diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml new file mode 100644 index 0000000..3409029 --- /dev/null +++ b/.github/workflows/ci.yml @@ -0,0 +1,67 @@ +name: CI Test for Cropped Example + +on: + push: + branches: + - '**' + pull_request: + branches: + - '**' + +jobs: + oregon_metro_mini_example: + env: + conda-env-prefix: /usr/share/miniconda3/envs/conda-abm_dev + label: linux-64 + strategy: + matrix: + region: + - oregon_metro + python-version: + - "3.10" + fail-fast: false + defaults: + run: + shell: bash -l {0} + + name: ${{ matrix.region }}-py${{ matrix.python-version }} + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v4 + + # Clone ActivitySim from SimOR_pnr branch + - name: Clone ActivitySim + run: | + cd .. + git clone --branch SimOR_pnr https://github.com/RSGInc/activitysim.git + pwd + tree -L 2 + + - name: Setup Miniforge + uses: conda-incubator/setup-miniconda@v3 + with: + auto-update-conda: true + miniforge-version: latest + mamba-version: "2.0.5" + conda-solver: classic + conda-remove-defaults: true + environment-file: ../activitysim/conda-environments/github-actions-tests.yml + activate-environment: asim-test + python-version: ${{ matrix.python-version }} + + # Install cloned fork of ActivitySim + - name: Install ActivitySim in editable mode + run: | + pwd + cd ../activitysim + python -m pip install -e . --no-deps + cd .. + cd SimOR + ls + conda info -a + conda list + + # Run the test + - name: Run cropped example test + run: python resident/test/test_cropped_dataset.py diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..8dbe99b --- /dev/null +++ b/.gitignore @@ -0,0 +1,16 @@ +*__pycache__* +.vscode/* +resident/model_data/metro/data_full/* +resident/outputs/* + +# Unit test / coverage reports +htmlcov/ +.tox/ +.coverage +.cache +nosetests.xml +coverage.xml + +Metro_outputs/ + +skimming_and_assignment/visum/*.ver diff --git a/.vscode/launch.json b/.vscode/launch.json new file mode 100644 index 0000000..727bd62 --- /dev/null +++ b/.vscode/launch.json @@ -0,0 +1,34 @@ +{ + // Use IntelliSense to learn about possible attributes. + // Hover to view descriptions of existing attributes. + // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387 + "version": "0.2.0", + "configurations": [ + { + "name": "Run Full Example", + "type": "debugpy", + "request": "launch", + "cwd": "${workspaceFolder}/resident", + "program": "simulation.py", + "console": "integratedTerminal", + "args": [ + "-c", "configs", // check the settings.yaml file for run settings! + "-d", "model_data/metro/data_full", + "-o", "outputs/full", + ] + }, + { + "name": "Run Cropped Example", + "type": "debugpy", + "request": "launch", + "cwd": "${workspaceFolder}/resident", + "program": "simulation.py", + "console": "integratedTerminal", + "args": [ + "-c", "configs", // check the settings.yaml file for run settings! + "-d", "model_data/metro/data_cropped", + "-o", "outputs/test", + ] + } + ] +} \ No newline at end of file diff --git a/README.md b/README.md index 278bafe..e16a437 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,6 @@ # SimOR Simulate Oregon (SimOR) - Oregon's Jointly Estimated ActivitySim Model ![image](SimOR.png) + +## Currently in development +To run the prototype mini example, download and install the correct fork of Activitysim from [here](https://github.com/RSGInc/activitysim/tree/SimOR_pnr) and follow the launch commands in the .vscode/launch.json file. This version of ActivitySim contains the necessary park-and-ride changes to run the SimOR model. \ No newline at end of file diff --git a/misc/bike_analysis/.gitignore b/misc/bike_analysis/.gitignore new file mode 100644 index 0000000..41b592c --- /dev/null +++ b/misc/bike_analysis/.gitignore @@ -0,0 +1 @@ +src/__pycache__/ \ No newline at end of file diff --git a/misc/bike_analysis/scripts/01_clean.py b/misc/bike_analysis/scripts/01_clean.py new file mode 100644 index 0000000..5dfe41c --- /dev/null +++ b/misc/bike_analysis/scripts/01_clean.py @@ -0,0 +1,241 @@ +import pandas as pd +import numpy as np +import os +pd.set_option('display.max_columns', None) +os.chdir(os.path.dirname(os.path.dirname(__file__))) + +# %% +# Directories +data_dir = 'data' +raw_dir = f'{data_dir}/raw' +interim_dir = f'{data_dir}/interim' +processed_dir = f'{data_dir}/processed' + +# %% +# Load data +hh = pd.read_csv(f'{raw_dir}/ex_hh.csv') +# day = pd.read_csv(f'{raw_dir}/ex_day.csv') +person = pd.read_csv(f'{raw_dir}/ex_person.csv') +trips = pd.read_csv(f'{raw_dir}/ex_trip_unlinked.csv') +ltrips = pd.read_csv(f'{raw_dir}/ex_trip_linked.csv') + +# %% Rename variables +ltrips.rename( + columns= + {'linked_trip_weight': 'ltrip_weight'}, + inplace=True +) + +# Re-map values to labels +person_mapping = { + 'gender': {1: 'Female', 2: 'Male', 3:'Non-binary', 995:'Missing', 997:'Other', 999:'PNTA'}, + 'age': {1:'Under5', 2:'5-10', 3:'11-15', 4:'16-17', 5:'18-24', 6:'25-34', 7:'35-44', 8:'45-54', 9:'55-64', 10:'65-74', 11:'75-84', 12:'85plus'}, + 'bike_freq': {1: '6-7days', 2: '5days', 3: '4days', 4: '3days', 5: '2days', 6: '1day', 7: '1-3month', 8: '<1month', 996: 'Never', 995: 'Missing'}, + 'bike_attitude': {1:'PhysicallyUnable', 2:'DoesNotKnowHow', 3:'NotInterested', 4:'WantBikeLess', 5:'HappyWithCurrent', 6:'WantBikeMore', 995:'Missing'} +} + +# Re-map to broader categories +bike_freq_map = { + '6-7days': 'VeryFrequent', + '5days': 'VeryFrequent', + '4days': 'VeryFrequent', + '3days': 'Occasional', + '2days': 'Occasional', + '1day': 'Occasional', + '1-3month': 'Infrequent', + '<1month': 'Infrequent', + 'Never': 'Never', + 'Missing': 'Missing' +} + +bike_att_bin = { + 'PhysicallyUnable': 'NotInterestedCant', + 'DoesNotKnowHow': 'NotInterestedCant', + 'NotInterested': 'NotInterestedCant', + 'WantBikeLess': 'WantBikeLess', + 'HappyWithCurrent': 'HappyWithCurrent', + 'WantBikeMore': 'WantBikeMore', + 'Missing': 'Missing' +} + +income_bin = { + 1: 'Low', + 2: 'Low', + 3: 'Mid', + 4: 'Mid', + 5: 'High', + 6: 'High', + 995: 'Missing', + 999: 'Missing' +} + +income_num = { + 1: 25000, + 2: 37500, + 3: 62500, + 4: 87500, + 5: 150000, + 6: 200000, + 995: np.nan, + 999: np.nan +} + +age_bin = { + 'Under5': 'Under10', + '5-10': 'Under10', + '11-15': '10-17', + '16-17': '10-17', + '18-24': '18-34', + '25-34': '18-34', + '35-44': '35-64', + '45-54': '35-64', + '55-64': '35-64', + '65-74': '65+', + '75-84': '65+', + '85plus': '65+' +} + +age_num = { + 'Under5': 2.5, + '5_10': 8, + '11-15': 13, + '16-17': 16, + '18-24': 21, + '25-34': 30, + '35-44': 40, + '45-54': 50, + '55-64': 60, + '65-74': 70, + '75-84': 80, + '85plus': 85 +} + +person['gender_num'] = np.where( + person['gender'].isin([1,3,997]), 1, # Female & Others + np.where(person['gender'] == 2, 2, 3) # Male and Missing +) +person['gender_lab'] = person['gender'].map(person_mapping['gender']) +person['gender_bin'] = person['gender_num'].map({1:'Female & Others', 2: 'Male', 3: 'Missing'}) + +person['age_lab'] = person['age'].map(person_mapping['age']) +person['age_bin'] = person['age_lab'].map(age_bin) +person['age_num'] = person['age_lab'].map(age_num) + +person['bike_freq_lab'] = person['bike_freq'].map(person_mapping['bike_freq']) +person['bike_freq_bin'] = person['bike_freq_lab'].map(bike_freq_map) + +person['bike_att'] = person['bike_attitude'].map(person_mapping['bike_attitude']) +person['bike_att_bin'] = person['bike_att'].map(bike_att_bin) + +hh['income_bin'] = hh['income_broad'].map(income_bin) +hh['income_num'] = hh['income_broad'].map(income_num) + +# %% +# Define comfort variable +comfort_cols = [col for col in person.columns if 'bike_comfort' in col] +likert_scale = {1:4, 2:3, 3:2, 4:1} # Reverse scoring for comfort +person[comfort_cols] = person[comfort_cols].replace(likert_scale) +person['avg_comfort'] = person[comfort_cols].replace(995, pd.NA).mean(axis=1, skipna=True).fillna(0) +comfort_labels = ['Uncomfortable', 'Comfortable'] +person['comfort_bin'] = np.where( + person['avg_comfort'] >= 3, 'Comfortable', + np.where(person['avg_comfort'] != 0 , 'Uncomfortable', pd.NA) +) + +# %% +# Identify complete rmove households (for comfort classification - 04_classify.py) +print(f"Total households: {hh['hh_weight'].sum():,.2f}") + +hh_rmove = hh[hh['diary_platform'] == 'rmove'] +print(f"Total households with rmove: {hh_rmove['hh_weight'].sum():,.2f}") + +hh_rmove_complete = hh_rmove[hh_rmove['num_days_complete'] == 7] +print(f"Total number of households with 7 complete days: {hh_rmove_complete['hh_weight'].sum():,.2f}") +print(f"Total sample size of households with 7 complete days: {hh_rmove_complete.shape[0]:,}") + +bike_complete_hh_ids = hh_rmove_complete['hh_id'].unique().tolist() + +# Add flag to identify complete records +person['bike_complete_flag'] = 0 +person.loc[person['hh_id'].isin(bike_complete_hh_ids), 'bike_complete_flag'] = 1 + +# %% Merge bike trips with person table +# Count bike trips +bike_trip_mask = (ltrips['linked_trip_mode'] == 11) +bike_trips= ltrips[bike_trip_mask].copy() +bike_counts = bike_trips.groupby('person_id').size().reset_index(name='bike_trips') + +# Keep relevant cols +per_cols = ['hh_id', 'person_id', 'age_lab', 'age_bin', 'age_num', + 'gender_bin', 'num_days_complete', 'bike_complete_flag', + 'avg_comfort', 'comfort_bin', + 'student', 'person_weight'] +bike_cols_per = [col for col in person.columns if 'bike' in col] + +# Merge bike trip info with person data +person_btrips = pd.merge( + person[per_cols + bike_cols_per], + bike_counts, + on='person_id', + how='left', + validate='1:1' +).fillna({'bike_trips': 0}) + +# Merge with hh data +hh_cols = ['hh_id', 'num_bicycle_adult', 'num_bicycle_child', 'income_broad', 'income_bin', 'income_num'] +person_btrips = pd.merge( + person_btrips, + hh[hh_cols], + on = 'hh_id', + how = 'left' +) + +# Add dummy if person reported bike trip +person_btrips['recorded_btrip'] = np.where(person_btrips['bike_trips'] > 0, 1, 0) + +# %% Merge bike trips with person info +btrips_person = pd.merge( + bike_trips.drop(columns = ['hh_id']), + person[per_cols + bike_cols_per], + how = 'left', + on = 'person_id' +) + +# Add hh data +btrips_person = pd.merge( + btrips_person, + hh[hh_cols], + how = 'left', + on = 'hh_id' +) + +# Add bike type to bike trip info +ltrip_bike_ids = bike_trips['linked_trip_id'].to_list() +subset = trips[trips['linked_trip_id'].isin(ltrip_bike_ids)] # bike type is unlinked trip table + +bike_modes = [2, 3, 4, 5, 69, 70, 82, 103, 300] # bike modes and hierarchy + +def get_highest_bike_mode(modes): + found_bikes = [m for m in modes if m in bike_modes] + if found_bikes: + return max(found_bikes) + else: + return None + +modes = subset.groupby('linked_trip_id')['mode_1'].agg(list).reset_index().rename(columns = {'mode_1':'mode_list'}) +modes['bike_mode'] = modes['mode_list'].apply(get_highest_bike_mode) + +# Add bike type +btrips_person = pd.merge( + btrips_person, + modes[['linked_trip_id', 'bike_mode']], + how = 'left', + on = 'linked_trip_id', +) + +ebikes = [70, 82] # ebike from bikeshare, ebike in household +btrips_person['bike_type'] = np.where(btrips_person['bike_mode'].isin(ebikes), 'ebike', 'standard') + +# %% Export +person_btrips.to_csv(f'{interim_dir}/person_btrips.csv', index=False) +btrips_person.to_csv(f'{interim_dir}/btrips_person.csv', index=False) diff --git a/misc/bike_analysis/scripts/02_visualize.py b/misc/bike_analysis/scripts/02_visualize.py new file mode 100644 index 0000000..b7891f5 --- /dev/null +++ b/misc/bike_analysis/scripts/02_visualize.py @@ -0,0 +1,233 @@ +import pandas as pd +import matplotlib.pyplot as plt +import seaborn as sns +import numpy as np +import os +import importlib + +pd.set_option('display.max_columns', None) +os.chdir(os.path.dirname(os.path.dirname(__file__))) + +import src.visualize as viz +importlib.reload(viz) + +# %% +# Define directories +data_dir = 'data' +raw_dir = f'{data_dir}/raw' +interim_dir = f'{data_dir}/interim' +processed_dir = f'{data_dir}/processed' +# %% +# Load data +person_btrips = pd.read_csv(f'{interim_dir}/person_btrips.csv') +btrips_person = pd.read_csv(f'{interim_dir}/btrips_person.csv') +ltrips = pd.read_csv(f'{raw_dir}/ex_trip_linked.csv') +value_labels = pd.read_csv(f'{raw_dir}/ex_value_labels.csv') + +# Convert to categorical +age_lab_cat = ['Under5', '5-10', '11-15', '16-17', '18-24', '25-34', '35-44', '45-54', '55-64', '65-74', '75-84'] +freq_bin_cat = ['VeryFrequent', 'Occasional', 'Infrequent', 'Never', 'Missing'] +att_cat = ['PhysicallyUnable', 'DoesNotKnowHow', 'NotInterested', 'WantBikeLess', 'HappyWithCurrent', 'WantBikeMore', 'Missing'] +att_bin_cat = ['NotInterestedCant', 'WantBikeLess', 'HappyWithCurrent', 'WantBikeMore', 'Missing'] + +person_btrips['age_lab'] = pd.Categorical(person_btrips['age_lab'], categories=age_lab_cat, ordered=True) +person_btrips['bike_freq_bin'] = pd.Categorical(person_btrips['bike_freq_bin'], categories=freq_bin_cat, ordered=True) +person_btrips['bike_att'] = pd.Categorical(person_btrips['bike_att'], categories=att_cat, ordered=True) +person_btrips['bike_att_bin'] = pd.Categorical(person_btrips['bike_att_bin'], categories=att_bin_cat, ordered=True) +# person['gender'] = pd.Categorical(person['gender'], categories=person_mapping['gender'], ordered=True) + +# %% =============== +# Basic Statistics +print(f"Number of bike trips: {btrips_person['ltrip_weight'].sum():,.2f}") +print(f"Percent bike trips: {btrips_person['ltrip_weight'].sum() / ltrips['linked_trip_weight'].sum() * 100:.2f}%") + +# Demographics of Revealed Bikers +bikers = person_btrips[person_btrips['bike_trips'] > 0] +bikers_gender = viz.weighted_total(bikers, 'gender_bin', 'person_weight') +bikers_age = viz.weighted_total(bikers, 'age_lab', 'person_weight') +bikers_attitude = viz.weighted_total(bikers, 'bike_att', 'person_weight') +bikers_income = viz.weighted_total(bikers, 'income_bin', 'person_weight') + +viz.plot_hist(bikers_gender, 'gender_bin', 'pct', title='Revealed Bikers by Gender') +viz.plot_hist(bikers_age, 'age_lab', 'pct', title='Revealed Bikers by Age') +viz.plot_hist(bikers_income, 'income_bin', 'pct', title='Revealed Bikers by Income') +viz.plot_hist(bikers_attitude, 'bike_att', 'pct', title='Revealed Bikers by Stated Attitude') + +# Num of bike trips per day +bikers_bike_count = viz.weighted_total(bikers, 'bike_trips', 'person_weight') +bikers_bike_count['bin'] = pd.cut(bikers_bike_count['bike_trips'], bins=[-0.1,0,1,2,3,4,5,50]) +bin_labels = ['0', '1', '2', '3', '4', '5', '6+'] +ax = (bikers_bike_count + .groupby('bin')['pct'] + .sum() + .plot(kind='bar', + color='skyblue', + xlabel='Number of Bike Trips', + ylabel='Pct', + title='Bike Trips per Day by Revealed Bikers')) +ax.set_xticklabels(bin_labels, rotation=0) +ax.bar_label(ax.containers[0], fmt='%.1f') +# %% +# Bike Trips by Purpose +bike_purpose = viz.weighted_total(btrips_person, 'd_purpose_category', 'ltrip_weight') +top_purpose = bike_purpose.sort_values(by='pct', ascending=False).head(10) +labels = value_labels[value_labels['variable'] == 'd_purpose_category'][['value', 'label']] +bin_labels = labels[labels['value'].isin(top_purpose['d_purpose_category'])]['label'].tolist() + +ax = top_purpose.plot( + kind='barh', + x='d_purpose_category', + y='pct', + color='skyblue', + title="Bike Trips by Purpose", + legend=False +) +ax.set_yticks(range(len(bin_labels))) +ax.set_yticklabels(bin_labels, rotation=0) +ax.bar_label(ax.containers[0], fmt='%.1f', label_type='edge') + +# %% ==================== +# Cross tabulations +df = person_btrips.copy() +df = df[df['comfort_bin'].notna()] + +freq_vs_trips_col = viz.weighted_crosstab(df, 'bike_freq_bin', 'recorded_btrip', 'person_weight', 'col', 0) +freq_vs_trips_row = viz.weighted_crosstab(df, 'bike_freq_bin', 'recorded_btrip', 'person_weight', 'row', 0) + +att_vs_trips_row = viz.weighted_crosstab(df, 'bike_att_bin', 'recorded_btrip', 'person_weight', 'row', 0) +att_vs_trips_col = viz.weighted_crosstab(df, 'bike_att_bin', 'recorded_btrip', 'person_weight', 'col', 0) + +comfort_vs_trips_row = viz.weighted_crosstab(df, 'comfort_bin', 'recorded_btrip', 'person_weight', 'row', 0) +comfort_vs_trips_col = viz.weighted_crosstab(df, 'comfort_bin', 'recorded_btrip', 'person_weight', 'col', 0) + +comfort_vs_att_row = viz.weighted_crosstab(df, 'bike_att_bin', 'comfort_bin', 'person_weight', 'row', 0) +comfort_vs_att_col = viz.weighted_crosstab(df, 'bike_att_bin', 'comfort_bin', 'person_weight', 'col', 0) + +# %% ==================================== +# Bike trip distance by reported frequency +df = btrips_person[(btrips_person['bike_freq_bin'] != 'Missing') & + (btrips_person['d_purpose_category'] != 14)] + +bins = [0, 1, 5, 10, np.inf] +labels2 = ['<1', '1-5', '5-10', '10+'] +df['dist_bin'] = pd.cut(df['distance_miles'], bins=bins, labels=labels2, right=False) + +summary = [] +for freq in df['bike_freq_bin'].unique(): + tbl = df[df['bike_freq_bin'] == freq].groupby('dist_bin')['ltrip_weight'].sum().reset_index() + tbl['pct'] = tbl['ltrip_weight'] / tbl['ltrip_weight'].sum() * 100 + tbl['bike_freq_bin'] = freq + summary.append(tbl) + +summary = pd.concat(summary) + +pivot = summary.pivot(index='dist_bin', columns='bike_freq_bin', values='pct').fillna(0) +pivot = pivot[df['bike_freq_bin'].unique()] + +ax = pivot.plot(kind='bar', figsize=(10,6), edgecolor='black') +ax.tick_params(axis='x', rotation=0) +ax.set_xlabel('Trip distance (miles)') +ax.set_ylabel('Percent') +ax.set_title('Bike Trip Distances by Reported Frequency') +ax.legend(title='Reported Frequency') +plt.tight_layout() +plt.show() + +# %% ========================= +# Trip Distance by Bike Type +df = btrips_person.copy() +bins = [0, 1, 2, 3, 4, 5, 10, np.inf] +labels = ['<1', '1-2', '2-3', '3-4', '4-5', '5-10', '10+'] +df['dist_bin'] = pd.cut(df['distance_miles'], bins=bins, labels=labels, right=False) + +std_dist = df[df['bike_type'] == 'standard'].groupby('dist_bin')['ltrip_weight'].sum().reset_index() +std_dist['pct'] = std_dist['ltrip_weight'] / std_dist['ltrip_weight'].sum() * 100 + +ebike_dist = df[df['bike_type'] == 'ebike'].groupby('dist_bin')['ltrip_weight'].sum().reset_index() +ebike_dist['pct'] = ebike_dist['ltrip_weight'] / ebike_dist['ltrip_weight'].sum() * 100 + +x = np.arange(len(labels)) +width = 0.35 +fig, ax = plt.subplots(figsize=(10,6)) +ax.bar(x - width/2, std_dist['pct'], width, label='Standard') +ax.bar(x + width/2, ebike_dist['pct'], width, label='E-bike') +ax.set_xticks(x) +ax.set_xticklabels(labels) +ax.set_xlabel('Bike trip distance (miles)') +ax.set_ylabel('Percent of Trips') +ax.set_title('Trip Distance by Bike Type') +ax.legend() +plt.show() + +# %% +# Gender vs Bike Type +df = btrips_person[btrips_person['gender_bin']!= "Missing"] +btype_fem = df[df['gender_bin'] == 'Female & Others'].groupby('bike_type')['ltrip_weight'].sum().reset_index() +btype_fem['pct'] = btype_fem['ltrip_weight'] / btype_fem['ltrip_weight'].sum() * 100 + +btype_male = df[df['gender_bin'] == 'Male'].groupby('bike_type')['ltrip_weight'].sum().reset_index() +btype_male['pct'] = btype_male['ltrip_weight'] / btype_male['ltrip_weight'].sum() * 100 + +btype_fem['gender'] = 'Female & Others' +btype_male['gender'] = 'Male' + +btype_combined = pd.concat([btype_fem, btype_male], ignore_index=True) +btype_pivot = btype_combined.pivot(index='bike_type', columns='gender', values='pct') +btype_pivot.plot(kind='bar', figsize=(8, 5)) +plt.ylabel('Percent of Trips (%)') +plt.title('Bike Type Share by Gender') +plt.legend(title='Gender') +plt.xticks(rotation=0) +plt.tight_layout() +plt.show() + +# %% ============================== +# Reported frequency vs comfort +df = person_btrips[(person_btrips['avg_comfort'] > 0)] +avg_comfort_freq_plt, avg_comfort_freq_tbl = viz.plot_weighted_comfort_by_group(df, 'bike_freq_bin', 'person_weight') + +# Age vs average comfort ratinga +avg_comfort_age_plt, avg_comfort_age_tbl = viz.plot_weighted_comfort_by_group(df, 'age_bin', 'person_weight') +# %% ===================== +# Avg comfort by gender +df = person_btrips.copy() +df = df[(df['gender_bin'] != 'Missing') & (df['avg_comfort'] > 0)] +bins = [0, 1, 2, 3, 4] +df['rate_bin'] = pd.cut(df['avg_comfort'], bins=bins, right=True) + +comfort_fem_rate = ( + df[df['gender_bin'] == 'Female & Others'] + .groupby('rate_bin')['person_weight'] + .sum() + .div(df[df['gender_bin'] == 'Female & Others']['person_weight'].sum()) + .mul(100) + .reset_index() +) + +comfort_male_rate = ( + df[df['gender_bin'] == 'Male'] + .groupby('rate_bin')['person_weight'] + .sum() + .div(df[df['gender_bin'] == 'Male']['person_weight'].sum()) + .mul(100) + .reset_index() +) + +comfort_fem_rate['gender'] = 'Female & Others' +comfort_male_rate['gender'] = 'Male' + +merge = pd.concat([comfort_male_rate, comfort_fem_rate], axis=0) + +sns.barplot( + merge, + x = 'rate_bin', + y = 'person_weight', + hue = 'gender' +) +plt.xticks( + ticks=range(len(merge['rate_bin'].unique())), + labels=['0–1', '1–2', '2–3', '3–4'] +) +plt.xlabel("Average comfort") +plt.title("Average Comfort by Gender") +plt.show() diff --git a/misc/bike_analysis/scripts/03_analyze.py b/misc/bike_analysis/scripts/03_analyze.py new file mode 100644 index 0000000..ada25a8 --- /dev/null +++ b/misc/bike_analysis/scripts/03_analyze.py @@ -0,0 +1,268 @@ +import pandas as pd +import numpy as np +import seaborn as sns +import importlib +import os +pd.set_option('display.max_columns', None) +pd.options.display.float_format = '{:.2f}'.format +os.chdir(os.path.dirname(os.path.dirname(__file__))) + +import src.visualize +importlib.reload(src.visualize) + +from src.stats import weighted_ttest, weighted_pt +importlib.reload(src.stats) + +from scipy.stats import chi2_contingency + +# %% +# Directoriess +data_dir = 'data' +raw_dir = f'{data_dir}/raw' +interim_dir = f'{data_dir}/interim' +processed_dir = f'{data_dir}/processed' + +# %% +# Load data +btrips_person = pd.read_csv(f"{interim_dir}/btrips_person.csv") +person_btrips = pd.read_csv(f"{interim_dir}/person_btrips.csv") +# %% +# Difference between male/female & level of comfort +df = person_btrips[person_btrips['avg_comfort'] > 0] +results1 = weighted_ttest( + df, + group_col = 'gender_bin', + value_col = 'avg_comfort', + group1_val = 'Male', + group2_val = 'Female & Others', + weight_col = 'person_weight' +) +results1 + +sns.histplot(df[df['gender_bin'] != "Missing"], + x = 'avg_comfort', + bins = [0, 1, 2, 3, 4], + weights='person_weight', + hue='gender_bin', + stat = 'percent', + multiple='dodge') + +# Alternative method +# group1 = df.loc[df['gender_bin'] == 'Male', 'avg_comfort'] +# group2 = df.loc[df['gender_bin'] == 'Female & Others', 'avg_comfort'] + +# weights1 = df.loc[df['gender_bin'] == 'Male', 'person_weight'] +# weights2 = df.loc[df['gender_bin'] == 'Female & Others', 'person_weight'] + +# t_stat, p_value, degrees_of_freedom = ttest_ind(group1, group2, weights = (weights1, weights2)) +# %% ==================================== +# Gender vs number of bike trips reported +results2 = weighted_ttest( + person_btrips, + group_col = 'gender_bin', + group1_val='Male', + group2_val='Female & Others', + value_col='bike_trips', + weight_col='person_weight' +) +results2 +# %% +# Differences between comfort level & bike trips +df = person_btrips[person_btrips['comfort_bin'].notna()] +results3 = weighted_ttest( + df, + group_col = 'comfort_bin', + value_col = 'bike_trips', + group1_val = 'Comfortable', + group2_val = 'Uncomfortable', + weight_col = 'person_weight' +) +results3 + +# %% +# Reported bike frequency & bike trips +df = person_btrips[person_btrips['bike_freq_bin'] != 'Missing'] +results4 = weighted_ttest( + df, + group_col = 'bike_freq_bin', + value_col = 'bike_trips', + group1_val = 'VeryFrequent', + group2_val = 'Occasional', + weight_col = 'person_weight' +) +results4 +# %% +# Reported attitude & bike trips +results4 = weighted_ttest( + person_btrips[person_btrips['bike_att_bin'] != "Missing"], + group_col = 'bike_att_bin', + value_col = 'bike_trips', + group1_val = 'HappyWithCurrent', + group2_val = 'WantBikeMore', + weight_col = 'person_weight' +) +results4 +# %% +# Frequency vs distance +df = btrips_person[ + (btrips_person['d_purpose_category'] != 14) & + (btrips_person['distance_miles'].notna())] + +results5 = weighted_ttest( + df, + group_col = 'bike_freq_bin', + value_col = 'distance_miles', + group1_val = 'VeryFrequent', + group2_val = 'Infrequent', + weight_col = 'ltrip_weight' +) +results5 + +results6 = weighted_ttest( + df, + group_col = 'bike_freq_bin', + value_col = 'distance_miles', + group1_val = 'VeryFrequent', + group2_val = 'Occasional', + weight_col = 'ltrip_weight' +) +results6 +# %% +# Bike comfort vs age +df = person_btrips[person_btrips['avg_comfort'] > 0] +results7 = weighted_ttest( + df, + group_col = 'age_bin', + value_col = 'avg_comfort', + group1_val = '18-34', + group2_val = '35-64', + weight_col = 'person_weight' +) +results7 + +# %% ====================== +# Chi-square test +# Reported biking vs gender +table = person_btrips[person_btrips['gender_bin'].isin(['Male', 'Female & Others'])].pivot_table( + index = 'gender_bin', + columns = 'reported_btrip', + values = 'person_weight', + aggfunc = 'sum', + fill_value=0 +) +chi2, p_value, dof, expected = chi2_contingency(table) +# %% +# Reported biking vs comfort +table2 = person_btrips[person_btrips['comfort_bin'].notna()].pivot_table( + index = 'bike_freq_bin', + columns = 'reported_btrip', + aggfunc = 'sum', + values = 'person_weight', + fill_value = 0 +) +chi2, p_value, dof, expected = chi2_contingency(table2) +print(f"chi-square: {chi2}, p-value: {p_value}, expected {expected}") + +# %% +# Comfort vs gender +table3 = person_btrips[(person_btrips['comfort_bin'].notna()) & (person_btrips['gender_bin'] != 'Missing')].pivot_table( + index = 'comfort_bin', + columns = 'gender_bin', + aggfunc = 'sum', + values = 'person_weight', + fill_value = 0 +) +chi2, p_value, dof, expected = chi2_contingency(table3) +print(f"chi-square: {chi2}, p-value: {p_value}, expected {expected}") + +print(f"chi-square: {chi2}, p-value: {p_value}, expected {expected}")# %% +# Reported btrip and attitude +table4 = person_btrips[(person_btrips['bike_att_bin'] != "Missing")].pivot_table( + index = 'bike_att_bin', + columns = 'reported_btrip', + aggfunc = 'sum', + values = 'person_weight', + fill_value = 0 +) +chi2, p_value, dof, expected = chi2_contingency(table4) +print(f"chi-square: {chi2}, p-value: {p_value}, expected {expected}") + +# %% +# Reported frequency and comfort +df = person_btrips[(person_btrips['bike_att_bin'] != "Missing") & (person_btrips['bike_att_bin'] != "Missing")] +table5 = df.pivot_table( + index = 'bike_att_bin', + columns = 'bike_freq_bin', + aggfunc = 'sum', + values = 'person_weight', + fill_value = 0 +) +chi2, p_value, dof, expected = chi2_contingency(table5) +print(f"chi-square: {chi2}, p-value: {p_value}, expected {expected}") + +weighted_pt( + df, + index = 'bike_att_bin', + cols='bike_freq_bin', + values = 'person_weight' +) + +weighted_pt( + df, + index = 'bike_att_bin', + cols='bike_freq_bin', + values = 'person_weight', + prop = 'col' +) + +# %% +bike_comf_cols = [col for col in person_btrips.columns if "bike_comfort_" in col] +df = person_btrips[person_btrips[bike_comf_cols] != 995] + +# %% ================================== +# Logistic regression +import statsmodels.api as sm +from sklearn.preprocessing import StandardScaler + +# Prepare and filter dataframe +df = person_btrips.copy() +df = df[(df['comfort_bin'].notna()) & + (df['gender_bin'] != "Missing") & + (df['age_num'].notna()) & + (df['income_num'].notna()) & + (df['bike_freq_bin'] != 'Missing') & + (df['bike_att_bin'] != "Missing") & + (df['avg_comfort'] > 0 ) + ] + +# Dummies for categorical predictors +X_dummies = pd.get_dummies( + df[['gender_bin', 'comfort_bin', 'bike_freq_bin', 'bike_att_bin', 'age_bin']], + drop_first=True +).astype(float) + +# Scaled continuous predictors +cont = df[['income_num', 'avg_comfort']] +scaler = StandardScaler() +cont_scaled = pd.DataFrame( + scaler.fit_transform(cont), + columns=cont.columns, + index=df.index +) + +# Combine predictors +X = pd.concat([X_dummies, cont_scaled], axis=1) + +# Remove NaN's +y = df['reported_btrip'].astype(float) +mask_nonnull = X.notna().all(axis=1) & y.notna() +X = X.loc[mask_nonnull] +y = y.loc[mask_nonnull] +weights = df.loc[mask_nonnull, 'person_weight'] + +# Add intercept +X = sm.add_constant(X, has_constant='add') + +model = sm.GLM(y, X, family=sm.families.Binomial(), freq_weights=weights) +result = model.fit() +print(result.summary()) \ No newline at end of file diff --git a/misc/bike_analysis/scripts/04_classify.py b/misc/bike_analysis/scripts/04_classify.py new file mode 100644 index 0000000..7035831 --- /dev/null +++ b/misc/bike_analysis/scripts/04_classify.py @@ -0,0 +1,133 @@ +import pandas as pd +import numpy as np +import matplotlib.pyplot as plt +import importlib +import os + +pd.set_option('display.max_columns', None) +os.chdir(os.path.dirname(os.path.dirname(__file__))) + +import src.visualize as viz +importlib.reload(viz) + +# %% +def pct_summary(df, col='biker_type', weight_col='person_weight', label=None): + """Return weighted percent share of each category.""" + weighted = ( + df.groupby(col, observed=True)[weight_col] + .sum() + .div(df[weight_col].sum()) + .mul(100) + ) + return weighted.rename(label) + +def classify_bikers(df, comfort_cols): + """Classify and reclassify bikers, tracking weighted % change after each step.""" + + biker_types = [ + 'NoWayNoHow', + 'InterestedButConcerned', + 'EnthusedAndConfident', + 'StrongAndFearless' + ] + + biker_type_map = { + 'Never': biker_types[0], + '<1month': biker_types[1], + '1-3month': biker_types[1], + '1day': biker_types[2], + '2days': biker_types[2], + '3days': biker_types[2], + '4days': biker_types[3], + '5days': biker_types[3], + '6-7days': biker_types[3], + } + + # Step 0: Initial classification + df['biker_type'] = df['bike_freq_lab'].map(biker_type_map) + pct_table = pct_summary(df, label='initial') + + # Step 1: Reclassify based on actual frequency + df.loc[(df['biker_type'] == 'Never') & (df['bike_trips'] > 0), 'biker_type'] = biker_types[1] + df.loc[(df['biker_type'] == biker_types[2]) & (df['bike_trips'] == 0), 'biker_type'] = biker_types[1] + df.loc[(df['biker_type'] == biker_types[3]) & (df['bike_trips'] == 0), 'biker_type'] = biker_types[1] + df.loc[(df['biker_type'] == biker_types[3]) & (df['bike_trips'] < 2), 'biker_type'] = biker_types[2] + pct_table = pd.concat([pct_table, pct_summary(df, label='after_actual_freq')], axis=1) + + # Step 2: Reclassify based on Bike Attitude + print(f"Attitudes before att: {df['bike_att'].unique()}") + mask = df['biker_type'].isin(biker_types[1:]) + df.loc[mask & df['bike_att'].isin(['NotInterested', 'PhysicallyUnable']), 'biker_type'] = 'NoWayNoHow' + pct_table = pd.concat([pct_table, pct_summary(df, label='after_att')], axis=1) + + # Step 3: Reclassify based on Bike Comfort + df.loc[ + (df['biker_type'] == biker_types[2]) & + (~df['bike_comfort_four_lanes'].isin([3, 4])), + 'biker_type' + ] = biker_types[1] + + df.loc[ + (df['biker_type'] == biker_types[3]) & + (~df[comfort_cols] == 4).all(axis=1), + 'biker_type' + ] = biker_types[2] + + pct_table = pd.concat([pct_table, pct_summary(df, label='after_comfort')], axis=1) + + # Step 4: Reclassify by Bike Ownership + mask = df['biker_type'].isin(biker_types[1:]) + df.loc[mask & (df['num_bicycle_adult'] == 0), 'biker_type'] = 'InterestedButConcerned' + pct_table = pd.concat([pct_table, pct_summary(df, label='after_ownership')], axis=1) + + # Final tidy-up + pct_table = pct_table.reindex(biker_types) + pct_table = pct_table.fillna(0).round(2) + + return df, pct_table +# %% +# Define directories +data_dir = 'data' +raw_dir = f'{data_dir}/raw' +interim_dir = f'{data_dir}/interim' +processed_dir = f'{data_dir}/processed' +# %% +# Load data +person_btrips = pd.read_csv(f'{interim_dir}/person_btrips.csv') +# %% ============ +# Clean dataframe +df = person_btrips.copy() + +# Keep complete records only +print(f"Total records before: {df.shape[0]:,}") +print(f"Total persons before: {df['person_weight'].sum():,.2f}") + +comfort_cols = [col for col in df.columns if 'bike_comfort' in col] +df = df[df['bike_complete_flag'] == 1] # defined in 01_cleaning.py +df = df[df['bike_freq_lab'] != 'Missing'] +df = df[~(df[comfort_cols] == 995).all(axis=1)] # Remove missing + +print(f"Total records after: {df.shape[0]:,}") +print(f"Total persons after: {df['person_weight'].sum():,.2f}") + +# %% +# Classify +df_classified, tbl = classify_bikers(df, comfort_cols) +# %% ============== +# Analysis +age_within = viz.weighted_crosstab(df, 'biker_type', 'age_bin', 'person_weight', 'col') +age_across = viz.weighted_crosstab(df, 'biker_type', 'age_bin', 'person_weight', 'row') + +viz.plot_stacked(age_within, 'Age Distribution Within Each Biker Type') +viz.plot_stacked(age_across, 'Biker Type Distribution Across Age Groups') +# %% +gender_within = viz.weighted_crosstab(df, 'biker_type', 'gender_bin', 'person_weight', 'col') +gender_across = viz.weighted_crosstab(df, 'biker_type', 'gender_bin', 'person_weight', 'row') +viz.plot_stacked(gender_within, 'Gender Distribution Within Each Biker Type') +viz.plot_stacked(gender_across, 'Biker Type Distribution Across Gender Groups') + +# %% +income_within = viz.weighted_crosstab(df, 'biker_type', 'income_bin', 'person_weight', 'col') +income_across = viz.weighted_crosstab(df, 'biker_type', 'income_bin', 'person_weight', 'row') +viz.plot_stacked(income_within, 'Income Distribution Within Each Biker Type') +viz.plot_stacked(income_across, 'Biker Type Distribution Across Income') diff --git a/misc/bike_analysis/src/__init__.py b/misc/bike_analysis/src/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/misc/bike_analysis/src/stats.py b/misc/bike_analysis/src/stats.py new file mode 100644 index 0000000..4cc8775 --- /dev/null +++ b/misc/bike_analysis/src/stats.py @@ -0,0 +1,85 @@ +from scipy import stats +import numpy as np +from statsmodels.stats.weightstats import ttest_ind + + +def weighted_mean(x, w): + return np.sum(w * x) / np.sum(w) + +def weighted_std(x, w, unbiased=True): + mean = weighted_mean(x, w) + var = np.sum(w * (x - mean)**2) + if unbiased: + eff_n = np.sum(w) - (np.sum(w**2) / np.sum(w)) + return np.sqrt(var / eff_n) + else: + return np.sqrt(var / np.sum(w)) + +def weighted_ttest(df, group_col, value_col, group1_val, group2_val, weight_col): + """ + Performs an independent t-test between two groups and returns key statistics. + """ + group1 = df.loc[df[group_col] == group1_val, value_col] + group2 = df.loc[df[group_col] == group2_val, value_col] + + weights1 = df.loc[df[group_col] == group1_val, weight_col] + weights2 = df.loc[df[group_col] == group2_val, weight_col] + + # Sample sizes + n1, n2 = weights1.sum(), weights2.sum() + + # Means and SDs + mean1, mean2 = weighted_mean(group1, weights1), weighted_mean(group2, weights2) + std1, std2 = weighted_std(group1, weights1), weighted_std(group2, weights2) + + # Mean difference + mean_diff = mean1 - mean2 + + # t-test + t_stat, p_value, degrees_of_freedom = ttest_ind(group1, group2, weights = (weights1, weights2)) + + results = { + 'group1_val': group1_val, + 'group2_val': group2_val, + 'n1': n1, + 'n2': n2, + 'mean1': mean1, + 'mean2': mean2, + 'std1': std1, + 'std2': std2, + 'mean_diff': mean_diff, + 't_stat': t_stat, + 'p_val': p_value, + # 'cohen_d': cohen_d + } + + return results + +def weighted_total(df, gb_col, wgt_col, ): + """ + Incomplete + """ + freq = df.groupby(gb_col)[wgt_col].sum().reset_index(name='total') + freq['pct'] = freq['total'] / freq['total'].sum() * 100 + return freq + +def weighted_pt(df, index, cols, values, prop='row'): + """ + Creates a weighted pivot table showing percentages. + prop (row, col) + """ + pt = df.pivot_table( + index = index, + columns = cols, + values = values, + aggfunc = 'sum' + ) + + if prop == 'row': + pct = pt.div(pt.sum(axis=1), axis=0) * 100 + elif prop == 'col': + pct = pt.div(pt.sum(axis=0), axis=1) * 100 + else: + raise ValueError("prop must be either 'row' or 'col'") + + return pct \ No newline at end of file diff --git a/misc/bike_analysis/src/visualize.py b/misc/bike_analysis/src/visualize.py new file mode 100644 index 0000000..70a52e7 --- /dev/null +++ b/misc/bike_analysis/src/visualize.py @@ -0,0 +1,137 @@ +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import seaborn as sns + +def weighted_total(df, gb_col, wgt_col): + freq = df.groupby(gb_col)[wgt_col].sum().reset_index(name='total') + freq['pct'] = freq['total'] / freq['total'].sum() * 100 + return freq + +def plot_hist(df, x, y, title = None): + fig, ax = plt.subplots(figsize=(8, 6)) + bars = ax.bar(df[x], df[y], color='skyblue') + for bar, pct in zip(bars, df['pct']): + ax.text( + bar.get_x() + bar.get_width() / 2, + bar.get_height(), + f"{pct:.1f}%", + ha='center', va='bottom', fontsize=10 + ) + ax.set_xlabel(f"{x}") + ax.set_ylabel(f"{y}") + ax.set_title(f"{title}") + plt.xticks(rotation=45) + plt.tight_layout() + plt.show() + + +def check_dup_cols(df): + if df.columns.duplicated().any(): + print("Duplicate column names:", df.columns[df.columns.duplicated()].unique()) + else: + print("No duplicate columns!") + +def plot_weighted_comfort_by_group( + df, + group_by, + weight_col='person_weight', + comfort_prefix='bike_comfort', + figsize=(10, 6), + ylabel='Average Comfort (1=Very Uncomfortable, 4=Very Comfortable)', + xlabel='Scenario', + title=None, + rotate_xticks=45, +): + """ + Compute weighted mean comfort for all columns containing `comfort_prefix`, grouped by `group_by`, + and plot a bar chart of the results. + """ + # comfort columns + comfort_cols = [col for col in df.columns if comfort_prefix in col] + + def weighted_mean(x): + w = df.loc[x.index, weight_col] + if (w.isna() | (w == 0)).all(): + return np.nan + return np.average(x.fillna(np.nan), weights=w) + + summary_comfort = pd.DataFrame() + for col in comfort_cols: + tbl = df.groupby(group_by)[col].apply(weighted_mean) + summary_comfort = pd.concat([summary_comfort, tbl], axis=1) + + summary_plot = summary_comfort.T + summary_plot.index = comfort_cols + + ax = summary_plot.plot( + kind='bar', + figsize=figsize, + ylabel=ylabel, + xlabel=xlabel, + title=title, + ) + ax.set_xticklabels(ax.get_xticklabels(), rotation=rotate_xticks, ha='right') + plt.tight_layout() + plt.show() + + return ax, summary_plot + +def weighted_crosstab(df, index, columns, weight=None, prop='col', fill_value=0): + """ + Creates a weighted cross-tab and returns percentages + either across rows or columns. + """ + pivot = pd.pivot_table( + df, + index=index, + columns=columns, + values=weight, + aggfunc='sum' if weight else 'count', + fill_value=fill_value + ) + + # Compute row or column percentages + if prop == 'row': + pct = pivot.div(pivot.sum(axis=1), axis=0) * 100 + elif prop == 'col': + pct = pivot.div(pivot.sum(axis=0), axis=1) * 100 + + return pct.round(0) + +def plot_stacked(pct_table, title, ylabel='Percent'): + pct_table = pct_table.reindex(biker_order) + + plt.figure(figsize=(8, 5)) + pct_table.T[biker_order].plot( + kind='bar', + stacked=True, + color=[biker_colors[b] for b in biker_order] + ) + + plt.title(title, fontsize=12) + plt.ylabel(ylabel) + plt.xlabel('') + plt.legend( + title='Biker Type', + bbox_to_anchor=(1.05, 1), + loc='upper left', + labels=biker_order + ) + plt.xticks(rotation = 0) + plt.tight_layout() + plt.show() + +biker_order = [ + 'NoWayNoHow', + 'InterestedButConcerned', + 'EnthusedAndConfident', + 'StrongAndFearless' +] + +biker_colors = { + 'NoWayNoHow': '#D9523B', + 'InterestedButConcerned': '#fc8d59', + 'EnthusedAndConfident': '#91bfdb', + 'StrongAndFearless': '#4575b4' +} \ No newline at end of file diff --git a/resident/configs/accessibility.csv b/resident/configs/accessibility.csv new file mode 100644 index 0000000..3aa88c5 --- /dev/null +++ b/resident/configs/accessibility.csv @@ -0,0 +1,53 @@ +Description,Target,Expression +#,, +#,, auto peak +#,, +#,, assume peak occurs in AM for outbound and PM for inbound +peak round trip distance,_auPkTime,"skim_od[('SOV_M_TIME', 'AM')] + skim_do[('SOV_M_TIME', 'PM')]" +decay function,_decay, exp(_auPkTime * dispersion_parameter_automobile) +auto peak retail,auPkRetail,df.EMP_RET * _decay +auto peak total,auPkTotal,df.EMP_TOT * _decay +#,, +#,, auto off-peak +#,, +#,, assume midday occurs entirely in the midday period +off-peak round trip distance,_auOpTime,"skim_od[('SOV_M_TIME', 'MD')] + skim_do[('SOV_M_TIME', 'MD')]" +decay function,_decay, exp(_auOpTime * dispersion_parameter_automobile) +auto off-peak retail,auOpRetail,df.EMP_RET * _decay +auto off-peak total,auOpTotal,df.EMP_TOT * _decay +#,, +#,, transit peak +#,, assume peak outbound transit occurs in AM +o-d peak transit time,_inVehicletime_od,"skim_od[('WTW_TIV', 'AM')]" +o-d out of vehicle transit time,_outOfVehicleTime_od,"skim_od[('WTW_FWT', 'AM')] + skim_od[('WTW_XWT', 'AM')] + reindex(df.walk_dist_local_bus, df.orig) + skim_od[('WTW_AUX', 'AM')] + reindex(df.walk_dist_local_bus, df.dest)" +total o-d peak transit time,_trPkTime_od,(_inVehicletime_od + out_of_vehicle_time_weight * _outOfVehicleTime_od) +#,, assume peak inbound transit occurs in PM +o-d peak transit time,_inVehicletime_do,"skim_do[('WTW_TIV', 'PM')]" +o-d out of vehicle transit time,_outOfVehicleTime_do,"skim_do[('WTW_FWT', 'PM')] + skim_do[('WTW_XWT', 'PM')] + reindex(df.walk_dist_local_bus, df.orig) + skim_do[('WTW_AUX', 'PM')] + reindex(df.walk_dist_local_bus, df.dest)" +total o-d peak transit time,_trPkTime_do,(_inVehicletime_do + out_of_vehicle_time_weight * _outOfVehicleTime_do) +#,, +peak transit time,_trPkTime,(_trPkTime_od + _trPkTime_do).clip(0) +round trip path is available,_rt_available,(_trPkTime_od > 0) & (_trPkTime_do > 0) +decay function,_decay,_rt_available * exp(_trPkTime * dispersion_parameter_transit) +transit peak retail,trPkRetail,df.EMP_RET * _decay +transit peak total,trPkTotal,df.EMP_TOT * _decay +transit peak total,trPkHH,df.TOTHHS * _decay +####,, +####,, transit off-peak +####,, +####,, assume off-peak inbound and outbound transit occurs in the MD time period +o-d off-peak transit time,_trOpTime_od,"skim_od[('WTW_TIV', 'MD')]" +d-o off-peak transit time,_trOpTime_do,"skim_do[('WTW_TIV', 'MD')]" +off-peak transit time,_trOpTime,(_trOpTime_od + _trPkTime_do).clip(0) +round trip path is available,_rt_available,(_trOpTime_od > 0) & (_trOpTime_do > 0) +decay function,_decay,_rt_available * exp(_trOpTime * dispersion_parameter_transit) +transit off-peak retail,trOpRetail,df.EMP_RET * _decay +transit off-peak total,trOpTotal,df.EMP_TOT * _decay +#,, +#,, non motorized +#,, +non-motorized round trip distance,_nmDist,"skim_od[('SOV_M_DIST', 'MD')] + skim_do[('SOV_M_DIST', 'MD')]" +round trip path is available,_rt_available,_nmDist <= max_walk_distance +decay function,_decay,_rt_available * exp(_nmDist * dispersion_parameter_walk) +retail accessibility,nmRetail,df.EMP_RET * _decay +total accessibility,nmTotal,df.EMP_TOT * _decay diff --git a/resident/configs/accessibility.yaml b/resident/configs/accessibility.yaml new file mode 100644 index 0000000..b39b24d --- /dev/null +++ b/resident/configs/accessibility.yaml @@ -0,0 +1,2 @@ +# columns from land_use table to add to df +land_use_columns: ['EMP_RET', 'EMP_TOT', 'TOTHHS', 'walk_dist_local_bus'] diff --git a/resident/configs/annotate_disaggregate_accessibility.csv b/resident/configs/annotate_disaggregate_accessibility.csv new file mode 100644 index 0000000..821dda4 --- /dev/null +++ b/resident/configs/annotate_disaggregate_accessibility.csv @@ -0,0 +1,11 @@ +# annotating the proto_disaggregate_accessibilty table +Description,Target,Expression +workplace location for zero auto only,workplace_location_accessibility_0,"np.where(df.auto_ownership == 0, df.workplace_location_accessibility, np.nan)" +workplace location for auto deficient,workplace_location_accessibility_1,"np.where(df.auto_ownership == 1, df.workplace_location_accessibility, np.nan)" +workplace location for auto sufficient,workplace_location_accessibility_2,"np.where(df.auto_ownership == 2, df.workplace_location_accessibility, np.nan)" +other discretionary for zero auto only,othdiscr_accessibility_0,"np.where(df.auto_ownership == 0, df.othdiscr_accessibility, np.nan)" +other discretionary for auto deficient,othdiscr_accessibility_1,"np.where(df.auto_ownership ==1, df.othdiscr_accessibility, np.nan)" +other discretionary for auto sufficient,othdiscr_accessibility_2,"np.where(df.auto_ownership == 2, df.othdiscr_accessibility, np.nan)" +shopping for zero auto only,shopping_accessibility_0,"np.where(df.auto_ownership == 0, df.shopping_accessibility, np.nan)" +shopping for auto deficient,shopping_accessibility_1,"np.where(df.auto_ownership ==1, df.shopping_accessibility, np.nan)" +shopping for auto sufficient,shopping_accessibility_2,"np.where(df.auto_ownership == 2, df.shopping_accessibility, np.nan)" \ No newline at end of file diff --git a/resident/configs/annotate_households.csv b/resident/configs/annotate_households.csv new file mode 100644 index 0000000..1f8040c --- /dev/null +++ b/resident/configs/annotate_households.csv @@ -0,0 +1,33 @@ +Description,Target,Expression +#,, annotate households table after import +,_PERSON_COUNT,"lambda query, persons, households: persons.query(query).groupby('household_id').size().reindex(households.index).fillna(0).astype(np.int8)" +#,,FIXME households.income can be negative - so we clip? +income_in_thousands,_income_in_thousands,(households.income / 1000).clip(lower=0) +income_segment,income_segment,"pd.cut(_income_in_thousands, bins=[-np.inf, 30, 60, 100, np.inf], labels=[INCOME_SEGMENT_LOW, INCOME_SEGMENT_MED, INCOME_SEGMENT_HIGH, INCOME_SEGMENT_VERYHIGH]).astype(int)" +# FIXME move below expressions to their respective preprocessor files,, +#,,we assume that everyone 16 and older is a potential driver +number of drivers,num_drivers,"_PERSON_COUNT('16 <= age', persons, households)" +num_adults,num_adults,"_PERSON_COUNT('age >= 18', persons, households)" +num_children,num_children,"_PERSON_COUNT('age < 18', persons, households)" +num_young_children,num_young_children,"_PERSON_COUNT('age <= 5', persons, households)" +num_children_6_to_12,num_children_6_to_12,"_PERSON_COUNT('6 <= age <= 12', persons, households)" +num_children_5_to_15,num_children_5_to_15,"_PERSON_COUNT('5 <= age <= 15', persons, households)" +num_children_16_to_17,num_children_16_to_17,"_PERSON_COUNT('16 <= age <= 17', persons, households)" +num_gradeschool,num_gradeschool,"_PERSON_COUNT('6 <= age <= 17', persons, households)" +num_highschool,num_highschool,"_PERSON_COUNT('14 <= age <= 17', persons, households)" +num_college_age,num_college_age,"_PERSON_COUNT('18 <= age <= 24', persons, households)" +num_young_adults,num_young_adults,"_PERSON_COUNT('25 <= age <= 34', persons, households)" +number of non_workers,num_non_workers,households.hhsize - households.num_workers +number of pre-driving age children in the household,num_predrive_child,"_PERSON_COUNT('ptype == 7', persons, households)" +number of non-working adult in the household,num_nonworker_adults,"_PERSON_COUNT('ptype == 4', persons, households)" +number of full time workers,num_fullTime_workers,"_PERSON_COUNT('pemploy == 1', persons, households)" +number of part time workers,num_partTime_workers,"_PERSON_COUNT('pemploy == 2', persons, households)" +number of retired adults in the household,num_retired_adults,"_PERSON_COUNT('ptype == 5', persons, households)" +#,,FIXME is this the best replacement for area_type?? +home_is_urban,home_is_urban,"reindex(land_use.pseudomsa, households.home_zone_id) <= urban_threshold" +home_is_rural,home_is_rural,"reindex(land_use.pseudomsa, households.home_zone_id) > rural_threshold" +#,, +,num_hh_in_zone,"reindex(households.groupby('home_zone_id').size(), households.home_zone_id)" +#,multiple_auto_hh_in_zone,"reindex_i(df.groupby('home_zone_id').multiple_auto_hh.sum(), df.index)" +#,multiple_auto_hh_in_zone_share,multiple_auto_hh_in_zone/num_hh_in_zone +hh owns an ebike,ebike_owner,"rng.random_for_df(households)[:,0] < ebikeownership" \ No newline at end of file diff --git a/resident/configs/annotate_households_auto_ownership.csv b/resident/configs/annotate_households_auto_ownership.csv new file mode 100644 index 0000000..d0c6e63 --- /dev/null +++ b/resident/configs/annotate_households_auto_ownership.csv @@ -0,0 +1,15 @@ +Description,Target,Expression +#,, annotate households table after auto_ownership model has run +#,, need to select the appropriate accessibility value from the persons table for the household +,_wla0,persons_merged.groupby('household_id').workplace_location_accessibility_0.mean().reindex(df.index) +,_wla1,persons_merged.groupby('household_id').workplace_location_accessibility_1.mean().reindex(df.index) +,_wla2,persons_merged.groupby('household_id').workplace_location_accessibility_2.mean().reindex(df.index) +,workplace_location_accessibility,"np.where(df.auto_ownership == 0, _wla0, np.where(df.auto_ownership == 1, _wla1, _wla2))" +,_sha0,persons_merged.groupby('household_id').shopping_accessibility_0.mean().reindex(df.index) +,_sha1,persons_merged.groupby('household_id').shopping_accessibility_1.mean().reindex(df.index) +,_sha2,persons_merged.groupby('household_id').shopping_accessibility_2.mean().reindex(df.index) +,shopping_accessibility,"np.where(df.auto_ownership == 0, _sha0, np.where(df.auto_ownership == 1, _sha1, _sha2))" +,_otha0,persons_merged.groupby('household_id').othdiscr_accessibility_0.mean().reindex(df.index) +,_otha1,persons_merged.groupby('household_id').othdiscr_accessibility_1.mean().reindex(df.index) +,_otha2,persons_merged.groupby('household_id').othdiscr_accessibility_2.mean().reindex(df.index) +,othdiscr_accessibility,"np.where(df.auto_ownership == 0, _otha0, np.where(df.auto_ownership == 1, _otha1, _otha2))" diff --git a/resident/configs/annotate_households_cdap.csv b/resident/configs/annotate_households_cdap.csv new file mode 100644 index 0000000..52be731 --- /dev/null +++ b/resident/configs/annotate_households_cdap.csv @@ -0,0 +1,8 @@ +Description,Target,Expression +#,, annotate households table after cdap model has run +num_travel_active,num_travel_active,persons.travel_active.astype(int).groupby(persons.household_id).sum().reindex(households.index).fillna(0).astype(np.int8) +num_travel_active_adults,num_travel_active_adults,(persons.adult & persons.travel_active).astype(int).groupby(persons.household_id).sum().reindex(households.index).fillna(0).astype(np.int8) +num_travel_active_preschoolers,num_travel_active_preschoolers,((persons.ptype == PTYPE_PRESCHOOL) & persons.travel_active).astype(int).groupby(persons.household_id).sum().reindex(households.index).fillna(0).astype(np.int8) +num_travel_active_children,num_travel_active_children,num_travel_active - num_travel_active_adults +num_travel_active_non_preschoolers,num_travel_active_non_preschoolers,num_travel_active - num_travel_active_preschoolers +joint tours determined in cdap model,participates_in_jtf_model,(df.has_joint_tour == 1) diff --git a/resident/configs/annotate_households_vehicle_type.csv b/resident/configs/annotate_households_vehicle_type.csv new file mode 100644 index 0000000..a3e0a26 --- /dev/null +++ b/resident/configs/annotate_households_vehicle_type.csv @@ -0,0 +1,2 @@ +Description,Target,Expression +number of household AVs,numAVowned,"vehicles[vehicles['vehicle_type'].str.contains('-AV', na=False)].groupby('household_id').size().reindex(households.index).fillna(0)" \ No newline at end of file diff --git a/resident/configs/annotate_landuse.csv b/resident/configs/annotate_landuse.csv new file mode 100644 index 0000000..eb499a5 --- /dev/null +++ b/resident/configs/annotate_landuse.csv @@ -0,0 +1,19 @@ +Description,Target,Expression +#,, annotate landuse table after import +household_density,household_density,land_use.TOTHHS / land_use.ACRES.clip(lower=1) +population_density,population_density,land_use['TOTPOP'] / land_use.ACRES.clip(lower=1) +# FIXME pseudomsa is used in a few places but there is no equivalent in Metro landuse,, +,pseudomsa,0 +# FIXME need to decide on what the preschool target looks like,, +preschool target for mandatory constraint - sum of all size terms,preschool_target,land_use['TOTPOP'] + land_use.EMP_TOT +Is a zone with parking,is_parking_zone,"np.where(land_use.PRKSPACES > 0, True, False)" +employment_density,employment_density,(land_use['EMP_TOT'] / land_use.ACRES.clip(lower=1)).clip(upper=500) +density_index,density_index,(household_density *employment_density) / (household_density + employment_density).clip(lower=1) +# FIXME not sure what the mapping between parking_type in the sandag model is and our current parkingConstrainedArea +temporary parking type,parking_type,land_use['PARKAREA'] +# FIXME temporary density calculation -- again I don't think this calculation matches what's in sandag,, +dwelling unit density,duden,(land_use['TOTHHS'] / land_use.ACRES.clip(lower=1)).clip(upper=500) +population density,popden,(land_use['TOTPOP'] / land_use.ACRES.clip(lower=1)).clip(upper=500) +employment unit density,empden,(land_use['EMP_TOT'] / land_use.ACRES.clip(lower=1)).clip(upper=500) +retail employment density,retempden,(land_use['EMP_RET'] / land_use.ACRES.clip(lower=1)).clip(upper=500) +total park and ride capacity,PNR_CAP_TOTAL,(land_use['PNR_CAP_FORMAL'] + land_use['PNR_CAP_INFORMAL']) \ No newline at end of file diff --git a/resident/configs/annotate_persons.csv b/resident/configs/annotate_persons.csv new file mode 100644 index 0000000..0a0902e --- /dev/null +++ b/resident/configs/annotate_persons.csv @@ -0,0 +1,68 @@ +Description,Target,Expression +#,, annotate persons table after import +age_16_to_19,age_16_to_19,"persons.age.between(16, 19)" +age_16_p,age_16_p,persons.age >= 16 +adult,adult,persons.age >= 18 +# FIXME --are these SEX categories correct?,, +male,male,persons.SEX == 1 +female,female,persons.SEX == 2 +employment status type,pemploy,"pd.Series(np.zeros(len(persons)), index=persons.index)" +,pemploy,"np.where(persons.age < 16, PEMPLOY_CHILD, PEMPLOY_PART)" +,pemploy,"np.where((persons.age >= 16) & ((persons.ESR == 3) | (persons.ESR == 6)), PEMPLOY_NOT, pemploy)" +,pemploy,"np.where((persons.age>=16) & (persons.ESR != 3) & (persons.ESR != 6) & (persons.WKHP >= 35) & (persons.WKW >= 1) & (persons.WKW <= 4), PEMPLOY_FULL, pemploy)" +student category,pstudent,"pd.Series(np.zeros(len(persons)), index=persons.index)" +,pstudent,"np.where((pemploy == 1) & (persons.age >= 16), PSTUDENT_NOT, pstudent)" +,pstudent,"np.where((pemploy == 1) & (persons.age < 16), PSTUDENT_GRADE_OR_HIGH, pstudent)" +,pstudent,"np.where((persons.SCHG < 1) & (persons.age >= 16), PSTUDENT_NOT, pstudent)" +,pstudent,"np.where((persons.SCHG < 1) & (persons.age < 16), PSTUDENT_GRADE_OR_HIGH, pstudent)" +,pstudent,"np.where((persons.SCHG >= 15) & (persons.age >= 16) & (pemploy != 1), PSTUDENT_UNIVERSITY, pstudent)" +,pstudent,"np.where((persons.SCHG >= 15) & (persons.age < 16) & (pemploy != 1), PSTUDENT_GRADE_OR_HIGH, pstudent)" +,pstudent,"np.where((persons.age <= 19) & (pemploy != 1) & (persons.SCHG >=1) & (persons.SCHG<=14), PSTUDENT_GRADE_OR_HIGH, pstudent)" +,pstudent,"np.where((persons.age > 19) & (pemploy != 1) & (persons.SCHG >=1) & (persons.SCHG<=14), PSTUDENT_UNIVERSITY, pstudent)" +,pstudent,"np.where(pstudent == 0, 3, pstudent)" +person type,ptype,"pd.Series(np.zeros(len(persons)), index=persons.index)" +,ptype,"np.where((pemploy == 1), PTYPE_FULL, PTYPE_NONWORK)" +,ptype,"np.where((pstudent == 3) & (pemploy == 2), PTYPE_PART, ptype)" +,ptype,"np.where((pstudent == 3) & (persons.age >= 65) & ((pemploy == 3) | (pemploy == 4)), PTYPE_RETIRED, ptype)" +,ptype,"np.where((pstudent == 3) & (persons.age < 6) & ((pemploy == 3) | (pemploy == 4)), PTYPE_PRESCHOOL, ptype)" +,ptype,"np.where((pstudent == 3) & (persons.age >= 6) & (persons.age <= 64) & ((pemploy == 3) | (pemploy == 4)), PTYPE_NONWORK, ptype)" +,ptype,"np.where((pstudent == 2) & ((pemploy == 2) | (pemploy == 3) | (pemploy == 4)), PTYPE_UNIVERSITY, ptype)" +,ptype,"np.where((pstudent == 1) & (persons.age < 6) & ((pemploy == 2) | (pemploy == 3) | (pemploy == 4)), PTYPE_PRESCHOOL, ptype)" +,ptype,"np.where((pstudent == 1) & (persons.age >= 6) & ((pemploy == 2) | (pemploy == 3) | (pemploy == 4)), PTYPE_SCHOOL, ptype)" +# FIXME are these rules for preschoolers still wanted?,, +is_student,is_student,"np.isin(pstudent, [PSTUDENT_GRADE_OR_HIGH, PSTUDENT_UNIVERSITY])" +preschool age can go to preschool,is_student,"np.where(persons.age > GRADE_SCHOOL_MIN_AGE, is_student, True)" +preschool age can go to preschool,pstudent,"np.where(persons.age > GRADE_SCHOOL_MIN_AGE, pstudent, PSTUDENT_GRADE_OR_HIGH)" +is_preschool,_is_preschool,(pstudent == PSTUDENT_GRADE_OR_HIGH) & (persons.age <= GRADE_SCHOOL_MIN_AGE) +is_gradeschool,_is_gradeschool,(pstudent == PSTUDENT_GRADE_OR_HIGH) & (persons.age <= GRADE_SCHOOL_MAX_AGE) & (persons.age > GRADE_SCHOOL_MIN_AGE) +is_highschool,_is_highschool,(pstudent == PSTUDENT_GRADE_OR_HIGH) & (persons.age > GRADE_SCHOOL_MAX_AGE) +# is_university is actually hard coded in mandatory scheduling(!) so need it here,, +is_university,is_university,pstudent == PSTUDENT_UNIVERSITY +school_segment preschool,school_segment,"np.where(_is_preschool, SCHOOL_SEGMENT_PREK, SCHOOL_SEGMENT_NONE)" +school_segment gradeschool,school_segment,"np.where(_is_gradeschool, SCHOOL_SEGMENT_GRADE, school_segment)" +school_segment highschool,school_segment,"np.where(_is_highschool, SCHOOL_SEGMENT_HIGH, school_segment)" +school_segment university,school_segment,"np.where(is_university, SCHOOL_SEGMENT_UNIV, school_segment).astype(np.int8)" +#,, +is_worker,is_worker,"np.isin(pemploy, [PEMPLOY_FULL, PEMPLOY_PART])" +#extrnal model variable initialization treating everyone as internal initially,, +,is_internal_worker,is_worker +,is_external_worker,0 +#,, +home_zone_id,home_zone_id,"reindex(households.home_zone_id, persons.household_id)" +#,, +,_mean_work,distributed_time_factor_work_mean +,_stddev_work,distributed_time_factor_work_stddev +,_mean_nonwork,distributed_time_factor_nonwork_mean +,_stddev_nonwork,distributed_time_factor_nonwork_stddev +,_min_time_factor,distributed_time_factor_min +,_max_time_factor,distributed_time_factor_max +#,,"Below expression was previously done using np.clip, but was producing a NotImplemented error for mixed DataFrame and Series Inputs. Reimplemented using min & max" +travel time sensitivity factor for work travel,time_factor_work,"np.minimum(_max_time_factor, np.maximum(rng.lognormal_for_df(persons, mu=_mean_work, sigma=_stddev_work, scale=True), _min_time_factor))" +travel time sensitivity factor for non-work travel,time_factor_nonwork,"np.minimum(_max_time_factor, np.maximum(rng.lognormal_for_df(persons, mu=_mean_nonwork, sigma=_stddev_nonwork, scale=True), _min_time_factor)) " +#,, +# FIXME need naics and SOC codes ,, +,_naics_code,"np.where(persons.naics2_original_code=='3M', 3000, np.where(persons.naics2_original_code=='4M', 4000, np.where(persons.naics2_original_code=='MIL',9000, persons.naics2_original_code))) if 'naics2_original_code' in persons.columns else 0" +,naics_code,_naics_code.astype('int') if all(_naics_code!=0) else 0 +#,, +# FIXME does filling with -1 make sense here? depends on what the missing values mean,, +occupation categories mapped to workplace segments,occupation,persons.OCCCAT.fillna(-1).map(occupation_xwalk) diff --git a/resident/configs/annotate_persons_after_hh.csv b/resident/configs/annotate_persons_after_hh.csv new file mode 100644 index 0000000..5123de4 --- /dev/null +++ b/resident/configs/annotate_persons_after_hh.csv @@ -0,0 +1,15 @@ +Description,Target,Expression +#,, annotate persons table after annotate_households +,_hh_income,"reindex(households.income, persons.household_id)" +,_num_adults,"reindex(households.num_adults, persons.household_id)" +,_num_predrive_child,"reindex(households.num_predrive_child, persons.household_id)" +,_num_nonworker_adults,"reindex(households.num_nonworker_adults, persons.household_id)" +,_num_full_time_workers,"reindex(households.num_fullTime_workers, persons.household_id)" +Income less than 25K,is_income_less25K,(_hh_income)<25000 +Income 25K to 60K,is_income_25K_to_60K,((_hh_income)>=25000) & ((_hh_income)<60000) +Income 60K to 120K,is_income_60K_to_120K, ((_hh_income)>=60000) & ((_hh_income)<120000) +Income greater than 60K,is_income_greater60K,((_hh_income)>=60000) +Income greater than 120K,is_income_greater120K,((_hh_income)>=120000) +Presence of nonworker in HHs,is_non_worker_in_HH,_num_nonworker_adults>0 +all the adults in the HH are full time workers,is_all_adults_full_time_workers,(_num_adults) == (_num_full_time_workers) +Presence of predrive child in HHs,is_pre_drive_child_in_HH,_num_predrive_child>0 diff --git a/resident/configs/annotate_persons_cdap.csv b/resident/configs/annotate_persons_cdap.csv new file mode 100644 index 0000000..38eb42e --- /dev/null +++ b/resident/configs/annotate_persons_cdap.csv @@ -0,0 +1,3 @@ +Description,Target,Expression +#,, annotate persons table after cdap model has run +travel_active,travel_active,persons.cdap_activity != CDAP_ACTIVITY_HOME \ No newline at end of file diff --git a/resident/configs/annotate_persons_jtp.csv b/resident/configs/annotate_persons_jtp.csv new file mode 100644 index 0000000..a72c866 --- /dev/null +++ b/resident/configs/annotate_persons_jtp.csv @@ -0,0 +1,3 @@ +Description,Target,Expression +#,, annotate persons table after joint_tour_participation model has run +num_joint_tours,num_joint_tours,"joint_tour_participants.groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8)" diff --git a/resident/configs/annotate_persons_mtf.csv b/resident/configs/annotate_persons_mtf.csv new file mode 100644 index 0000000..47e4160 --- /dev/null +++ b/resident/configs/annotate_persons_mtf.csv @@ -0,0 +1,10 @@ +Description,Target,Expression +#,, annotate persons table after mandatory_tour_frequency model has run +,_PERSON_TOUR_COUNT,"lambda exp, persons, tours: tours.query(exp).groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8)" +,_Q,lambda s: "'{}'".format(s) +work_and_school_and_worker,work_and_school_and_worker,(persons.mandatory_tour_frequency == 'work_and_school') & persons.is_worker +work_and_school_and_student,work_and_school_and_student,(persons.mandatory_tour_frequency == 'work_and_school') & persons.is_student +number of mandatory tours for each person,num_mand,"_PERSON_TOUR_COUNT('tour_category==%s' % _Q('mandatory'), persons, tours).fillna(0)" +number of work tours for each person,num_work_tours,"_PERSON_TOUR_COUNT('tour_type==%s' % _Q('work'), persons, tours).fillna(0)" +presence of pre school kid with mandatory tours,has_pre_school_child_with_mandatory,"other_than(persons.household_id, (persons.ptype == 8) & (num_mand > 0))" +presense of driving age school children with mandatory tours,has_driving_age_child_with_mandatory,"other_than(persons.household_id, (persons.ptype == 6) & (num_mand > 0))" diff --git a/resident/configs/annotate_persons_nmtf.csv b/resident/configs/annotate_persons_nmtf.csv new file mode 100644 index 0000000..11f8b11 --- /dev/null +++ b/resident/configs/annotate_persons_nmtf.csv @@ -0,0 +1,15 @@ +Description,Target,Expression +#,, annotate persons table after non_mandatory_tour_frequency model has run +num_non_mand,num_non_mand,tours[tours.tour_category=='non_mandatory'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8) +num_escort_tours,num_escort_tours,tours[tours.tour_type == 'escort'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8) +num_eatout_tours,num_eatout_tours,tours[tours.tour_type == 'eatout'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8) +num_shop_tours,num_shop_tours,tours[tours.tour_type == 'shopping'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8) +num_maint_tours,num_maint_tours,tours[tours.tour_type == 'othmaint'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8) +num_discr_tours,num_discr_tours,tours[tours.tour_type == 'othdiscr'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8) +num_social_tours,num_social_tours,tours[tours.tour_type == 'social'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8) +num_non_escort_tours,num_non_escort_tours,num_non_mand-num_escort_tours +total shopping and maintenance tours,num_shop_maint_tours,num_shop_tours + num_maint_tours +"total shopping, maintenance and escort tours",num_shop_maint_escort_tours,num_shop_tours + num_maint_tours + num_escort_tours +number of additional shopping and maintenance tours,num_add_shop_maint_tours,"np.where (num_shop_maint_tours>0, 1, 0) * (num_shop_maint_tours - 1)" +total social and discretionary tours,num_soc_discr_tours,num_social_tours + num_discr_tours +number of additional social and discretionary,num_add_soc_discr_tours,"np.where (num_soc_discr_tours>0, 1, 0) * (num_soc_discr_tours - 1)" diff --git a/resident/configs/annotate_persons_school.csv b/resident/configs/annotate_persons_school.csv new file mode 100644 index 0000000..d6c18fe --- /dev/null +++ b/resident/configs/annotate_persons_school.csv @@ -0,0 +1,7 @@ +Description,Target,Expression +#,, annotate persons table after school_location model has run +,distance_to_school,"np.where(persons.school_zone_id >= 0,skim_dict.lookup(persons.home_zone_id, persons.school_zone_id, ('SOV_M_DIST', 'MD')), np.nan)" +#,, this uses the free flow travel time in both directions. MTC TM1 was MD and MD +temp auto_time_to_school,_auto_time_to_school,"skim_dict.lookup(persons.home_zone_id, persons.school_zone_id, ('SOV_M_TIME', 'MD'))" +temp auto_time_return,_auto_time_return,"skim_dict.lookup(persons.school_zone_id, persons.home_zone_id, ('SOV_M_TIME', 'MD'))" +free flow roundtrip_auto_time_to_school,roundtrip_auto_time_to_school,"np.where(persons.school_zone_id >= 0,_auto_time_to_school + _auto_time_return,0)" diff --git a/resident/configs/annotate_persons_workplace.csv b/resident/configs/annotate_persons_workplace.csv new file mode 100644 index 0000000..133f853 --- /dev/null +++ b/resident/configs/annotate_persons_workplace.csv @@ -0,0 +1,13 @@ +Description,Target,Expression +#,, annotate persons table after workplace_location model has run +,workplace_zone_id,"np.where(persons.get('external_workplace_zone_id',-1) > 0, persons.get('external_workplace_zone_id',-1), persons.workplace_zone_id)" +,workplace_location_logsum,"np.where(persons.get('external_workplace_zone_id',-1) > 0, persons.get('external_workplace_location_logsum',-1), persons.workplace_location_logsum)" +,workplace_modechoice_logsum,"np.where(persons.get('external_workplace_zone_id',-1) > 0, persons.get('external_workplace_modechoice_logsum',-1), persons.workplace_modechoice_logsum)" +,distance_to_work,"np.where(workplace_zone_id>=0,skim_dict.lookup(persons.home_zone_id, workplace_zone_id, ('SOV_M_DIST', 'MD')),np.nan)" +work_zone_area_type,work_zone_area_type,"reindex(land_use.pseudomsa, persons.workplace_zone_id)" +#,, auto time to work - free flow travel time in both directions. MTC TM1 was MD and MD +#,,roundtrip_auto_time_to_work +,auto_time_home_to_work,"skim_dict.lookup(persons.home_zone_id, workplace_zone_id, ('SOV_M_TIME', 'AM'))" +,_auto_time_work_to_home,"skim_dict.lookup(workplace_zone_id, persons.home_zone_id, ('SOV_M_TIME', 'PM'))" +,roundtrip_auto_time_to_work,"np.where(workplace_zone_id>=0, auto_time_home_to_work + _auto_time_work_to_home,0)" +Parking cost at work,exp_daily_work,"reindex(land_use.PRKCST_DAY, df.workplace_zone_id).fillna(0)" diff --git a/resident/configs/annotate_proto_persons.csv b/resident/configs/annotate_proto_persons.csv new file mode 100644 index 0000000..12ec1f4 --- /dev/null +++ b/resident/configs/annotate_proto_persons.csv @@ -0,0 +1,4 @@ +Description,Target,Expression +flag denoting proto person,is_proto_person,True +setting work time factor to 1 for disaggregate accessibilities,time_factor_work,1 +setting non-work time factor to 1 for disaggregate accessibilities,time_factor_nonwork,1 diff --git a/resident/configs/annotate_tours_external_joint_identification.csv b/resident/configs/annotate_tours_external_joint_identification.csv new file mode 100644 index 0000000..84de424 --- /dev/null +++ b/resident/configs/annotate_tours_external_joint_identification.csv @@ -0,0 +1,9 @@ +Description,Target,Expression +#,, annotate tours table after first external tour identification model has run +finding external workers,_is_external_worker,"reindex(persons.get('is_external_worker', False), df.person_id)" +#,, want to set external tour indicators for mandatory tours too +setting external work trips,is_external_tour,"np.where(_is_external_worker & (df.tour_category == 'mandatory') & (df.tour_type == 'work'), True, is_external_tour)" +setting internal work trips,is_external_tour,"np.where((~_is_external_worker) & (df.tour_category == 'mandatory') & (df.tour_type == 'work'), False, is_external_tour)" +#atwork subtours not created yet...,, +#atwork tours are internal,is_external_tour,"np.where((df.tour_category == 'atwork'), False, is_external_tour)" +setting corresponding internal tour indicator,is_internal_tour,"np.where(is_external_tour == True, False, True)" diff --git a/resident/configs/annotate_tours_tour_mode_choice.csv b/resident/configs/annotate_tours_tour_mode_choice.csv new file mode 100644 index 0000000..1ebe015 --- /dev/null +++ b/resident/configs/annotate_tours_tour_mode_choice.csv @@ -0,0 +1,9 @@ +Description,Target,Expression +non-motorized and walk tran and tnc set to null,selected_vehicle,np.nan +tours using sov option,selected_vehicle,"np.where(df.tour_mode.isin(['DRIVEALONE','PNR_LOC','PNR_PRM','PNR_MIX']), df.vehicle_occup_1, selected_vehicle)" +tours using sr2 option,selected_vehicle,"np.where(df.tour_mode.isin(['SHARED2','KNR_LOC', 'KNR_PRM', 'KNR_MIX']), df.vehicle_occup_2, selected_vehicle)" +tours using sr3 option,selected_vehicle,"np.where(df.tour_mode.isin(['SHARED3']), df['vehicle_occup_3.5'], selected_vehicle)" +parent tour vehicle,_parent_selected_vehicle,"reindex(df.selected_vehicle, df.parent_tour_id) if 'selected_vehicle' in df.columns else np.nan" +assign parent tour vehicle to atwork subtours,selected_vehicle,"np.where(df.tour_category == 'atwork', _parent_selected_vehicle, selected_vehicle)" +is non-driving tour,_is_non_driving_tour,"df.tour_mode.isin(['WALK','BIKE','WALK_LOC','WALK_PRM','WALK_MIX','TAXI','TNC_SINGLE','TNC_SHARED','SCH_BUS'])" +atwork subtours whose parent tour did not use a vehicle use non_hh_veh,selected_vehicle,"np.where(~_is_non_driving_tour & (df.tour_category == 'atwork') & pd.isna(selected_vehicle),'non_hh_veh', selected_vehicle)" diff --git a/resident/configs/atwork_subtour_destination.csv b/resident/configs/atwork_subtour_destination.csv new file mode 100644 index 0000000..bef8820 --- /dev/null +++ b/resident/configs/atwork_subtour_destination.csv @@ -0,0 +1,26 @@ +Label,Description,Expression,atwork +,,"_DIST@np.minimum(skims[('SOV_M_DIST', 'MD')], 20)",1 +,,_DIST_SQUARED@_DIST**2,1 +,,_DIST_CUBED@_DIST**3,1 +,,_DIST_LOGGED@np.log(_DIST + 0.001),1 +util_Distance,Distance,@_DIST,coef_dist_atwork +util_Distance_squared,Distance_squared,@_DIST_SQUARED,coef_distsqrd_atwork +util_Distance_fulltime_worker,Distance_fulltime_worker,@_DIST * (df.ptype==1),coef_dist_ftworker_atwork +util_Size_variable,Size_variable,@df['size_term'].apply(np.log1p),1 +util_No_attractions,No_attractions,@df['size_term']==0,-999 +util_mode_choice_logsum,Mode choice logsum,mode_choice_logsum,coef_mode_choice_logsum +#,,, +util_Calibration_Distance,Calibration_Distance,@_DIST,coef_Calibration_Distance_atwork +util_Calibration_Distance_squared,Calibration_Distance_squared,@_DIST_SQUARED,coef_Calibration_Distance_squared_atwork +util_Calibration_Distance_cubed,Calibration_Distance_cubed,@_DIST_CUBED,coef_Calibration_Distance_cubed_atwork +util_Calibration_Distance_logged,Calibration_Distance_logged,@_DIST_LOGGED,coef_Calibration_Distance_logged_atwork +util_ABM2_calibration_distance,ABM2_calibration_distance,@_DIST,coef_ABM2_calibration_distance_atwork +util_ABM2_calibration_distance_squared,ABM2_calibration_distance_squared,@_DIST_SQUARED,coef_ABM2_calibration_distance_squared_atwork +util_ABM2_calibration_distance_cubed,ABM2_calibration_distance_cubed,@_DIST_CUBED,coef_ABM2_calibration_distance_cubed_atwork +util_ABM2_calibration_distance_logged,ABM2_calibration_distance_logged,@_DIST_LOGGED,coef_ABM2_calibration_distance_logged_atwork +util_ABM2_calibration_0_1_miles,ABM2_calibration_0_1_miles,@(_DIST < 1),coef_ABM2_calibration_0_1_miles_atwork +util_ABM2_calibration_1_2_miles,ABM2_calibration_1_2_miles,@(_DIST < 2) * (_DIST >= 1),coef_ABM2_calibration_1_2_miles_atwork +util_ABM2_calibration_2_3_miles,ABM2_calibration_2_3_miles,@(_DIST < 3) * (_DIST >= 2),coef_ABM2_calibration_2_3_miles_atwork +util_ABM2_calibration_3_4_miles,ABM2_calibration_3_4_miles,@(_DIST < 4) * (_DIST >= 3),coef_ABM2_calibration_3_4_miles_atwork +util_ABM2_calibration_4_5_miles,ABM2_calibration_4_5_miles,@(_DIST < 5) * (_DIST >= 4),coef_ABM2_calibration_4_5_miles_atwork +util_ABM2_calibration_0_8_miles,ABM2_calibration_0_8_miles,@(_DIST < 8) * _DIST,coef_ABM2_calibration_0_8_miles_atwork diff --git a/resident/configs/atwork_subtour_destination.yaml b/resident/configs/atwork_subtour_destination.yaml new file mode 100644 index 0000000..232e1dc --- /dev/null +++ b/resident/configs/atwork_subtour_destination.yaml @@ -0,0 +1,33 @@ + +SPEC: atwork_subtour_destination.csv +SAMPLE_SPEC: atwork_subtour_destination_sample.csv +COEFFICIENTS: atwork_subtour_destination_coefficients.csv + +SAMPLE_SIZE: 30 + +SIZE_TERM_SELECTOR: atwork + +SEGMENTS: + - atwork + +ORIG_ZONE_ID: workplace_zone_id + +SIMULATE_CHOOSER_COLUMNS: + - person_id + - income_segment + - workplace_zone_id + - ptype + +LOGSUM_SETTINGS: tour_mode_choice.yaml + +# model-specific logsum-related settings +CHOOSER_ORIG_COL_NAME: workplace_zone_id +ALT_DEST_COL_NAME: alt_dest +IN_PERIOD: 14 +OUT_PERIOD: 14 + +# optional (comment out if not desired) +DEST_CHOICE_LOGSUM_COLUMN_NAME: destination_logsum + +# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if saved alt logsum table +DEST_CHOICE_SAMPLE_TABLE_NAME: tour_destination_sample diff --git a/resident/configs/atwork_subtour_destination_coefficients.csv b/resident/configs/atwork_subtour_destination_coefficients.csv new file mode 100644 index 0000000..7b13c01 --- /dev/null +++ b/resident/configs/atwork_subtour_destination_coefficients.csv @@ -0,0 +1,20 @@ +coefficient_name,value,constrain +coef_dist_atwork,-0.705802227,F +coef_distsqrd_atwork,0.014952721,F +coef_dist_ftworker_atwork,0.119028062,F +coef_mode_choice_logsum,0.5,T +#,, +coef_Calibration_Distance_atwork,-0.5666,T +coef_Calibration_Distance_squared_atwork,0.13,T +coef_Calibration_Distance_cubed_atwork,-0.0047,T +coef_Calibration_Distance_logged_atwork,-0.3494,T +coef_ABM2_calibration_distance_atwork,0.3065,T +coef_ABM2_calibration_distance_squared_atwork,-0.0037,T +coef_ABM2_calibration_distance_cubed_atwork,0,T +coef_ABM2_calibration_distance_logged_atwork,-1.5153,T +coef_ABM2_calibration_0_1_miles_atwork,-0.2192,T +coef_ABM2_calibration_1_2_miles_atwork,0.0836,T +coef_ABM2_calibration_2_3_miles_atwork,0.5432,T +coef_ABM2_calibration_3_4_miles_atwork,0.6058,T +coef_ABM2_calibration_4_5_miles_atwork,0.7145,T +coef_ABM2_calibration_0_8_miles_atwork,0.1737,T diff --git a/resident/configs/atwork_subtour_destination_sample.csv b/resident/configs/atwork_subtour_destination_sample.csv new file mode 100644 index 0000000..8c700b7 --- /dev/null +++ b/resident/configs/atwork_subtour_destination_sample.csv @@ -0,0 +1,7 @@ +Label,Description,Expression,atwork +,,"_DIST@skims[('SOV_M_DIST', 'MD')]",1 +util_Distance,Distance,"@_DIST.clip(0,20)",coef_dist_atwork +util_Distance_squared,Distance_squared,"@_DIST.clip(0,20)**2",coef_distsqrd_atwork +util_Distance_fulltime_worker,Distance_fulltime_worker,"@_DIST.clip(0,20) * (df.ptype==1)",coef_dist_ftworker_atwork +util_Size_variable,Size_variable,@df['size_term'].apply(np.log1p),1 +util_No_attractions,No_attractions,@df['size_term']==0,-999 diff --git a/resident/configs/atwork_subtour_frequency.csv b/resident/configs/atwork_subtour_frequency.csv new file mode 100644 index 0000000..36b1bf2 --- /dev/null +++ b/resident/configs/atwork_subtour_frequency.csv @@ -0,0 +1,23 @@ +Label,Expression,no_subtours,eat,business1,maint,business2,eat_business +util_dummy_for_full_time_worker,pemploy==1,coefficient_dummy_for_full_time_worker_no_subtours,coefficient_dummy_for_full_time_worker_eat,coefficient_dummy_for_full_time_worker_business1,coefficient_dummy_for_full_time_worker_maint,coefficient_dummy_for_full_time_worker_business2,coefficient_dummy_for_full_time_worker_eat_business +util_dummy_for_non_full_time_worker,pemploy!=1,coefficient_dummy_for_non_full_time_worker_no_subtours,coefficient_dummy_for_non_full_time_worker_eat,coefficient_dummy_for_non_full_time_worker_business1,coefficient_dummy_for_non_full_time_worker_maint,coefficient_dummy_for_non_full_time_worker_business2,coefficient_dummy_for_non_full_time_worker_eat_business +util_dummy_for_non_workers,"ptype in [4, 5]",coefficient_dummy_for_non_workers_no_subtours,coefficient_dummy_for_non_workers_eat,coefficient_dummy_for_non_workers_business1,coefficient_dummy_for_non_workers_maint,coefficient_dummy_for_non_workers_business2,coefficient_dummy_for_non_workers_eat_business +util_medium_hh_income_dummy,income_segment == 2,coefficient_medium_hh_income_dummy_no_subtours,coefficient_medium_hh_income_dummy_eat,coefficient_medium_hh_income_dummy_business1,coefficient_medium_hh_income_dummy_maint,coefficient_medium_hh_income_dummy_business2,coefficient_medium_hh_income_dummy_eat_business +util_high_hh_income_dummy,(income_segment > 2) & (income_segment < 5),coefficient_high_hh_income_dummy_no_subtours,coefficient_high_hh_income_dummy_eat,coefficient_high_hh_income_dummy_business1,coefficient_high_hh_income_dummy_maint,coefficient_high_hh_income_dummy_business2,coefficient_high_hh_income_dummy_eat_business +util_zero_cars_owned_by_hh_dummy, auto_ownership == 0,coefficient_zero_cars_owned_by_hh_dummy_no_subtours,coefficient_zero_cars_owned_by_hh_dummy_eat,coefficient_zero_cars_owned_by_hh_dummy_business1,coefficient_zero_cars_owned_by_hh_dummy_maint,coefficient_zero_cars_owned_by_hh_dummy_business2,coefficient_zero_cars_owned_by_hh_dummy_eat_business +util_individual_discretionary_tours_made_by_full_time_worker,@(df.pemploy==1)*df.num_discr_tours,coefficient_individual_discretionary_tours_made_by_full_time_worker_no_subtours,coefficient_individual_discretionary_tours_made_by_full_time_worker_eat,coefficient_individual_discretionary_tours_made_by_full_time_worker_business1,coefficient_individual_discretionary_tours_made_by_full_time_worker_maint,coefficient_individual_discretionary_tours_made_by_full_time_worker_business2,coefficient_individual_discretionary_tours_made_by_full_time_worker_eat_business +util_individual_discretionary_tours_made_by_part_time_worker,@(df.pemploy==2)*df.num_discr_tours,coefficient_individual_discretionary_tours_made_by_part_time_worker_no_subtours,coefficient_individual_discretionary_tours_made_by_part_time_worker_eat,coefficient_individual_discretionary_tours_made_by_part_time_worker_business1,coefficient_individual_discretionary_tours_made_by_part_time_worker_maint,coefficient_individual_discretionary_tours_made_by_part_time_worker_business2,coefficient_individual_discretionary_tours_made_by_part_time_worker_eat_business +util_individual_eating_out_tours_made_by_person,num_eatout_tours,coefficient_individual_eating_out_tours_made_by_person_no_subtours,coefficient_individual_eating_out_tours_made_by_person_eat,coefficient_individual_eating_out_tours_made_by_person_business1,coefficient_individual_eating_out_tours_made_by_person_maint,coefficient_individual_eating_out_tours_made_by_person_business2,coefficient_individual_eating_out_tours_made_by_person_eat_business +util_main_shop_escort_tours_allocated_to_full_time_worker,@(df.pemploy==1)*df.num_maint_shop_escort,coefficient_main_shop_escort_tours_allocated_to_full_time_worker_no_subtours,coefficient_main_shop_escort_tours_allocated_to_full_time_worker_eat,coefficient_main_shop_escort_tours_allocated_to_full_time_worker_business1,coefficient_main_shop_escort_tours_allocated_to_full_time_worker_maint,coefficient_main_shop_escort_tours_allocated_to_full_time_worker_business2,coefficient_main_shop_escort_tours_allocated_to_full_time_worker_eat_business +util_main_shop_escort_tours_allocated_to_part_time_worker,@(df.pemploy==2)*df.num_maint_shop_escort,coefficient_main_shop_escort_tours_allocated_to_part_time_worker_no_subtours,coefficient_main_shop_escort_tours_allocated_to_part_time_worker_eat,coefficient_main_shop_escort_tours_allocated_to_part_time_worker_business1,coefficient_main_shop_escort_tours_allocated_to_part_time_worker_maint,coefficient_main_shop_escort_tours_allocated_to_part_time_worker_business2,coefficient_main_shop_escort_tours_allocated_to_part_time_worker_eat_business +util_participation_in_joint_shop_main_eat_tours,num_joint_maint_shop_eat,coefficient_participation_in_joint_shop_main_eat_tours_no_subtours,coefficient_participation_in_joint_shop_main_eat_tours_eat,coefficient_participation_in_joint_shop_main_eat_tours_business1,coefficient_participation_in_joint_shop_main_eat_tours_maint,coefficient_participation_in_joint_shop_main_eat_tours_business2,coefficient_participation_in_joint_shop_main_eat_tours_eat_business +util_participation_in_joint_discretionary_tours,num_joint_discr,coefficient_participation_in_joint_discretionary_tours_no_subtours,coefficient_participation_in_joint_discretionary_tours_eat,coefficient_participation_in_joint_discretionary_tours_business1,coefficient_participation_in_joint_discretionary_tours_maint,coefficient_participation_in_joint_discretionary_tours_business2,coefficient_participation_in_joint_discretionary_tours_eat_business +util_log_of_the_work_tour_duration,@np.log(df.duration+0.5),coefficient_log_of_the_work_tour_duration_no_subtours,coefficient_log_of_the_work_tour_duration_eat,coefficient_log_of_the_work_tour_duration_business1,coefficient_log_of_the_work_tour_duration_maint,coefficient_log_of_the_work_tour_duration_business2,coefficient_log_of_the_work_tour_duration_eat_business +util_dummy_for_drive_alone_mode_for_work_tour,work_tour_is_SOV,coefficient_dummy_for_drive_alone_mode_for_work_tour_no_subtours,coefficient_dummy_for_drive_alone_mode_for_work_tour_eat,coefficient_dummy_for_drive_alone_mode_for_work_tour_business1,coefficient_dummy_for_drive_alone_mode_for_work_tour_maint,coefficient_dummy_for_drive_alone_mode_for_work_tour_business2,coefficient_dummy_for_drive_alone_mode_for_work_tour_eat_business +util_two_work_tours_by_person,num_work_tours==2,coefficient_two_work_tours_by_person_no_subtours,coefficient_two_work_tours_by_person_eat,coefficient_two_work_tours_by_person_business1,coefficient_two_work_tours_by_person_maint,coefficient_two_work_tours_by_person_business2,coefficient_two_work_tours_by_person_eat_business +util_workplace_urban_area_dummy,work_zone_area_type<4,coefficient_workplace_urban_area_dummy_no_subtours,coefficient_workplace_urban_area_dummy_eat,coefficient_workplace_urban_area_dummy_business1,coefficient_workplace_urban_area_dummy_maint,coefficient_workplace_urban_area_dummy_business2,coefficient_workplace_urban_area_dummy_eat_business +util_workplace_suburban_area_dummy,(work_zone_area_type>3) & (work_zone_area_type<6),coefficient_workplace_suburban_area_dummy_no_subtours,coefficient_workplace_suburban_area_dummy_eat,coefficient_workplace_suburban_area_dummy_business1,coefficient_workplace_suburban_area_dummy_maint,coefficient_workplace_suburban_area_dummy_business2,coefficient_workplace_suburban_area_dummy_eat_business +util_auto_accessibility_to_retail_for_work_taz,auOpRetail,coefficient_auto_accessibility_to_retail_for_work_taz_no_subtours,coefficient_auto_accessibility_to_retail_for_work_taz_eat,coefficient_auto_accessibility_to_retail_for_work_taz_business1,coefficient_auto_accessibility_to_retail_for_work_taz_maint,coefficient_auto_accessibility_to_retail_for_work_taz_business2,coefficient_auto_accessibility_to_retail_for_work_taz_eat_business +util_walk_accessibility_to_retail_for_work_taz,nmRetail,coefficient_walk_accessibility_to_retail_for_work_taz_no_subtours,coefficient_walk_accessibility_to_retail_for_work_taz_eat,coefficient_walk_accessibility_to_retail_for_work_taz_business1,coefficient_walk_accessibility_to_retail_for_work_taz_maint,coefficient_walk_accessibility_to_retail_for_work_taz_business2,coefficient_walk_accessibility_to_retail_for_work_taz_eat_business +util_dummy_for_worker_or_student_with_non_mandatory_tour,(is_worker | is_student) * num_non_mand,coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_no_subtours,coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_eat,coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_business1,coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_maint,coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_business2,coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_eat_business +util_at_work_sub_tour_alternative_specific_constant,1,coefficient_at_work_sub_tour_asc_no_subtours,coefficient_at_work_sub_tour_asc_eat,coefficient_at_work_sub_tour_asc_business1,coefficient_at_work_sub_tour_asc_maint,coefficient_at_work_sub_tour_asc_business2,coefficient_at_work_sub_tour_asc_eat_business diff --git a/resident/configs/atwork_subtour_frequency.yaml b/resident/configs/atwork_subtour_frequency.yaml new file mode 100644 index 0000000..ed3ab3e --- /dev/null +++ b/resident/configs/atwork_subtour_frequency.yaml @@ -0,0 +1,11 @@ + +SPEC: atwork_subtour_frequency.csv +COEFFICIENTS: atwork_subtour_frequency_coefficients.csv + +preprocessor: + SPEC: atwork_subtour_frequency_annotate_tours_preprocessor + DF: df + TABLES: + - land_use + - tours + - joint_tour_participants diff --git a/resident/configs/atwork_subtour_frequency_alternatives.csv b/resident/configs/atwork_subtour_frequency_alternatives.csv new file mode 100644 index 0000000..ba99419 --- /dev/null +++ b/resident/configs/atwork_subtour_frequency_alternatives.csv @@ -0,0 +1,8 @@ +#,,,alt file for building tours even though simulation is simple_simulate not interaction_simulate +alt,eat,business,maint +no_subtours,0,0,0 +eat,1,0,0 +business1,0,1,0 +maint,0,0,1 +business2,0,2,0 +eat_business,1,1,0 diff --git a/resident/configs/atwork_subtour_frequency_annotate_tours_preprocessor.csv b/resident/configs/atwork_subtour_frequency_annotate_tours_preprocessor.csv new file mode 100644 index 0000000..3e4f151 --- /dev/null +++ b/resident/configs/atwork_subtour_frequency_annotate_tours_preprocessor.csv @@ -0,0 +1,9 @@ +Description,Target,Expression +,num_maint_shop_escort,df.num_maint_tours+df.num_shop_tours+df.num_escort_tours +#,num_joint_maint_shop_eat,"reindex_i(tours[(tours.tour_category=='joint') & tours.tour_type.isin(['othmaint', 'shopping', 'eatout'])].groupby('person_id').size(), df.person_id)" +#,num_eatout_tours,"reindex_i(tours[~tours.is_joint & (tours.tour_type==EATOUT_TOUR)].groupby('person_id').size(), df.person_id)" +joint tour participants annotated with tour type,_PARTICIPANTS,"pd.merge(joint_tour_participants[['tour_id', 'person_id']], tours[['tour_type']], left_on='tour_id', right_index=True, how='left')" +,num_joint_discr,"reindex_i(_PARTICIPANTS[_PARTICIPANTS.tour_type=='othdiscr'].groupby('person_id').size(), df.person_id)" +,num_joint_maint_shop_eat,"reindex_i(_PARTICIPANTS[_PARTICIPANTS.tour_type.isin(['othmaint', 'shopping', 'eatout'])].groupby('person_id').size(), df.person_id)" +#,, +,work_tour_is_SOV,"df.tour_mode.isin(['DRIVEALONEFREE','DRIVEALONEPAY'])" diff --git a/resident/configs/atwork_subtour_frequency_coefficients.csv b/resident/configs/atwork_subtour_frequency_coefficients.csv new file mode 100644 index 0000000..f719a69 --- /dev/null +++ b/resident/configs/atwork_subtour_frequency_coefficients.csv @@ -0,0 +1,133 @@ +coefficient_name,value,constrain +coefficient_dummy_for_full_time_worker_business1,-7.375,F +coefficient_dummy_for_full_time_worker_business2,-14.28,F +coefficient_dummy_for_full_time_worker_eat,-7.28,F +coefficient_dummy_for_full_time_worker_eat_business,-14.79,F +coefficient_dummy_for_full_time_worker_maint,-8.093,F +coefficient_dummy_for_full_time_worker_no_subtours,-0.6,F +coefficient_dummy_for_non_full_time_worker_business1,-8.319,F +coefficient_dummy_for_non_full_time_worker_business2,-14.28,F +coefficient_dummy_for_non_full_time_worker_eat,-8.604,F +coefficient_dummy_for_non_full_time_worker_eat_business,-14.79,F +coefficient_dummy_for_non_full_time_worker_maint,-8.214,F +coefficient_dummy_for_non_full_time_worker_no_subtours,-0.6,F +coefficient_dummy_for_non_workers_business1,-5,T +coefficient_dummy_for_non_workers_business2,-5,T +coefficient_dummy_for_non_workers_eat,0,T +coefficient_dummy_for_non_workers_eat_business,-5,T +coefficient_dummy_for_non_workers_maint,-5,T +coefficient_dummy_for_non_workers_no_subtours,0,T +coefficient_medium_hh_income_dummy_business1,0.5555,F +coefficient_medium_hh_income_dummy_business2,1.111,F +coefficient_medium_hh_income_dummy_eat,0.61,F +coefficient_medium_hh_income_dummy_eat_business,1.1655,F +coefficient_medium_hh_income_dummy_maint,0.1527,F +coefficient_medium_hh_income_dummy_no_subtours,0,T +coefficient_high_hh_income_dummy_business1,1.066,F +coefficient_high_hh_income_dummy_business2,2.132,F +coefficient_high_hh_income_dummy_eat,0.8693,F +coefficient_high_hh_income_dummy_eat_business,1.9353,F +coefficient_high_hh_income_dummy_maint,0.1651,F +coefficient_high_hh_income_dummy_no_subtours,0,T +coefficient_zero_cars_owned_by_hh_dummy_business1,-0.3391,F +coefficient_zero_cars_owned_by_hh_dummy_business2,0,T +coefficient_zero_cars_owned_by_hh_dummy_eat,0,T +coefficient_zero_cars_owned_by_hh_dummy_eat_business,-0.3391,F +coefficient_zero_cars_owned_by_hh_dummy_maint,0.1762,F +coefficient_zero_cars_owned_by_hh_dummy_no_subtours,0,T +coefficient_individual_discretionary_tours_made_by_full_time_worker_business1,0.7045,F +coefficient_individual_discretionary_tours_made_by_full_time_worker_business2,1.409,F +coefficient_individual_discretionary_tours_made_by_full_time_worker_eat,0.2334,F +coefficient_individual_discretionary_tours_made_by_full_time_worker_eat_business,0.9379,F +coefficient_individual_discretionary_tours_made_by_full_time_worker_maint,0.5061,F +coefficient_individual_discretionary_tours_made_by_full_time_worker_no_subtours,0,T +coefficient_individual_discretionary_tours_made_by_part_time_worker_business1,0.7045,F +coefficient_individual_discretionary_tours_made_by_part_time_worker_business2,1.409,F +coefficient_individual_discretionary_tours_made_by_part_time_worker_eat,0.6776,F +coefficient_individual_discretionary_tours_made_by_part_time_worker_eat_business,1.3821,F +coefficient_individual_discretionary_tours_made_by_part_time_worker_maint,0.5061,F +coefficient_individual_discretionary_tours_made_by_part_time_worker_no_subtours,0,T +coefficient_individual_eating_out_tours_made_by_person_business1,0.5434,F +coefficient_individual_eating_out_tours_made_by_person_business2,1.0868,F +coefficient_individual_eating_out_tours_made_by_person_eat,0.5491,F +coefficient_individual_eating_out_tours_made_by_person_eat_business,1.0925,F +coefficient_individual_eating_out_tours_made_by_person_maint,0.9166,F +coefficient_individual_eating_out_tours_made_by_person_no_subtours,0,T +coefficient_main_shop_escort_tours_allocated_to_full_time_worker_business1,-0.1903,F +coefficient_main_shop_escort_tours_allocated_to_full_time_worker_business2,-0.3806,F +coefficient_main_shop_escort_tours_allocated_to_full_time_worker_eat,0.052,F +coefficient_main_shop_escort_tours_allocated_to_full_time_worker_eat_business,-0.2423,F +coefficient_main_shop_escort_tours_allocated_to_full_time_worker_maint,0.1446,F +coefficient_main_shop_escort_tours_allocated_to_full_time_worker_no_subtours,0,T +coefficient_main_shop_escort_tours_allocated_to_part_time_worker_business1,-0.1903,F +coefficient_main_shop_escort_tours_allocated_to_part_time_worker_business2,-0.3806,F +coefficient_main_shop_escort_tours_allocated_to_part_time_worker_eat,-0.3099,F +coefficient_main_shop_escort_tours_allocated_to_part_time_worker_eat_business,-0.5002,F +coefficient_main_shop_escort_tours_allocated_to_part_time_worker_maint,-0.2723,F +coefficient_main_shop_escort_tours_allocated_to_part_time_worker_no_subtours,0,T +coefficient_participation_in_joint_shop_main_eat_tours_business1,0.083,F +coefficient_participation_in_joint_shop_main_eat_tours_business2,0.166,F +coefficient_participation_in_joint_shop_main_eat_tours_eat,0.2458,F +coefficient_participation_in_joint_shop_main_eat_tours_eat_business,0.3288,F +coefficient_participation_in_joint_shop_main_eat_tours_maint,0.0803,F +coefficient_participation_in_joint_shop_main_eat_tours_no_subtours,0,T +coefficient_participation_in_joint_discretionary_tours_business1,-0.2637,F +coefficient_participation_in_joint_discretionary_tours_business2,-0.5274,F +coefficient_participation_in_joint_discretionary_tours_eat,0.3588,F +coefficient_participation_in_joint_discretionary_tours_eat_business,0.0951,F +coefficient_participation_in_joint_discretionary_tours_maint,0.5822,F +coefficient_participation_in_joint_discretionary_tours_no_subtours,0,T +coefficient_log_of_the_work_tour_duration_business1,1.142,F +coefficient_log_of_the_work_tour_duration_business2,2.284,F +coefficient_log_of_the_work_tour_duration_eat,1.55,F +coefficient_log_of_the_work_tour_duration_eat_business,2.692,F +coefficient_log_of_the_work_tour_duration_maint,1.659,F +coefficient_log_of_the_work_tour_duration_no_subtours,0,T +coefficient_dummy_for_drive_alone_mode_for_work_tour_business1,0.9901,F +coefficient_dummy_for_drive_alone_mode_for_work_tour_business2,1.9802,F +coefficient_dummy_for_drive_alone_mode_for_work_tour_eat,0.4804,F +coefficient_dummy_for_drive_alone_mode_for_work_tour_eat_business,1.4705,F +coefficient_dummy_for_drive_alone_mode_for_work_tour_maint,1.153,F +coefficient_dummy_for_drive_alone_mode_for_work_tour_no_subtours,0,T +coefficient_two_work_tours_by_person_business1,0.3753,F +coefficient_two_work_tours_by_person_business2,0.7506,F +coefficient_two_work_tours_by_person_eat,-0.9862,F +coefficient_two_work_tours_by_person_eat_business,-0.6109,F +coefficient_two_work_tours_by_person_maint,-0.2312,F +coefficient_two_work_tours_by_person_no_subtours,0,T +coefficient_workplace_urban_area_dummy_business1,-0.2235,F +coefficient_workplace_urban_area_dummy_business2,-0.447,F +coefficient_workplace_urban_area_dummy_eat,-0.4182,F +coefficient_workplace_urban_area_dummy_eat_business,-0.6417,F +coefficient_workplace_urban_area_dummy_maint,-0.1479,F +coefficient_workplace_urban_area_dummy_no_subtours,0,T +coefficient_workplace_suburban_area_dummy_business1,-0.1102,F +coefficient_workplace_suburban_area_dummy_business2,-0.2204,F +coefficient_workplace_suburban_area_dummy_eat,-0.2916,F +coefficient_workplace_suburban_area_dummy_eat_business,-0.4018,F +coefficient_workplace_suburban_area_dummy_maint,0,T +coefficient_workplace_suburban_area_dummy_no_subtours,0,T +coefficient_auto_accessibility_to_retail_for_work_taz_business1,0.0534,F +coefficient_auto_accessibility_to_retail_for_work_taz_business2,0.1067,F +coefficient_auto_accessibility_to_retail_for_work_taz_eat,0.015,F +coefficient_auto_accessibility_to_retail_for_work_taz_eat_business,0.0683,F +coefficient_auto_accessibility_to_retail_for_work_taz_maint,0.0265,F +coefficient_auto_accessibility_to_retail_for_work_taz_no_subtours,0,T +coefficient_walk_accessibility_to_retail_for_work_taz_business1,0,T +coefficient_walk_accessibility_to_retail_for_work_taz_business2,0,T +coefficient_walk_accessibility_to_retail_for_work_taz_eat,0.06,F +coefficient_walk_accessibility_to_retail_for_work_taz_eat_business,0.06,F +coefficient_walk_accessibility_to_retail_for_work_taz_maint,0.04,F +coefficient_walk_accessibility_to_retail_for_work_taz_no_subtours,0,T +coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_business1,0,T +coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_business2,0,T +coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_eat,0,T +coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_eat_business,0,T +coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_maint,-0.3573,F +coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_no_subtours,0,T +coefficient_at_work_sub_tour_asc_business1,-0.5372,F +coefficient_at_work_sub_tour_asc_business2,-2.1337,F +coefficient_at_work_sub_tour_asc_eat,0.8576,F +coefficient_at_work_sub_tour_asc_eat_business,-0.9721,F +coefficient_at_work_sub_tour_asc_maint,-0.6198,F +coefficient_at_work_sub_tour_asc_no_subtours,0,T diff --git a/resident/configs/auto_ownership.csv b/resident/configs/auto_ownership.csv new file mode 100644 index 0000000..144f617 --- /dev/null +++ b/resident/configs/auto_ownership.csv @@ -0,0 +1,31 @@ +Label,Description,Expression,cars0,cars1,cars2,cars3,cars4 +util_drivers_1,alternative specific constant for 1 driver household,@df.num_drivers==1,coef_alternative_specific_constant_for_1_driver_household_0_CARS,,coef_alternative_specific_constant_for_1_driver_household_2_CARS,coef_alternative_specific_constant_for_1_driver_household_3_CARS,coef_alternative_specific_constant_for_1_driver_household_4_CARS +util_drivers_2,alternative specific constant for 2 driver household,@df.num_drivers==2,coef_alternative_specific_constant_for_2_driver_household_0_CARS,coef_alternative_specific_constant_for_2_driver_household_1_CAR,,coef_alternative_specific_constant_for_2_driver_household_3_CARS,coef_alternative_specific_constant_for_2_driver_household_4_CARS +util_drivers_3,alternative specific constant for 3 driver household,@df.num_drivers==3,coef_alternative_specific_constant_for_3_driver_household_0_CARS,coef_alternative_specific_constant_for_3_driver_household_1_CAR,coef_alternative_specific_constant_for_3_driver_household_2_CARS,,coef_alternative_specific_constant_for_3_driver_household_4_CARS +util_drivers_4p,alternative specific constant for 4+ driver household,@df.num_drivers>=4,coef_alternative_specific_constant_for_4_driver_household_0_CARS,coef_alternative_specific_constant_for_4_driver_household_1_CAR,coef_alternative_specific_constant_for_4_driver_household_2_CARS,coef_alternative_specific_constant_for_4_driver_household_3_CARS, +util_has_0_4,Presence of children age 0-4,(num_young_children>0),coef_cars0_has_0_4,,coef_cars2_has_0_4,coef_cars34_has_0_4,coef_cars34_has_0_4 +util_has_18_24,Presence of persons age 18-24,(num_college_age > 0),coef_cars0_has_18_24,,coef_cars2_has_18_24,coef_cars34_has_18_24,coef_cars34_has_18_24 +util_has_25_34,Presence of persons age 35-34,(num_young_adults > 0),coef_cars0_has_25_34,,coef_cars2_has_25_34,coef_cars34_has_25_34,coef_cars34_has_25_34 +util_has_80plus,Presence of persons age 80+,(num_old_retirees > 0),coef_cars0_has_80plus,,coef_cars2_has_80plus,coef_cars34_has_80plus,coef_cars34_has_80plus +util_has_1_worker,Has one worker,@df.num_workers==1,coef_cars0_workers_1,,coef_cars2_workers_1,coef_cars34_workers_1,coef_cars34_workers_1 +util_has_2_workers,Has two workers,@df.num_workers==2,coef_cars0_workers_2,,coef_cars2_workers_2,coef_cars34_workers_2,coef_cars34_workers_1 +util_has_3plus_workers,Has three or more workers,@df.num_workers>2,coef_cars0_workers_3plus,,coef_cars2_workers_3plus,coef_cars34_workers_3plus,coef_cars34_workers_3plus +util_hh_income_verylow,household income <15k,@(df.income<15000),coef_household_income_15k_0_CARS,,coef_household_income_15k_2_CARS,coef_household_income_15k_3_CARS,coef_household_income_15k_4_CARS +util_hh_income_low,household income 15k-30k,@((df.income>=15000) & (df.income<30000)),coef_household_income_1530k_0_CARS,,coef_household_income_1530k_2_CARS,coef_household_income_1530k_3_CARS,coef_household_income_1530k_4_CARS +util_hh_income_mid,household income 30-60k,@((df.income>=30000) & (df.income<60000)),coef_household_income_3060k_0_CARS,,coef_household_income_3060k_2_CARS,coef_household_income_3060k_3_CARS,coef_household_income_3060k_4_CARS +util_hh_income_veryhigh,household income 100k+,@(df.income>=100000),coef_household_income_100k_0_CARS,,coef_household_income_100k_2_CARS,coef_household_income_100k_3_CARS,coef_household_income_100k_4_CARS +# FIXME no bldgsz equivalent in the Metro data,,,,,, +# util_attached,attached dwelling,"@np.where(df.bldgsz == 2, 0, 1)",coef_attached_0_CARS,,coef_attached_2_CARS,coef_attached_3_CARS,coef_attached_4_CARS +# 0.785 sq miles in 1/2 mile radius * 640 acres per square mile = 502,,,,,,, +util_intersection_density,Intersection count in 1/2 mile radius of household,totint/502,coef_intersection_density_0_CARS,,coef_intersection_density_2_CARS,coef_intersection_density_3_CARS,coef_intersection_density_4_CARS +util_pop_density,Population Density in 1/2 mile radius of household,popden,coef_population_density_0_CARS,,coef_population_density_2_CARS,coef_population_density_34_CARS,coef_population_density_34_CARS +util_emp_density,Employment Density in 1/2 mile radius of household,empden,coef_emp_density_0_CARS,,coef_emp_density_2_CARS,coef_emp_density_3_CARS,coef_emp_density_4_CARS +util_nm_accessibility,Non-motorized zonal accessbility,nmRetail,coef_nonmotorized_zonal_accessbility_0_CARS,,coef_nonmotorized_zonal_accessbility_2_CARS,coef_nonmotorized_zonal_accessbility_3_CARS,coef_nonmotorized_zonal_accessbility_4_CARS +# Disaggregate accessibilities,,,,,,, +util_nonmand_logsum_0auto,Average non-mandatory disaggrage accessibility -- 0 autos,(shopping_accessibility_0 + othdiscr_accessibility_0) / 2,coef_nonmand_logsum_0,,,, +util_nonmand_logsum_1auto,Average non-mandatory disaggrage accessibility -- 1 autos,(shopping_accessibility_1 + othdiscr_accessibility_1) / 2,,coef_nonmand_logsum_1,,, +util_nonmand_logsum_2pauto,Average non-mandatory disaggrage accessibility -- 2+ autos,(shopping_accessibility_2 + othdiscr_accessibility_2) / 2,,,coef_nonmand_logsum_2p,coef_nonmand_logsum_2p,coef_nonmand_logsum_2p +util_asc,alternative specific constant adjustment for all households,1,asc_allhhs_0_autos,,asc_allhhs_2_autos,asc_allhs_3_autos,asc_allhhs_4plus_autos +util_asc_gq,alternative specific constant adjustment for GQ households,@(df.HHT==0),asc_GQ_0_autos,,coef_unavailable,coef_unavailable,coef_unavailable +# coefficients for av ownership,,,,,,, +#util_household_owns_av,Decreasing the number of household autos if hh owns an AV,av_ownership,coef_unavailable,,coef_cars2_av_ownership,coef_cars3_av_ownership,coef_unavailable diff --git a/resident/configs/auto_ownership.yaml b/resident/configs/auto_ownership.yaml new file mode 100644 index 0000000..ecfc122 --- /dev/null +++ b/resident/configs/auto_ownership.yaml @@ -0,0 +1,19 @@ + +SPEC: auto_ownership.csv +COEFFICIENTS: auto_ownership_coefficients.csv + +LOGIT_TYPE: MNL + +preprocessor: + SPEC: auto_ownership_preprocessor + DF: choosers + TABLES: + - persons + - households + - persons_merged + +annotate_households: + SPEC: annotate_households_auto_ownership + DF: households + TABLES: + - persons_merged \ No newline at end of file diff --git a/resident/configs/auto_ownership_coefficients.csv b/resident/configs/auto_ownership_coefficients.csv new file mode 100644 index 0000000..fe526af --- /dev/null +++ b/resident/configs/auto_ownership_coefficients.csv @@ -0,0 +1,99 @@ +coefficient_name,value,constrain +coef_alternative_specific_constant_for_1_driver_household_0_CARS,-3.400614855,F +coef_alternative_specific_constant_for_1_driver_household_2_CARS,-0.352797459,F +coef_alternative_specific_constant_for_1_driver_household_3_CARS,-1.65228754,F +coef_alternative_specific_constant_for_1_driver_household_4_CARS,-2.217521436,F +coef_alternative_specific_constant_for_2_driver_household_0_CARS,-5.644614423,F +coef_alternative_specific_constant_for_2_driver_household_1_CAR,-2.078124802,F +coef_alternative_specific_constant_for_2_driver_household_3_CARS,-1.110305396,F +coef_alternative_specific_constant_for_2_driver_household_4_CARS,-1.672613333,F +coef_alternative_specific_constant_for_3_driver_household_0_CARS,-5.663986002,F +coef_alternative_specific_constant_for_3_driver_household_1_CAR,-2.219196761,F +coef_alternative_specific_constant_for_3_driver_household_2_CARS,-0.428191009,F +coef_alternative_specific_constant_for_3_driver_household_4_CARS,-0.158614724,F +coef_alternative_specific_constant_for_4_driver_household_0_CARS,-6.09109227,F +coef_alternative_specific_constant_for_4_driver_household_1_CAR,-3.245812862,F +coef_alternative_specific_constant_for_4_driver_household_2_CARS,-1.256377783,F +coef_alternative_specific_constant_for_4_driver_household_3_CARS,-0.736107251,F +coef_cars0_has_16_17,0,F +coef_cars0_has_18_24,0.303347961,F +coef_cars0_has_25_34,0,T +coef_cars0_has_65_79,0,T +coef_cars0_has_80plus,0,T +coef_cars0_has_0_4,0,T +coef_cars0_has_5_17,0,T +coef_cars0_workers_1,-0.626860919,F +coef_cars0_workers_2,-0.687643006,F +coef_cars0_workers_3plus,1.247591794,F +coef_household_income_15k_0_CARS,2.026792987,F +coef_household_income_1530k_0_CARS,1.115116386,F +coef_household_income_3060k_0_CARS,0,T +coef_household_income_100k_0_CARS,-0.901938407,F +coef_attached_0_CARS,0.583266391,F +coef_intersection_density_0_CARS,0.788935628,F +coef_population_density_0_CARS,0.016016885,F +coef_emp_density_0_CARS,0.014187024,F +coef_nonmotorized_zonal_accessbility_0_CARS,0.304730094,F +coef_autotransit_zonal_accessibility_0_CARS,0,T +coef_cars2_has_16_17,0,F +coef_cars2_has_18_24,0,T +coef_cars2_has_25_34,0,T +coef_cars2_has_0_4,0.588834468,F +coef_cars2_has_5_17,0,T +coef_cars2_has_65_79,0,T +coef_cars2_has_80plus,-0.718935811,F +coef_cars2_workers_1,0,T +coef_cars2_workers_2,0.775530112,F +coef_cars2_workers_3plus,0,T +coef_household_income_15k_2_CARS,-1.264502448,F +coef_household_income_1530k_2_CARS,-0.823659861,F +coef_household_income_3060k_2_CARS,-0.284592043,F +coef_household_income_100k_2_CARS,0,T +coef_attached_2_CARS,-0.65382238,F +coef_intersection_density_2_CARS,0.00678398,F +coef_population_density_2_CARS,-0.012314708,F +coef_emp_density_2_CARS,0,T +coef_nonmotorized_zonal_accessbility_2_CARS,-0.051503169,F +coef_autotransit_zonal_accessibility_2_CARS,0,T +coef_cars34_has_16_17,0,T +coef_cars34_has_18_24,0,T +coef_cars34_has_25_34,-0.205410684,F +coef_cars34_has_0_4,0,T +coef_cars34_has_5_17,0,T +coef_cars34_has_65_79,0,T +coef_cars34_has_80plus,-0.973202303,F +coef_cars34_workers_1,0.415094751,F +coef_cars34_workers_2,0.964206165,F +coef_cars34_workers_3plus,1.190507925,F +coef_household_income_15k_3_CARS,-1.827402022,F +coef_household_income_1530k_3_CARS,-1.243285944,F +coef_household_income_3060k_3_CARS,-0.510748344,F +coef_household_income_100k_3_CARS,0,T +coef_attached_3_CARS,-1.426386743,F +coef_intersection_density_3_CARS,-0.875111252,F +coef_population_density_34_CARS,-0.015701116,F +coef_emp_density_3_CARS,-0.01090242,F +coef_nonmotorized_zonal_accessbility_3_CARS,-0.068584652,F +coef_autotransit_zonal_accessibility_3_CARS,-0.000872604,F +coef_cars4_num_workers_clip_3,0,F +coef_household_income_15k_4_CARS,-2.743515835,F +coef_household_income_1530k_4_CARS,-2.050237487,F +coef_household_income_3060k_4_CARS,-0.903012312,F +coef_household_income_100k_4_CARS,0,T +coef_attached_4_CARS,-1.85939905,F +coef_intersection_density_4_CARS,-0.593060324,F +coef_emp_density_4_CARS,-0.016815877,F +coef_nonmotorized_zonal_accessbility_4_CARS,-0.133879458,F +coef_autotransit_zonal_accessibility_4_CARS,-0.002525807,F +coef_nonmand_logsum_0,0.422778026,F +coef_nonmand_logsum_1,0.517335299,F +coef_nonmand_logsum_2p,0.495923942,F +coef_cars2_av_ownership,0,T +coef_cars3_av_ownership,0,T +coef_unavailable,-999,T +asc_GQ_0_autos,2.468181736,F +# Calibration constants (2021 ACS Data),, +asc_allhhs_0_autos,-0.205031923,F +asc_allhhs_2_autos,0.103098218,F +asc_allhs_3_autos,0.217732491,F +asc_allhhs_4plus_autos,0.178545411,F diff --git a/resident/configs/auto_ownership_preprocessor.csv b/resident/configs/auto_ownership_preprocessor.csv new file mode 100644 index 0000000..9d7b904 --- /dev/null +++ b/resident/configs/auto_ownership_preprocessor.csv @@ -0,0 +1,10 @@ +Description,Target,Expression +,_PERSON_COUNT,"lambda query, persons, households: persons.query(query).groupby('household_id').size().reindex(households.index).fillna(0).astype(np.int8)" +,num_old_retirees,"_PERSON_COUNT('80 <= age', persons, households)" +# averaging disaggregate accessibilities across all household members +,shopping_accessibility_0,persons_merged.groupby('household_id').shopping_accessibility_0.mean().reindex(df.index) +,shopping_accessibility_1,persons_merged.groupby('household_id').shopping_accessibility_1.mean().reindex(df.index) +,shopping_accessibility_2,persons_merged.groupby('household_id').shopping_accessibility_2.mean().reindex(df.index) +,othdiscr_accessibility_0,persons_merged.groupby('household_id').othdiscr_accessibility_0.mean().reindex(df.index) +,othdiscr_accessibility_1,persons_merged.groupby('household_id').othdiscr_accessibility_1.mean().reindex(df.index) +,othdiscr_accessibility_2,persons_merged.groupby('household_id').othdiscr_accessibility_2.mean().reindex(df.index) \ No newline at end of file diff --git a/resident/configs/av_ownership.csv b/resident/configs/av_ownership.csv new file mode 100644 index 0000000..677db13 --- /dev/null +++ b/resident/configs/av_ownership.csv @@ -0,0 +1,8 @@ +Label,Description,Expression,owns_av,does_not_own_av +util_low_income,Household income less than 50K,income < 50000,coef_low_income,0 +util_high_income,Household income greater than 100K,income > 100000,coef_high_income,0 +# util_hh_head_age_lt_35,Head of household under age 35,hhhead_age < 35,coef_hh_head_age_lt_35,0 +# util_hh_head_age_gt_65,Head of household over age 65,hhhead_age > 65,coef_hh_head_age_gt_65,0 +# util_per_hour_commute_time,Coefficient per hour of total household commute time,hh_commute_time,coef_per_hour_commute_time,0 +util_static_constant,Static constant that does not change via iterations,1,coef_static,0 +util_av_target_share,Constant to adjust to match set AV target,1,coef_av_target_share,0 diff --git a/resident/configs/av_ownership.yaml b/resident/configs/av_ownership.yaml new file mode 100644 index 0000000..b72482d --- /dev/null +++ b/resident/configs/av_ownership.yaml @@ -0,0 +1,18 @@ + +SPEC: av_ownership.csv +COEFFICIENTS: av_ownership_coeffs.csv + +LOGIT_TYPE: MNL + +preprocessor: + SPEC: av_ownership_preprocessor + DF: choosers + TABLES: + - persons + +# iterative what-if analysis example +# omit these settings to not iterate +AV_OWNERSHIP_ITERATIONS: 5 +AV_OWNERSHIP_TARGET_PERCENT: 0.0 +AV_OWNERSHIP_TARGET_PERCENT_TOLERANCE: 0.01 +AV_OWNERSHIP_COEFFICIENT_CONSTANT: coef_av_target_share \ No newline at end of file diff --git a/resident/configs/av_ownership_coeffs.csv b/resident/configs/av_ownership_coeffs.csv new file mode 100644 index 0000000..18a6a7a --- /dev/null +++ b/resident/configs/av_ownership_coeffs.csv @@ -0,0 +1,8 @@ +coefficient_name,value,constrain +coef_low_income,-1.0,F +coef_high_income,1.0,F +coef_hh_head_age_lt_35,0.5,F +coef_hh_head_age_gt_65,-1.0,F +coef_per_hour_commute_time,0.25,F +coef_static,-3.5,F +coef_av_target_share,0,F diff --git a/resident/configs/av_ownership_preprocessor.csv b/resident/configs/av_ownership_preprocessor.csv new file mode 100644 index 0000000..f6db3c2 --- /dev/null +++ b/resident/configs/av_ownership_preprocessor.csv @@ -0,0 +1,3 @@ +Description,Target,Expression +age of the head of household,hhhead_age,persons.groupby('household_id').age.max().reindex(df.index) +#total household commute time -- cant compute if before workplace location choice,hh_commute_time,persons.groupby('household_id').roundtrip_auto_time_to_work.sum().reindex(df.index) \ No newline at end of file diff --git a/resident/configs/cdap.yaml b/resident/configs/cdap.yaml new file mode 100644 index 0000000..ea6ae20 --- /dev/null +++ b/resident/configs/cdap.yaml @@ -0,0 +1,47 @@ + +INDIV_AND_HHSIZE1_SPEC: cdap_indiv_and_hhsize1.csv +COEFFICIENTS: cdap_coefficients.csv +INTERACTION_COEFFICIENTS: cdap_interaction_coefficients.csv + +FIXED_RELATIVE_PROPORTIONS_SPEC: cdap_fixed_relative_proportions.csv + +# set to True if want to introduce joint tour utility in cdap +# otherwise set to False or comment out, defaulted to False +ADD_JOINT_TOUR_UTILITY: True +JOINT_TOUR_COEFFICIENTS: cdap_joint_tour_coefficients.csv +JOINT_TOUR_USEFUL_COLUMNS: + - auto_ownership + - income + - num_workers + - shopping_accessibility + - workplace_modechoice_logsum + +CONSTANTS: + FULL: 1 + PART: 2 + UNIVERSITY: 3 + NONWORK: 4 + RETIRED: 5 + DRIVING: 6 + SCHOOL: 7 + PRESCHOOL: 8 + +PERSON_TYPE_MAP: + WORKER: + - 1 + - 2 + CHILD: + - 6 + - 7 + - 8 + +annotate_persons: + SPEC: annotate_persons_cdap + DF: persons + + +annotate_households: + SPEC: annotate_households_cdap + DF: households + TABLES: + - persons diff --git a/resident/configs/cdap_coefficients.csv b/resident/configs/cdap_coefficients.csv new file mode 100644 index 0000000..af7e246 --- /dev/null +++ b/resident/configs/cdap_coefficients.csv @@ -0,0 +1,198 @@ +coefficient_name,value,constrain +coef_UNAVAILABLE,-999.0,T +coef_age_80_plus_N,-0.40552014545569237,F +coef_base_work_from_home_2016_N,-0.412755915492793,F +coef_base_work_from_home_N,-0.36137098196813544,F +coef_base_zero_auto_M,-0.3750683512059161,F +coef_base_zero_auto_N,-0.5866902617568075,F +coef_driving_age_child_2016_asc_M,0.9762795235480467,F +coef_driving_age_child_2016_asc_N,-0.24488773870065403,F +coef_driving_age_child_asc_M,-0.028791867283176956,F +coef_driving_age_child_asc_N,-0.35221765455627785,F +coef_driving_age_child_auto_deficient_N,-0.9468282864008857,F +coef_full_time_worker_2016_asc_M,1.223481320989442,F +coef_full_time_worker_2016_asc_N,0.6920533294338312,F +coef_full_time_worker_age_less_than_35_M,-0.12285898207075491,F +coef_full_time_worker_age_less_than_35_N,-0.15920673068983657,F +coef_full_time_worker_asc_M,1.5644720845560531,F +coef_full_time_worker_asc_N,1.4614984268627733,F +coef_full_time_worker_income_less_than_30k_H,0.15736137368023284,F +coef_non_working_adult_2016_asc_N,0.232621494267051,F +coef_non_working_adult_asc_N,1.1808900957814006,F +coef_non_working_adult_auto_deficient_N,-0.8370587331043843,F +coef_non_working_adult_zero_auto_N,0.0,F +coef_part_time_worker_2016_asc_M,0.2983857953450747,F +coef_part_time_worker_2016_asc_N,0.35204682792828224,F +coef_part_time_worker_age_less_than_35_M,0.23178722435239257,F +coef_part_time_worker_asc_M,1.6387513010169674,F +coef_part_time_worker_asc_N,1.606230763394354,F +coef_part_time_worker_auto_deficient_M,0.37748443629482326,F +coef_part_time_worker_income_less_than_30k_H,0.23400742652772027,F +coef_part_time_worker_work_from_home_2016_N,1.4299461243823042,F +coef_part_time_worker_work_from_home_N,-1.2281808814800665,F +coef_pre_driving_age_child_2016_asc_M,0.11101687817213937,F +coef_pre_driving_age_child_2016_asc_N,0.10384140613549096,F +coef_pre_driving_age_child_asc_M,0.8049646260769578,F +coef_pre_driving_age_child_asc_N,-0.06982784895274624,F +coef_pre_driving_age_child_auto_deficient_M,0.0,F +coef_pre_driving_age_child_auto_deficient_N,0.0,F +coef_preschool_child_2016_asc_M,0.06913803289094414,F +coef_preschool_child_2016_asc_N,0.8594372063809559,F +coef_preschool_child_asc_M,-0.4237607056861521,F +coef_preschool_child_asc_N,-0.7825949113888206,F +coef_preschool_child_auto_deficient_M,-0.6176520350813967,F +coef_preschool_child_income_between_60k_and_100k_M,0.27169762977541834,F +coef_retired_2016_asc_N,0.3104586071157866,F +coef_retired_asc_N,1.1538566746686725,F +coef_retired_auto_deficient_N,-0.5763380569296244,F +coef_telecommute_1_day_week_2016_H,0.6205919905910637,F +coef_telecommute_1_day_week_2016_N,0.5297551190509434,F +coef_telecommute_1_day_week_H,-0.7795490402549963,F +coef_telecommute_1_day_week_N,-0.7150941297946883,F +coef_telecommute_2_3_days_week_2016_H,-0.4085117546195023,F +coef_telecommute_2_3_days_week_2016_N,-0.5948535892511913,F +coef_telecommute_2_3_days_week_H,1.487776947249398,F +coef_telecommute_2_3_days_week_N,0.995563845622819,F +coef_telecommute_4_days_week_2016_H,0.8369274593715018,F +coef_telecommute_4_days_week_2016_N,1.022480425972746,F +coef_telecommute_4_days_week_H,0.7932098123998844,F +coef_telecommute_4_days_week_N,0.2758341263673416,F +coef_university_student_2016_asc_M,1.3474703710372755,F +coef_university_student_2016_asc_N,0.6441076940029465,F +coef_university_student_asc_M,0.2934044679640938,F +coef_university_student_asc_N,0.7161370279501159,F +coef_university_student_auto_deficient_M,0.0,F +coef_university_student_auto_deficient_N,-0.4793921704199631,F +coef_university_student_income_between_60k_and_100k_H,0.0,F +coef_university_student_zero_auto_N,0.0,F +coef_non_mand_accessibility_med_low_N,0.2098418874105526,F +coef_non_mand_accessibility_med_high_N,0.23778715336968032,F +coef_non_mand_accessibility_high_N,0.4489226773950751,F +coef_non_mand_accessibility_med_low_2016_N,-0.14676390165726438,F +coef_non_mand_accessibility_med_high_2016_N,-0.1657971018672975,F +coef_non_mand_accessibility_high_2016_N,-0.3075771697132568,F +coef_H_11,0.9081676720163869,F +coef_H_12,0.5879605906687824,F +coef_H_13,0.5293653823036347,F +coef_H_14,0.6121582837956262,F +coef_H_15,0.785784651471339,F +coef_H_16,0.0,F +coef_H_17,0.1687232310036398,F +coef_H_18,0.370497961792488,F +coef_H_22,1.0023866203429688,F +coef_H_23,0.0,F +coef_H_24,0.0,F +coef_H_25,0.5841783984603512,F +coef_H_26,0.0,F +coef_H_27,0.0,F +coef_H_28,0.0,F +coef_H_33,1.3535972783148953,F +coef_H_34,0.4815185221939276,F +coef_H_35,0.0,F +coef_H_36,0.0,T +coef_H_37,0.0,T +coef_H_38,0.0,T +coef_H_44,0.5566224376246068,F +coef_H_45,0.5008668115587485,F +coef_H_46,0.0,T +coef_H_47,0.0,T +coef_H_48,0.0,T +coef_H_55,0.27176137157094504,F +coef_H_56_57,0.0,T +coef_H_58,0.0,T +coef_H_66,3.14482613013081,F +coef_H_67,0.0,F +coef_H_68,0.0,F +coef_H_77,1.9693172113603012,F +coef_H_78,1.622709457576422,F +coef_H_88,1.9456615126911383,F +coef_M_11,0.20582236458467118,F +coef_M_12,0.0,F +coef_M_13,0.13759875304842933,F +coef_M_16,0.0,F +coef_M_17,0.1008692647165656,F +coef_M_18,0.30906951422329104,F +coef_M_22,0.0,F +coef_M_23,0.0,F +coef_M_26,0.0,F +coef_M_27,0.24621546849867038,F +coef_M_28,0.0,F +coef_M_33,0.2552795327531223,F +coef_M_36,0.0,T +coef_M_37,0.0,T +coef_M_38,0.0,T +coef_M_66,0.0,F +coef_M_67,0.0,F +coef_M_68,0.0,F +coef_M_77,1.180347121732678,F +coef_M_78,0.5390396802361401,F +coef_M_88,1.3066654391714445,F +coef_N_11,0.0,F +coef_N_12,0.0,F +coef_N_13,0.0,F +coef_N_14,0.0,F +coef_N_15,-0.3059631767785066,F +coef_N_16,0.0,F +coef_N_17,0.14867426687105245,F +coef_N_18,0.22534260567363343,F +coef_N_22,0.0,F +coef_N_23,0.0,F +coef_N_24,0.0,F +coef_N_25,0.0,F +coef_N_26,0.9791798564652399,F +coef_N_27,0.0,F +coef_N_28,0.3010315611792873,F +coef_N_33,0.0,F +coef_N_34,0.0,F +coef_N_35,-0.8021689612128808,F +coef_N_36,0.0,T +coef_N_37,0.0,T +coef_N_38,0.0,T +coef_N_44,0.0,F +coef_N_45,0.0,F +coef_N_46,0.0,T +coef_N_47,0.0,T +coef_N_48,0.0,T +coef_N_55,-0.15036765019338055,F +coef_N_56_57_58,0.0,T +coef_N_66,2.241353046656697,F +coef_N_67,1.9957786284419086,F +coef_N_68,0.0,F +coef_N_77,1.6220342829913563,F +coef_N_78,1.1156499097895638,F +coef_N_88,1.2180412202476305,F +coef_H_114,0.0,F +coef_H_117_118,0.0,F +coef_H_127_128,0.0,F +coef_H_147_148,0.0,T +coef_H_177_178_187_188,0.0,F +coef_H_277_278_287_288,0.0,F +coef_H_447_448,0.0,T +coef_H_477_478_487_488,0.0,T +coef_H_777_778_788_888,-1.1692289537913036,F +coef_M_111,-0.32284228918318064,F +coef_M_112,0.0,F +coef_M_122,0.5302446008838457,F +coef_M_127_128,0.2847930736739325,F +coef_M_177_178_187_188,0.0,F +coef_M_227_228,0.0,F +coef_M_277_278_287_288,0.0,F +coef_M_777_778_788_888,-0.5669303177188842,F +coef_N_117_118,0.0,F +coef_N_144,0.0,F +coef_N_127_128,0.0,F +coef_N_147_148,0.0,T +coef_N_177_178_187_188,0.0,F +coef_N_222,0.0,T +coef_N_277_278_287_288,0.0,F +coef_N_477_478_487_488,0.0,T +coef_N_777_778_788_888,-0.7322489904595986,F +coef_M_xxx,0.0,T +coef_N_xxx,0.0,T +coef_H_xxx,0.0,T +coef_M_xxxx,0.0,T +coef_N_xxxx,0.0,T +coef_H_xxxx,0.0,T +coef_M_xxxxx,0.3270563409817982,F +coef_N_xxxxx,0.3500149649035247,F +coef_H_xxxxx,0.0,F diff --git a/resident/configs/cdap_fixed_relative_proportions.csv b/resident/configs/cdap_fixed_relative_proportions.csv new file mode 100644 index 0000000..49ab7c4 --- /dev/null +++ b/resident/configs/cdap_fixed_relative_proportions.csv @@ -0,0 +1,13 @@ +Description,Expression,M,N,H +Full-time worker,(ptype == 1) & ~work_from_home,0.79647,0.09368,0.10985 +Full-time worker,(ptype == 1) & work_from_home,0,0.460276126,0.539723874 +Part-time worker,(ptype == 2) & ~work_from_home,0.61678,0.25757,0.12565 +Part-time worker,(ptype == 2) & work_from_home,0,0.672120453,0.327879547 +University student,ptype == 3,0.69229,0.15641,0.1513 +Non-working adult,ptype == 4,0,0.67169,0.32831 +Retired,ptype == 5,0,0.54295,0.45705 +Driving-age child who is in school,ptype == 6,0.77609,0.06004,0.16387 +Pre-driving-age child who is in school,ptype == 7,0.68514,0.09144,0.22342 +#Child who is too young for school,ptype == 8,0.14056,0.06512,0.79432 +Child who is too young for school,(ptype == 8) & (school_zone_id >= 0),0.14056,0.06512,0.79432 +Child who is too young for school,(ptype == 8) & (school_zone_id < 0),0,0.1354,0.8646 \ No newline at end of file diff --git a/resident/configs/cdap_indiv_and_hhsize1.csv b/resident/configs/cdap_indiv_and_hhsize1.csv new file mode 100644 index 0000000..90f7ae0 --- /dev/null +++ b/resident/configs/cdap_indiv_and_hhsize1.csv @@ -0,0 +1,66 @@ +Description,Expression,M,N,H +Full-time worker ASC,ptype == 1,coef_full_time_worker_asc_M,coef_full_time_worker_asc_N, +Part-time worker ASC,ptype == 2,coef_part_time_worker_asc_M,coef_part_time_worker_asc_N, +University student ASC,ptype == 3,coef_university_student_asc_M,coef_university_student_asc_N, +Non-working adult ASC,ptype == 4,,coef_non_working_adult_asc_N, +Retired ASC,ptype == 5,,coef_retired_asc_N, +Driving-age child who is in school ASC,ptype == 6,coef_driving_age_child_asc_M,coef_driving_age_child_asc_N, +Pre-driving-age child who is in school ASC,ptype == 7,coef_pre_driving_age_child_asc_M,coef_pre_driving_age_child_asc_N, +Preschool child ASC,ptype == 8,coef_preschool_child_asc_M,coef_preschool_child_asc_N, +Full-time worker interaction with age less than 35,(ptype == 1) & (age < 35),coef_full_time_worker_age_less_than_35_M,coef_full_time_worker_age_less_than_35_N, +Part-time worker interaction with age less than 35,(ptype == 2) & (age < 35),coef_part_time_worker_age_less_than_35_M,coef_full_time_worker_age_less_than_35_N, +Age over 80,(age >= 80),,coef_age_80_plus_N, +Zero auto ownership base,(auto_ownership == 0),coef_base_zero_auto_M,coef_base_zero_auto_N, +Auto Deficient part-time worker,(ptype == 2) & (auto_ownership > 0) & (auto_ownership < num_adults),coef_part_time_worker_auto_deficient_M,, +Auto Deficient university_student,(ptype == 3) & (auto_ownership > 0) & (auto_ownership < num_adults),,coef_university_student_auto_deficient_N, +Auto Deficient non_working_adult,(ptype == 4) & (auto_ownership > 0) & (auto_ownership < num_adults),,coef_non_working_adult_auto_deficient_N, +Auto Deficient retired,(ptype == 5) & (auto_ownership > 0) & (auto_ownership < num_adults),,coef_retired_auto_deficient_N, +Auto Deficient driving_age_child,(ptype == 6) & (auto_ownership > 0) & (auto_ownership < num_adults),,coef_driving_age_child_auto_deficient_N, +Auto Deficient preschool_child,(ptype == 8) & (auto_ownership > 0) & (auto_ownership < num_adults),coef_preschool_child_auto_deficient_M,, +Full-time worker interaction with income less than $30k,(ptype == 1) & (income < 30000),,,coef_full_time_worker_income_less_than_30k_H +Part-time worker interaction with income less than $30k,(ptype == 2) & (income < 30000),,,coef_part_time_worker_income_less_than_30k_H +Preschool child interaction with income between $60k and $100k,(ptype == 8) & (income >= 60000) & (income <= 100000),coef_preschool_child_income_between_60k_and_100k_M,, +Base works from home,(work_from_home),,coef_base_work_from_home_N, +Base works from home 2016,(work_from_home),,coef_base_work_from_home_2016_N, +Part time worker who works from home,(ptype == 2) & (work_from_home),,coef_part_time_worker_work_from_home_N, +Part time worker who works from home,@(df.ptype == 2) & (df.work_from_home) & (PRE_COVID),,coef_part_time_worker_work_from_home_2016_N, +Telecommutes 1 day per week,telecommute_frequency=='1_day_week',,coef_telecommute_1_day_week_N,coef_telecommute_1_day_week_H +Telecommutes 2-3 days per week,telecommute_frequency=='2_3_days_week',,coef_telecommute_2_3_days_week_N,coef_telecommute_2_3_days_week_H +Telecommutes 4 days per week,telecommute_frequency=='4_days_week',,coef_telecommute_4_days_week_N,coef_telecommute_4_days_week_H +Full-time worker 2016 ASC,@(df.ptype == 1) & (PRE_COVID),coef_full_time_worker_2016_asc_M,coef_full_time_worker_2016_asc_N, +Part-time worker 2016 ASC,@(df.ptype == 2) & (PRE_COVID),coef_part_time_worker_2016_asc_M,coef_part_time_worker_2016_asc_N, +University student 2016 ASC,@(df.ptype == 3) & (PRE_COVID),coef_university_student_2016_asc_M,coef_university_student_2016_asc_N, +Non-working adult 2016 ASC,@(df.ptype == 4) & (PRE_COVID),,coef_non_working_adult_2016_asc_N, +Retired 2016 ASC,@(df.ptype == 5) & (PRE_COVID),,coef_retired_2016_asc_N, +Driving-age child who is in school 2016 ASC,@(df.ptype == 6) & (PRE_COVID),coef_driving_age_child_2016_asc_M,coef_driving_age_child_2016_asc_N, +Pre-driving-age child who is in school 2016 ASC,@(df.ptype == 7) & (PRE_COVID),coef_pre_driving_age_child_2016_asc_M,coef_pre_driving_age_child_2016_asc_N, +Preschool child 2016 ASC,@(df.ptype == 8) & (PRE_COVID),coef_preschool_child_2016_asc_M,coef_preschool_child_2016_asc_N, +Telecommutes 1 day per week 2016,@(df.telecommute_frequency=='1_day_week') & (PRE_COVID),,coef_telecommute_1_day_week_2016_N,coef_telecommute_1_day_week_2016_H +Telecommutes 2-3 days per week 2016,@(df.telecommute_frequency=='2_3_days_week') & (PRE_COVID),,coef_telecommute_2_3_days_week_2016_N,coef_telecommute_2_3_days_week_2016_H +Telecommutes 4 days per week 2016,@(df.telecommute_frequency=='4_days_week') & (PRE_COVID),,coef_telecommute_4_days_week_2016_N,coef_telecommute_4_days_week_2016_H +Mandatory pattern unavailable if not worker or student,(is_student == False) & (is_worker == False),coef_UNAVAILABLE,, +Mandatory pattern unavailable if not works from home,(work_from_home == True) & (is_student == False),coef_UNAVAILABLE,, +# cutting non-mandatory accessibility into roughly quartiles with low as base,,,, +Non-mandatory accessibility med low,@(((df.shopping_accessibility + df.othdiscr_accessibility) / 2) >= 12) & (((df.shopping_accessibility + df.othdiscr_accessibility) / 2) < 13.5),,coef_non_mand_accessibility_med_low_N, +Non-mandatory accessibility med high,@(((df.shopping_accessibility + df.othdiscr_accessibility) / 2) >= 13.5) & (((df.shopping_accessibility + df.othdiscr_accessibility) / 2) < 15),,coef_non_mand_accessibility_med_high_N, +Non-mandatory accessibility high,@(((df.shopping_accessibility + df.othdiscr_accessibility) / 2) > 15),,coef_non_mand_accessibility_high_N, +Non-mandatory accessibility med low,@(((df.shopping_accessibility + df.othdiscr_accessibility) / 2) >= 12) & (((df.shopping_accessibility + df.othdiscr_accessibility) / 2) < 13.5) & (PRE_COVID),,coef_non_mand_accessibility_med_low_2016_N, +Non-mandatory accessibility med high,@(((df.shopping_accessibility + df.othdiscr_accessibility) / 2) >= 13.5) & (((df.shopping_accessibility + df.othdiscr_accessibility) / 2) < 15) & (PRE_COVID),,coef_non_mand_accessibility_med_high_2016_N, +Non-mandatory accessibility high,@(((df.shopping_accessibility + df.othdiscr_accessibility) / 2) > 15) & (PRE_COVID),,coef_non_mand_accessibility_high_2016_N, +#,,,, +Full-time worker weighted target ASC,ptype == 1,-0.076,0.033,0.288 +Part-time worker weighted target ASC,ptype == 2,0.118,-0.124,0.029 +University student weighted target ASC,ptype == 3,-0.081,0.038,0.164 +Non-working adult weighted target ASC,ptype == 4,,-0.110,0.284 +Retired weighted target ASC,ptype == 5,,-0.131,0.313 +Driving-age child who is in school weighted target ASC,ptype == 6,-0.134,0.314,0.026 +Pre-driving-age child who is in school weighted target ASC,ptype == 7,0.028,-0.084,0.010 +Preschool child weighted target ASC,ptype == 8,0.104,-0.223,0.116 +ABM3 calibration ASC ptype=1,ptype==1,0.600,0.00,-0.527 +ABM3 calibration ASC ptype=2,ptype==2,-0.107,0.0276,0.190 +ABM3 calibration ASC ptype=3,ptype==3,0.427,-0.120,-0.200 +ABM3 calibration ASC ptype=4,ptype==4,0.0,0.00,0.0889 +ABM3 calibration ASC ptype=5,ptype==5,0.0,0.134,-0.227 +ABM3 calibration ASC ptype=6,ptype==6,3.750,-0.241,0.298 +ABM3 calibration ASC ptype=7,ptype==7,2.200,-0.171,0.096 +ABM3 calibration ASC ptype=8,ptype==8,0.536,0.454,-0.731 \ No newline at end of file diff --git a/resident/configs/cdap_interaction_coefficients.csv b/resident/configs/cdap_interaction_coefficients.csv new file mode 100644 index 0000000..f94fa17 --- /dev/null +++ b/resident/configs/cdap_interaction_coefficients.csv @@ -0,0 +1,177 @@ +activity,interaction_ptypes,coefficient +# 2-way interactions,, +H,11,coef_H_11 +H,12,coef_H_12 +H,13,coef_H_13 +H,14,coef_H_14 +H,15,coef_H_15 +H,16,coef_H_16 +H,17,coef_H_17 +H,18,coef_H_18 +H,22,coef_H_22 +H,23,coef_H_23 +H,24,coef_H_24 +H,25,coef_H_25 +H,26,coef_H_26 +H,27,coef_H_27 +H,28,coef_H_28 +H,33,coef_H_33 +H,34,coef_H_34 +H,35,coef_H_35 +H,36,coef_H_36 +H,37,coef_H_37 +H,38,coef_H_38 +H,44,coef_H_44 +H,45,coef_H_45 +H,46,coef_H_46 +H,47,coef_H_47 +H,48,coef_H_48 +H,55,coef_H_55 +H,56,coef_H_56_57 +H,57,coef_H_56_57 +H,58,coef_H_58 +H,66,coef_H_66 +H,67,coef_H_67 +H,68,coef_H_68 +H,77,coef_H_77 +H,78,coef_H_78 +H,88,coef_H_88 +M,11,coef_M_11 +M,12,coef_M_12 +M,13,coef_M_13 +M,16,coef_M_16 +M,17,coef_M_17 +M,18,coef_M_18 +M,22,coef_M_22 +M,23,coef_M_23 +M,26,coef_M_26 +M,27,coef_M_27 +M,28,coef_M_28 +M,33,coef_M_33 +M,36,coef_M_36 +M,37,coef_M_37 +M,38,coef_M_38 +M,66,coef_M_66 +M,67,coef_M_67 +M,68,coef_M_68 +M,77,coef_M_77 +M,78,coef_M_78 +M,88,coef_M_88 +N,11,coef_N_11 +N,12,coef_N_12 +N,13,coef_N_13 +N,14,coef_N_14 +N,15,coef_N_15 +N,16,coef_N_16 +N,17,coef_N_17 +N,18,coef_N_18 +N,22,coef_N_22 +N,23,coef_N_23 +N,24,coef_N_24 +N,25,coef_N_25 +N,26,coef_N_26 +N,27,coef_N_27 +N,28,coef_N_28 +N,33,coef_N_33 +N,34,coef_N_34 +N,35,coef_N_35 +N,36,coef_N_36 +N,37,coef_N_37 +N,38,coef_N_38 +N,44,coef_N_44 +N,45,coef_N_45 +N,46,coef_N_46 +N,47,coef_N_47 +N,48,coef_N_48 +N,55,coef_N_55 +N,56,coef_N_56_57_58 +N,57,coef_N_56_57_58 +N,58,coef_N_56_57_58 +N,66,coef_N_66 +N,67,coef_N_67 +N,68,coef_N_68 +N,77,coef_N_77 +N,78,coef_N_78 +N,88,coef_N_88 +# 3-way interactions,, +H,114,coef_H_114 +H,117,coef_H_117_118 +H,118,coef_H_117_118 +H,127,coef_H_127_128 +H,128,coef_H_127_128 +H,147,coef_H_147_148 +H,148,coef_H_147_148 +H,177,coef_H_177_178_187_188 +H,178,coef_H_177_178_187_188 +H,187,coef_H_177_178_187_188 +H,188,coef_H_177_178_187_188 +H,277,coef_H_277_278_287_288 +H,278,coef_H_277_278_287_288 +H,287,coef_H_277_278_287_288 +H,288,coef_H_277_278_287_288 +H,447,coef_H_447_448 +H,448,coef_H_447_448 +H,477,coef_H_477_478_487_488 +H,478,coef_H_477_478_487_488 +H,487,coef_H_477_478_487_488 +H,488,coef_H_477_478_487_488 +H,777,coef_H_777_778_788_888 +H,778,coef_H_777_778_788_888 +H,788,coef_H_777_778_788_888 +H,888,coef_H_777_778_788_888 +M,111,coef_M_111 +M,112,coef_M_112 +M,122,coef_M_122 +M,127,coef_M_127_128 +M,128,coef_M_127_128 +M,177,coef_M_177_178_187_188 +M,178,coef_M_177_178_187_188 +M,187,coef_M_177_178_187_188 +M,188,coef_M_177_178_187_188 +M,227,coef_M_227_228 +M,228,coef_M_227_228 +M,277,coef_M_277_278_287_288 +M,278,coef_M_277_278_287_288 +M,287,coef_M_277_278_287_288 +M,288,coef_M_277_278_287_288 +M,777,coef_M_777_778_788_888 +M,778,coef_M_777_778_788_888 +M,788,coef_M_777_778_788_888 +M,888,coef_M_777_778_788_888 +N,117,coef_N_117_118 +N,118,coef_N_117_118 +N,144,coef_N_144 +N,127,coef_N_127_128 +N,128,coef_N_127_128 +N,147,coef_N_147_148 +N,148,coef_N_147_148 +N,177,coef_N_177_178_187_188 +N,178,coef_N_177_178_187_188 +N,187,coef_N_177_178_187_188 +N,188,coef_N_177_178_187_188 +N,222,coef_N_222 +N,277,coef_N_277_278_287_288 +N,278,coef_N_277_278_287_288 +N,287,coef_N_277_278_287_288 +N,288,coef_N_277_278_287_288 +N,477,coef_N_477_478_487_488 +N,478,coef_N_477_478_487_488 +N,487,coef_N_477_478_487_488 +N,488,coef_N_477_478_487_488 +N,777,coef_N_777_778_788_888 +N,778,coef_N_777_778_788_888 +N,788,coef_N_777_778_788_888 +N,888,coef_N_777_778_788_888 +# cdap_final_rules,, +M,5,coef_UNAVAILABLE +M,4,coef_UNAVAILABLE +# cdap_all_people,, +M,***,coef_M_xxx +N,***,coef_N_xxx +H,***,coef_H_xxx +M,****,coef_M_xxxx +N,****,coef_N_xxxx +H,****,coef_H_xxxx +M,*****,coef_M_xxxxx +N,*****,coef_N_xxxxx +H,*****,coef_H_xxxxx diff --git a/resident/configs/cdap_joint_tour_coefficients.csv b/resident/configs/cdap_joint_tour_coefficients.csv new file mode 100644 index 0000000..1eb8d6a --- /dev/null +++ b/resident/configs/cdap_joint_tour_coefficients.csv @@ -0,0 +1,19 @@ +Label,description,Expression,dependency,coefficient +,constant - joint tour,1,,-3.1506 +,joint tour for hhsize 2,hhsize == 2,,-0.550050686 +,joint tour for hhsize 3,hhsize == 3,,-1.066203116 +,joint tour for hhsize 4,hhsize == 4,,-1.041167491 +,joint tour for hhsize 5+,hhsize >= 5,,-1.281586989 +,person x is adult and DAP is M,ptype_px < 7,M_px,0.008 +,person x is adult and DAP is N,ptype_px < 7,N_px,1.2557 +,person x is kid and DAP is M,ptype_px > 6,M_px,0.1088 +,person x is kid and DAP is N,ptype_px > 6,N_px,1.6898 +,Accessibility to retail employment/Non-Mandatory Attractions,shopping_accessibility_p1,,0.055031985 +,Income less than $30k,income_p1 < 30000,,-0.192506367 +,Income between $60k and $100k,(income_p1 >= 60000) & (income_p1 < =100000),,0.104325349 +,Income more than $100k,income_p1 > 100000,,0.104325349 +,No Car Households,auto_ownership_p1 == 0,,0 +,Cars Less than Workers,auto_ownership_p1 < num_workers_p1,,0.088402389 +,Cars More than Workers,auto_ownership_p1 > num_workers_p1,,-0.005896499 +,WorkAccessForMandatoryDap,"@df.workplace_modechoice_logsum_px.fillna(0)",M_px,0.17217579 +,If All Adults stay at Home/ None of the Adults have Dap 1 or 2,(ptype_pxprod < 7) | (ptype_pxprod > 6),H_px,-0.988838929 diff --git a/resident/configs/constants.yaml b/resident/configs/constants.yaml new file mode 100644 index 0000000..3519117 --- /dev/null +++ b/resident/configs/constants.yaml @@ -0,0 +1,257 @@ +## ActivitySim +## See full license in LICENSE.txt. + +scenarioYear: 2022 +# set below to 1 for everything to be internal, 0 otherwise +NO_EXTERNAL: 0 + +# set below to be 1 for years before 2021, 0 otherwise +PRE_COVID: 0 + +# convenience for expression files +HHT_NONFAMILY: [4, 5, 6, 7] +HHT_FAMILY: [1, 2, 3] + +PSTUDENT_GRADE_OR_HIGH: 1 +PSTUDENT_UNIVERSITY: 2 +PSTUDENT_NOT: 3 + +GRADE_SCHOOL_MAX_AGE: 14 +GRADE_SCHOOL_MIN_AGE: 5 + +SCHOOL_SEGMENT_NONE: 0 +SCHOOL_SEGMENT_PREK: 1 +SCHOOL_SEGMENT_GRADE: 2 +SCHOOL_SEGMENT_HIGH: 3 +SCHOOL_SEGMENT_UNIV: 4 + + +INCOME_SEGMENT_LOW: 1 +INCOME_SEGMENT_MED: 2 +INCOME_SEGMENT_HIGH: 3 +INCOME_SEGMENT_VERYHIGH: 4 + +PEMPLOY_FULL: 1 +PEMPLOY_PART: 2 +PEMPLOY_NOT: 3 +PEMPLOY_CHILD: 4 + +PTYPE_FULL: 1 +PTYPE_PART: 2 +PTYPE_UNIVERSITY: 3 +PTYPE_NONWORK: 4 +PTYPE_RETIRED: 5 +PTYPE_SCHOOL: 6 +PTYPE_PRESCHOOL: 7 + +PTYPE_VALUE: + 1: PTYPE_FULL + 2: PTYPE_PART + 3: PTYPE_UNIVERSITY + 4: PTYPE_NONWORK + 5: PTYPE_RETIRED + 6: PTYPE_SCHOOL + 7: PTYPE_PRESCHOOL + + +CDAP_ACTIVITY_MANDATORY: M +CDAP_ACTIVITY_NONMANDATORY: N +CDAP_ACTIVITY_HOME: H + +# Defines cuts on psedomsa in landuse file. Replacement for area_type +cbd_threshold: 2 +urban_threshold: 5 +rural_threshold: 5 + +distributed_time_factor_work_mean: 1.0 +distributed_time_factor_work_stddev: 0.7 +distributed_time_factor_nonwork_mean: 1.0 +distributed_time_factor_nonwork_stddev: 0.6 +distributed_time_factor_min: 0.1 +distributed_time_factor_max: 10 + +c_drive: 1.5 + +AV_OWNERSHIP_TARGET_PERCENT: 0.0 +autoIVTFactorAV: 0.75 +autoParkingCostFactorAV: 0.5 +autoCostPerMileFactorAV: 0.7 +autoTerminalTimeFactorAV: 0.65 +minAgeDriveAloneAV: 13 + + +#valueOfTime: 8.00 +costPerMile: 32.5 +shortWalk: 0.333 +walkSpeed: 3.00 +bikeSpeed: 7.80 +ebikeSpeed: 10.00 +escooterSpeed: 6.70 +driveSpeed: 25.00 +indivTour: 1.00000 + +### Crosswalk between OCCCAT and size terms +# FIXME -- this xwalk is completely made up!! Needs to be updated with real data. +occupation_xwalk: + 1: mngt_busi_scic_arts + 2: services + 3: health + 4: services + 5: sales_office + 6: constr_maint + 7: prod_trans_move + 999: military + -1: # no occupation + +# RIDEHAIL Settings +Taxi_baseFare: 3.00 +Taxi_costPerMile: 3.3 +Taxi_costPerMinute: 0.46 +Taxi_waitTime_mean: + 1: 5.5 + 2: 9.5 + 3: 13.3 + 4: 17.3 + 5: 26.5 +#### [NICK] Updated all SD numbers from sandag_abm.properties. Were all 0 before +Taxi_waitTime_sd: + 1: 6.4 + 2: 6.4 + 3: 6.4 + 4: 6.4 + 5: 6.4 +TNC_single_baseFare: 3.31 +TNC_single_costPerMile: 0.96 +TNC_single_costPerMinute: 0.33 +TNC_single_costMinimum: 9.19 +TNC_single_waitTime_mean: + 1: 3.0 + 2: 6.3 + 3: 8.4 + 4: 8.5 + 5: 10.3 +TNC_single_waitTime_sd: + 1: 4.1 + 2: 4.1 + 3: 4.1 + 4: 4.1 + 5: 4.1 +TNC_shared_baseFare: 1.66 +TNC_shared_costPerMile: 0.48 +TNC_shared_costPerMinute: 0.16 +TNC_shared_costMinimum: 4.6 +TNC_shared_IVTFactor: 1.5 +TNC_shared_waitTime_mean: + 1: 5.0 + 2: 8.0 + 3: 11.0 + 4: 15.0 + 5: 15.0 +TNC_shared_waitTime_sd: + 1: 4.1 + 2: 4.1 + 3: 4.1 + 4: 4.1 + 5: 4.1 +min_waitTime: 0 +max_waitTime: 50 + +# rapid bus wait time discount +WAIT_TIME_DISC: 1.0 + +ivt_lrt_multiplier: 0.85 +ivt_brt_multiplier: 0.95 +ivt_cmr_multiplier: 0.85 +ivt_ltd_multiplier: 1.0 +# line-haul mode constants; note commuter rail is based on CMRIVTT. Also currently hyperloop is not applied because we do not skim hyperloop IVTT +eb_equiv_asc: 10 +brt_equiv_asc: -5 +lrt_equiv_asc: -30 +pnr_lrt_equiv_asc: -20 +# +density_index_multiplier: -0.2 +# joint_sr2_ASC_no_auto: 0 +# joint_sr2_ASC_auto_deficient: 0 +# joint_sr2_ASC_auto_sufficient: 0 +# joint_drive_transit_ASC_no_auto: 0 +cost_share_s2: 0.57 +cost_share_s3: 0.37 +vot_threshold_low: 8.81 +vot_threshold_med: 18.00 +max_walk_time: 60 +max_bike_time: 60 +max_walk_distance: 3 +max_bike_distance: 12 +# Location of attractions for associated special size terms +cabrillo_maz: 23831 +seaworld_maz: 8127 +legoland_maz: 24245 +safari_maz: 14345 +midway_maz: 10646 +zoo_maz: 10561 +torrypines_maz: 4093 + +# Micromobility constants +microVarCost: 39 #cents +microFixedCost: 100 #cents +microRentTime: 1 +microAccessThreshold: 100 +ebikeownership: 0.008 +ebikeMaxDist: 10.50 +escooterMaxDist: 2.00 + +# Microtransit and NEV constants +microtransitSpeed: 30 +microtransitCost: 125 #cents +microtransitWaitTime: 12 +microtransitMaxDist: 4.5 +microtransitDiversionConstant: 6 +microtransitDiversionFactor: 1.25 +microtransitStartPeriod: 9 +microtransitEndPeriod: 32 +nevSpeed: 17 +nevCost: 125 #cents +nevWaitTime: 12 +nevMaxDist: 3 +nevDwellMinutesPerMile: 0.5 +nevDiversionConstant: 6 +nevDiversionFactor: 1.25 +nevStartPeriod: 9 +nevEndPeriod: 38 +maxWalkIfMTAccessAvailable: 1.0 # Maximum distance to walk to premium transit if microtransit access is available + +# cost of "average" monthly transit pass cost. Used in transit pass ownership model. +# cost of pass divided by 2 for age < 18 and 65+. +monthly_transit_pass_cost: 100 + +# year after which a vehicle (therefore its household) must have transponder +hhTR_Vehyear: 2029 + +# SHARED2 and SHARED3 Occupancies +OCC_SHARED2: 2.0 +OCC_SHARED3: 3.33 +# add TNC and Taxi Occupancies +OCC_TAXI: 1.1 +OCC_TNC_SINGLE: 1.2 +OCC_TNC_SHARED: 2.0 + +# EV Rebate constants +LowIncomeEVRebateCutoff: 3 +MedIncomeEVRebateCutoff: 4 +LowIncomeBEVRebate: 0 +LowIncomePEVRebate: 0 +MedIncomeBEVRebate: 0 +MedIncomePEVRebate: 0 + +# MGRAs for Ports of Entry +SanYsidro_maz_id: 9279 +OtayMesa_maz_id: 9387 +Tecate_maz_id: 22324 +OtayMesaEast_maz_id: 7123 + +# dispersion parameters used for accessibility calculations +dispersion_parameter_automobile: -0.05 +dispersion_parameter_transit: -0.05 +dispersion_parameter_walk: -1.00 +# perceived minute of in-vehicle time for every minute of out-of-vehicle time +out_of_vehicle_time_weight: 2.0 \ No newline at end of file diff --git a/resident/configs/destination_choice_size_terms.csv b/resident/configs/destination_choice_size_terms.csv new file mode 100644 index 0000000..68dbb84 --- /dev/null +++ b/resident/configs/destination_choice_size_terms.csv @@ -0,0 +1,38 @@ +model_selector,segment,TOTHHS,TOTPOP,EMP_NRM,EMP_CON,EMP_NHTMFG,EMP_HTMFG,EMP_WT,EMP_TWU,EMP_RET,EMP_IFRPBS,EMP_HCS,EMP_OSV,EMP_GOV,EMP_EDU,EMP_AER,EMP_ACC,EMP_FSD,EMP_TOT,ENROLL_K8,ENROLL_912,ENROLL_COLL,ACTIVE_ACRES,external_work,external_nonwork +workplace,constr_maint,0,0,0.114467809,1,0.019444044,0.078208267,0.053921216,0.042038744,0.057720006,0.149701138,0.007849633,0.160935475,0.137710644,0.014673052,0.021406453,0.007723607,0.001836382,0,0,0,0,0,0,0 +workplace,health,0,0,0,0.000229486,0.000272515,0.003012005,0.028269818,0,0.056869523,0.064585993,1,0.078627673,0.181566529,0.019735804,0.006167439,0.001161773,8.61E-05,0,0,0,0,0,0,0 +workplace,military,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0 +workplace,mngt_busi_scic_arts,0,0,0.023150473,0.105478261,0.033921273,0.345664415,0.520588696,0.019155288,0.063194096,1,0.12997306,0.101893393,0.357056089,0.046020203,0.044384337,0.014582422,0.071675764,0,0,0,0,0,0,0 +workplace,prod_trans_move,0,0,0.044941354,0.059838173,0.160289782,1,0.055761149,0.692090573,0.577996613,0.289719626,0.051119614,0.300100358,0.169102427,0.010851157,0.045537226,0.039515775,0.116759706,0,0,0,0,0,0,0 +workplace,sales_office,0,0,0.011768937,0.051276887,0.174045033,0.201999856,1,0.206618229,0.914135674,0.494698223,0.170534494,0.142953744,0.337702324,0.048183584,0.052183296,0.031177613,0.216603122,0,0,0,0,0,0,0 +workplace,services,0,0,0.03214445,0.013782902,0.002031041,0.02819977,0.064277161,0.007689779,0.04669046,0.477412008,0.145096152,0.403566649,0.565075489,0.264904083,0.154746531,0.079950222,1,0,0,0,0,0,0,0 +school,preschool,0,0.1888,0,0,0,0,0,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0 +school,gradeschool,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +school,highschool,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0 +school,university,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0 +non_mandatory,escort,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +non_mandatory,shopping,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +non_mandatory,othmaint,0,0,0,0,0,0,0,0,1.60296,0.42255,0.42255,0,0.24001,0,0,0,0,0,0,0,0,0,0,0 +non_mandatory,eatout,0.5512,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0 +non_mandatory,social,0.3006,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.1,0,0,0,0,0,0,0 +non_mandatory,othdiscr,0.04333,0,0,0,0,0,0.004465,0,0.042025,0.004465,0,1,0.005953,0,0.20337,0.03453,0.15911,0,0.03167,0.05136,0.02258,3.71685,0,0 +atwork,atwork,0,0,0,0,0,0,0,0,0.104,0.0145,0,0,0,0,0,0,0.367,0,0,0,0,0,0,0 +trip,work,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +trip,escort,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +trip,shopping,0.000001,0,0,0,0,0,0,0,0.375,0,0,0,0,0,0,0,0.214689,0.0001,0,0,0,0,0,0 +trip,othmaint,0.000001,0,0,0,0,0,0,0,1.2379255,0.025,1.464014,0,0.661904,0,0,0,0,0.0001,0,0,0,0,0,0 +trip,eatout,0.010162,0,0,0,0,0,0,0,0.0689145,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0 +trip,social,0.495249,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0 +trip,othdiscr,0.027342,0,0,0,0,0,0,0,0.0194215,0,0,1,0,0,0.473744,0.092343,0.122872,0,0,0,0,3.71685,0.05,0 +trip,univ,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0 +# not needed as school is not chosen as an intermediate trip destination,,,,,,,,,,,,,,,,,,,,,,,,, +#trip,gradeschool,0,,,,,,,,,,,,,,,,,,,,,,, +#trip,highschool,0,,,,,,,,,,,,,,,,,,,,,,, +external_workplace,external_workplace,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +external_nonwork,external_nonwork,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +external_non_mandatory,escort,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +external_non_mandatory,shopping,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +external_non_mandatory,othmaint,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +external_non_mandatory,eatout,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +external_non_mandatory,social,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +external_non_mandatory,othdiscr,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1 diff --git a/resident/configs/disaggregate_accessibility.yaml b/resident/configs/disaggregate_accessibility.yaml new file mode 100644 index 0000000..e87dbcd --- /dev/null +++ b/resident/configs/disaggregate_accessibility.yaml @@ -0,0 +1,192 @@ +# Sampling size. 0 = no limit +# can be whole integer value or a sample rate (percent of zones) +# zero or missing defaults to full sample! +# DESTINATION_SAMPLE_SIZE: 100 +# ORIGIN_SAMPLE_SIZE: 5000 +DESTINATION_SAMPLE_SIZE: 10 +ORIGIN_SAMPLE_SIZE: 100 + +# select origin zones weighted by population (or another landuse variable) +ORIGIN_WEIGHTING_COLUMN: TOTPOP + +# skim to use to find nearest zone when distributing accessibilities to un-sampled origins +NEAREST_ZONE_SKIM: SOV_M_DIST__MD + +# Specify the tables to be created and their variables. +# Single values are constants +# Lists are varying and will generate the cartesian product (i.e., all possible non-repeating combinations) + +# Example result for households below: +#hhid veh hinccat1 hinc hworkers persons hht bldgsz +# 1 0 1 14000 1 2 1 2 +# 2 1 1 14000 1 2 1 2 +# 3 2 1 14000 1 2 1 2 +# 4 0 2 67000 1 2 1 2 +# 5 1 2 67000 1 2 1 2 +# 6 2 2 67000 1 2 1 2 +# .... + +#Note: parameters in ALL CAPS below are required by the program for synthesis +CREATE_TABLES: + PROTO_HOUSEHOLDS: + index_col: proto_household_id + zone_col: home_zone_id + rename_columns: + zone_id: home_zone_id + VARIABLES: + hinccat1: [1, 2, 3, 4] # Income categories + hworkers: 1 # Household workers + auto_ownership: [0, 1, 2] # Household vehicles + persons: 2 # Two persons household + HHT: 1 # Married-couple family household + bldgsz: 2 # Building size - Single family detached + # Additional columns that are mapped to another (e.g., hhinccat1: 1 = hinc: 14000) + mapped_fields: + hinccat1: # List new fields mapped to this field + income: # Median income within each quartile + 1: 15000 # 14000 (for 3 level) + 2: 45000 # 67000 (for 3 level) + 3: 80000 # 120000 (for 3 level) + 4: 150000 + persons: + hhsize: + 1: 1 + 2: 2 + 3: 3 + hworkers: + num_workers: + 1: 1 + 2: 2 + 3: 3 + + PROTO_PERSONS: + index_col: proto_person_id + VARIABLES: + PERSON_NUM: [1, 2] # Person number + MIL: 4 # Not military + SCHG: -1 # Not attending school + mapped_fields: + PERSON_NUM: + age: + 1: 35 + 2: 55 + SEX: # Female male + 1: 2 + 2: 1 + ptype: # Person type, full-time and non-working adult + 1: 1 + 2: 4 + pemploy: # Full-time and unemployed + 1: 1 + 2: 3 + WKW: # 50-52 weeks, none + 1: 1 + 2: 0 + WKHP: # Hours per week + 1: 35 + 2: 0 + ESR: # Employment code, employed and unemployed + 1: 1 + 2: 3 + DAP: # Mandatory, Non-mandatory + 1: "M" + 2: "N" + # FIXME this should be updated when occupation_xwalk is updated + OCCCAT: # occupation codes + 1: 1 # Management Occupations + 2: -1 # Non-worker + + PROTO_TOURS: + index_col: proto_tour_id + VARIABLES: + tour_num: 1 # Tour number, 1 tour per person + purpose: [1, 2, 3] + mapped_fields: + purpose: + person_num: # In this case it was easier to map the person number directly to the purposes + 1: 1 + 2: 2 + 3: 2 + tour_type: + 1: "work" + 2: "shopping" + 3: "othdiscr" + tour_category: # tour purpose category, mandatory/non-mandatory + 1: "mandatory" + 2: "non_mandatory" + 3: "non_mandatory" + filter_rows: # list any matching conditions as pandas expression + - ~((df.tour_type == "work") & (df.person_num == 1)) + JOIN_ON: + person_num: PERSON_NUM # Specifies which person variable to join the tours on + +# Merge on variables +# will merge onto the persons table +MERGE_ON: + by: # These should be categorical variables at the household level + - home_zone_id + - income_segment +# Note that auto ownership is not included in the merge above. +# Below columns are created in the post-processor and separate out accessibility by auto ownership. +# Since auto ownership is not yet decided in the synthetic population, we want them all. +# Disaggreate accessibility table is grouped by the "by" cols above and the KEEP_COLS are averaged +# across the group. Initializing the below as NA if not in the auto ownership level, they are skipped +# in the groupby mean and the values are correct. +# (It's a way to avoid having to update code to reshape the table and introduce new functionality there.) +KEEP_COLS: + - workplace_location_accessibility_0 + - workplace_location_accessibility_1 + - workplace_location_accessibility_2 + - othdiscr_accessibility_0 + - othdiscr_accessibility_1 + - othdiscr_accessibility_2 + - shopping_accessibility_0 + - shopping_accessibility_1 + - shopping_accessibility_2 + +# Include any annotations for persons, households, land_use, or tours. +# The purpose of a separate annotation setup is that annotation expressions for the main model +# may require data that aren't in the proto population. +# This step enables users to annotate the proto-population by referencing custom annotation scripts. +# Of course, users can also just reference existing configs if they work, but they must be referenced here too. + +annotate_proto_tables: + - tablename: proto_persons + annotate: + SPEC: annotate_persons + DF: proto_persons + TABLES: + - proto_households + +# specific annotation for proto_persons + - tablename: proto_persons + annotate: + SPEC: annotate_proto_persons + DF: proto_persons + TABLES: + - proto_households + +# Annotate the proto_households table using the main model annotations files + - tablename: proto_households + annotate: + SPEC: annotate_households + DF: proto_households + TABLES: + - proto_persons + - land_use + +# Annotate the proto_persons table using the main model annotations files + - tablename: proto_persons + annotate: + SPEC: annotate_persons_after_hh + DF: proto_persons + TABLES: + - proto_households + +postprocess_proto_tables: + - tablename: proto_disaggregate_accessibility + annotate: + SPEC: annotate_disaggregate_accessibility + DF: proto_disaggregate_accessibility + TABLES: + - land_use \ No newline at end of file diff --git a/resident/configs/external_joint_tour_destination.yaml b/resident/configs/external_joint_tour_destination.yaml new file mode 100644 index 0000000..ca25967 --- /dev/null +++ b/resident/configs/external_joint_tour_destination.yaml @@ -0,0 +1 @@ +include_settings: external_non_mandatory_destination.yaml diff --git a/resident/configs/external_joint_tour_identification.yaml b/resident/configs/external_joint_tour_identification.yaml new file mode 100644 index 0000000..1b9e997 --- /dev/null +++ b/resident/configs/external_joint_tour_identification.yaml @@ -0,0 +1 @@ +include_settings: external_non_mandatory_identification.yaml diff --git a/resident/configs/external_non_mandatory_destination.csv b/resident/configs/external_non_mandatory_destination.csv new file mode 100644 index 0000000..e521cb1 --- /dev/null +++ b/resident/configs/external_non_mandatory_destination.csv @@ -0,0 +1,5 @@ +Label,Description,Expression,escort,shopping,eatout,othmaint,social,othdiscr +local_dist,Distance capped at 20 miles,"_DIST@np.minimum(skims[('SOV_M_DIST', 'MD')], 20)",1,1,1,1,1,1 +util_dist,dist,@_DIST,coef_dist_escort,coef_dist_shopping,coef_dist_eatout,coef_dist_maint,coef_dist_social,coef_dist_discr +util_size_term,Size variable,@df['size_term'].apply(np.log1p),coef_size,coef_size,coef_size,coef_size,coef_size,coef_size +util_mode_choice_logsum,Mode choice logsum,mode_choice_logsum,coef_mode_logsum,coef_mode_logsum,coef_mode_logsum,coef_mode_logsum,coef_mode_logsum,coef_mode_logsum diff --git a/resident/configs/external_non_mandatory_destination.yaml b/resident/configs/external_non_mandatory_destination.yaml new file mode 100644 index 0000000..82af7ed --- /dev/null +++ b/resident/configs/external_non_mandatory_destination.yaml @@ -0,0 +1,43 @@ +SAMPLE_SPEC: external_non_mandatory_destination_sample.csv +SPEC: external_non_mandatory_destination.csv +COEFFICIENTS: external_non_mandatory_destination_coefficients.csv + +SAMPLE_SIZE: 30 + +SIZE_TERM_SELECTOR: external_non_mandatory + +CHOOSER_SEGMENT_COLUMN_NAME: tour_type + +# optional (comment out if not desired) +DEST_CHOICE_LOGSUM_COLUMN_NAME: destination_logsum + +# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if saved alt logsum table +DEST_CHOICE_SAMPLE_TABLE_NAME: external_tour_destination_sample + +# these segments need to match in the destination size term table +SEGMENTS: + - shopping + - othmaint + - othdiscr + - eatout + - social + - escort + +SIMULATE_CHOOSER_COLUMNS: + - tour_type + - home_zone_id + - person_id + - female + - income + - income_segment + - age + - tour_type_count + - tour_type_num + +LOGSUM_SETTINGS: tour_mode_choice.yaml + +# model-specific logsum-related settings +CHOOSER_ORIG_COL_NAME: home_zone_id +ALT_DEST_COL_NAME: alt_dest +IN_PERIOD: 14 +OUT_PERIOD: 14 diff --git a/resident/configs/external_non_mandatory_destination_coefficients.csv b/resident/configs/external_non_mandatory_destination_coefficients.csv new file mode 100644 index 0000000..62c9a42 --- /dev/null +++ b/resident/configs/external_non_mandatory_destination_coefficients.csv @@ -0,0 +1,9 @@ +coefficient_name,value,constrain +coef_mode_logsum,0.27,F +coef_dist_escort,-0.356,F +coef_dist_shopping,-0.42,F +coef_dist_maint,-0.18,F +coef_dist_eatout,-0.312,F +coef_dist_social,-0.273,F +coef_dist_discr,-0.345,F +coef_size,1,T diff --git a/resident/configs/external_non_mandatory_destination_sample.csv b/resident/configs/external_non_mandatory_destination_sample.csv new file mode 100644 index 0000000..f3a22a7 --- /dev/null +++ b/resident/configs/external_non_mandatory_destination_sample.csv @@ -0,0 +1,5 @@ +Label,Description,Expression,escort,shopping,eatout,othmaint,social,othdiscr +local_dist,Distance capped at 20 miles,"_DIST@np.minimum(skims[('SOV_M_DIST', 'MD')], 20)",1,1,1,1,1,1 +util_dist,dist,@_DIST,coef_dist_escort,coef_dist_shopping,coef_dist_eatout,coef_dist_maint,coef_dist_social,coef_dist_discr +util_size_term,Size variable,@df['size_term'].apply(np.log1p),coef_size,coef_size,coef_size,coef_size,coef_size,coef_size +utl_zero_size_term,,@df['size_term']==0,-999,-999,-999,-999,-999,-999 \ No newline at end of file diff --git a/resident/configs/external_non_mandatory_identification.csv b/resident/configs/external_non_mandatory_identification.csv new file mode 100644 index 0000000..52278ab --- /dev/null +++ b/resident/configs/external_non_mandatory_identification.csv @@ -0,0 +1,18 @@ +Label,Description,Expression,external_tour,internal_tour +util_dist_to_nearest_ext_station,Distance to nearest external station,dist_to_external_zone,coef_dist_to_nearest_ext_station, +util_log_size,Log size of nearest station,"@np.log1p(reindex(land_use.external_nonwork, df.closest_external_zone))",coef_log_size_of_nearest_ext_station, +util_escort,escort tour ASC,"@np.where(df.tour_type == 'escort', 1, 0)",coef_escort, +util_shopping,shopping tour ASC,"@np.where(df.tour_type == 'shopping', 1, 0)",coef_shopping, +util_othmaint,othmaint tour ASC,"@np.where(df.tour_type == 'othmaint', 1, 0)",coef_othmaint, +util_eatout,eatout tour ASC,"@np.where(df.tour_type == 'eatout', 1, 0)",coef_eatout, +util_social,social tour ASC,"@np.where(df.tour_type == 'social', 1, 0)",coef_social, +util_othdiscr,othdiscr tour ASC,"@np.where(df.tour_type == 'othdiscr', 1, 0)",coef_othdiscr, +util_inc_lt15,Income less than $15k,"@np.where(df['income']<15000,1,0)",coef_income_lt_15, +util_inc_15_50,Income 15 to 50k,"@np.where((df['income']>=15000) * (df['income']<=49999),1,0)",coef_income_15_50, +util_inc_100_250,Income 100k to 250k,"@np.where((df['income']>=100000) * (df['income']<=249999),1,0)",coef_income_100_250, +util_inc_250plus,Income 250k plus,"@np.where(df['income']>=250000,1,0)",coef_income_250_plus, +util_autos_0,0 auto household,"@np.where(df.auto_ownership==0,1,0)",coef_autos_0, +util_is_pure_escort,Pure escort tours cannot have an external destination,"@np.where((df.school_esc_outbound == 'pure_escort')|(df.school_esc_inbound == 'pure_escort'), 1, 0)",-999, +util_global_switch,Global switch to have everything internal,@NO_EXTERNAL,-999, +util_dist_lt_2p5,Distance less than 2.5 miles,"@np.where(df.dist_to_external_zone<2.5,1,0)",coef_dist_lt_2p5, +util_calibration,,1,0.43, diff --git a/resident/configs/external_non_mandatory_identification.yaml b/resident/configs/external_non_mandatory_identification.yaml new file mode 100644 index 0000000..c2a882b --- /dev/null +++ b/resident/configs/external_non_mandatory_identification.yaml @@ -0,0 +1,11 @@ +# using the same spec for both non-mandatory and joint tours +SPEC: external_non_mandatory_identification.csv +COEFFICIENTS: external_non_mandatory_identification_coefficients.csv + +#LOGIT_TYPE: NL +LOGIT_TYPE: MNL + +# Adds this column to the persons table and is set to True if workplace is external +EXTERNAL_COL_NAME: is_external_tour +# set to True if not external but CHOOSER_FILTER_COLUMN_NAME is True +INTERNAL_COL_NAME: is_internal_tour diff --git a/resident/configs/external_non_mandatory_identification_coefficients.csv b/resident/configs/external_non_mandatory_identification_coefficients.csv new file mode 100644 index 0000000..be5f021 --- /dev/null +++ b/resident/configs/external_non_mandatory_identification_coefficients.csv @@ -0,0 +1,15 @@ +coefficient_name,value,constrain +coef_dist_to_nearest_ext_station,-0.06913447619232407,F +coef_log_size_of_nearest_ext_station,0.40435031197856747,F +coef_escort,-8.056823449462565,F +coef_shopping,-7.413630670471193,F +coef_othmaint,-6.453644115005783,F +coef_eatout,-7.27668098767437,F +coef_social,-6.566754062811491,F +coef_othdiscr,-7.415691288097906,F +coef_income_lt_15,-0.706806612224066,F +coef_income_15_50,-0.2533987244055551,F +coef_income_100_250,0.11811329788815791,F +coef_income_250_plus,0.6221129017397624,F +coef_autos_0,-0.5505407390480237,F +coef_dist_lt_2p5,2.6495984313112557,F diff --git a/resident/configs/external_worker_identification.csv b/resident/configs/external_worker_identification.csv new file mode 100644 index 0000000..8cc45a1 --- /dev/null +++ b/resident/configs/external_worker_identification.csv @@ -0,0 +1,14 @@ +Label,Description,Expression,work_external,work_internal +util_dist_to_nearest_ext_station,Distance to nearest external station,dist_to_external_zone,coef_dist_to_nearest_ext_station, +util_size_of_nearest_ext_station,Size of nearest external station,"@np.log1p(reindex(land_use.external_work,df.closest_external_zone))",coef_size_of_nearest_ext_station, +util_dist_lt_2p5,Distance less than 2.5 miles,"@np.where(df.dist_to_external_zone<2.5,1,0)",coef_dist_lt_2p5, +util_part_time,Part time worker,"@np.where(df.pemploy == 2,1,0)",coef_part_time, +util_inc_lt15,Household Income less than $15k,@(df.income<15000),coef_inc_lt15, +util_inc_15_25,Household income $15k-$25k,@(df.income>=15000) & (df.income<25000) ,coef_inc_15_25, +util_inc_25_50,Household Income $25k-$50k,@(df.income>=25000) & (df.income<50000) ,coef_inc_25_50, +util_inc_150_250,Household Income $150k-$50k,@(df.income>=150000) * (df.income<250000) ,coef_inc_150_250, +util_inc_250plus,Household Income $250k+,@(df.income>=250000),coef_inc_250plus, +util_asc,Alternative-specific constant for external worker,1,asc_external_worker, +util_global_switch,Global switch to have everything internal,@NO_EXTERNAL,-999, +util_2016,Constant for pre-COVID conditions,@PRE_COVID,asc_external_2016, +util_calib,Constant for calibration,1,0.35, diff --git a/resident/configs/external_worker_identification.yaml b/resident/configs/external_worker_identification.yaml new file mode 100644 index 0000000..e9660b6 --- /dev/null +++ b/resident/configs/external_worker_identification.yaml @@ -0,0 +1,14 @@ + +SPEC: external_worker_identification.csv +COEFFICIENTS: external_worker_identification_coeffs.csv + +LOGIT_TYPE: MNL + +# boolean column to filter choosers (True means keep) +# will only expose these people to the model +CHOOSER_FILTER_COLUMN_NAME: is_out_of_home_worker + +# Adds this column to the persons table and is set to True if workplace is external +EXTERNAL_COL_NAME: is_external_worker +# set to True if not external but CHOOSER_FILTER_COLUMN_NAME is True +INTERNAL_COL_NAME: is_internal_worker diff --git a/resident/configs/external_worker_identification_coeffs.csv b/resident/configs/external_worker_identification_coeffs.csv new file mode 100644 index 0000000..d316d86 --- /dev/null +++ b/resident/configs/external_worker_identification_coeffs.csv @@ -0,0 +1,12 @@ +coefficient_name,value,constrain +coef_dist_to_nearest_ext_station,-0.03637843085617849,F +coef_size_of_nearest_ext_station,0.7890441071064493,F +coef_part_time,-0.22281386510906526,F +asc_external_worker,-10.723198329563822,F +asc_external_2016,0.7108412488374917,F +coef_inc_lt15,-1.3314204997581536,F +coef_inc_25_50,-0.5733965244215453,F +coef_inc_250plus,1.2296738201382527,F +coef_dist_lt_2p5,2.302815581601645,F +coef_inc_15_25,-0.9334826063624445,F +coef_inc_150_250,0.8316476615543997,F diff --git a/resident/configs/external_workplace_location.csv b/resident/configs/external_workplace_location.csv new file mode 100644 index 0000000..f8b270d --- /dev/null +++ b/resident/configs/external_workplace_location.csv @@ -0,0 +1,6 @@ +Label,Description,Expression,external_workplace +local_dist,,"_DIST@skims[('SOV_M_DIST', 'MD')]",1 +util_dist,,"@np.minimum(_DIST,10)",coef_dist_capped +util_size_variable,Size variable,@(df['size_term']).apply(np.log1p),coef_size +util_no_attractions,No attractions,@df['size_term']==0,-999 +mode_choice_logsum,Mode choice logsum,@df.mode_choice_logsum if 'mode_choice_logsum' in df.columns else 0,coef_mode_logsum diff --git a/resident/configs/external_workplace_location.yaml b/resident/configs/external_workplace_location.yaml new file mode 100644 index 0000000..f106547 --- /dev/null +++ b/resident/configs/external_workplace_location.yaml @@ -0,0 +1,53 @@ +SAMPLE_SIZE: 30 + +SIMULATE_CHOOSER_COLUMNS: + - home_zone_id + - household_id + +# model-specific logsum-related settings +CHOOSER_ORIG_COL_NAME: home_zone_id +ALT_DEST_COL_NAME: alt_dest +IN_PERIOD: 14 +OUT_PERIOD: 8 + +DEST_CHOICE_COLUMN_NAME: external_workplace_zone_id +# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if not desired in persons table +DEST_CHOICE_LOGSUM_COLUMN_NAME: external_workplace_location_logsum +# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if not desired in persons table +MODE_CHOICE_LOGSUM_COLUMN_NAME: external_workplace_modechoice_logsum + +# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if saved alt logsum table +DEST_CHOICE_SAMPLE_TABLE_NAME: external_workplace_location_sample + +SAMPLE_SPEC: external_workplace_location.csv +SPEC: external_workplace_location.csv +COEFFICIENTS: external_workplace_location_coefficients.csv + +LOGSUM_SETTINGS: tour_mode_choice.yaml +LOGSUM_PREPROCESSOR: nontour_preprocessor + +LOGSUM_TOUR_PURPOSE: work + +# required by initialize_households when creating workplace_destination_size table +CHOOSER_TABLE_NAME: persons + +# size_terms model_selector +MODEL_SELECTOR: external_workplace + +# chooser column with segment_id for this segment type +CHOOSER_SEGMENT_COLUMN_NAME: is_external_worker + +# boolean column to filter choosers (True means keep) +# CHOOSER_FILTER_COLUMN_NAME: is_worker +CHOOSER_FILTER_COLUMN_NAME: is_external_worker + +# FIXME - these are assigned to persons in annotate_persons. we need a better way to manage this +SEGMENT_IDS: + external_workplace: True # using only one segment + +# model adds these tables (informational - not added if commented out) +# SHADOW_PRICE_TABLE: workplace_shadow_prices +# MODELED_SIZE_TABLE: workplace_modeled_size + +# not loaded if commented out +# SAVED_SHADOW_PRICE_TABLE_NAME: workplace_shadow_prices.csv diff --git a/resident/configs/external_workplace_location_archive.csv b/resident/configs/external_workplace_location_archive.csv new file mode 100644 index 0000000..1b113c4 --- /dev/null +++ b/resident/configs/external_workplace_location_archive.csv @@ -0,0 +1,6 @@ +Label,Description,Expression,external_workplace +local_dist,,"_DIST@skims[('SOV_M_DIST', 'MD')]",1 +util_dist,,"@np.minimum(_DIST,10)",coef_dist_capped +util_size_variable,Size variable,@(df['size_term']).apply(np.log1p),coef_size +util_no_attractions,No attractions,@df['size_term']==0,-999 +mode_choice_logsum not available for sample,Mode choice logsum,@df.mode_choice_logsum if 'mode_choice_logsum' in df.columns else 0,coef_mode_logsum \ No newline at end of file diff --git a/resident/configs/external_workplace_location_coefficients.csv b/resident/configs/external_workplace_location_coefficients.csv new file mode 100644 index 0000000..7ba9aa4 --- /dev/null +++ b/resident/configs/external_workplace_location_coefficients.csv @@ -0,0 +1,4 @@ +coefficient_name,value,constrain +coef_mode_logsum,0.064105,F +coef_dist_capped,-0.519016,F +coef_size,1,T diff --git a/resident/configs/free_parking.csv b/resident/configs/free_parking.csv new file mode 100644 index 0000000..8fa0f37 --- /dev/null +++ b/resident/configs/free_parking.csv @@ -0,0 +1,6 @@ +Label,Description,Expression,free,pay +util_income_very_high,Very high income household dummy,@df.income>=100000,coef_income_very_high,0.0 +util_income_high,High income housheold dummy,@(df.income>=60000) & (df.income<100000),coef_income_high,0.0 +util_hh_size_4_up,Household size is greater than 3 dummy,@df.hhsize>3,coef_hh_size_4_up,0.0 +util_more_autos_than_workers,More automobiles than workers dummy,@df.auto_ownership>df.num_workers,coef_more_autos_than_workers,0.0 +util_fewer_autos_than_workers,Fewer automobiles than workers dummy,@df.auto_ownership num_workers) * othmaint,coef_hh_has_more_autos_than_workers_maintenance +util_income_gtr_100k_maintenance,Income > $100k /Maintenance,(income>100000) * othmaint,coef_income_gtr_100k_maintenance +util_income_less_than_29999__eating_out,"Income Less than $29,999 / Eating Out",(income<30000) * eatout,coef_income_less_than_29999__eating_out +util_income_between_30000_to_59999__eating_out,"Income Between $30,000 to $59,999 / Eating Out",((income>=30000) & (income<60000)) * othmaint,coef_income_between_30000_to_59999__eating_out +util_income_less_than_29999__discretionary,"Income Less than $29,999 / Discretionary",(income<30000) * othdiscr,coef_income_less_than_29999__discretionary +util_income_between_30000_to_59999__discretionary,"Income Between $30,000 to $59,999 / Discretionary",((income>=30000) & (income<60000)) * othdiscr,coef_income_between_30000_to_59999__discretionary +# util_shopping_hov_accessibility_for_2_tours,Shopping HOV accessibility for 2 Tours,((auto_ownershipnum_workers)*shop_hov_oversufficient_accessibility)*(num_joint_tours==2)*shopping,coef_shopping_hov_accessibility_for_2_tours +# util_maintenance_hov_accessibility,Maintenance HOV Accessibility,((auto_ownershipnum_workers)*maint_hov_oversufficient_accessibility)*othmaint,coef_maintenance_hov_accessibility +# util_discretionary_hov_accessibility,Discretionary HOV Accessibility,((auto_ownershipnum_workers)*discr_hov_oversufficient_accessibility)*othdiscr,coef_discretionary_hov_accessibility +util_shopping_hov_accessibility_for_2_tours,Shopping HOV accessibility for 2 Tours,(shopping_accessibility) * (num_joint_tours == 2) * shopping,coef_shopping_hov_accessibility_for_2_tours +util_maintenance_hov_accessibility,Maintenance HOV Accessibility,shopping_accessibility * othmaint,coef_maintenance_hov_accessibility +util_discretionary_hov_accessibility,Discretionary HOV Accessibility,othdiscr_accessibility * othdiscr,coef_discretionary_hov_accessibility +util_constant_for_children_party_shopping_tour,Constant for Children Party/ Shopping Tour,@(df.purpose1==5)*(df.party1==2)+(df.purpose2==5)*(df.party2==2),coef_constant_for_children_party_shopping_tour +util_constant_for_children_party_maintenance_tour,Constant for Children Party/ Maintenance Tour,@(df.purpose1==6)*(df.party1==2)+(df.purpose2==6)*(df.party2==2),coef_constant_for_children_party_maintenance_tour +util_constant_for_children_party_eating_out_tour,Constant for Children Party/ Eating Out Tour,@(df.purpose1==7)*(df.party1==2)+(df.purpose2==7)*(df.party2==2),coef_constant_for_children_party_eating_out_tour +util_constant_for_children_party_visiting_tour,Constant for Children Party/ Visiting Tour,@(df.purpose1==8)*(df.party1==2)+(df.purpose2==8)*(df.party2==2),coef_constant_for_children_party_visiting_tour +util_constant_for_children_party_discretionary_tour,Constant for Children Party/ Discretionary Tour,@(df.purpose1==9)*(df.party1==2)+(df.purpose2==9)*(df.party2==2),coef_constant_for_children_party_discretionary_tour +util_constant_for_mixed_party_shopping_tour,Constant for Mixed Party/ Shopping Tour,@(df.purpose1==5)*(df.party1==3)+(df.purpose2==5)*(df.party2==3),coef_constant_for_mixed_party_shopping_tour +util_constant_for_mixed_party_maintenance_tour,Constant for Mixed Party/ Maintenance Tour,@(df.purpose1==6)*(df.party1==3)+(df.purpose2==6)*(df.party2==3),coef_constant_for_mixed_party_maintenance_tour +util_constant_for_mixed_party_eating_out_tour,Constant for Mixed Party/ Eating Out Tour,@(df.purpose1==7)*(df.party1==3)+(df.purpose2==7)*(df.party2==3),coef_constant_for_mixed_party_eating_out_tour +util_constant_for_mixed_party_visiting_tour,Constant for Mixed Party/ Visiting Tour,@(df.purpose1==8)*(df.party1==3)+(df.purpose2==8)*(df.party2==3),coef_constant_for_mixed_party_visiting_tour +util_constant_for_mixed_party_discretionary_tour,Constant for Mixed Party/ Discretionary Tour,@(df.purpose1==9)*(df.party1==3)+(df.purpose2==9)*(df.party2==3),coef_constant_for_mixed_party_discretionary_tour +util_number_of_active_full_time_workers_adult_party,Number of Active Full time workers /Adult Party,num_travel_active_full_time_workers * (party1==1) + num_travel_active_full_time_workers * (party2==1),coef_number_of_active_full_time_workers_adult_party +util_number_of_active_part_time_workers_adult_party,Number of Active Part time workers /Adult Party,num_travel_active_part_time_workers * (party1==1) + num_travel_active_part_time_workers * (party2==1),coef_number_of_active_part_time_workers_adult_party +util_number_of_active_university_students_adult_party,Number of Active University Students /Adult Party,num_travel_active_university_students * (party1==1) + num_travel_active_university_students * (party2==1),coef_number_of_active_university_students_adult_party +util_number_of_active_nonworkers_adult_party,Number of Active Non-workers /Adult Party,num_travel_active_non_workers * (party1==1) + num_travel_active_non_workers * (party2==1),coef_number_of_active_nonworkers_adult_party +util_number_of_active_retirees_adult_party,Number of Active Retirees /Adult Party,num_travel_active_retirees * (party1==1) + num_travel_active_retirees * (party2==1),coef_number_of_active_retirees_adult_party +util_number_of_active_driving_age_school_children_children_party,Number of Active Driving Age School Children /Children Party,num_travel_active_driving_age_students * (party1==1) + num_travel_active_driving_age_students * (party2==1),coef_number_of_active_driving_age_school_children_children_party +util_number_of_active_pre_driving_age_school_children_children_party,Number of Active Pre- Driving Age School Children /Children Party,num_travel_active_pre_driving_age_school_kids * (party1==2) + num_travel_active_pre_driving_age_school_kids * (party2==2),coef_number_of_active_pre_driving_age_school_children_children_party +util_number_of_active_part_time_workers_mixed_party,Number of Active Part time workers /Mixed Party,num_travel_active_part_time_workers * (party1==3) + num_travel_active_part_time_workers * (party2==3),coef_number_of_active_part_time_workers_mixed_party +util_number_of_active_driving_age_school_children_mixed_party,Number of Active Driving Age School Children /Mixed Party,num_travel_active_driving_age_students * (party1==3) + num_travel_active_driving_age_students * (party2==3),coef_number_of_active_driving_age_school_children_mixed_party +util_number_of_active_pre_driving_age_school_children_mixed_party,Number of Active Pre- Driving Age School Children /Mixed Party,num_travel_active_pre_driving_age_school_kids * (party1==3) + num_travel_active_pre_driving_age_school_kids * (party2==3),coef_number_of_active_pre_driving_age_school_children_mixed_party +util_number_of_active_preschool_children_mixed_party,Number of Active Preschool Children /Mixed Party,num_travel_active_pre_school_kids * (party1==3) + num_travel_active_pre_school_kids * (party2==3),coef_number_of_active_preschool_children_mixed_party +util_hh_has_no_autos__mixed_party,HH has no autos / Mixed Party,@(df.auto_ownership==0)*(df.party1==3)+(df.auto_ownership==0)*(df.party2==3),coef_hh_has_no_autos__mixed_party +util_hh_has_less_autos_than_workers_mixed_party,HH has less autos than workers/ Mixed Party,@(df.auto_ownership100000)*(df.party1==2)+(df.income>100000)*(df.party2==2),coef_income_more_than_100k_child_party +util_income_more_than_100k_mixed_party,Income more than $100k /Mixed Party,@(df.income>100000)*(df.party1==3)+(df.income>100000)*(df.party2==3),coef_income_more_than_100k_mixed_party +util_log_of_max_window_overlaps_between_adults,Log of max window overlaps between adults,@df.log_time_window_overlap_adult*((df.party1==1)+(df.party2==1)),coef_log_of_max_window_overlaps_between_adults +util_log_of_max_window_overlaps_between_children,Log of max window overlaps between children,@df.log_time_window_overlap_child*((df.party1==2)+(df.party2==2)),coef_log_of_max_window_overlaps_between_children +util_log_of_max_window_overlaps_between_adult_child,Log of max window overlaps between adult & child,@df.log_time_window_overlap_adult_child*((df.party1==3)+(df.party2==3)),coef_log_of_max_window_overlaps_between_adult_child +util_not_more_than_one_active_adult,Not more than 1 travel active adult in HH,@(df.num_travel_active_adults < 2)*(((df.party1==1)+(df.party2==1))>0),coef_unavailable +util_not_more_than_one_active_child,Not more than 1 travel active child in HH,@(df.num_travel_active_children < 2)*(((df.party1==2)+(df.party2==2))>0),coef_unavailable +util_not_more_than_one_active_mixed,No travel-active pair adult-child in HH ,@((df.num_travel_active_adults*df.num_travel_active_children) ==0)*(((df.party1==3)+(df.party2==3))>0),coef_unavailable +util_adjustment_for_children_party_shopping_tour,Adjustment for Children Party/ Shopping Tour,@(df.purpose1==5)*(df.party1==2)+(df.purpose2==5)*(df.party2==2),coef_adjustment_for_children_party_shopping_tour +util_adjustment_for_children_party_maintenance_tour,Adjustment for Children Party/ Maintenance Tour,@(df.purpose1==6)*(df.party1==2)+(df.purpose2==6)*(df.party2==2),coef_adjustment_for_children_party_maintenance_tour +util_adjustment_for_children_party_eating_out_tour,Adjustment for Children Party/ Eating Out Tour,@(df.purpose1==7)*(df.party1==2)+(df.purpose2==7)*(df.party2==2),coef_adjustment_for_children_party_eating_out_tour +util_adjustment_for_children_party_visiting_tour,Adjustment for Children Party/ Visiting Tour,@(df.purpose1==8)*(df.party1==2)+(df.purpose2==8)*(df.party2==2),coef_adjustment_for_children_party_visiting_tour +util_adjustment_for_children_party_discretionary_tour,Adjustment for Children Party/ Discretionary Tour,@(df.purpose1==9)*(df.party1==2)+(df.purpose2==9)*(df.party2==2),coef_adjustment_for_children_party_discretionary_tour +util_adjustment_for_mixed_party_shopping_tour,Adjustment for Mixed Party/ Shopping Tour,@(df.purpose1==5)*(df.party1==3)+(df.purpose2==5)*(df.party2==3),coef_adjustment_for_mixed_party_shopping_tour +util_adjustment_for_mixed_party_maintenance_tour,Adjustment for Mixed Party/ Maintenance Tour,@(df.purpose1==6)*(df.party1==3)+(df.purpose2==6)*(df.party2==3),coef_adjustment_for_mixed_party_maintenance_tour +util_adjustment_for_mixed_party_eating_out_tour,Adjustment for Mixed Party/ Eating Out Tour,@(df.purpose1==7)*(df.party1==3)+(df.purpose2==7)*(df.party2==3),coef_adjustment_for_mixed_party_eating_out_tour +util_adjustment_for_mixed_party_visiting_tour,Adjustment for Mixed Party/ Visiting Tour,@(df.purpose1==8)*(df.party1==3)+(df.purpose2==8)*(df.party2==3),coef_adjustment_for_mixed_party_visiting_tour +util_adjustment_for_mixed_party_discretionary_tour,Adjustment for Mixed Party/ Discretionary Tour,@(df.purpose1==9)*(df.party1==3)+(df.purpose2==9)*(df.party2==3),coef_adjustment_for_mixed_party_discretionary_tour +util_adjustment_for_shopping_tour,Adjustment for shopping tour,shopping,coef_adjustment_for_shopping_tour +util_adjustment_for_maintenance_tour,Adjustment for Maintenance tour,othmaint,coef_adjustment_for_maintenance_tour +util_adjustment_for_eating_out_tour,Adjustment for eating out tour,eatout,coef_adjustment_for_eating_out_tour +util_adjustment_for_visiting_tour,Adjustment for visiting tour,social,coef_adjustment_for_visiting_tour +util_adjustment_for_discretionary_tour,Adjustment for discretionary tour,othdiscr,coef_adjustment_for_discretionary_tour +util_adjustment_for_share_of_2_joint_tours,Adjustment for share of 2 Joint Tours,num_joint_tours==2,coef_adjustment_for_share_of_2_joint_tours +util_tm2_adjustment_for_children_party,TM2 Adjustment for Children Party,@(df.party1==2)+(df.party2==2),coef_tm2_adjustment_for_children_party +util_tm2_adjustment_for_mixed_party,TM2 Adjustment for Mixed Party,@(df.party1==3)+(df.party2==3),coef_tm2_adjustment_for_mixed_party +util_tm2_adjustment_for_share_of_2_joint_tours,TM2 adjustment for share of 2 Joint Tours,num_joint_tours==2,coef_tm2_adjustment_for_share_of_2_joint_tours +util_tm2_adjustment_for_share_of_1_joint_tours,TM2 adjustment for share of 1 Joint Tours,num_joint_tours==1,coef_tm2_adjustment_for_share_of_1_joint_tours +util_tm2_adjustment_for_share_of_0_joint_tours,TM2 adjustment for share of 0 Joint Tours,num_joint_tours==0,coef_tm2_adjustment_for_share_of_0_joint_tours diff --git a/resident/configs/joint_tour_frequency_composition.yaml b/resident/configs/joint_tour_frequency_composition.yaml new file mode 100644 index 0000000..452ec92 --- /dev/null +++ b/resident/configs/joint_tour_frequency_composition.yaml @@ -0,0 +1,35 @@ +LOGIT_TYPE: MNL + +SPEC: joint_tour_frequency_composition.csv +COEFFICIENTS: joint_tour_frequency_composition_coeffs.csv + +preprocessor: + SPEC: joint_tour_frequency_composition_annotate_households_preprocessor.csv + DF: households + TABLES: + - persons_merged + +ALTS_PREPROCESSOR: + SPEC: joint_tour_frequency_composition_annotate_alt_preprocessor.csv + DF: alt_tdd + +# define the structure of alternative table +ALTS_TABLE_STRUCTURE: + PURPOSE: + COLUMNS: + - purpose1 + - purpose2 + VALUE_MAP: + 5: shopping + 6: othmaint + 7: eatout + 8: social + 9: othdiscr + COMPOSITION: + COLUMNS: + - party1 + - party2 + VALUE_MAP: + 1: adults + 2: children + 3: mixed \ No newline at end of file diff --git a/resident/configs/joint_tour_frequency_composition_alternatives.csv b/resident/configs/joint_tour_frequency_composition_alternatives.csv new file mode 100644 index 0000000..657f987 --- /dev/null +++ b/resident/configs/joint_tour_frequency_composition_alternatives.csv @@ -0,0 +1,151 @@ +alt,purpose1,purpose2,party1,party2 +1,5,0,1,0 +2,5,0,2,0 +3,5,0,3,0 +4,6,0,1,0 +5,6,0,2,0 +6,6,0,3,0 +7,7,0,1,0 +8,7,0,2,0 +9,7,0,3,0 +10,8,0,1,0 +11,8,0,2,0 +12,8,0,3,0 +13,9,0,1,0 +14,9,0,2,0 +15,9,0,3,0 +16,5,5,1,1 +17,5,5,1,2 +18,5,5,1,3 +19,5,5,2,1 +20,5,5,2,2 +21,5,5,2,3 +22,5,5,3,1 +23,5,5,3,2 +24,5,5,3,3 +25,5,6,1,1 +26,5,6,1,2 +27,5,6,1,3 +28,5,6,2,1 +29,5,6,2,2 +30,5,6,2,3 +31,5,6,3,1 +32,5,6,3,2 +33,5,6,3,3 +34,5,7,1,1 +35,5,7,1,2 +36,5,7,1,3 +37,5,7,2,1 +38,5,7,2,2 +39,5,7,2,3 +40,5,7,3,1 +41,5,7,3,2 +42,5,7,3,3 +43,5,8,1,1 +44,5,8,1,2 +45,5,8,1,3 +46,5,8,2,1 +47,5,8,2,2 +48,5,8,2,3 +49,5,8,3,1 +50,5,8,3,2 +51,5,8,3,3 +52,5,9,1,1 +53,5,9,1,2 +54,5,9,1,3 +55,5,9,2,1 +56,5,9,2,2 +57,5,9,2,3 +58,5,9,3,1 +59,5,9,3,2 +60,5,9,3,3 +61,6,6,1,1 +62,6,6,1,2 +63,6,6,1,3 +64,6,6,2,1 +65,6,6,2,2 +66,6,6,2,3 +67,6,6,3,1 +68,6,6,3,2 +69,6,6,3,3 +70,6,7,1,1 +71,6,7,1,2 +72,6,7,1,3 +73,6,7,2,1 +74,6,7,2,2 +75,6,7,2,3 +76,6,7,3,1 +77,6,7,3,2 +78,6,7,3,3 +79,6,8,1,1 +80,6,8,1,2 +81,6,8,1,3 +82,6,8,2,1 +83,6,8,2,2 +84,6,8,2,3 +85,6,8,3,1 +86,6,8,3,2 +87,6,8,3,3 +88,6,9,1,1 +89,6,9,1,2 +90,6,9,1,3 +91,6,9,2,1 +92,6,9,2,2 +93,6,9,2,3 +94,6,9,3,1 +95,6,9,3,2 +96,6,9,3,3 +97,7,7,1,1 +98,7,7,1,2 +99,7,7,1,3 +100,7,7,2,1 +101,7,7,2,2 +102,7,7,2,3 +103,7,7,3,1 +104,7,7,3,2 +105,7,7,3,3 +106,7,8,1,1 +107,7,8,1,2 +108,7,8,1,3 +109,7,8,2,1 +110,7,8,2,2 +111,7,8,2,3 +112,7,8,3,1 +113,7,8,3,2 +114,7,8,3,3 +115,7,9,1,1 +116,7,9,1,2 +117,7,9,1,3 +118,7,9,2,1 +119,7,9,2,2 +120,7,9,2,3 +121,7,9,3,1 +122,7,9,3,2 +123,7,9,3,3 +124,8,8,1,1 +125,8,8,1,2 +126,8,8,1,3 +127,8,8,2,1 +128,8,8,2,2 +129,8,8,2,3 +130,8,8,3,1 +131,8,8,3,2 +132,8,8,3,3 +133,8,9,1,1 +134,8,9,1,2 +135,8,9,1,3 +136,8,9,2,1 +137,8,9,2,2 +138,8,9,2,3 +139,8,9,3,1 +140,8,9,3,2 +141,8,9,3,3 +142,9,9,1,1 +143,9,9,1,2 +144,9,9,1,3 +145,9,9,2,1 +146,9,9,2,2 +147,9,9,2,3 +148,9,9,3,1 +149,9,9,3,2 +150,9,9,3,3 diff --git a/resident/configs/joint_tour_frequency_composition_annotate_alt_preprocessor.csv b/resident/configs/joint_tour_frequency_composition_annotate_alt_preprocessor.csv new file mode 100644 index 0000000..389d28a --- /dev/null +++ b/resident/configs/joint_tour_frequency_composition_annotate_alt_preprocessor.csv @@ -0,0 +1,7 @@ +Description,Target,Expression +,shopping,"np.where(alt_tdd.purpose1 == 5, 1, 0) + np.where(alt_tdd.purpose2 == 5, 1, 0)" +,othmaint,"np.where(alt_tdd.purpose1 == 6, 1, 0) + np.where(alt_tdd.purpose2 == 6, 1, 0)" +,eatout,"np.where(alt_tdd.purpose1 == 7, 1, 0) + np.where(alt_tdd.purpose2 == 7, 1, 0)" +,social,"np.where(alt_tdd.purpose1 == 8, 1, 0) + np.where(alt_tdd.purpose2 == 8, 1, 0)" +,othdiscr,"np.where(alt_tdd.purpose1 == 9, 1, 0) + np.where(alt_tdd.purpose2 == 9, 1, 0)" +,num_joint_tours,shopping+othmaint+eatout+social+othdiscr \ No newline at end of file diff --git a/resident/configs/joint_tour_frequency_composition_annotate_households_preprocessor.csv b/resident/configs/joint_tour_frequency_composition_annotate_households_preprocessor.csv new file mode 100644 index 0000000..2185a09 --- /dev/null +++ b/resident/configs/joint_tour_frequency_composition_annotate_households_preprocessor.csv @@ -0,0 +1,21 @@ +Description,Target,Expression +,_HH_OVERLAPS,"hh_time_window_overlap(households, persons_merged)" +,time_window_overlap_adult,_HH_OVERLAPS['aa'] +,time_window_overlap_child,_HH_OVERLAPS['cc'] +,time_window_overlap_adult_child,_HH_OVERLAPS['ac'] +logTimeWindowOverlapAdult,log_time_window_overlap_adult,np.log1p(time_window_overlap_adult) +logTimeWindowOverlapChild,log_time_window_overlap_child,np.log1p(time_window_overlap_child) +logTimeWindowOverlapAdultChild,log_time_window_overlap_adult_child,np.log1p(time_window_overlap_adult_child) +,_PERSON_COUNT,"lambda query, persons_merged, households: persons_merged.query(query).groupby('household_id').size().reindex(households.index).fillna(0).astype(np.int8)" +number of travel active fulltime workers,num_travel_active_full_time_workers,"_PERSON_COUNT('(ptype == 1) & (cdap_activity != \'H\')', persons_merged, households)" +number of travel active parttime workers,num_travel_active_part_time_workers,"_PERSON_COUNT('(ptype == 2) & (cdap_activity != \'H\')', persons_merged, households)" +number of travel active university student,num_travel_active_university_students,"_PERSON_COUNT('(ptype == 3) & (cdap_activity != \'H\')', persons_merged, households)" +number of travel active non-workers,num_travel_active_non_workers,"_PERSON_COUNT('(ptype == 4) & (cdap_activity != \'H\')', persons_merged, households)" +number of travel active retirees,num_travel_active_retirees,"_PERSON_COUNT('(ptype == 5) & (cdap_activity != \'H\')', persons_merged, households)" +number of travel active driving age students,num_travel_active_driving_age_students,"_PERSON_COUNT('(ptype == 6) & (cdap_activity != \'H\')', persons_merged, households)" +number of travel active pre-driving age school kids,num_travel_active_pre_driving_age_school_kids,"_PERSON_COUNT('(ptype == 7) & (cdap_activity != \'H\')', persons_merged, households)" +number of travel active pre-school kids,num_travel_active_pre_school_kids,"_PERSON_COUNT('(ptype == 8) & (cdap_activity != \'H\')', persons_merged, households)" +number of travel active adults,num_travel_active_adults,"_PERSON_COUNT('adult & (cdap_activity != \'H\')', persons_merged, households)" +number of travel active chilren,num_travel_active_children,"_PERSON_COUNT('~adult & (cdap_activity != \'H\')', persons_merged, households)" +shopping_accessibility,shopping_accessibility,"persons_merged.groupby('household_id')['shopping_accessibility'].mean().reindex(households.index)" +othdiscr_accessibility,othdiscr_accessibility,"persons_merged.groupby('household_id')['othdiscr_accessibility'].mean().reindex(households.index)" \ No newline at end of file diff --git a/resident/configs/joint_tour_frequency_composition_coeffs.csv b/resident/configs/joint_tour_frequency_composition_coeffs.csv new file mode 100644 index 0000000..e0d15c2 --- /dev/null +++ b/resident/configs/joint_tour_frequency_composition_coeffs.csv @@ -0,0 +1,102 @@ +coefficient_name,value,constrain +coef_unavailable,-999,T +coef_constant_for_shopping_tour,0,T +coef_constant_for_maintenance_tour,-1.477,F +coef_constant_for_eating_out_tour,0.5796,F +coef_constant_for_visiting_tour,-1.0037,F +coef_constant_for_discretionary_tour,-1.1195,F +coef_constant_for_2_shopping_tour,-12.009,F +coef_1_shopping_and_1_maintenance_tour,-12.13684801,F +coef_1_shopping_and_1_eating_out_tour,-12.79892713,F +coef_1_shopping_and_1_visiting_tour,-12.22077221,F +coef_1_shopping_and_1_discretionary_tour,-12.57867869,F +coef_constant_for_2_maintenance_tour,-13.43572169,F +coef_1_maintenance_and_1_eating_out_tour,-12.18915033,F +coef_1_maintenance_and_1_visiting_tour,-11.81267905,F +coef_1_maintenance_and_1_discretionary_tour,-12.28867197,F +coef_constant_for_2_eating_out_tour,-13.15388273,F +coef_1_eating_out_and_1_visiting_tour,-13.15388273,F +coef_1_eating_out_and_1_discretionary_tour,-12.56102569,F +coef_constant_for_2_visiting_tour,-13.15388273,F +coef_1_visiting_and_1_discretionary_tour,-12.37222202,F +coef_constant_for_2_discretionary_tour,-13.23532342,F +coef_constant_for_1_discretionary_tour,-1.200,F +coef_constant_for_1_eating_out_tour,0.460,F +coef_constant_for_1_visiting_tour,1.720,F +coef_number_of_active_full_time_workers_shopping,0.09864381,F +coef_number_of_active_nonworkers_shopping,0.393630718,F +coef_number_of_active_pre_driving_age_school_children_shopping,-0.313021042,F +coef_number_of_active_preschool_children_shopping,-1.213950718,F +coef_number_of_active_nonworkers_maintenance,0.322738327,F +coef_number_of_active_retirees_maintenance,0.298632515,F +coef_number_of_active_driving_age_school_children_maintenance,0.503901856,F +coef_number_of_active_preschool_children_maintenance,-1.160523204,F +coef_number_of_active_full_time_workers_eating_out,-0.305934663,F +coef_number_of_active_university_students_eating_out,-0.65706029,F +coef_number_of_active_retirees_eating_out,-0.391710731,F +coef_number_of_active_pre_driving_age_school_children_eating_out,-0.25140082,F +coef_number_of_active_preschool_children_eating_out,-1.700731169,F +coef_number_of_active_pre_driving_age_school_children_visiting,0.162335324,F +coef_number_of_active_preschool_children_visiting,-0.96955678,F +coef_number_of_active_part_time_workers_discretionary,0.217898126,F +coef_number_of_active_university_students_discretionary,-0.610578234,F +coef_number_of_active_driving_age_school_children_discretionary,0.359485499,F +coef_number_of_active_preschool_children_discretionary,-1.244458661,F +coef_hh_has_more_autos_than_workers_maintenance,-0.336188392,F +coef_income_gtr_100k_maintenance,-0.475730683,F +coef_income_less_than_29999__eating_out,-1.282190776,F +coef_income_between_30000_to_59999__eating_out,-0.275046208,F +coef_income_less_than_29999__discretionary,-0.352579013,F +coef_income_between_30000_to_59999__discretionary,-0.191735343,F +coef_shopping_hov_accessibility_for_2_tours,0.039513822,F +coef_maintenance_hov_accessibility,0.128132691,F +coef_discretionary_hov_accessibility,0.089590553,F +coef_constant_for_children_party_shopping_tour,-5.374907972,F +coef_constant_for_children_party_maintenance_tour,-5.144798184,F +coef_constant_for_children_party_eating_out_tour,-4.09806907,F +coef_constant_for_children_party_visiting_tour,-4.09806907,F +coef_constant_for_children_party_discretionary_tour,-4.09806907,F +coef_constant_for_mixed_party_shopping_tour,0.575879495,F +coef_constant_for_mixed_party_maintenance_tour,0.515873866,F +coef_constant_for_mixed_party_eating_out_tour,0.168592084,F +coef_constant_for_mixed_party_visiting_tour,0.078060666,F +coef_constant_for_mixed_party_discretionary_tour,0.856042068,F +coef_number_of_active_full_time_workers_adult_party,0.599335502,F +coef_number_of_active_part_time_workers_adult_party,1.113944272,F +coef_number_of_active_university_students_adult_party,0.231138167,F +coef_number_of_active_nonworkers_adult_party,0.341446999,F +coef_number_of_active_retirees_adult_party,0.657220801,F +coef_number_of_active_driving_age_school_children_children_party,0.580109231,F +coef_number_of_active_pre_driving_age_school_children_children_party,0.580109231,F +coef_number_of_active_part_time_workers_mixed_party,0.522327137,F +coef_number_of_active_driving_age_school_children_mixed_party,0.216908669,F +coef_number_of_active_pre_driving_age_school_children_mixed_party,0.31440104,F +coef_number_of_active_preschool_children_mixed_party,0.897670235,F +coef_hh_has_no_autos__mixed_party,-2.920728233,F +coef_hh_has_less_autos_than_workers_mixed_party,-0.546339245,F +coef_income_more_than_100k_child_party,-1.189112151,F +coef_income_more_than_100k_mixed_party,-0.303217156,F +coef_log_of_max_window_overlaps_between_adults,2.96902119,F +coef_log_of_max_window_overlaps_between_children,4.673601828,F +coef_log_of_max_window_overlaps_between_adult_child,3.523795377,F +coef_adjustment_for_children_party_shopping_tour,0.407745781,F +coef_adjustment_for_children_party_maintenance_tour,0.284925252,F +coef_adjustment_for_children_party_eating_out_tour,0,T +coef_adjustment_for_children_party_visiting_tour,0.966264444,F +coef_adjustment_for_children_party_discretionary_tour,0.144740532,F +coef_adjustment_for_mixed_party_shopping_tour,0.681178558,F +coef_adjustment_for_mixed_party_maintenance_tour,0.476324741,F +coef_adjustment_for_mixed_party_eating_out_tour,0.827297043,F +coef_adjustment_for_mixed_party_visiting_tour,0.691826384,F +coef_adjustment_for_mixed_party_discretionary_tour,0.697808415,F +coef_adjustment_for_shopping_tour,0,T +coef_adjustment_for_maintenance_tour,0.06575155,F +coef_adjustment_for_eating_out_tour,0.0643679,F +coef_adjustment_for_visiting_tour,0.00785518,F +coef_adjustment_for_discretionary_tour,0.076075677,F +coef_adjustment_for_share_of_2_joint_tours,-1.000,F +coef_tm2_adjustment_for_children_party,-4.0,F +coef_tm2_adjustment_for_mixed_party,-1.800,F +coef_tm2_adjustment_for_share_of_2_joint_tours,0.00,F +coef_tm2_adjustment_for_share_of_1_joint_tours,1.50,F +coef_tm2_adjustment_for_share_of_0_joint_tours,-1.00,F \ No newline at end of file diff --git a/resident/configs/joint_tour_participation.csv b/resident/configs/joint_tour_participation.csv new file mode 100644 index 0000000..29f5548 --- /dev/null +++ b/resident/configs/joint_tour_participation.csv @@ -0,0 +1,67 @@ +Label,Description,Expression,participate,not_participate +util_full_time_worker_adults_only_party,Dummy for full-time worker in adult party,(ptype==1) & (composition=='adults'),coef_full_time_worker_adults_only_party, +util_part_time_worker_adults_only_party,Dummy for part-time worker in adult party,(ptype==2) & (composition=='adults'),coef_part_time_worker_adults_only_party, +util_university_students_adults_only_party,Dummy for university student in adult party,(ptype==3) & (composition=='adults'),coef_university_students_adults_only_party, +util_non_worker_adults_only_party,Dummy for nonworker in adult party,(ptype==4) & (composition=='adults'),coef_non_worker_adults_only_party, +util_retiree_adults_only_party,Dummy for retiree in adult party,(ptype==5) & (composition=='adults'),coef_retiree_adults_only_party, +util_driving_age_student_children_only_party,Dummy for driving school child in children party,(ptype==6) & (composition=='children'),coef_driving_age_student_children_only_party, +util_pre_driving_age_student_children_only_party,Dummy for pre-driving school child in children party,(ptype==7) & (composition=='children'),coef_pre_driving_age_student_children_only_party, +util_child_too_young_for_school_children_only_party,Dummy for preschool child in children party,(ptype==8) & (composition=='children'),coef_child_too_young_for_school_children_only_party, +util_full_time_worker_mixed_party,Dummy for full-time worker in mixed party,(ptype==1) & (composition=='mixed'),coef_full_time_worker_mixed_party, +util_part_time_worker_mixed_party,Dummy for part-time worker in mixed party,(ptype==2) & (composition=='mixed'),coef_part_time_worker_mixed_party, +util_university_student_mixed_party,Dummy for university student in mixed party,(ptype==3) & (composition=='mixed'),coef_university_student_mixed_party, +util_non_worker_mixed_party,Dummy for nonworker in mixed party,(ptype==4) & (composition=='mixed'),coef_non_worker_mixed_party, +util_retiree_mixed_party,Dummy for retiree in mixed party,(ptype==5) & (composition=='mixed'),coef_retiree_mixed_party, +util_child_too_young_for_school_mixed_party,Dummy for driving school child in mixed party,(ptype==6) & (composition=='mixed'),coef_child_too_young_for_school_mixed_party, +util_pre_driving_age_student_mixed_party,Dummy for pre-driving school child in mixed party,(ptype==7) & (composition=='mixed'),coef_pre_driving_age_student_mixed_party, +util_driving_age_student_mixed_party,Dummy for preschool child in mixed party,(ptype==8) & (composition=='mixed'),coef_driving_age_student_mixed_party, +#,,,, +util_part_time_worker_specific_to_maintenance_joint_tours,Dummy for part-time worker on maintenance joint tour ,(ptype==2) & (tour_type=='othmaint'),coef_part_time_worker_specific_to_maintenance_joint_tours, +util_non_worker_specific_to_maintenance_joint_tours,Dummy for non-worker on maintenance joint tour ,(ptype==4) & (tour_type=='othmaint'),coef_non_worker_specific_to_maintenance_joint_tours, +util_pre_school_kid_specific_to_maintenance_joint_tours,Dummy for preschool child on maintenance joint tour ,(ptype==8) & (tour_type=='othmaint'),coef_pre_school_kid_specific_to_maintenance_joint_tours, +util_full_time_worker_specific_to_eating_out_joint_tours,Dummy for full-time worker on eating out joint tour ,(ptype==1) & (tour_type=='eatout'),coef_full_time_worker_specific_to_eating_out_joint_tours, +util_part_time_worker_specific_to_eating_out_joint_tours,Dummy for part-time worker on eating out joint tour ,(ptype==2) & (tour_type=='eatout'),coef_part_time_worker_specific_to_eating_out_joint_tours, +util_pre_driving_age_student_specific_to_eating_out_joint_tours,Dummy for pre-driving age school child on eating out joint tour ,(ptype==7) & (tour_type=='eatout'),coef_pre_driving_age_student_specific_to_eating_out_joint_tours, +util_part_time_worker_specific_to_discretionary_joint_tours,Dummy for part-time worker on discretionary joint tour ,(ptype==2) & (tour_type=='othdiscr'),coef_part_time_worker_specific_to_discretionary_joint_tours, +util_retiree_specific_to_discretionary_joint_tours,Dummy for retiree on discretionary joint tour ,(ptype==5) & (tour_type=='othdiscr'),coef_retiree_specific_to_discretionary_joint_tours, +util_driving_age_student_specific_to_discretionary_joint_tours,Dummy for driving school child on discretionary joint tour ,(ptype==6) & (tour_type=='othdiscr'),coef_driving_age_student_specific_to_discretionary_joint_tours, +util_pre_driving_age_student_specific_to_discretionary_joint_tours,Dummy for pre-driving school child on discretionary joint tour ,(ptype==7) & (tour_type=='othdiscr'),coef_pre_driving_age_student_specific_to_discretionary_joint_tours, +util_part_time_worker_specific_to_visiting_joint_tours,Dummy for part-time worker on visiting joint tour ,(ptype==2) & (tour_type=='social'),coef_part_time_worker_specific_to_visiting_joint_tours, +util_non_worker_specific_to_visiting_joint_tours,Dummy for non-worker on visiting joint tour ,(ptype==4) & (tour_type=='social'),coef_non_worker_specific_to_visiting_joint_tours, +util_retiree_specific_to_visiting_joint_tours,Dummy for retiree on visiting joint tour ,(ptype==5) & (tour_type=='social'),coef_retiree_specific_to_visiting_joint_tours, +util_driving_age_student_specific_to_visiting_joint_tours,Dummy for driving school child on visiting joint tour ,(ptype==6) & (tour_type=='social'),coef_driving_age_student_specific_to_visiting_joint_tours, +util_pre_school_kid_specific_to_visiting_joint_tours,Dummy for preschool child on visiting joint tour ,(ptype==8) & (tour_type=='social'),coef_pre_school_kid_specific_to_visiting_joint_tours, +#,,,, +util_adult_fewer_automobiles_than_workers_adult_only_party,Dummy for low car ownership for adult in adult party,(auto_ownershipnum_workers)&(composition=='mixed')&adult,coef_adult_more_automobiles_than_workers_mixed_party, +util_child_zero_automobiles_mixed_party,Dummy for no car for child in mixed party,(auto_ownership==0)&(composition=='mixed')&child,coef_child_zero_automobiles_mixed_party, +#,,,, +util_dummy_for_low_income_for_adult_in_adult_party,Dummy for low income for adult in adult party,(income<30000)&(composition=='adults')&adult,coef_dummy_for_low_income_for_adult_in_adult_party, +util_dummy_for_high_income_for_adult_in_mixed_party,Dummy for high income for adult in mixed party,(income>100000)&(composition=='mixed')&adult,coef_dummy_for_high_income_for_adult_in_mixed_party, +util_dummy_for_high_income_for_child_in_mixed_party,Dummy for high income for child in mixed party,(income>100000)&(composition=='mixed')&child,coef_dummy_for_high_income_for_child_in_mixed_party, +#,,,, +util_adult_number_of_joint_tours_adult_only,No of HH joint tours for adult in adult party,(adult&(composition=='adults'))*num_hh_joint_tours,coef_adult_number_of_joint_tours_adult_only, +util_adult_number_of_joint_tours_mixed,No of HH joint tours for adult in mixed party,(adult&(composition=='mixed'))*num_hh_joint_tours,coef_adult_number_of_joint_tours_mixed, +util_child_number_of_joint_tours_child_only,No of HH joint tours for child in children party,(child&(composition=='children'))*num_hh_joint_tours,coef_child_number_of_joint_tours_child_only, +util_child_number_of_joint_tours_mixed,No of HH joint tours for child in mixed party,(child&(composition=='mixed'))*num_hh_joint_tours,coef_child_number_of_joint_tours_mixed, +#,,,, +util_adult_number_of_other_adults_in_the_household_adults_only_party,No of other HH adults for adult in adult party,(adult & (composition=='adults')) * (num_adults.clip(upper=4) - 1),coef_adult_number_of_other_adults_in_the_household_adults_only_party, +util_adult_number_of_other_adults_in_the_household_mixed_party,No of other HH adults for adult in mixed party,(adult & (composition=='mixed')) * (num_adults.clip(upper=4) - 1),coef_adult_number_of_other_adults_in_the_household_mixed_party, +util_child_number_of_other_children_in_the_household_child_only_party,No of other HH children for child in children party,(~adult & (composition=='children')) * (num_children.clip(upper=4) - 1),coef_child_number_of_other_children_in_the_household_child_only_party, +util_child_number_of_other_children_in_the_household_mixed,No of other HH children for child in mixed party,(~adult & (composition=='mixed')) * (num_children.clip(upper=4) - 1),coef_child_number_of_other_children_in_the_household_mixed, +#,,,, +util_adult_log_of_max_window_overlap_with_an_adult_adult_only_party,"Adult, log of max window overlap with an adult, adult-only party",(adult & (composition=='adults')) * log_time_window_overlap_adult,coef_adult_log_of_max_window_overlap_with_an_adult_adult_only_party, +util_adult_log_of_max_window_overlap_with_a_child_mixed,"Adult, log of max window overlap with a child, mixed",(adult & (composition=='mixed')) * log_time_window_overlap_adult_child,coef_adult_log_of_max_window_overlap_with_a_child_mixed, +util_child_log_of_max_window_overlap_with_an_adult_mixed,"Child, log of max window overlap with an adult, mixed",(~adult &(composition=='mixed')) * log_time_window_overlap_adult_child,coef_child_log_of_max_window_overlap_with_an_adult_mixed, +util_child_log_of_max_window_overlap_with_a_child_child,"Child, log of max window overlap with a child, child",(~adult & (composition=='children')) * log_time_window_overlap_child,coef_child_log_of_max_window_overlap_with_a_child_child, +#,,,, +util_adults_are_prohibited_in_participating_in_child_only_tours,Adults are prohibited in participating in child-only tours,adult & (composition=='children'),coef_unavailable, +util_children_are_prohibited_in_participating_in_adult_only_tours,Children are prohibited in participating in adult-only tours,~adult & (composition=='adults'),coef_unavailable, +util_persons_with_home_activity_patterns_are_prohibilted_from_participating,Persons with Home activity patterns are prohibilted from participating,~travel_active,coef_unavailable, +#,,,, +util_if_only_two_available_adults_both_must_participate_in_adult_only_tour,"If only two available adults, both must participate in adult-only tour",adult & travel_active & (composition=='adults') & (num_travel_active_adults<3),,coef_unavailable +util_if_only_one_available_adult_traveler_must_participate_in_mixed_tour,"If only one available adult, traveler must participate in mixed tour",adult & travel_active & (composition=='mixed') & (num_travel_active_adults<2),,coef_unavailable +util_if_only_two_available_children_both_must_participate_in_child_only_tour,"If only two available children, both must participate in child-only tour",~adult & travel_active & (composition=='children') & (num_travel_active_children<3),,coef_unavailable +util_if_only_one_available_child_traveler_must_participate_in_mixed_tour,"If only one available child, traveler must participate in mixed tour",~adult & travel_active & (composition == 'mixed') & (num_travel_active_children<2),,coef_unavailable +#,,,, +util_globabl_adjustment_constant,Global adjustment constant for whether person participated or not,1,coef_global_adj_for_paticipation, diff --git a/resident/configs/joint_tour_participation.yaml b/resident/configs/joint_tour_participation.yaml new file mode 100644 index 0000000..1d17a22 --- /dev/null +++ b/resident/configs/joint_tour_participation.yaml @@ -0,0 +1,24 @@ + +SPEC: joint_tour_participation.csv +COEFFICIENTS: joint_tour_participation_coefficients.csv + +LOGIT_TYPE: MNL + +max_participation_choice_iterations: 1000 + +# will force anyone with a probability > 0 onto the tour after the max iterations +FORCE_PARTICIPATION: True + +preprocessor: + SPEC: joint_tour_participation_annotate_participants_preprocessor + DF: participants + TABLES: + - tours +# - persons +# - accessibility + +annotate_persons: + SPEC: annotate_persons_jtp + DF: persons + TABLES: + - joint_tour_participants diff --git a/resident/configs/joint_tour_participation_annotate_participants_preprocessor.csv b/resident/configs/joint_tour_participation_annotate_participants_preprocessor.csv new file mode 100644 index 0000000..38d26c7 --- /dev/null +++ b/resident/configs/joint_tour_participation_annotate_participants_preprocessor.csv @@ -0,0 +1,14 @@ +Description,Target,Expression +,_P_OVERLAPS,person_time_window_overlap(persons) +,time_window_overlap_adult,"reindex(_P_OVERLAPS.aa, participants.person_id)" +,time_window_overlap_child,"reindex(_P_OVERLAPS.cc, participants.person_id)" +,time_window_overlap_adult_child,"reindex(_P_OVERLAPS.ac, participants.person_id)" +logTimeWindowOverlapAdult,log_time_window_overlap_adult,np.log1p(time_window_overlap_adult) +logTimeWindowOverlapChild,log_time_window_overlap_child,np.log1p(time_window_overlap_child) +logTimeWindowOverlapAdultChild,log_time_window_overlap_adult_child,np.log1p(time_window_overlap_adult_child) +#,, +,_JOINT_TOURS,tours[tours.tour_category=='joint'] +,num_hh_joint_tours,"reindex_i(_JOINT_TOURS.groupby('household_id').size(), participants.household_id)" +#,, +,person_is_preschool,participants.ptype == 8 +,child,~participants.adult \ No newline at end of file diff --git a/resident/configs/joint_tour_participation_coefficients.csv b/resident/configs/joint_tour_participation_coefficients.csv new file mode 100644 index 0000000..8126d30 --- /dev/null +++ b/resident/configs/joint_tour_participation_coefficients.csv @@ -0,0 +1,59 @@ +coefficient_name,value,constrain +coef_unavailable,-999,T +coef_full_time_worker_adults_only_party,-0.844885174,F +coef_part_time_worker_adults_only_party,-1.837563723,F +coef_university_students_adults_only_party,-0.970251833,F +coef_non_worker_adults_only_party,-0.757939204,F +coef_retiree_adults_only_party,1.196798335,F +coef_driving_age_student_children_only_party,-12.08924396,F +coef_pre_driving_age_student_children_only_party,-16.16999328,F +coef_child_too_young_for_school_children_only_party,-16.16999328,F +coef_full_time_worker_mixed_party,0.453048547,F +coef_part_time_worker_mixed_party,1.263225489,F +coef_university_student_mixed_party,1.561741231,F +coef_non_worker_mixed_party,2.90040237,F +coef_retiree_mixed_party,1.043276837,F +coef_child_too_young_for_school_mixed_party,-1.916267403,F +coef_pre_driving_age_student_mixed_party,-1.91645719,F +coef_driving_age_student_mixed_party,-0.933863842,F +#,, +coef_part_time_worker_specific_to_maintenance_joint_tours,0.766072366,F +coef_non_worker_specific_to_maintenance_joint_tours,0.971450303,F +coef_pre_school_kid_specific_to_maintenance_joint_tours,-0.52817951,F +coef_full_time_worker_specific_to_eating_out_joint_tours,0.535579735,F +coef_part_time_worker_specific_to_eating_out_joint_tours,1.23257663,F +coef_pre_driving_age_student_specific_to_eating_out_joint_tours,1.535608884,F +coef_part_time_worker_specific_to_discretionary_joint_tours,0.539337302,F +coef_retiree_specific_to_discretionary_joint_tours,1.105118702,F +coef_driving_age_student_specific_to_discretionary_joint_tours,-1.151066894,F +coef_pre_driving_age_student_specific_to_discretionary_joint_tours,0.798684189,F +coef_part_time_worker_specific_to_visiting_joint_tours,1.075535443,F +coef_non_worker_specific_to_visiting_joint_tours,1.075535443,F +coef_retiree_specific_to_visiting_joint_tours,-1.930328716,F +coef_driving_age_student_specific_to_visiting_joint_tours,-1.334515344,F +coef_pre_school_kid_specific_to_visiting_joint_tours,1.552698221,F +#,, +coef_adult_fewer_automobiles_than_workers_adult_only_party,1.292641814,F +coef_adult_more_automobiles_than_workers_mixed_party,-0.390671553,F +coef_child_zero_automobiles_mixed_party,-1.546687521,F +#,, +coef_dummy_for_low_income_for_adult_in_adult_party,-0.680688378,F +coef_dummy_for_high_income_for_adult_in_mixed_party,-0.20257569,F +coef_dummy_for_high_income_for_child_in_mixed_party,-0.74160668,F +#,, +coef_adult_number_of_joint_tours_adult_only,-0.599443484,F +coef_adult_number_of_joint_tours_mixed,-0.218541079,F +coef_child_number_of_joint_tours_child_only,-0.313832442,F +coef_child_number_of_joint_tours_mixed,-0.241748337,F +#,, +coef_adult_number_of_other_adults_in_the_household_adults_only_party,-0.747528682,F +coef_adult_number_of_other_adults_in_the_household_mixed_party,-0.285988025,F +coef_child_number_of_other_children_in_the_household_child_only_party,-2.305908575,F +coef_child_number_of_other_children_in_the_household_mixed,-0.471545203,F +#,, +coef_adult_log_of_max_window_overlap_with_an_adult_adult_only_party,1.634378325,F +coef_adult_log_of_max_window_overlap_with_a_child_mixed,0.057012356,F +coef_child_log_of_max_window_overlap_with_an_adult_mixed,1.616796615,F +coef_child_log_of_max_window_overlap_with_a_child_child,10.70256432,F +#,, +coef_global_adj_for_paticipation,-1,T diff --git a/resident/configs/joint_tour_scheduling.yaml b/resident/configs/joint_tour_scheduling.yaml new file mode 100644 index 0000000..1a8cdcd --- /dev/null +++ b/resident/configs/joint_tour_scheduling.yaml @@ -0,0 +1,13 @@ + +SPEC: tour_scheduling_joint.csv +COEFFICIENTS: tour_scheduling_joint_coefficients.csv + +LOGIT_TYPE: MNL + +preprocessor: + SPEC: joint_tour_scheduling_annotate_tours_preprocessor + DF: joint_tours + TABLES: + - land_use + - households + - joint_tour_participants diff --git a/resident/configs/joint_tour_scheduling_annotate_tours_preprocessor.csv b/resident/configs/joint_tour_scheduling_annotate_tours_preprocessor.csv new file mode 100644 index 0000000..c220297 --- /dev/null +++ b/resident/configs/joint_tour_scheduling_annotate_tours_preprocessor.csv @@ -0,0 +1,2 @@ +Description,Target,Expression +,origin_to_destination_distance,"skim_dict.lookup(joint_tours.origin, joint_tours.destination, ('SOV_M_DIST', 'MD'))" diff --git a/resident/configs/logging.yaml b/resident/configs/logging.yaml new file mode 100644 index 0000000..71162f1 --- /dev/null +++ b/resident/configs/logging.yaml @@ -0,0 +1,59 @@ +# Config for logging +# ------------------ +# See http://docs.python.org/2.7/library/logging.config.html#configuration-dictionary-schema + +logging: + version: 1 + disable_existing_loggers: true + + + # Configuring the default (root) logger is highly recommended + root: + level: NOTSET + handlers: [console] + + loggers: + + activitysim: + level: DEBUG + handlers: [console, logfile] + propagate: false + + sharrow: + level: INFO + handlers: [console, logfile] + propagate: false + + handlers: + + logfile: + class: logging.FileHandler + filename: + get_log_file_path: 'activitysim.log' + mode: w + formatter: fileFormatter + level: NOTSET + + console: + class: logging.StreamHandler + stream: ext://sys.stdout + formatter: elapsedFormatter + level: INFO + + formatters: + + simpleFormatter: + class: logging.Formatter + # format: '%(levelname)s - %(name)s - %(message)s' + format: '%(levelname)s - %(message)s' + datefmt: '%d/%m/%Y %H:%M:%S' + + fileFormatter: + class: logging.Formatter + format: '%(asctime)s - %(levelname)s - %(name)s - %(message)s' + datefmt: '%d/%m/%Y %H:%M:%S' + + elapsedFormatter: + (): activitysim.core.tracing.ElapsedTimeFormatter + format: '[{elapsedTime}] {levelname:s}: {message:s}' + style: '{' \ No newline at end of file diff --git a/resident/configs/mandatory_tour_frequency.csv b/resident/configs/mandatory_tour_frequency.csv new file mode 100644 index 0000000..548e2dd --- /dev/null +++ b/resident/configs/mandatory_tour_frequency.csv @@ -0,0 +1,78 @@ +Label,Description,Expression,work1,work2,school1,school2,work_and_school +# availiability conditions just based on worker or student status,,,,,,, +util_work_unavailable_for_non_workers,work not available for non-workers,is_worker == False,coef_unavailable,coef_unavailable,,,coef_unavailable +util_work_unavailable_for_wfh,work not available if work from home,work_from_home == True,coef_unavailable,coef_unavailable,,,coef_unavailable +util_school_unavailable_for_non_students,school unavailable for non-students,is_student == False,,,coef_unavailable,coef_unavailable,coef_unavailable +#,,,,,,, +util_alternative_specific_constant_for_full_time_work,alternative specific constant for full_time work,@df.ptype==1,coef_zero,coef_ft_worker_work2_asc,coef_ft_worker_school1_asc,,coef_ft_worker_work_and_school_asc +util_alternative_specific_constant_for_part_time_work,alternative specific constant for part_time work,@df.ptype==2,coef_zero,coef_pt_worker_work2_asc,coef_pt_worker_school1_asc,,coef_pt_worker_work_and_school_asc +util_alternative_specific_constant_for_university_student,alternative specific constant for university student,@df.ptype==3,coef_univ_work1_asc,,coef_zero,coef_univ_school2_asc,coef_univ_work_and_school_asc +util_alternative_specific_constant_for_non_worker_U65,alternative specific constant for non_worker U65,@df.ptype==4,coef_zero,,coef_non_worker_school1_asc,coef_zero, +util_alternative_specific_constant_for_retiree_65,alternative specific constant for retiree 65+,@df.ptype==5,coef_zero,,coef_ret_worker_school1_asc,coef_zero, +util_alternative_specific_constant_for_school_child_16_17,alternative specific constant for school child 16_17,@df.ptype==6,coef_driving_age_child_work1_asc,,coef_zero,coef_driving_age_child_school2_asc,coef_driving_age_child_work_and_school_asc +util_alternative_specific_constant_for_school_child_6_15,alternative specific constant for school child 6_15,@df.ptype==7,coef_unavailable,coef_unavailable,coef_zero,coef_pre_driving_age_child_school2_asc,coef_unavailable +#,,,,,,, +util_person_is_female_full_time_worker,person is female_ full time worker,@(df.female==1)*(df.ptype==1),coef_zero,coef_female_ftworker_work2,coef_female_ftworker_school1,,coef_female_ftworker_work_and_school +util_person_is_female_part_time_worker,person is female_ part time worker,@(df.female==1)*(df.ptype==2),coef_zero,coef_female_ptworker_work2,coef_female_ptworker_school1,,coef_zero +util_person_is_female_university_student,person is female_ university student,@(df.female==1)*(df.ptype==3),coef_female_univ_work1,,coef_zero,coef_female_univ_school2,coef_female_univ_work_and_school +util_person_is_female_non_worker_Under_65,person is female_ non_worker Under 65,@(df.female==1)*(df.ptype==4),,,,, +util_person_is_female_retiree_65_and_plus,person is female_ retiree 65 and plus,@(df.female==1)*(df.ptype==5),,,,, +util_person_is_female_school_child_16_17,person is female_ school child 16_17,@(df.female==1)*(df.ptype==6),coef_female_driving_age_child_work1,,coef_zero,coef_female_driving_age_child_school2,coef_female_driving_age_child_work_and_school +util_person_is_female_school_child_6_15,person is female_ school child 6_15,@(df.female==1)*(df.ptype==7),,,coef_zero,coef_female_child_school2, +util_young_adult_full_time_worker,young adult_ full time worker,@(df.age>17)*(df.age<=35)*(df.ptype==1),coef_zero,coef_zero,coef_youngadult_ftworker_school1,,coef_zero +util_older_adult_university_student,older adult_ university student,@(df.age>35)*(df.ptype==3),coef_olderadult_univ_work1,,coef_zero,coef_zero, +#,,,,,,, +util_workplace_within_distance_band_1full_time_worker,workplace within distance band 1 (0 to 0.5 mile)_ full time worker,@(df.distance_to_work<=0.5)*(df.ptype==1),,coef_distband1_ftworker_work2,,, +util_workplace_within_distance_band_1part_time_worker,workplace within distance band 1 (0 to 0.5 mile)_ part time worker,@(df.distance_to_work<=0.5)*(df.ptype==2),,coef_distband1_ptworker_work2,,, +util_workplace_within_distance_band_2_full_time_worker,workplace within distance band 2 (0.5 to 3 mile)_ full time worker,@(df.distance_to_work>0.5)*(df.distance_to_work<=3)*(df.ptype==1),,coef_distband2_ftworker_work2,,, +util_workplace_within_distance_band_2_part_time_worker,workplace within distance band 2 (0.5 to 3 mile)_ part time worker,@(df.distance_to_work>0.5)*(df.distance_to_work<=3)*(df.ptype==2),,coef_distband2_ptworker_work2,,, +util_school_wtihin_distance_band_2_university_student,school wtihin distance band 2 (0.5 to 2 mile)_ university student,@(df.distance_to_school>0.5)*(df.distance_to_school<=2)*(df.ptype==3),,,,coef_distband2_univ_school2, +util_school_wtihin_distance_band_2_school_child_16_17,school wtihin distance band 2 (0.5 to 2 mile)_ school child 16_17,@(df.distance_to_school>0.5)*(df.distance_to_school<=2)*(df.ptype==6),,,,coef_distband2_driving_age_child_school2, +util_school_wtihin_distance_band_2_school_child_6_15,school wtihin distance band 2 (0.5 to 2 mile)_ school child 6_15,@(df.distance_to_school>0.5)*(df.distance_to_school<=2)*(df.ptype==7),,,,coef_distband2_child_school2, +util_work_or_school_distance_within_distance_band_1_full_time_worker,work or school distance within distance band 1_ full time worker,@((df.distance_to_work<=0.5)+(df.distance_to_school<=0.5))*(df.ptype==1),,,,,coef_distband1_ftworker_work_and_school +util_work_or_school_distance_within_distance_band_1_part_time_worker,work or school distance within distance band 1_ part time worker,@((df.distance_to_work<=0.5)+(df.distance_to_school<=0.5))*(df.ptype==2),,,,,coef_distband1_ptworker_work_and_school +util_work_or_school_distance_within_distance_band_1_university_student,work or school distance within distance band 1_ university student,@((df.distance_to_work<=0.5)+(df.distance_to_school<=0.5))*(df.ptype==3),,,,,coef_distband1_univ_work_and_school +util_work_or_school_distance_within_distance_band_1_school_child_16_17,work or school distance within distance band 1_ school child 16_17,@((df.distance_to_work<=0.5)+(df.distance_to_school<=0.5))*(df.ptype==6),,,,,coef_distband1_driving_age_child_work_and_school +util_work_or_school_distance_within_distance_band_2_full_time_worker,work or school distance within distance band 2_ full time worker,@((df.distance_to_work>0.5)*(df.distance_to_work<=3)+(df.distance_to_school>0.5)*(df.distance_to_school<=2))*(df.ptype==1),,,,,coef_distband2_ftworker_work_and_school +util_work_or_school_distance_within_distance_band_2_part_time_worker,work or school distance within distance band 2_ part time worker,@((df.distance_to_work>0.5)*(df.distance_to_work<=3)+(df.distance_to_school>0.5)*(df.distance_to_school<=2))*(df.ptype==2),,,,,coef_distband2_ptworker_work_and_school +util_work_or_school_distance_within_distance_band_2_university_student,work or school distance within distance band 2_ university student,@((df.distance_to_work>0.5)*(df.distance_to_work<=3)+(df.distance_to_school>0.5)*(df.distance_to_school<=2))*(df.ptype==3),,,,,coef_distband2_univ_work_and_school +util_work_or_school_distance_within_distance_band_2_school_child_16_17,work or school distance within distance band 2_ school child 16_17,@((df.distance_to_work>0.5)*(df.distance_to_work<=3)+(df.distance_to_school>0.5)*(df.distance_to_school<=2))*(df.ptype==6),,,,,coef_distband2_driving_age_child_work_and_school +#,,,,,,, +util_minimum_travel_time_to_workplace_full_time_worker,minimum travel time to workplace (exclues non_motorized)_ full time worker,@df.ptype==1 * df.roundtrip_auto_time_to_work,,coef_ftworker_timetowork_work2,,,coef_ftworker_timetowork_work_and_school +util_minimum_travel_time_to_workplace_part_time_worker,minimum travel time to workplace (excludes non_motorized)_ part time worker,@df.ptype==2 * df.roundtrip_auto_time_to_work,,coef_ptworker_timetowork_work2,,,coef_ptworker_timetowork_work_and_school +util_minimum_travel_time_to_workplace_university_student,minimum travel time to workplace (excludes non_motorized)_ university student,@df.ptype==3 * df.roundtrip_auto_time_to_work,,,,,coef_univ_timetowork_work_and_school +util_minimum_travel_time_to_workplace_non_worker_under_65,minimum travel time to workplace (excludes non_motorized)_ non_worker under 65,@df.ptype==4 * df.roundtrip_auto_time_to_work,,,,, +util_minimum_travel_time_to_workplace_retiree_65_and_plus,minimum travel time to workplace (excludes non_motorized)_ retiree 65 and plus,@df.ptype==5 * df.roundtrip_auto_time_to_work,,,,, +#,,,,,,, +util_zero_car_full_time_worker,zero car full time worker,@(df.auto_ownership==0)*(df.ptype==1),,coef_ftworker_zerocar_work2,,,coef_ftworker_zerocar_work_and_school +util_zero_car_part_time_worker,zero car part time worker,@(df.auto_ownership==0)*(df.ptype==2),,coef_ptworker_zerocar_work2,,,coef_ptworker_zerocar_work_and_school +util_zero_car_university_student,zero car university student,@(df.auto_ownership==0)*(df.ptype==3),,,,coef_univ_zerocar_school2,coef_univ_zerocar_work_and_school +util_zero_car_non_worker_under_65,zero car non_worker under 65,@(df.auto_ownership==0)*(df.ptype==4),,,,, +util_zero_car_retiree_65_and_plus,zero car retiree 65 and plus,@(df.auto_ownership==0)*(df.ptype==5),,,,, +util_zero_car_school_child_16_17,zero car school child 16_17,@(df.auto_ownership==0)*(df.ptype==6),,,,coef_driving_age_child_zerocar_school2,coef_driving_age_child_zerocar_work_and_school +util_zero_car_school_child_6_15,zero car school child 6_15,@(df.auto_ownership==0)*(df.ptype==7),,,,coef_child_zerocar_school2, +util_car_fewer_than_drivers_university_student,car fewer than drivers_ university student,@(df.auto_ownership=60000)*(df.ptype==1),,,,, +util_mid_and_high_household_income_part_time_worker,mid and high household income_ part time worker,@(df.income>=60000)*(df.ptype==2),,,,, +util_mid_and_high_household_income_university_student,mid and high household income_ university student,@(df.income>=60000)*(df.ptype==3),coef_univ_midhighincome_work1,,,, +#,,,,,,, +util_alternative_specific_constant_adjustment_for_full_time_work,alternative specific constant adjustment for full_time work,@df.ptype==1,,0.1423726,,, +util_alternative_specific_constant_adjustment_for_part_time_work,alternative specific constant adjustment for part_time work,@df.ptype==2,,0.2684171,,, +util_alternative_specific_constant_adjustment_for_university_student,alternative specific constant adjustment for university student,@df.ptype==3,0.362762501,,,0.52545937,1.0066602 +util_alternative_specific_constant_adjustment_for_school_child_16_17,alternative specific constant adjustment for school child 16_17,@df.ptype==6,,,,0.1478192,0.4715931 +util_alternative_specific_constant_adjustment_for_school_child_6_15,alternative specific constant adjustment for school child 6_15,@df.ptype==7,,,,0.0969167, +util_alternative_specific_constant_adjustment_for_school_child_0_5,alternative specific constant adjustment for school child 0_5,@df.ptype==8,,,0.72,-0.67, +#,,,,,,, diff --git a/resident/configs/mandatory_tour_frequency.yaml b/resident/configs/mandatory_tour_frequency.yaml new file mode 100644 index 0000000..a0d7fa6 --- /dev/null +++ b/resident/configs/mandatory_tour_frequency.yaml @@ -0,0 +1,15 @@ + +SPEC: mandatory_tour_frequency.csv +COEFFICIENTS: mandatory_tour_frequency_coeffs.csv + +preprocessor: + SPEC: mandatory_tour_frequency_preprocessor + DF: persons + TABLES: + - households + +annotate_persons: + SPEC: annotate_persons_mtf + DF: persons + TABLES: + - tours diff --git a/resident/configs/mandatory_tour_frequency_alternatives.csv b/resident/configs/mandatory_tour_frequency_alternatives.csv new file mode 100644 index 0000000..025decb --- /dev/null +++ b/resident/configs/mandatory_tour_frequency_alternatives.csv @@ -0,0 +1,7 @@ +#,,alt file for building tours even though simulation is simple_simulate not interaction_simulate +alt,work,school +work1,1,0 +work2,2,0 +school1,0,1 +school2,0,2 +work_and_school,1,1 diff --git a/resident/configs/mandatory_tour_frequency_coeffs.csv b/resident/configs/mandatory_tour_frequency_coeffs.csv new file mode 100644 index 0000000..d01a9e3 --- /dev/null +++ b/resident/configs/mandatory_tour_frequency_coeffs.csv @@ -0,0 +1,83 @@ +coefficient_name,value,constrain,,,, +coef_zero,0,T,,,, +coef_unavailable,-999,T,,,, +#,,,,,, +coef_ft_worker_work2_asc,-2.548,F,,,, +coef_ft_worker_school1_asc,-2.29548389,F,,,, +coef_ft_worker_work_and_school_asc,-2.124547196,F,,,, +coef_pt_worker_work2_asc,-3.255,F,,,, +coef_pt_worker_school1_asc,-1.523338502,F,,,, +coef_pt_worker_work_and_school_asc,-1.916270321,F,,,, +coef_univ_work1_asc,-0.935,F,,,, +coef_driving_age_child_work1_asc,-2.81,F,,,, +coef_univ_school2_asc,-2.329,F,,,, +coef_univ_work_and_school_asc,-0.898,F,,,, +coef_driving_age_child_school2_asc,-2.230,F,,,, +coef_pre_driving_age_child_school2_asc,-3.838,F,,,, +coef_driving_age_child_work_and_school_asc,-3.449,F,,,, +coef_non_worker_school1_asc,14.600,F,,,, +coef_ret_worker_school1_asc,14.600,F,,,, +#,,,,,, +coef_female_ftworker_work2,-0.171976,F,,,, +coef_female_ftworker_school1,-0.372102879,F,,,, +coef_female_ftworker_work_and_school,0.656500245,F,,,, +coef_female_ptworker_work2,0.72635447,F,,,, +coef_female_ptworker_school1,0.634251329,F,,,, +coef_female_univ_work1,-0.186104106,F,,,, +coef_female_univ_school2,-1.207464296,F,,,, +coef_female_univ_work_and_school,-0.627268064,F,,,, +coef_female_driving_age_child_work1,-0.84458299,F,,,, +coef_female_driving_age_child_school2,-1.565680968,F,,,, +coef_female_driving_age_child_work_and_school,2.22484406,F,,,, +coef_female_child_school2,-1.124163745,F,,,, +coef_youngadult_ftworker_school1,0.336552714,F,,,, +coef_olderadult_univ_work1,1.373765885,F,,,, +#,,,,,, +coef_distband1_ftworker_work2,0.64195998,F,,,, +coef_distband1_ptworker_work2,0.64195998,F,,,, +coef_distband2_ftworker_work2,1.217340344,F,,,, +coef_distband2_ptworker_work2,1.217340344,F,,,, +coef_distband2_univ_school2,0.491551442,F,,,, +coef_distband2_driving_age_child_school2,0.491551442,F,,,, +coef_distband2_child_school2,0.491551442,F,,,, +coef_distband1_ftworker_work_and_school,0.194304616,F,,,, +coef_distband1_ptworker_work_and_school,0.194304616,F,,,, +coef_distband1_univ_work_and_school,0.194304616,F,,,, +coef_distband1_driving_age_child_work_and_school,0.194304616,F,,,, +coef_distband2_ftworker_work_and_school,0.183705706,F,,,, +coef_distband2_ptworker_work_and_school,0.183705706,F,,,, +coef_distband2_univ_work_and_school,0.183705706,F,,,, +coef_distband2_driving_age_child_work_and_school,0.183705706,F,,,, +#,,,,,, +coef_ftworker_timetowork_work2,-0.022406282,F,,,, +coef_ptworker_timetowork_work2,-0.022406282,F,,,, +coef_ftworker_timetowork_work_and_school,-0.007707408,F,,,, +coef_ptworker_timetowork_work_and_school,-0.007707408,F,,,, +coef_univ_timetowork_work_and_school,-0.007707408,F,,,, +#,,,,,, +coef_ftworker_zerocar_work2,-0.661753779,F,,,, +coef_ptworker_zerocar_work2,-0.661753779,F,,,, +coef_univ_zerocar_school2,-0.661753779,F,,,, +coef_driving_age_child_zerocar_school2,-0.661753779,F,,,, +coef_child_zerocar_school2,-0.661753779,F,,,, +coef_univ_autodef_school2,-0.955356561,F,,,, +coef_driving_age_child_autodef_school2,-0.955356561,F,,,, +coef_child_autodef_school2,-0.955356561,F,,,, +coef_ftworker_zerocar_work_and_school,-0.661753779,F,,,, +coef_ptworker_zerocar_work_and_school,-0.661753779,F,,,, +coef_univ_zerocar_work_and_school,-0.661753779,F,,,, +coef_driving_age_child_zerocar_work_and_school,-0.661753779,F,,,, +#,,,,,, +coef_univ_nonfamily_work1,-1.094011735,F,,,, +coef_driving_age_child_nonfamily_work2,-1.094011735,F,,,, +coef_univ_midhighincome_work1,0.688596236,F,,,, +coef_ftworker_numprekchildren_work2,-0.039040872,F,,,, +coef_ptworker_numprekchildren_work2,-0.039040872,F,,,, +coef_ftworker_scng2school_work2,-0.436718886,F,,,, +coef_univ_numprekchildren_school2,-1.533952899,F,,,, +coef_ftworker_numprekchildren_work_and_school,-0.142979462,F,,,, +coef_ptworker_numprekchildren_work_and_school,-0.142979462,F,,,, +coef_univ_numprekchildren_work_and_school,-0.142979462,F,,,, +coef_univ_nonfamily_work_and_school,-1.094011735,F,,,, +coef_driving_age_child_nonfamily_work_and_school,-1.094011735,F,,,, +#,,,,,, diff --git a/resident/configs/mandatory_tour_frequency_preprocessor.csv b/resident/configs/mandatory_tour_frequency_preprocessor.csv new file mode 100644 index 0000000..d777580 --- /dev/null +++ b/resident/configs/mandatory_tour_frequency_preprocessor.csv @@ -0,0 +1,4 @@ +Description,Target,Expression +HHT from households,_HHT,"reindex(households.HHT, persons.household_id)" +non_family,non_family,_HHT.isin(HHT_NONFAMILY) +family,family,_HHT.isin(HHT_FAMILY) \ No newline at end of file diff --git a/resident/configs/mandatory_tour_scheduling.yaml b/resident/configs/mandatory_tour_scheduling.yaml new file mode 100644 index 0000000..4063f07 --- /dev/null +++ b/resident/configs/mandatory_tour_scheduling.yaml @@ -0,0 +1,51 @@ + +SIMULATE_CHOOSER_COLUMNS: + - age + - female + - ptype + - is_income_less25K + - is_income_25K_to_60K + - is_income_60K_to_120K + - is_income_greater120K + - is_pre_drive_child_in_HH + - is_non_worker_in_HH + - auto_ownership + - is_all_adults_full_time_workers + - distance_to_school + - roundtrip_auto_time_to_work + - roundtrip_auto_time_to_school + - free_parking_at_work + - workplace_zone_id + - school_zone_id + - home_zone_id + - TAZ + - transit_pass_ownership + - transit_pass_subsidy + +LOGSUM_SETTINGS: tour_mode_choice.yaml + +TOUR_SPEC_SEGMENTS: + work: work + school: school + univ: univ + +ALTS_PREPROCESSOR: + work: + SPEC: mandatory_tour_scheduling_annotate_alts_preprocessor.csv + DF: alt_tdd + +SPEC_SEGMENTS: + work: + 'SPEC': tour_scheduling_work.csv + 'COEFFICIENTS': tour_scheduling_work_coeffs.csv + school: + 'SPEC': tour_scheduling_school.csv + 'COEFFICIENTS': tour_scheduling_school_coeffs.csv + univ: + 'SPEC': tour_scheduling_university.csv + 'COEFFICIENTS': tour_scheduling_university_coeffs.csv + +DESTINATION_FOR_TOUR_PURPOSE: + work: workplace_zone_id + school: school_zone_id + univ: school_zone_id diff --git a/resident/configs/mandatory_tour_scheduling_annotate_alts_preprocessor.csv b/resident/configs/mandatory_tour_scheduling_annotate_alts_preprocessor.csv new file mode 100644 index 0000000..d961142 --- /dev/null +++ b/resident/configs/mandatory_tour_scheduling_annotate_alts_preprocessor.csv @@ -0,0 +1,15 @@ +Description,Target,Expression +# make expressions are consistent with reference bins,, +departure_reference_bin,departureRefBin,9 +arrival_reference_bin,arrivalRefBin,29 +duration_reference_bin,durationRefBin,20 +departure_shift,departureLinearShift1,"(9-df.start)*(df.start<=9) + (df.start-9)*(df.start>9)" +arrival_shift,arrivalLinearShift1,"(29-df.end)*(df.end<=29) + (df.end-29)*(df.end>29)" +duration_shift,durationShift,"(20-df.duration)*(df.duration<=20) + (df.duration-20)*(df.duration>20)" +# school specific bins +departure_reference_bin_school,departureRefBin_school,9 +arrival_reference_bin_school,arrivalRefBin_school,29 +duration_reference_bin_school,durationRefBin_school,16 +departure_shift_school,departureLinearShift1_school,"(9-df.start)*(df.start<=9) + (df.start-9)*(df.start>9)" +arrival_shift_school,arrivalLinearShift1_school,"(29-df.end)*(df.end<=29) + (df.end-29)*(df.end>29)" +duration_shift_school,durationShift_school,"(16-df.duration)*(df.duration<=16) + (df.duration-16)*(df.duration>16)" diff --git a/resident/configs/network_los.yaml b/resident/configs/network_los.yaml new file mode 100644 index 0000000..97494d4 --- /dev/null +++ b/resident/configs/network_los.yaml @@ -0,0 +1,49 @@ +inherit_settings: True + +zone_system: 2 + +skim_dict_factory: NumpyArraySkimFactory +#skim_dict_factory: MemMapSkimFactory + +# read cached skims (using numpy memmap) from output directory (memmap is faster than omx ) +read_skim_cache: False +# write memmapped cached skims to output directory after reading from omx, for use in subsequent runs +write_skim_cache: False + +# series15 +taz_skims: + - autoSkims*.omx + - transitSkims*.omx + - walkSkim.omx + +maz: land_use.csv + +maz_to_maz: + tables: + - maz_maz_walk.csv + + # maz_to_maz blending distance (missing or 0 means no blending) + # max_blend_distance: + # DIST: 5 + # # blend distance of 0 means no blending + # DISTBIKE: 0 + # DISTWALK: 1 + + +skim_time_periods: + time_window: 1440 + period_minutes: 30 + # Metro time periods: + # EA: midnight to 5 AM + # AM: 5 AM to 9 AM + # MD: 9 AM to 3 PM + # PM: 3 PM to 7 PM + # EV: 7 PM to midnight + periods: [0, 6, 12, 24, 32, 42, 48] # time periods to match documentation + labels: &skim_time_period_labels ['EA', 'AM', 'MD', 'PM', 'EV', 'EA'] + +demographic_segments: &demographic_segments + - &low_income_segment_id 0 + - &high_income_segment_id 1 + + diff --git a/resident/configs/non_mandatory_tour_destination.csv b/resident/configs/non_mandatory_tour_destination.csv new file mode 100644 index 0000000..116360a --- /dev/null +++ b/resident/configs/non_mandatory_tour_destination.csv @@ -0,0 +1,74 @@ +Label,Description,Expression,escort,shopping,eatout,othmaint,social,othdiscr +,,"_DIST@np.minimum(skims[('SOV_M_DIST', 'MD')], 20)",1,1,1,1,1,1 +,,_DIST_SQUARED@_DIST**2,1,1,1,1,1,1 +,,_DIST_CUBED@_DIST**3,1,1,1,1,1,1 +,,_DIST_LOGGED@np.log(_DIST + 0.001),1,1,1,1,1,1 +,,"_CALIB_DIST@np.where(df.tour_type == 'escort', 20, np.where(df.tour_type == 'othmaint', 8, 10))",1,1,1,1,1,1 +util_dist,dist,@_DIST,coef_dist_escort,coef_dist_shopping,coef_dist_eatout,coef_dist_maint,coef_dist_social,coef_dist_discr +util_dist_squared,dist_squared,@_DIST_SQUARED,coef_distsqrd_escort,coef_distsqrd_shopping,coef_distsqrd_eatout,coef_distsqrd_maint,coef_distsqrd_social,coef_distsqrd_discr +util_dist_cubed,dist_cubed,@_DIST_CUBED,coef_zero,coef_distcubed_shopping,coef_distcubed_eatout,coef_zero,coef_distcubed_social,coef_distcubed_discr +util_dist_logged,dist_logged,@_DIST_LOGGED,coef_zero,coef_distlogged_shopping,coef_distlogged_eatout,coef_zero,coef_distlogged_social,coef_distlogged_discr +util_dist_female,dist_female,"@_DIST* df.female * np.where(df.get('tour_category', default=False) == 'joint', 0, 1)",coef_zero,coef_distfemale_shopping,coef_zero,coef_zero,coef_zero,coef_distfemale_disc +util_dist_income_segment_escort,Distance - low income_segment_escort,@_DIST * (df.income < 30000),coef_distlowinc_escort,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero +util_dist_lowincome_segment_maint,Distance - low income_segment_maint,@_DIST * (df.income_segment <= 2),coef_zero,coef_zero,coef_zero,coef_distlowinc_maint,coef_distlowinc_social,coef_zero +util_dist_lowmed_income_segment_maint,Distance - lowmed income_segment_maint,@_DIST * (df.income_segment <= 3),coef_zero,coef_zero,coef_zero,coef_zero,coef_distmidlowinc_social,coef_zero +#Distance - Time Pressure calculated as the log of the maxtime over tours left,#Distance - Time Pressure calculated as the log of the maxtime over tours left,@_DIST* np.log((person_max_window(persons)/(df.tour_type_count - df.tour_type_num + 1))+0.0001),coef_zero,coef_disttimepressure_shopping,coef_disttimepressure_eatout,coef_disttimepressure_maint,coef_zero,coef_disttimepressure_disc +Accessibility,#Accessibility,shopping_accessibility,coef_zero,coef_accessibility_shopping,coef_zero,coef_zero,coef_zero,coef_zero +util_dist_age_16to24,Distance - age 16 to 24,"@_DIST * df.age.between(16, 24) * np.where(df.get('tour_category', default=False) == 'joint', 0, 1)",coef_zero,coef_zero,coef_zero,coef_distage1624_maint,coef_zero,coef_zero +util_dist_age_56to64,Distance - age 56 to 64,"@_DIST * (df.age.between(56, 64))",coef_distage5664_escort,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero +util_dist_age_65+,Distance - age 65+,@_DIST * (df.age > 64),coef_distage64plus_escort,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero +#escort size is zero for escort tour,#escort size is zero for escort tour,@(@@householdsDestAlt * 1 + df.num_young_children * @@householdsDestAlt * 1 + df.num_gradeschool * @@gradeSchoolEnrollmentDestAlt * 100 + df.numHighSchoolStudents * @@highSchoolEnrollmentDestAlt * 100) == 0,-999,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero +#escort size is non-zero for escort tour,#escort size is non-zero for escort tour,"@np.where(@@householdsDestAlt * 1 + df.num_young_children * @@householdsDestAlt * 1 + df.num_gradeschool * @@gradeSchoolEnrollmentDestAlt * 100 + df.numHighSchoolStudents * @@highSchoolEnrollmentDestAlt * 100 > 0, np.log(@@householdsDestAlt * 1 + df.num_y",1,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero +util_size_term,Size variable,@df['size_term'].apply(np.log1p),1,1,1,1,1,1 +util_no_attractions,No attractions,@df['size_term']==0,-999,-999,-999,-999,-999,-999 +util_mode_choice_logsum,Mode choice logsum,mode_choice_logsum,coef_mode_logsum,coef_mode_logsum,coef_mode_logsum,coef_mode_logsum,coef_mode_logsum_socdisc,coef_mode_logsum_socdisc +util_sample_alts_correction,Sample of alternatives correction factor,"@np.minimum(np.log(df.pick_count/df.prob), 60)",1,1,1,1,1,1 +#Calibration constants,#Calibration constants,,,,,,, +util_calib_dist,Calibration - Distance,"@np.minimum(_DIST, _CALIB_DIST)",coef_Calibration_Distance_escort,coef_Calibration_Distance_shopping,coef_Calibration_Distance_eatout,coef_Calibration_Distance_maint,coef_Calibration_Distance_social,coef_Calibration_Distance_disc +util_calib_dist_squared,Calibration - Distance_squared,"@np.minimum(_DIST_SQUARED, _CALIB_DIST**2)",coef_Calibration_Distance_squared_escort,coef_Calibration_Distance_squared_shopping,coef_Calibration_Distance_squared_eatout,coef_Calibration_Distance_squared_maint,coef_Calibration_Distance_squared_social,coef_Calibration_Distance_squared_disc +util_calib_dist_cubed,Calibration - Distance_cubed,"@np.minimum(_DIST_CUBED, _CALIB_DIST**3)",coef_Calibration_Distance_cubed_escort,coef_Calibration_Distance_cubed_shopping,coef_Calibration_Distance_cubed_eatout,coef_Calibration_Distance_cubed_maint,coef_Calibration_Distance_cubed_social,coef_Calibration_Distance_cubed_disc +util_calib_dist_logged,Calibration - Distance_logged,"@np.minimum(_DIST_LOGGED, np.log(_CALIB_DIST))",coef_Calibration_Distance_logged_escort,coef_Calibration_Distance_logged_shopping,coef_Calibration_Distance_logged_eatout,coef_Calibration_Distance_logged_maint,coef_Calibration_Distance_logged_social,coef_Calibration_Distance_logged_disc +util_calib_0_1_miles,Calibration - 0-1 miles,@(_DIST<1),,coef_Calibration_0_1_miles_shopping,coef_Calibration_0_1_miles_eatout,coef_Calibration_0_1_miles_maint,coef_Calibration_0_1_miles_social, +util_calib_1_2_miles,Calibration - 1-2 miles,@(_DIST>=1) * (_DIST<2),,coef_Calibration_1_2_miles_shopping,,coef_Calibration_1_2_miles_maint,coef_Calibration_1_2_miles_social, +util_calib_2_5_miles,Calibration - 2-5 miles,@(_DIST>=2) * (_DIST<5),,coef_Calibration_2_5_miles_shopping,,coef_Calibration_2_5_miles_maint,, +util_abm2_calib_0_5_miles,ABM2 calibration - 0-5 miles,@(_DIST < 5) * _DIST,,,coef_ABM2_calibration_0_5_miles_eatout,,coef_ABM2_calibration_0_5_miles_social,coef_ABM2_calibration_0_5_miles_disc +util_abm2_calib_0to1_miles_all,"ABM2 calibration - 0-1 miles, all tours",@(_DIST < 1),,,coef_ABM2_calibration_0_1_miles_eatout,,coef_ABM2_calibration_0_1_miles_social,coef_ABM2_calibration_0_1_miles_disc +util_abm2_calib_1to2_miles_all,"ABM2 calibration - 1-2 miles, all tours",@(_DIST < 2) * (_DIST >= 1),,,coef_ABM2_calibration_1_2_miles_eatout,,coef_ABM2_calibration_1_2_miles_social,coef_ABM2_calibration_1_2_miles_disc +util_abm2_calib_2to3_miles_all,"ABM2 calibration - 2-3 miles, all tours",@(_DIST < 3) * (_DIST >= 2),,,,,coef_ABM2_calibration_2_3_miles_social,coef_ABM2_calibration_2_3_miles_disc +util_abm2_calib_3to4_miles_all,"ABM2 calibration - 3-4 miles, all tours",@(_DIST < 4) * (_DIST >= 3),,,,,coef_ABM2_calibration_3_4_miles_social,coef_ABM2_calibration_3_4_miles_disc +util_abm2_calib_0to1_miles_joint,"ABM2 calibration - 0-1 miles, joint tour","@(_DIST < 1) * np.where(df.get('tour_category', default=False) == 'joint', 0, 1)",coef_ABM2_calibration_0_1_miles_escort,,,,, +util_abm2_calib_1to2_miles_joint,"ABM2 calibration - 1-2 miles, joint tour","@(_DIST < 2) * (_DIST >= 1) * np.where(df.get('tour_category', default=False) == 'joint', 0, 1)",coef_ABM2_calibration_1_2_miles_escort,,,coef_ABM2_calibration_1_2_miles_maint,, +util_abm2_calib_2to3_miles_joint,"ABM2 calibration - 2-3 miles, joint tour","@(_DIST < 3) * (_DIST >= 2) * np.where(df.get('tour_category', default=False) == 'joint', 0, 1)",coef_ABM2_calibration_2_3_miles_escort,,,coef_ABM2_calibration_2_3_miles_maint,, +util_abm2_calib_distance_logged,"ABM2 calibration - distance logged, indiv tour","@np.minimum(_DIST_LOGGED, np.log(_CALIB_DIST)) * np.where(df.get('tour_category', default=False) == 'joint', 0, 1)",,,,coef_ABM2_calibration_distance_logged_maint,, +util_abm2_calib_4to5_miles,ABM2 calibration - 4-5 miles,@(_DIST < 5) * (_DIST >= 4),,coef_ABM2_calibration_4_5_miles_shopping,coef_ABM2_calibration_4_5_miles_eatout,coef_ABM2_calibration_4_5_miles_maint,coef_ABM2_calibration_4_5_miles_social,coef_ABM2_calibration_4_5_miles_disc +util_abm2_calib_5to6_miles,ABM2 calibration - 5-6 miles,@(_DIST < 6) * (_DIST >= 5),,coef_ABM2_calibration_5_6_miles_shopping,,coef_ABM2_calibration_5_6_miles_maint,, +util_abm2_calib_6to7_miles,ABM2 calibration - 6-7 miles,@(_DIST < 7) * (_DIST >= 6),,coef_ABM2_calibration_6_7_miles_shopping,,coef_ABM2_calibration_6_7_miles_maint,, +util_abm2_calib_0to20_miles,ABM2 calibration - 0-20 miles,@(_DIST < 20) * _DIST,coef_ABM2_calibration_0_20_miles_escort,coef_ABM2_calibration_0_20_miles_shopping,,coef_ABM2_calibration_0_20_miles_maint,, +util_abm2_calib_distance,ABM2 calibration - distance,@_DIST,coef_ABM2_calibration_distance_escort,coef_ABM2_calibration_distance_shopping,coef_ABM2_calibration_distance_eatout,coef_ABM2_calibration_distance_maint,coef_ABM2_calibration_distance_social,coef_ABM2_calibration_distance_disc +util_abm2_calib_distance_squared,ABM2 calibration - distance squared,@_DIST_SQUARED,coef_ABM2_calibration_distance_squared_escort,coef_ABM2_calibration_distance_squared_shopping,coef_ABM2_calibration_distance_squared_eatout,coef_ABM2_calibration_distance_squared_maint,coef_ABM2_calibration_distance_squared_social,coef_ABM2_calibration_distance_squared_disc +util_abm2_calib_distance_cubed,ABM2 calibration - distance cubed,@_DIST_CUBED,coef_ABM2_calibration_distance_cubed_escort,coef_ABM2_calibration_distance_cubed_shopping,coef_ABM2_calibration_distance_cubed_eatout,coef_ABM2_calibration_distance_cubed_maint,coef_ABM2_calibration_distance_cubed_social,coef_ABM2_calibration_distance_cubed_disc +util_abm2_calib_distance_logged2,ABM2 calibration - distance logged,"@np.minimum(_DIST_LOGGED, np.log(_CALIB_DIST)) ",coef_ABM2_calibration_distance_logged_escort,coef_ABM2_calibration_distance_logged_shopping,coef_ABM2_calibration_distance_logged_eatout,,coef_ABM2_calibration_distance_logged_social,coef_ABM2_calibration_distance_logged_disc +"util_abm2_calib_0to1_miles_joint_tour""","ABM2 calibration - 0-1 miles, joint tour","@np.where(df.get('tour_category', default=False) == 'joint', 1, 0) * (_DIST<1)",,coef_ABM2_calibration_0_1_miles_joint_shopping,coef_ABM2_calibration_0_1_miles_joint_eatout,coef_ABM2_calibration_0_1_miles_joint_maint,coef_ABM2_calibration_0_1_miles_joint_social,coef_ABM2_calibration_0_1_miles_joint_disc +"util_abm2_calib_1to2_miles_joint_tour""","ABM2 calibration - 1-2 miles, joint tour","@np.where(df.get('tour_category', default=False) == 'joint', 1, 0) * (_DIST>=1) * (_DIST<2)",,coef_ABM2_calibration_1_2_miles_joint_shopping,coef_ABM2_calibration_1_2_miles_joint_eatout,coef_ABM2_calibration_1_2_miles_joint_maint,coef_ABM2_calibration_1_2_miles_joint_social,coef_ABM2_calibration_1_2_miles_joint_disc +"util_abm2_calib_2to3_miles_joint_tour""","ABM2 calibration - 2-3 miles, joint tour","@np.where(df.get('tour_category', default=False) == 'joint', 1, 0) * (_DIST>=2) * (_DIST<3)",,coef_ABM2_calibration_2_3_miles_joint_shopping,,coef_ABM2_calibration_2_3_miles_joint_maint,, +"util_abm2_calib_1to2_miles_indiv_tour""","ABM2 calibration - 1-2 miles, indiv tour","@(_DIST < 2) * (_DIST >= 1) * np.where(df.get('tour_category', default=False) == 'joint', 0, 1)",,coef_ABM2_calibration_1_2_miles_shopping,,,, +"util_abm2_calib_2to3_miles_indiv_tour""","ABM2 calibration - 2-3 miles, indiv tour","@(_DIST < 3) * (_DIST >= 2) * np.where(df.get('tour_category', default=False) == 'joint', 0, 1)",,coef_ABM2_calibration_2_3_miles_shopping,,,, +util_abm2_calib_distance_logged3,ABM2+ calibration - distance logged,"@(_DIST < 20) * np.minimum(_DIST_LOGGED, np.log(_CALIB_DIST))",coef_ABM2p_calibration_distance_logged_escort,,,,, +util_abm2_calib_0to1_miles,ABM2+ calibration - 0-1 miles,@(_DIST < 1),coef_ABM2p_calibration_0_1_miles_escort,coef_ABM2p_calibration_0_1_miles_shopping,coef_ABM2p_calibration_0_1_miles_eatout,coef_ABM2p_calibration_0_1_miles_maint,coef_ABM2p_calibration_0_1_miles_social,coef_ABM2p_calibration_0_1_miles_disc +util_abm2_calib_1to2_miles,ABM2+ calibration - 1-2 miles,@(_DIST < 2) * (_DIST >= 1),coef_ABM2p_calibration_1_2_miles_escort,coef_ABM2p_calibration_1_2_miles_shopping,coef_ABM2p_calibration_1_2_miles_eatout,coef_ABM2p_calibration_1_2_miles_maint,coef_ABM2p_calibration_1_2_miles_social,coef_ABM2p_calibration_1_2_miles_disc +util_abm2_calib_2to3_miles,ABM2+ calibration - 2-3 miles,@(_DIST < 3) * (_DIST >= 2),coef_ABM2p_calibration_2_3_miles_escort,coef_ABM2p_calibration_2_3_miles_shopping,coef_ABM2p_calibration_2_3_miles_eatout,coef_ABM2p_calibration_2_3_miles_maint,coef_ABM2p_calibration_2_3_miles_social,coef_ABM2p_calibration_2_3_miles_disc +util_abm2_calib_3to4_miles,ABM2+ calibration - 3-4 miles,@(_DIST < 4) * (_DIST >= 3),coef_ABM2p_calibration_3_4_miles_escort,coef_ABM2p_calibration_3_4_miles_shopping,coef_ABM2p_calibration_3_4_miles_eatout,coef_ABM2p_calibration_3_4_miles_maint,coef_ABM2p_calibration_3_4_miles_social,coef_ABM2p_calibration_3_4_miles_disc +#,#,,,,,,, +#util_mission_valley_mall_constant,#Mission Valley Mall Constant,@df.destination==6187,,coef_Mission_Valley_Mall_Constant_shopping_6187,,,, +#util_mission_valley_mall_constant,#Mission Valley Mall Constant,@df.destination==6196,,coef_Mission_Valley_Mall_Constant_shopping_6196,,,, +#,#,,,,,,, +util_indiv_0_2_ASC,indiv tours in 0_2bin calibration constant,"@(_DIST.between(0,2)) * (df.get('tour_category', default=False) != 'joint')",coef_abm3_dist_0_2indivescort_asc,coef_abm3_dist_0_2indivmaint_asc,coef_abm3_dist_0_2indivdisc_asc,coef_abm3_dist_0_2indivmaint_asc,coef_abm3_dist_0_2indivdisc_asc,coef_abm3_dist_0_2indivdisc_asc +util_indiv_2_5_ASC,indiv tours in 2_5bin calibration constant,"@(_DIST.between(2,5)) * (df.get('tour_category', default=False) != 'joint')",coef_abm3_dist_2_5indivescort_asc,coef_abm3_dist_2_5indivmaint_asc,coef_abm3_dist_2_5indivdisc_asc,coef_abm3_dist_2_5indivmaint_asc,coef_abm3_dist_2_5indivdisc_asc,coef_abm3_dist_2_5indivdisc_asc +util_indiv_5_10_ASC,indiv tours in 5_10bin calibration constant,"@(_DIST.between(5,10)) * (df.get('tour_category', default=False) != 'joint')",coef_abm3_dist_5_10indivescort_asc,coef_abm3_dist_5_10indivmaint_asc,coef_abm3_dist_5_10indivdisc_asc,coef_abm3_dist_5_10indivmaint_asc,coef_abm3_dist_5_10indivdisc_asc,coef_abm3_dist_5_10indivdisc_asc +util_indiv_10_30_ASC,indiv tours in 10_30bin calibration constant,"@(_DIST.between(10,30)) * (df.get('tour_category', default=False) != 'joint')",coef_abm3_dist_10_30indivescort_asc,coef_abm3_dist_10_30indivmaint_asc,coef_abm3_dist_10_30indivdisc_asc,coef_abm3_dist_10_30indivmaint_asc,coef_abm3_dist_10_30indivdisc_asc,coef_abm3_dist_10_30indivdisc_asc +util_indiv_dist_ASC,indiv tours in distance calibration constant,"@(_DIST) * (df.get('tour_category', default=False) != 'joint')",,0.05,,0.05,, +util_joint_0_2_ASC,joint tours in 0_2bin calibration constant,"@(_DIST.between(0,2)) * (df.get('tour_category', default=False) == 'joint')",,coef_abm3_dist_0_2jointmaint_asc,coef_abm3_dist_0_2jointdisc_asc,coef_abm3_dist_0_2jointmaint_asc,coef_abm3_dist_0_2jointdisc_asc,coef_abm3_dist_0_2jointdisc_asc +util_joint_2_5_ASC,joint tours in 2_5bin calibration constant,"@(_DIST.between(2,5)) * (df.get('tour_category', default=False) == 'joint')",,coef_abm3_dist_2_5jointmaint_asc,coef_abm3_dist_2_5jointdisc_asc,coef_abm3_dist_2_5jointmaint_asc,coef_abm3_dist_2_5jointdisc_asc,coef_abm3_dist_2_5jointdisc_asc +util_joint_5_10_ASC,joint tours in 5_10bin calibration constant,"@(_DIST.between(5,10)) * (df.get('tour_category', default=False) == 'joint')",,coef_abm3_dist_5_10jointmaint_asc,coef_abm3_dist_5_10jointdisc_asc,coef_abm3_dist_5_10jointmaint_asc,coef_abm3_dist_5_10jointdisc_asc,coef_abm3_dist_5_10jointdisc_asc +util_joint_10_30_ASC,joint tours in 10_30bin calibration constant,"@(_DIST.between(10,30)) * (df.get('tour_category', default=False) == 'joint')",,coef_abm3_dist_10_30jointmaint_asc,coef_abm3_dist_10_30jointdisc_asc,coef_abm3_dist_10_30jointmaint_asc,coef_abm3_dist_10_30jointdisc_asc,coef_abm3_dist_10_30jointdisc_asc +util_indiv_dist_ASC,indiv tours in distance calibration constant,"@(_DIST) * (df.get('tour_category', default=False) == 'joint')",,0.10,,0.10,, \ No newline at end of file diff --git a/resident/configs/non_mandatory_tour_destination.yaml b/resident/configs/non_mandatory_tour_destination.yaml new file mode 100644 index 0000000..f8daa2c --- /dev/null +++ b/resident/configs/non_mandatory_tour_destination.yaml @@ -0,0 +1,58 @@ +SAMPLE_SPEC: non_mandatory_tour_destination_sample.csv +SPEC: non_mandatory_tour_destination.csv +COEFFICIENTS: non_mandatory_tour_destination_coefficients.csv + +SAMPLE_SIZE: 30 + +SIZE_TERM_SELECTOR: non_mandatory + +# we can't use use household income_segment as this will also be set for non-workers +CHOOSER_SEGMENT_COLUMN_NAME: tour_type + +# optional (comment out if not desired) +DEST_CHOICE_LOGSUM_COLUMN_NAME: destination_logsum + +# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if saved alt logsum table +DEST_CHOICE_SAMPLE_TABLE_NAME: tour_destination_sample + + +SEGMENTS: + - shopping + - othmaint + - othdiscr + - eatout + - social + - escort + +SIMULATE_CHOOSER_COLUMNS: + - tour_type + - home_zone_id + - person_id + - female + - income + - income_segment + - age + - tour_type_count + - tour_type_num + - tour_category + + +LOGSUM_SETTINGS: tour_mode_choice.yaml + +# model-specific logsum-related settings +CHOOSER_ORIG_COL_NAME: home_zone_id +ALT_DEST_COL_NAME: alt_dest +IN_PERIOD: + shopping: 14 + othmaint: 14 + othdiscr: 14 + eatout: 14 + social: 14 + escort: 14 +OUT_PERIOD: + shopping: 14 + othmaint: 14 + othdiscr: 14 + eatout: 14 + social: 14 + escort: 14 \ No newline at end of file diff --git a/resident/configs/non_mandatory_tour_destination_coefficients.csv b/resident/configs/non_mandatory_tour_destination_coefficients.csv new file mode 100644 index 0000000..09b4e02 --- /dev/null +++ b/resident/configs/non_mandatory_tour_destination_coefficients.csv @@ -0,0 +1,206 @@ +coefficient_name,value,constrain +coef_zero,0.0,T +coef_mode_logsum,0.5,T +coef_mode_logsum_socdisc,0.4,T +#,, +coef_dist_escort,-1.09538,F +coef_distsqrd_escort,0.022130838,F +coef_distlowinc_escort,0.157496938,F +coef_distage5664_escort,0.189508203,F +coef_distage64plus_escort,0.309309986,F +#,, +coef_dist_shopping,-0.27116,F +coef_distsqrd_shopping,-0.003098511,F +coef_distcubed_shopping,0.0,F +coef_distlogged_shopping,-0.229414306,F +coef_distfemale_shopping,0.0,F +coef_disttimepressure_shopping,0.029450792,F +coef_accessibility_shopping,0.377323356,F +#,, +coef_dist_eatout,0.10041,F +coef_distsqrd_eatout,-0.029120711,F +coef_distcubed_eatout,0.000647926,F +coef_distlogged_eatout,-0.664600521,F +coef_disttimepressure_eatout,0.027648186,F +#,, +coef_dist_maint,-0.03745,F +coef_distsqrd_maint,-0.007959,F +coef_distlowinc_maint,0.0,F +coef_disttimepressure_maint,0.025736,F +coef_distage1624_maint,-0.503857,F +#,, +coef_dist_social,-0.08731,F +coef_distsqrd_social,-0.003052,F +coef_distcubed_social,0.0,F +coef_distlogged_social,-0.430261,F +coef_distlowinc_social,0.038684,F +coef_distmidlowinc_social,0.0,F +#,, +coef_dist_discr,0.57603,F +coef_distsqrd_discr,-0.065310929,F +coef_distcubed_discr,0.001719762,F +coef_distlogged_discr,-1.524851692,F +coef_distfemale_disc,0.0,F +coef_disttimepressure_disc,0.054073655,F +#,, +coef_ABM2_calibration_0_1_miles_escort,-0.1714,T +coef_ABM2_calibration_1_2_miles_escort,-0.0966,T +coef_ABM2_calibration_2_3_miles_escort,-0.0739,T +coef_ABM2_calibration_0_20_miles_escort,0.13377,T +coef_ABM2_calibration_distance_escort,0.0,T +coef_ABM2_calibration_distance_squared_escort,-0.007376946,T +coef_ABM2_calibration_distance_cubed_escort,0.0,T +coef_ABM2_calibration_distance_logged_escort,-0.1373,T +coef_Calibration_Distance_squared_escort,0.0732726,T +coef_Calibration_Distance_cubed_escort,-0.0026709,T +coef_Calibration_Distance_escort,-0.2321275,T +coef_Calibration_Distance_logged_escort,-0.0801,T +coef_ABM2p_calibration_distance_logged_escort,0.1648,T +coef_ABM2p_calibration_0_1_miles_escort,0.222,T +coef_ABM2p_calibration_1_2_miles_escort,0.1599,T +coef_ABM2p_calibration_2_3_miles_escort,-0.0749,T +coef_ABM2p_calibration_3_4_miles_escort,-0.0095,T +#,, +coef_ABM2_calibration_0_1_miles_joint_shopping,-0.137065295,T +coef_ABM2_calibration_2_3_miles_joint_shopping,0.0,T +coef_ABM2_calibration_1_2_miles_joint_shopping,-0.389228729,T +coef_ABM2_calibration_0_1_miles_shopping,-0.338567459,T +coef_ABM2_calibration_1_2_miles_shopping,0.380266207,T +coef_ABM2_calibration_0_20_miles_shopping,0.0,T +coef_ABM2_calibration_2_3_miles_shopping,0.233495387,T +coef_ABM2_calibration_3_4_miles_shopping,-0.236188729,T +coef_ABM2_calibration_4_5_miles_shopping,-0.178169632,T +coef_ABM2_calibration_5_6_miles_shopping,0.0,T +coef_ABM2_calibration_6_7_miles_shopping,0.0,T +coef_Calibration_0_1_miles_shopping,-0.3477,T +coef_Calibration_1_2_miles_shopping,-0.2341,T +coef_Calibration_2_5_miles_shopping,-0.0208,T +coef_ABM2_calibration_distance_shopping,0.0,T +coef_ABM2_calibration_distance_squared_shopping,-0.0005121,T +coef_ABM2_calibration_distance_cubed_shopping,-7.584e-05,T +#coef_Mission_Valley_Mall_Constant_shopping_6187,-0.5,T +#coef_Mission_Valley_Mall_Constant_shopping_6196,-0.5,T +coef_Calibration_Distance_squared_shopping,-0.1357403,T +coef_Calibration_Distance_cubed_shopping,0.0090424,T +coef_Calibration_Distance_shopping,0.5816206,T +coef_Calibration_Distance_logged_shopping,-1.332207,T +coef_ABM2_calibration_distance_logged_shopping,-0.23912,T +coef_ABM2p_calibration_0_1_miles_shopping,-0.3386,T +coef_ABM2p_calibration_1_2_miles_shopping,-0.2771,T +coef_ABM2p_calibration_2_3_miles_shopping,-0.3181,T +coef_ABM2p_calibration_3_4_miles_shopping,-0.2362,T +#,, +coef_ABM2_calibration_1_2_miles_joint_eatout,-0.3004,T +coef_ABM2_calibration_0_1_miles_joint_eatout,-0.4739,T +coef_ABM2_calibration_0_1_miles_eatout,-0.7869,T +coef_ABM2_calibration_1_2_miles_eatout,0.5209,T +coef_ABM2_calibration_2_3_miles_eatout,0.4549,T +coef_ABM2_calibration_3_4_miles_eatout,0.1109,T +coef_ABM2_calibration_4_5_miles_eatout,-0.027,T +coef_ABM2_calibration_0_5_miles_eatout,0.107,T +coef_Calibration_0_1_miles_eatout,0.1074,T +coef_ABM2_calibration_distance_eatout,0.8713,T +coef_ABM2_calibration_distance_squared_eatout,-0.0353,T +coef_ABM2_calibration_distance_cubed_eatout,0.0004,T +coef_Calibration_Distance_squared_eatout,0.0333,T +coef_Calibration_Distance_cubed_eatout,-0.001,T +coef_Calibration_Distance_eatout,-0.19,T +coef_ABM2_calibration_distance_logged_eatout,-2.349,T +coef_Calibration_Distance_logged_eatout,-0.2569,T +coef_ABM2p_calibration_0_1_miles_eatout,-0.0563,T +coef_ABM2p_calibration_1_2_miles_eatout,-0.0582,T +coef_ABM2p_calibration_2_3_miles_eatout,-0.1056,T +coef_ABM2p_calibration_3_4_miles_eatout,-0.0262,T +#,, +coef_ABM2_calibration_0_1_miles_joint_maint,-0.1371,T +coef_ABM2_calibration_2_3_miles_joint_maint,0.0,T +coef_ABM2_calibration_1_2_miles_joint_maint,-0.3892,T +coef_ABM2_calibration_0_1_miles_maint,-0.3386,T +coef_ABM2_calibration_1_2_miles_maint,0.3803,T +coef_ABM2_calibration_0_20_miles_maint,0.0,T +coef_ABM2_calibration_2_3_miles_maint,0.2335,T +coef_ABM2_calibration_3_4_miles_maint,-0.2362,T +coef_ABM2_calibration_4_5_miles_maint,0.0,T +coef_ABM2_calibration_5_6_miles_maint,0.0,T +coef_ABM2_calibration_6_7_miles_maint,0.0,T +coef_Calibration_0_1_miles_maint,-0.0214,T +coef_Calibration_1_2_miles_maint,-0.1938,T +coef_Calibration_2_5_miles_maint,0.0,T +coef_ABM2_calibration_distance_maint,0.0,T +coef_ABM2_calibration_distance_squared_maint,-0.0005,T +coef_ABM2_calibration_distance_cubed_maint,-0.0001,T +coef_Calibration_Distance_squared_maint,-0.0396,T +coef_Calibration_Distance_cubed_maint,0.0023,T +coef_Calibration_Distance_maint,0.2641,T +coef_Calibration_Distance_logged_maint,-1.4297,T +coef_ABM2_calibration_distance_logged_maint,-0.2391,T +coef_ABM2p_calibration_0_1_miles_maint,-0.3386,T +coef_ABM2p_calibration_1_2_miles_maint,-0.2771,T +coef_ABM2p_calibration_2_3_miles_maint,-0.3181,T +coef_ABM2p_calibration_3_4_miles_maint,-0.2362,T +#,, +coef_ABM2_calibration_0_1_miles_joint_social,-0.4739,T +coef_ABM2_calibration_1_2_miles_joint_social,-0.3004,T +coef_ABM2_calibration_0_1_miles_social,-0.7869,T +coef_ABM2_calibration_1_2_miles_social,0.5209,T +coef_ABM2_calibration_2_3_miles_social,0.5605,T +coef_ABM2_calibration_3_4_miles_social,0.1371,T +coef_ABM2_calibration_4_5_miles_social,-0.027,T +coef_ABM2_calibration_0_5_miles_social,0.107,T +coef_Calibration_0_1_miles_social,1.0697,T +coef_Calibration_1_2_miles_social,0.5699,T +coef_ABM2_calibration_distance_social,0.8713,T +coef_ABM2_calibration_distance_squared_social,-0.0353,T +coef_ABM2_calibration_distance_cubed_social,0.0004,T +coef_Calibration_Distance_squared_social,0.0,T +coef_Calibration_Distance_cubed_social,0.0,T +coef_Calibration_Distance_social,0.0,T +coef_ABM2_calibration_distance_logged_social,-2.349,T +coef_Calibration_Distance_logged_social,0.0,T +coef_ABM2p_calibration_0_1_miles_social,-0.0563,T +coef_ABM2p_calibration_1_2_miles_social,-0.0582,T +coef_ABM2p_calibration_2_3_miles_social,-0.1056,T +coef_ABM2p_calibration_3_4_miles_social,-0.0262,T +#,, +coef_Distance_Calibration_adjustment_disc,0.0066,T +coef_ABM2_calibration_0_1_miles_joint_disc,-0.4739,T +coef_ABM2_calibration_1_2_miles_joint_disc,-0.3004,T +coef_ABM2_calibration_0_1_miles_disc,-0.7869,T +coef_ABM2_calibration_1_2_miles_disc,0.5209,T +coef_ABM2_calibration_2_3_miles_disc,0.5605,T +coef_ABM2_calibration_3_4_miles_disc,0.1371,T +coef_ABM2_calibration_4_5_miles_disc,-0.027,T +coef_ABM2_calibration_0_5_miles_disc,0.107,T +coef_ABM2_calibration_distance_disc,0.8713,T +coef_ABM2_calibration_distance_squared_disc,-0.0353,T +coef_ABM2_calibration_distance_cubed_disc,0.0004,T +coef_Calibration_Distance_squared_disc,-0.0523,T +coef_Calibration_Distance_cubed_disc,0.0027,T +coef_Calibration_Distance_disc,0.2181,T +coef_ABM2_calibration_distance_logged_disc,-2.349,T +coef_Calibration_Distance_logged_disc,-0.1883,T +coef_ABM2p_calibration_0_1_miles_disc,-0.0563,T +coef_ABM2p_calibration_1_2_miles_disc,-0.0582,T +coef_ABM2p_calibration_2_3_miles_disc,-0.1056,T +coef_ABM2p_calibration_3_4_miles_disc,-0.0262,T +#,, +coef_abm3_dist_0_2indivescort_asc,0.0743894542403208,F +coef_abm3_dist_0_2indivmaint_asc,-1.1013926972835268,F +coef_abm3_dist_0_2indivdisc_asc,-0.5188544554826798,F +coef_abm3_dist_2_5indivescort_asc,0.3267263770696683,F +coef_abm3_dist_2_5indivmaint_asc,-0.1229585362100356,F +coef_abm3_dist_2_5indivdisc_asc,0.3658142479785504,F +coef_abm3_dist_5_10indivescort_asc,0.3268135840383142,F +coef_abm3_dist_5_10indivmaint_asc,1.2615942662937147,F +coef_abm3_dist_5_10indivdisc_asc,0.970850923095182,F +coef_abm3_dist_10_30indivescort_asc,-1.210160116890714,F +coef_abm3_dist_10_30indivmaint_asc,2.244671931775837,F +coef_abm3_dist_10_30indivdisc_asc,1.7449730188184471,F +coef_abm3_dist_0_2jointmaint_asc,0.10814282191363894,F +coef_abm3_dist_0_2jointdisc_asc,-0.3257531020899993,F +coef_abm3_dist_2_5jointmaint_asc,-0.2256620842333652,F +coef_abm3_dist_2_5jointdisc_asc,0.7878311118085077,F +coef_abm3_dist_5_10jointmaint_asc,0.26451614721496275,F +coef_abm3_dist_5_10jointdisc_asc,0.7566707355720486,F +coef_abm3_dist_10_30jointmaint_asc,1.1623489449822964,F +coef_abm3_dist_10_30jointdisc_asc,0.17580063767755247,F diff --git a/resident/configs/non_mandatory_tour_destination_sample.csv b/resident/configs/non_mandatory_tour_destination_sample.csv new file mode 100644 index 0000000..e82f800 --- /dev/null +++ b/resident/configs/non_mandatory_tour_destination_sample.csv @@ -0,0 +1,19 @@ +Description,Expression,escort,shopping,eatout,othmaint,social,othdiscr +,"_DIST@skims[('SOV_M_DIST', 'MD')]",1,1,1,1,1,1 +Distance,"@_DIST.clip(0,20)",coef_dist_escort,coef_dist_shopping,coef_dist_eatout,coef_dist_maint,coef_dist_social,coef_dist_discr +Distance_squared,"@_DIST.clip(0,20)**2",coef_distsqrd_escort,coef_distsqrd_shopping,coef_distsqrd_eatout,coef_distsqrd_maint,coef_distsqrd_social,coef_distsqrd_discr +Distance_cubed,"@_DIST.clip(0,20)**3",coef_zero,coef_distcubed_shopping,coef_distcubed_eatout,coef_zero,coef_distcubed_social,coef_distcubed_discr +Distance_logged,"@np.log(_DIST.clip(0,20) + 0.001)",coef_zero,coef_distlogged_shopping,coef_distlogged_eatout,coef_zero,coef_distlogged_social,coef_distlogged_discr +Distance_female,"@_DIST.clip(0,20) * df.female * np.where(df.get('tour_category', default=False) == 'joint', 0, 1)",coef_zero,coef_distfemale_shopping,coef_zero,coef_zero,coef_zero,coef_distfemale_disc +Distance - low income_segment_escort,"@_DIST.clip(0,20) * (df.income < 30000)",coef_distlowinc_escort,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero +Distance - low income_segment_maint,"@_DIST.clip(0,20) * (df.income_segment <= 2)",coef_zero,coef_zero,coef_zero,coef_distlowinc_maint,coef_distlowinc_social,coef_zero +Distance - lowmed income_segment_maint,"@_DIST.clip(0,20) * (df.income_segment <= 3)",coef_zero,coef_zero,coef_zero,coef_zero,coef_distmidlowinc_social,coef_zero +#Distance - Time Pressure calculated as the log of the maxtime over tours left,"@_DIST.clip(0,20) * np.log((df.maxWindow/df.toursLeft)+0.0001)",coef_zero,coef_disttimepressure_shopping,coef_disttimepressure_eatout,coef_disttimepressure_maint,coef_zero,coef_disttimepressure_disc +#Accessibility,@@nonMandatoryAccessibilityAlt,coef_zero,coef_accessibility_shopping,coef_zero,coef_zero,coef_zero,coef_zero +Distance - age 16 to 24,"@_DIST.clip(0,20) * df.age.between(16, 24) * np.where(df.get('tour_category', default=False) == 'joint', 0, 1)",coef_zero,coef_zero,coef_zero,coef_distage1624_maint,coef_zero,coef_zero +Distance - age 56 to 64,"@_DIST.clip(0,20) * (df.age.between(56, 64))",coef_distage5664_escort,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero +Distance - age 65+,"@_DIST.clip(0,20) * (df.age > 64)",coef_distage64plus_escort,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero +#escort size is zero for escort tour,@(@@householdsDestAlt * 1 + df.num_young_children * @@householdsDestAlt * 1 + df.num_gradeschool * @@gradeSchoolEnrollmentDestAlt * 100 + df.numHighSchoolStudents * @@highSchoolEnrollmentDestAlt * 100) == 0,-999,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero +#escort size is non-zero for escort tour,"@np.where(@@householdsDestAlt * 1 + df.num_young_children * @@householdsDestAlt * 1 + df.num_gradeschool * @@gradeSchoolEnrollmentDestAlt * 100 + df.numHighSchoolStudents * @@highSchoolEnrollmentDestAlt * 100 > 0, np.log(@@householdsDestAlt * 1 + df.num_y",1,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero +Size variable,@df['size_term'].apply(np.log1p),coef_zero,1,1,1,1,1 +No attractions,@df['size_term']==0,coef_zero,-999,-999,-999,-999,-999 diff --git a/resident/configs/non_mandatory_tour_frequency.csv b/resident/configs/non_mandatory_tour_frequency.csv new file mode 100644 index 0000000..55db7ac --- /dev/null +++ b/resident/configs/non_mandatory_tour_frequency.csv @@ -0,0 +1,106 @@ +Label,Description,Expression,PTYPE_FULL,PTYPE_PART,PTYPE_UNIVERSITY,PTYPE_NONWORK,PTYPE_RETIRED,PTYPE_SCHOOL,PTYPE_PRESCHOOL +util_naming_alts_explicitly,Explicitly naming what tot_tours is defined as,_total_indNM_tours@df.tot_tours,1,1,1,1,1,1,1 +util_escorting_tour,Escorting Tour,escort,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour +util_discretionary_tour,Discretionary Tour,othdiscr,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour +util_shopping_tour,Shopping Tour,shopping,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour +util_maintenance_tour,Maintenance Tour,othmaint,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour +util_visiting_or_social_tour,Visiting/Social Tour,social,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour +util_eating_out_tour,Eating Out Tour,eatout,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour +util_total_indNM_tours_is_0_prior_tours,Total individual NM Tours = 0 (1 or more Prior Tours),(_total_indNM_tours == 0) & (has_previous_tour == 1),,coef_total_indNM_tours_is_0_prior_tours,coef_total_indNM_tours_is_0_prior_tours,,,coef_total_indNM_tours_is_0_prior_tours,coef_total_indNM_tours_is_0_prior_tours +util_total_indNM_tours_is_1,Total individual NM Tours = 1,(_total_indNM_tours == 1),coef_total_indNM_tours_is_1,coef_total_indNM_tours_is_1,coef_total_indNM_tours_is_1,coef_total_indNM_tours_is_1,coef_total_indNM_tours_is_1,coef_total_indNM_tours_is_1,coef_total_indNM_tours_is_1 +util_total_indNM_tours_is_2,Total individual NM Tours = 2,(_total_indNM_tours == 2),coef_total_indNM_tours_is_2,coef_total_indNM_tours_is_2,coef_total_indNM_tours_is_2,coef_total_indNM_tours_is_2,coef_total_indNM_tours_is_2,, +util_total_indNM_tours_is_2p,Total individual NM Tours >= 2,(_total_indNM_tours >= 2),,,,,,coef_total_indNM_tours_is_2p,coef_total_indNM_tours_is_2p +util_total_indNM_tours_is_3,Total individual NM Tours = 3,(_total_indNM_tours == 3),coef_total_indNM_tours_is_3,,,,coef_total_indNM_tours_is_3,, +util_total_indNM_tours_is_3p,Total individual NM Tours >= 3,(_total_indNM_tours >= 3),,coef_total_indNM_tours_is_3p,coef_total_indNM_tours_is_3p,coef_total_indNM_tours_is_3p,,, +util_total_indNM_tours_is_4,Total individual NM Tours = 4,(_total_indNM_tours >= 4),coef_total_indNM_tours_is_4p,,,,coef_total_indNM_tours_is_4p,, +util_has_mandatory_tours_and_iNM_tour_freq_1,One or more Mandatory tour & tour frequency =1,(num_mandatory_tours > 0) & (_total_indNM_tours == 1),coef_has_mandatory_tours_and_iNM_tour_freq_1,,,,,, +util_has_mandatory_tours_and_iNM_tour_freq_2p,One or more Mandatory tour & tour frequency =2p,(num_mandatory_tours > 0) & (_total_indNM_tours >= 2),coef_has_mandatory_tours_and_iNM_tour_freq_2p,,,,,, +util_has_joint_tours_and_iNM_tour_freq_1,One or more Joint tour & tour frequency =1,(num_person_joint_tours > 0) & (_total_indNM_tours == 1),coef_has_joint_tours_and_iNM_tour_freq_1,coef_has_joint_tours_and_iNM_tour_freq_1,,coef_has_joint_tours_and_iNM_tour_freq_1,coef_has_joint_tours_and_iNM_tour_freq_1,coef_has_joint_tours_and_iNM_tour_freq_1, +util_has_joint_tours_and_iNM_tour_freq_2p,One or more Joint tour & tour frequency =2p,(num_person_joint_tours > 0) & (_total_indNM_tours >= 2),coef_has_joint_tours_and_iNM_tour_freq_2p,,,coef_has_joint_tours_and_iNM_tour_freq_2p,coef_has_joint_tours_and_iNM_tour_freq_2p,, +util_number_of_school_escort_tours,Number of School Escort Tours,escort * num_school_escort_tours,coef_prev_school_escort_tours,coef_prev_school_escort_tours,,coef_prev_school_escort_tours,,, +# availability conditions,,,,,,,,, +util_availability_of_zero_tours,Requires NM tour if no previous tours,(has_previous_tour == 0) & (_total_indNM_tours == 0),coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable, +# income coefficients with medium as base,,,,,,,,, +util_low_income_escort,Low income household * number of escort tours,low_income * escort,,coef_low_income_escort,,coef_low_income_escort,coef_low_income_escort,, +util_low_income_shopping,Low income household * number of shopping tours,low_income * shopping,,,,,coef_low_income_shopping,, +util_low_income_othmaint,Low income household * number of othmaint tours,low_income * othmaint,coef_low_income_othmaint,,,coef_low_income_othmaint,coef_low_income_othmaint,, +util_low_income_eatout,Low income household * number of eatout tours,low_income * eatout,,,,,coef_low_income_eatout,, +util_low_income_othdiscr,Low income household * number of othdiscr tours,low_income * othdiscr,coef_low_income_othdiscr,coef_low_income_othdiscr,,,coef_low_income_othdiscr,, +util_medium_low_income_escort,Medium-low income household * number of escort tours,medium_low_income * escort,coef_medium_low_income_escort,coef_medium_low_income_escort,,coef_medium_low_income_escort,,, +util_medium_low_income_othmaint,Medium-low income household * number of othmaint tours,medium_low_income * othmaint,,,,coef_medium_low_income_othmaint,coef_medium_low_income_othmaint,, +util_medium_low_income_othdiscr,Medium-low income household * number of othdiscr tours,medium_low_income * othdiscr,coef_medium_low_income_othdiscr,,,,,, +util_medium_high_income_othdiscr,Medium-high income household * number of othdiscr tours,medium_high_income * othdiscr,coef_medium_high_income_othdiscr,,,coef_medium_high_income_othdiscr,,, +util_high_income_escort,High income household * number of escort tours,high_income * escort,coef_high_income_escort,,,,,, +util_high_income_shopping,High income household * number of shopping tours,high_income * shopping,,,,,coef_high_income_shopping,, +util_high_income_othdiscr,High income household * number of othdiscr tours,high_income * othdiscr,coef_high_income_othdiscr,,,,,, +# car ownership coefficients,,,,,,,,, +util_zero_auto_iNM_tour_freq_1,zero auto ownership & tour freq is 1,(auto_ownership == 0) & (_total_indNM_tours == 1),coef_zero_auto_iNM_tour_freq_1,coef_zero_auto_iNM_tour_freq_1,coef_zero_auto_iNM_tour_freq_1,,,, +util_zero_auto_iNM_tour_freq_2,zero auto ownership & tour freq is 2,(auto_ownership == 0) & (_total_indNM_tours == 2),coef_zero_auto_iNM_tour_freq_2,,,,,, +util_zero_auto_iNM_tour_freq_2p,zero auto ownership & tour freq is 2p,(auto_ownership == 0) & (_total_indNM_tours >= 2),,coef_zero_auto_iNM_tour_freq_2p,coef_zero_auto_iNM_tour_freq_2p,,,, +util_zero_auto_iNM_tour_freq_3p,zero auto ownership & tour freq is 3p,(auto_ownership == 0) & (_total_indNM_tours >= 3),coef_zero_auto_iNM_tour_freq_3p,,,coef_zero_auto_iNM_tour_freq_3p,,, +util_auto_deficient_iNM_tour_freq_1,auto deficient & tour freq is 1,(auto_ownership > 0) & (auto_ownership < num_adults) & (_total_indNM_tours == 1),coef_auto_deficient_iNM_tour_freq_1,coef_auto_deficient_iNM_tour_freq_1,coef_auto_deficient_iNM_tour_freq_1,coef_auto_deficient_iNM_tour_freq_1,coef_auto_deficient_iNM_tour_freq_1,, +util_auto_deficient_iNM_tour_freq_2,auto deficient & tour freq is 2,(auto_ownership > 0) & (auto_ownership < num_adults) & (_total_indNM_tours == 2),,,coef_auto_deficient_iNM_tour_freq_2,,coef_auto_deficient_iNM_tour_freq_2,, +util_auto_deficient_iNM_tour_freq_2p,auto deficient & tour freq is 2p,(auto_ownership > 0) & (auto_ownership < num_adults) & (_total_indNM_tours >= 2),coef_auto_deficient_iNM_tour_freq_2p,coef_auto_deficient_iNM_tour_freq_2p,,coef_auto_deficient_iNM_tour_freq_2p,,, +util_auto_deficient_iNM_tour_freq_3p,auto deficient & tour freq is 3p,(auto_ownership > 0) & (auto_ownership < num_adults) & (_total_indNM_tours >= 3),,,coef_auto_deficient_iNM_tour_freq_3p,,coef_auto_deficient_iNM_tour_freq_3p,, +# household member interactions,,,,,,,,, +util_num_children_escort,Number of household children * number of escort tours,num_children * escort,coef_num_children_escort,coef_num_children_escort,coef_num_children_escort,coef_num_children_escort,coef_num_children_escort,,coef_num_children_escort +util_num_children_shopping,Number of household children * number of shopping tours,num_children * shopping,coef_num_children_shopping,coef_num_children_shopping,,,,, +util_num_children_othmaint,Number of household children * number of othmaint tours,num_children * othmaint,coef_num_children_othmaint,,,,,, +util_num_children_social,Number of household children * number of social tours,num_children * social,coef_num_children_social,,,coef_num_children_social,,, +util_num_children_othdiscr,Number of household children * number of othdiscr tours,num_children * othdiscr,coef_num_children_othdiscr,coef_num_children_othdiscr,,,,, +util_num_full_time_workers_escort,Number of household full time workers * number of escort tours,num_fullTime_workers * escort,coef_num_full_time_workers_escort,,,coef_num_full_time_workers_escort,coef_num_full_time_workers_escort,coef_num_full_time_workers_escort, +util_num_full_time_workers_othmaint,Number of household full time workers * number of othmaint tours,num_fullTime_workers * othmaint,,,,coef_num_full_time_workers_othmaint,,, +util_num_full_time_workers_eatout,Number of household full time workers * number of eatout tours,num_fullTime_workers * eatout,,coef_num_full_time_workers_eatout,coef_num_full_time_workers_eatout,,,, +oef_num_full_time_workers_social,,,,,,,,, +util_num_full_time_workers_othdiscr,Number of household full time workers * number of othdiscr tours,num_fullTime_workers * othdiscr,coef_num_full_time_workers_othdiscr,,,,,, +util_hhsize_escort,household size * number of escort tours,hhsize * escort,,,,coef_hhsize_escort,,coef_hhsize_escort, +util_hhsize_shopping,household size * number of shopping tours,hhsize * shopping,coef_hhsize_shopping,,,,coef_hhsize_shopping,, +util_hhsize_othmaint,household size * number of othmaint tours,hhsize * othmaint,coef_hhsize_othmaint,,,coef_hhsize_othmaint,coef_hhsize_othmaint,, +util_hhsize_eatout,household size * number of eatout tours,hhsize * eatout,coef_hhsize_eatout,,coef_hhsize_eatout,coef_hhsize_eatout,coef_hhsize_eatout,, +util_hhsize_social,household size * number of social tours,hhsize * social,coef_hhsize_social,,,,,, +util_hhsize_othdiscr,household size * number of othdiscr tours,hhsize * othdiscr,coef_hhsize_othdiscr,,,,coef_hhsize_othdiscr,,coef_hhsize_othdiscr +util_retired_hh_escort,Retirees and Non-workers only household * Escorting,retiredHh * escort,,,,,coef_retired_hh_escort,, +# age effects by purpose,,,,,,,,, +util_retired_80p_escort,Retiree over age 80 escort,(age >= 80) * escort,,,,,coef_age_80p_escort,, +util_retired_80p_eatout,Retiree over age 80 eatout,(age >= 80) * eatout,,,,,coef_age_80p_eatout,, +util_adult_18to24_escort,Age 18 to 24 escort,((age > 18) & (age <= 24)) * escort,coef_age_18to24_escort,coef_age_18to24_escort,,,,, +util_adult_18to24_shopping,Age 18 to 24 shopping,((age > 18) & (age <= 24)) * shopping,,,coef_age_18to24_shopping,,,, +util_adult_18to24_eatout,Age 18 to 24 eatout,((age > 18) & (age <= 24)) * eatout,,,coef_age_18to24_eatout,,,, +util_adult_18to24_social,Age 18 to 24 social,((age > 18) & (age <= 24)) * social,,,,coef_age_18to24_social,,, +util_child_24to35_escort,Age 24 to 35 escort,((age > 24) & (age <= 35)) * escort,coef_age_24to35_escort,coef_age_24to35_escort,,coef_age_24to35_escort,,, +util_child_24to35_shopping,Age 24 to 35 shopping,((age > 24) & (age <= 35)) * shopping,,coef_age_24to35_shopping,coef_age_24to35_shopping,coef_age_24to35_shopping,,, +util_child_24to35_eatout,Age 24 to 35 eatout,((age > 24) & (age <= 35)) * eatout,coef_age_24to35_eatout,,,,,, +util_child_24to35_othdiscr,Age 24 to 35 othdiscr,((age > 24) & (age <= 35)) * othdiscr,,coef_age_24to35_othdiscr,,coef_age_24to35_othdiscr,,, +util_child_50to79_escort,Age 50 to 79 escort,((age >= 50) & (age <= 79)) * escort,coef_age_50to79_escort,coef_age_50to79_escort,,coef_age_50to79_escort,,, +util_child_50to79_eatout,Age 50 to 79 eatout,((age >= 50) & (age <= 79)) * eatout,coef_age_50to79_eatout,,,,,, +util_child_50to79_othdiscr,Age 50 to 79 othdiscr,((age >= 50) & (age <= 79)) * othdiscr,coef_age_50to79_othdiscr,,coef_age_50to79_othdiscr,,,, +# age effects by frequency,,,,,,,,, +util_retired_80p_and_iNM_tour_freq_2,Age 80+ & Tour Frequency =2,(age >= 80) & (_total_indNM_tours == 2),,,,,coef_age_80p_and_iNM_tour_freq_2,, +util_adult_18to24_and_iNM_tour_freq_2,Age 18 to 24 & Tour Frequency =2,((age > 18) & (age <= 24)) & (_total_indNM_tours == 2),coef_age_18to24_and_iNM_tour_freq_2,,,coef_age_18to24_and_iNM_tour_freq_2,,, +# residual time window,,,,,,,,, +util_log_total_hours_available_7am_to_10pm_window_escort,log_Total hours available 1am to 10pm tours escort,log_total_hours_available_7am_to_10pm * escort,coef_log_total_hours_available_7am_to_10pm_escort,coef_log_total_hours_available_7am_to_10pm_escort,coef_log_total_hours_available_7am_to_10pm_escort,coef_log_total_hours_available_7am_to_10pm_escort,coef_log_total_hours_available_7am_to_10pm_escort,coef_log_total_hours_available_7am_to_10pm_escort,coef_log_total_hours_available_7am_to_10pm_escort +util_log_total_hours_available_7am_to_10pm_window_shopping,log_Total hours available 1am to 10pm tours shopping,log_total_hours_available_7am_to_10pm * shopping,coef_log_total_hours_available_7am_to_10pm_shopping,coef_log_total_hours_available_7am_to_10pm_shopping,coef_log_total_hours_available_7am_to_10pm_shopping,,coef_log_total_hours_available_7am_to_10pm_shopping,coef_log_total_hours_available_7am_to_10pm_shopping,coef_log_total_hours_available_7am_to_10pm_shopping +util_log_total_hours_available_7am_to_10pm_window_othmaint,log_Total hours available 1am to 10pm tours othmaint,log_total_hours_available_7am_to_10pm * othmaint,coef_log_total_hours_available_7am_to_10pm_othmaint,coef_log_total_hours_available_7am_to_10pm_othmaint,coef_log_total_hours_available_7am_to_10pm_othmaint,,coef_log_total_hours_available_7am_to_10pm_othmaint,coef_log_total_hours_available_7am_to_10pm_othmaint,coef_log_total_hours_available_7am_to_10pm_othmaint +util_log_total_hours_available_7am_to_10pm_window_eatout,log_Total hours available 1am to 10pm tours eatout,log_total_hours_available_7am_to_10pm * eatout,coef_log_total_hours_available_7am_to_10pm_eatout,coef_log_total_hours_available_7am_to_10pm_eatout,coef_log_total_hours_available_7am_to_10pm_eatout,,,coef_log_total_hours_available_7am_to_10pm_eatout,coef_log_total_hours_available_7am_to_10pm_eatout +util_log_total_hours_available_7am_to_10pm_window_social,log_Total hours available 1am to 10pm tours social,log_total_hours_available_7am_to_10pm * social,coef_log_total_hours_available_7am_to_10pm_social,coef_log_total_hours_available_7am_to_10pm_social,,,,coef_log_total_hours_available_7am_to_10pm_social, +util_log_total_hours_available_7am_to_10pm_window_othdiscr,log_Total hours available 1am to 10pm tours othdiscr,log_total_hours_available_7am_to_10pm * othdiscr,coef_log_total_hours_available_7am_to_10pm_othdiscr,coef_log_total_hours_available_7am_to_10pm_othdiscr,,,coef_log_total_hours_available_7am_to_10pm_othdiscr,coef_log_total_hours_available_7am_to_10pm_othdiscr,coef_log_total_hours_available_7am_to_10pm_othdiscr +util_consecutive_hours_available_7am_to_10pm_window_escort,Consecutive hours available 1am to 10pmtours escort,consecutive_hours_available_7am_to_10pm * escort,,,,,coef_consecutive_hours_available_7am_to_10pm_escort,, +util_consecutive_hours_available_7am_to_10pm_window_shopping,Consecutive hours available 1am to 10pmtours shopping,consecutive_hours_available_7am_to_10pm * shopping,coef_consecutive_hours_available_7am_to_10pm_shopping,coef_consecutive_hours_available_7am_to_10pm_shopping,,coef_consecutive_hours_available_7am_to_10pm_shopping,,, +util_consecutive_hours_available_7am_to_10pm_window_othmaint,Consecutive hours available 1am to 10pmtours othmaint,consecutive_hours_available_7am_to_10pm * othmaint,coef_consecutive_hours_available_7am_to_10pm_othmaint,,,coef_consecutive_hours_available_7am_to_10pm_othmaint,,coef_consecutive_hours_available_7am_to_10pm_othmaint, +util_consecutive_hours_available_7am_to_10pm_window_eatout,Consecutive hours available 1am to 10pmtours eatout,consecutive_hours_available_7am_to_10pm * eatout,,,,,coef_consecutive_hours_available_7am_to_10pm_eatout,, +util_consecutive_hours_available_7am_to_10pm_window_othdiscr,Consecutive hours available 1am to 10pmtours othdiscr,consecutive_hours_available_7am_to_10pm * othdiscr,,,coef_consecutive_hours_available_7am_to_10pm_othdiscr,coef_consecutive_hours_available_7am_to_10pm_othdiscr,,, +# accessibilities,,,,,,,,, +util_othdiscr_accessibility_for_escorting,Retail Accessibility for Escorting,shopping_accessibility * escort,,coef_shopping_accessibility_escorting,,,,coef_shopping_accessibility_escorting, +util_shopping_accessibility_shopping,Retail Accessibility for Shopping,shopping_accessibility * shopping,coef_shopping_accessibility_shopping,coef_shopping_accessibility_shopping,coef_shopping_accessibility_shopping,coef_shopping_accessibility_shopping,,,coef_shopping_accessibility_shopping +util_shopping_accessibility_maintenance,Retail Accessibility for Maintenance,shopping_accessibility * othmaint,,,coef_shopping_accessibility_maintenance,,,coef_shopping_accessibility_maintenance,coef_shopping_accessibility_maintenance +util_othdiscr_accessibility_eating_out,Retail Accessibility for Eating Out,othdiscr_accessibility * eatout,coef_othdiscr_accessibility_eating_out,coef_othdiscr_accessibility_eating_out,coef_othdiscr_accessibility_eating_out,,coef_othdiscr_accessibility_eating_out,, +util_othdiscr_accessibility_discretionary,Retail Accessibility for Discretionary,othdiscr_accessibility * othdiscr,coef_othdiscr_accessibility_othdiscr,coef_othdiscr_accessibility_othdiscr,coef_othdiscr_accessibility_othdiscr,coef_othdiscr_accessibility_othdiscr,,,coef_othdiscr_accessibility_othdiscr +util_othdiscr_accessibility_visiting,Retail Accessibility for Visiting,othdiscr_accessibility * social,coef_othdiscr_accessibility_social,coef_othdiscr_accessibility_social,,coef_othdiscr_accessibility_social,,coef_othdiscr_accessibility_social,coef_othdiscr_accessibility_social +# work from home and telecommute_frequency,,,,,,,,, +util_work_from_home_and_iNM_tour_freq_2,Work From Home & Tour Frequency =2,(work_from_home) & (_total_indNM_tours == 2),coef_work_from_home_and_iNM_tour_freq_2,,,,,, +util_work_from_home_and_iNM_tour_freq_3p,Work From Home & Tour Frequency =3p,(work_from_home) & (_total_indNM_tours >= 3),coef_work_from_home_and_iNM_tour_freq_3p,,,,,, +util_telecommute_2_3_days_week_and_iNM_tour_freq_2,Telecommute 1 day per week & Tour Frequency =2,(telecommute_frequency == '2_3_days_week') & (_total_indNM_tours == 2),coef_telecommute_2_3_4_days_week_and_iNM_tour_freq_2,,,,,, +util_telecommute_2_3_days_week_and_iNM_tour_freq_3p,Telecommute 1 day per week & Tour Frequency =3p,(telecommute_frequency == '2_3_days_week') & (_total_indNM_tours >= 3),coef_telecommute_2_3_4_days_week_and_iNM_tour_freq_3p,,,,,, +util_telecommute_4_days_week_and_iNM_tour_freq_1,Telecommute 1 day per week & Tour Frequency =1,(telecommute_frequency == '4_days_week') & (_total_indNM_tours == 1),,coef_telecommute_4_days_week_and_iNM_tour_freq_1,,,,, +util_telecommute_4_days_week_and_iNM_tour_freq_2,Telecommute 1 day per week & Tour Frequency =2,(telecommute_frequency == '4_days_week') & (_total_indNM_tours == 2),coef_telecommute_2_3_4_days_week_and_iNM_tour_freq_2,,,,,, +util_telecommute_4_days_week_and_iNM_tour_freq_3p,Telecommute 1 day per week & Tour Frequency =3p,(telecommute_frequency == '4_days_week') & (_total_indNM_tours >= 3),coef_telecommute_2_3_4_days_week_and_iNM_tour_freq_3p,,,,,, diff --git a/resident/configs/non_mandatory_tour_frequency.yaml b/resident/configs/non_mandatory_tour_frequency.yaml new file mode 100644 index 0000000..d459bcc --- /dev/null +++ b/resident/configs/non_mandatory_tour_frequency.yaml @@ -0,0 +1,40 @@ + +SEGMENT_COL: ptype +SPEC: non_mandatory_tour_frequency.csv + +SPEC_SEGMENTS: + - NAME: PTYPE_FULL + PTYPE: 1 + COEFFICIENTS: non_mandatory_tour_frequency_coefficients_PTYPE_FULL.csv + - NAME: PTYPE_PART + PTYPE: 2 + COEFFICIENTS: non_mandatory_tour_frequency_coefficients_PTYPE_PART.csv + - NAME: PTYPE_UNIVERSITY + PTYPE: 3 + COEFFICIENTS: non_mandatory_tour_frequency_coefficients_PTYPE_UNIVERSITY.csv + - NAME: PTYPE_NONWORK + PTYPE: 4 + COEFFICIENTS: non_mandatory_tour_frequency_coefficients_PTYPE_NONWORK.csv + - NAME: PTYPE_RETIRED + PTYPE: 5 + COEFFICIENTS: non_mandatory_tour_frequency_coefficients_PTYPE_RETIRED.csv + - NAME: PTYPE_SCHOOL + PTYPE: 6 + COEFFICIENTS: non_mandatory_tour_frequency_coefficients_PTYPE_SCHOOL.csv + - NAME: PTYPE_PRESCHOOL + PTYPE: 7 + COEFFICIENTS: non_mandatory_tour_frequency_coefficients_PTYPE_PRESCHOOL.csv + +annotate_persons: + SPEC: annotate_persons_nmtf + DF: persons + TABLES: + - tours + +preprocessor: + SPEC: non_mandatory_tour_frequency_annotate_persons_preprocessor + DF: persons + TABLES: + - tours + - households + - joint_tour_participants diff --git a/resident/configs/non_mandatory_tour_frequency_alternatives.csv b/resident/configs/non_mandatory_tour_frequency_alternatives.csv new file mode 100644 index 0000000..e4d4f2e --- /dev/null +++ b/resident/configs/non_mandatory_tour_frequency_alternatives.csv @@ -0,0 +1,198 @@ +escort,shopping,othmaint,eatout,social,othdiscr +0,0,0,0,0,0 +0,0,0,0,0,1 +0,0,0,0,0,2 +0,0,0,0,1,0 +0,0,0,0,1,1 +0,0,0,0,1,2 +0,0,0,1,0,0 +0,0,0,1,0,1 +0,0,0,1,0,2 +0,0,0,1,1,0 +0,0,0,1,1,1 +0,0,0,1,1,2 +0,0,1,0,0,0 +0,0,1,0,0,1 +0,0,1,0,0,2 +0,0,1,0,1,0 +0,0,1,0,1,1 +0,0,1,0,1,2 +0,0,1,1,0,0 +0,0,1,1,0,1 +0,0,1,1,0,2 +0,0,1,1,1,0 +0,0,1,1,1,1 +0,0,1,1,1,2 +0,0,2,0,0,0 +0,0,2,0,0,1 +0,0,2,0,0,2 +0,0,2,0,1,0 +0,0,2,0,1,1 +0,0,2,0,1,2 +0,0,2,1,0,0 +0,0,2,1,0,1 +0,0,2,1,0,2 +0,0,2,1,1,0 +0,0,2,1,1,1 +0,1,0,0,0,0 +0,1,0,0,0,1 +0,1,0,0,0,2 +0,1,0,0,1,0 +0,1,0,0,1,1 +0,1,0,0,1,2 +0,1,0,1,0,0 +0,1,0,1,0,1 +0,1,0,1,0,2 +0,1,0,1,1,0 +0,1,0,1,1,1 +0,1,0,1,1,2 +0,1,1,0,0,0 +0,1,1,0,0,1 +0,1,1,0,0,2 +0,1,1,0,1,0 +0,1,1,0,1,1 +0,1,1,0,1,2 +0,1,1,1,0,0 +0,1,1,1,0,1 +0,1,1,1,0,2 +0,1,1,1,1,0 +0,1,1,1,1,1 +0,1,2,0,0,0 +0,1,2,0,0,1 +0,1,2,0,0,2 +0,1,2,0,1,0 +0,1,2,0,1,1 +0,1,2,1,0,0 +0,1,2,1,0,1 +0,1,2,1,1,0 +0,2,0,0,0,0 +0,2,0,0,0,1 +0,2,0,0,0,2 +0,2,0,0,1,0 +0,2,0,0,1,1 +0,2,0,0,1,2 +0,2,0,1,0,0 +0,2,0,1,0,1 +0,2,0,1,0,2 +0,2,0,1,1,0 +0,2,0,1,1,1 +0,2,1,0,0,0 +0,2,1,0,0,1 +0,2,1,0,0,2 +0,2,1,0,1,0 +0,2,1,0,1,1 +0,2,1,1,0,0 +0,2,1,1,0,1 +0,2,1,1,1,0 +0,2,2,0,0,0 +0,2,2,0,0,1 +0,2,2,0,1,0 +0,2,2,1,0,0 +1,0,0,0,0,0 +1,0,0,0,0,1 +1,0,0,0,0,2 +1,0,0,0,1,0 +1,0,0,0,1,1 +1,0,0,0,1,2 +1,0,0,1,0,0 +1,0,0,1,0,1 +1,0,0,1,0,2 +1,0,0,1,1,0 +1,0,0,1,1,1 +1,0,0,1,1,2 +1,0,1,0,0,0 +1,0,1,0,0,1 +1,0,1,0,0,2 +1,0,1,0,1,0 +1,0,1,0,1,1 +1,0,1,0,1,2 +1,0,1,1,0,0 +1,0,1,1,0,1 +1,0,1,1,0,2 +1,0,1,1,1,0 +1,0,1,1,1,1 +1,0,2,0,0,0 +1,0,2,0,0,1 +1,0,2,0,0,2 +1,0,2,0,1,0 +1,0,2,0,1,1 +1,0,2,1,0,0 +1,0,2,1,0,1 +1,0,2,1,1,0 +1,1,0,0,0,0 +1,1,0,0,0,1 +1,1,0,0,0,2 +1,1,0,0,1,0 +1,1,0,0,1,1 +1,1,0,0,1,2 +1,1,0,1,0,0 +1,1,0,1,0,1 +1,1,0,1,0,2 +1,1,0,1,1,0 +1,1,0,1,1,1 +1,1,1,0,0,0 +1,1,1,0,0,1 +1,1,1,0,0,2 +1,1,1,0,1,0 +1,1,1,0,1,1 +1,1,1,1,0,0 +1,1,1,1,0,1 +1,1,1,1,1,0 +1,1,2,0,0,0 +1,1,2,0,0,1 +1,1,2,0,1,0 +1,1,2,1,0,0 +1,2,0,0,0,0 +1,2,0,0,0,1 +1,2,0,0,0,2 +1,2,0,0,1,0 +1,2,0,0,1,1 +1,2,0,1,0,0 +1,2,0,1,0,1 +1,2,0,1,1,0 +1,2,1,0,0,0 +1,2,1,0,0,1 +1,2,1,0,1,0 +1,2,1,1,0,0 +1,2,2,0,0,0 +2,0,0,0,0,0 +2,0,0,0,0,1 +2,0,0,0,0,2 +2,0,0,0,1,0 +2,0,0,0,1,1 +2,0,0,0,1,2 +2,0,0,1,0,0 +2,0,0,1,0,1 +2,0,0,1,0,2 +2,0,0,1,1,0 +2,0,0,1,1,1 +2,0,1,0,0,0 +2,0,1,0,0,1 +2,0,1,0,0,2 +2,0,1,0,1,0 +2,0,1,0,1,1 +2,0,1,1,0,0 +2,0,1,1,0,1 +2,0,1,1,1,0 +2,0,2,0,0,0 +2,0,2,0,0,1 +2,0,2,0,1,0 +2,0,2,1,0,0 +2,1,0,0,0,0 +2,1,0,0,0,1 +2,1,0,0,0,2 +2,1,0,0,1,0 +2,1,0,0,1,1 +2,1,0,1,0,0 +2,1,0,1,0,1 +2,1,0,1,1,0 +2,1,1,0,0,0 +2,1,1,0,0,1 +2,1,1,0,1,0 +2,1,1,1,0,0 +2,1,2,0,0,0 +2,2,0,0,0,0 +2,2,0,0,0,1 +2,2,0,0,1,0 +2,2,0,1,0,0 +2,2,1,0,0,0 diff --git a/resident/configs/non_mandatory_tour_frequency_annotate_persons_preprocessor.csv b/resident/configs/non_mandatory_tour_frequency_annotate_persons_preprocessor.csv new file mode 100644 index 0000000..01895ec --- /dev/null +++ b/resident/configs/non_mandatory_tour_frequency_annotate_persons_preprocessor.csv @@ -0,0 +1,24 @@ +Description,Target,Expression +#,, +,low_income,persons.income<=30000 +,medium_low_income,(persons.income > 30000) & (persons.income <= 60000) +,medium_income,(persons.income > 60000) & (persons.income <= 100000) +,medium_high_income,(persons.income > 100000) & (persons.income <= 150000) +,high_income,(persons.income > 150000) +,total_hours_available_7am_to_10pm,"person_available_periods(persons, start_bin=8, end_bin=37, continuous=False) / 2" +,consecutive_hours_available_7am_to_10pm,"person_available_periods(persons, start_bin=8, end_bin=37, continuous=True) / 2" +,log_total_hours_available_7am_to_10pm,"np.log1p(total_hours_available_7am_to_10pm)" +#,, +# joint tour counts per persons,, +join joint tour participants with tour purpose,_JOINT_TOURS_PERSONS,"pd.merge(joint_tour_participants[['person_id', 'tour_id']], tours.reset_index()[['tour_id', 'tour_type']], on='tour_id', how='left')" +,num_person_joint_tours,"reindex_i(_JOINT_TOURS_PERSONS.groupby(['person_id']).size(), persons.index)" +,_TOUR_COUNT,"lambda query, tours, persons: tours.query(query).groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8)" +# non_mandatory tour frequency extension,, +,_INDIV_TOURS,tours[tours.tour_category!='joint'] +,num_mandatory_tours,"_TOUR_COUNT('(tour_category == \'mandatory\')', tours, persons)" +,num_school_escort_tours,"_TOUR_COUNT('(tour_category == \'non_mandatory\') & (tour_type == \'escort\')', tours, persons)" +,has_mandatory_tour,(num_mandatory_tours > 0) * 1 +,has_joint_tour,(num_person_joint_tours > 0) * 1 +,has_school_escort_tour,(num_school_escort_tours > 0) * 1 +,has_previous_tour,((has_mandatory_tour + has_joint_tour + has_school_escort_tour) > 0) * 1 +,retiredHh,"np.where(df.num_retired_adults == df.hhsize, 1, 0)" diff --git a/resident/configs/non_mandatory_tour_frequency_coefficients_PTYPE_FULL.csv b/resident/configs/non_mandatory_tour_frequency_coefficients_PTYPE_FULL.csv new file mode 100644 index 0000000..18e2d7e --- /dev/null +++ b/resident/configs/non_mandatory_tour_frequency_coefficients_PTYPE_FULL.csv @@ -0,0 +1,64 @@ +coefficient_name,value,constrain +coef_unavailable,-999.0,T +coef_escorting_tour,-9.980738057374221,F +coef_discretionary_tour,-7.464263172179179,F +coef_shopping_tour,-8.726268980359634,F +coef_maintenance_tour,-11.50076886788746,F +coef_visiting_or_social_tour,-9.13878408618917,F +coef_eating_out_tour,-7.996439827456814,F +coef_total_indNM_tours_is_1,3.65902242986596,F +coef_total_indNM_tours_is_2,3.783116973334075,F +coef_total_indNM_tours_is_3,3.94980139461646,F +coef_total_indNM_tours_is_4p,4.044250929541874,F +coef_has_mandatory_tours_and_iNM_tour_freq_1,-2.712110237880168,F +coef_has_mandatory_tours_and_iNM_tour_freq_2p,-1.8591259334530463,F +coef_has_joint_tours_and_iNM_tour_freq_1,-2.681436185164753,F +coef_has_joint_tours_and_iNM_tour_freq_2p,-2.582456735203676,F +coef_prev_school_escort_tours,-0.5659071632851075,F +coef_low_income_othmaint,0.1684373052095534,F +coef_low_income_othdiscr,-0.4247110056225974,F +coef_medium_low_income_escort,-0.26216121389680647,F +coef_medium_low_income_othdiscr,-0.2178944567340515,F +coef_medium_high_income_othdiscr,0.28578380808780474,F +coef_high_income_escort,0.5293466734142718,F +coef_high_income_othdiscr,0.5523603590834895,F +coef_zero_auto_iNM_tour_freq_1,-0.7778478707312145,F +coef_zero_auto_iNM_tour_freq_2,-1.745830549610859,F +coef_zero_auto_iNM_tour_freq_3p,-1.493273297127197,F +coef_auto_deficient_iNM_tour_freq_1,-0.13330115546054844,F +coef_auto_deficient_iNM_tour_freq_2p,-0.16552557917619873,F +coef_num_children_escort,0.7840653709271845,F +coef_num_children_shopping,0.16433950279556886,F +coef_num_children_othmaint,0.23002702126710234,F +coef_num_children_social,0.4360126244341865,F +coef_num_children_othdiscr,0.21305244576581855,F +coef_num_full_time_workers_escort,0.26574742217239844,F +coef_num_full_time_workers_othdiscr,-0.1268307960384887,F +coef_hhsize_shopping,-0.12965078886635895,F +coef_hhsize_othmaint,-0.11388617419409013,F +coef_hhsize_eatout,-0.26843735070052444,F +coef_hhsize_social,-0.4955951679184127,F +coef_hhsize_othdiscr,-0.18715371728147756,F +coef_age_18to24_escort,-0.7850576862260017,F +coef_age_24to35_escort,-0.33234113400691667,F +coef_age_24to35_eatout,-0.14650997264922844,F +coef_age_50to79_escort,-0.16636961878236434,F +coef_age_50to79_eatout,-0.33718093997956605,F +coef_age_50to79_othdiscr,-0.11702301453418044,F +coef_age_18to24_and_iNM_tour_freq_2,0.5184071993755616,F +coef_log_total_hours_available_7am_to_10pm_escort,2.3084987446913487,F +coef_log_total_hours_available_7am_to_10pm_shopping,1.9947536383662492,F +coef_log_total_hours_available_7am_to_10pm_othmaint,3.361513223893925,F +coef_log_total_hours_available_7am_to_10pm_eatout,1.8577971133678188,F +coef_log_total_hours_available_7am_to_10pm_social,1.6066161223464683,F +coef_log_total_hours_available_7am_to_10pm_othdiscr,1.935955432868534,F +coef_consecutive_hours_available_7am_to_10pm_shopping,0.03850875066248657,F +coef_consecutive_hours_available_7am_to_10pm_othmaint,0.05407491523536202,F +coef_shopping_accessibility_shopping,0.024876328142334057,F +coef_othdiscr_accessibility_eating_out,0.046921175676984284,F +coef_othdiscr_accessibility_othdiscr,0.029252086458666476,F +coef_othdiscr_accessibility_social,0.07973729159196855,F +coef_work_from_home_and_iNM_tour_freq_2,0.2882503610075806,F +coef_work_from_home_and_iNM_tour_freq_3p,0.43258258391404014,F +coef_telecommute_2_3_4_days_week_and_iNM_tour_freq_2,0.060220889952691474,F +coef_telecommute_2_3_4_days_week_and_iNM_tour_freq_3p,0.1399978999743526,F diff --git a/resident/configs/non_mandatory_tour_frequency_coefficients_PTYPE_NONWORK.csv b/resident/configs/non_mandatory_tour_frequency_coefficients_PTYPE_NONWORK.csv new file mode 100644 index 0000000..c9a06db --- /dev/null +++ b/resident/configs/non_mandatory_tour_frequency_coefficients_PTYPE_NONWORK.csv @@ -0,0 +1,42 @@ +coefficient_name,value,constrain +coef_escorting_tour,-11.210780876486327,F +coef_discretionary_tour,-2.787013651357596,F +coef_shopping_tour,-3.3897422131085118,F +coef_maintenance_tour,-2.0561696310183835,F +coef_visiting_or_social_tour,-4.541591324213227,F +coef_eating_out_tour,-2.105295178854616,F +coef_total_indNM_tours_is_1,3.1482892220784664,F +coef_total_indNM_tours_is_2,3.200495227239918,F +coef_total_indNM_tours_is_3p,3.1567943211556018,F +coef_has_joint_tours_and_iNM_tour_freq_1,-3.603962491982708,F +coef_has_joint_tours_and_iNM_tour_freq_2p,-3.4626515562619895,F +coef_prev_school_escort_tours,-1.0061285103185091,F +coef_unavailable,-999.0,T +coef_low_income_escort,-0.35229582496605166,F +coef_low_income_othmaint,-0.4037879626637974,F +coef_medium_low_income_escort,-0.23160471596989218,F +coef_medium_low_income_othmaint,-0.2805507112503287,F +coef_medium_high_income_othdiscr,0.32498217994094,F +coef_zero_auto_iNM_tour_freq_3p,-1.062908160511384,F +coef_auto_deficient_iNM_tour_freq_1,-0.21649670970431778,F +coef_auto_deficient_iNM_tour_freq_2p,-0.158,F +coef_num_children_escort,0.5581986955579746,F +coef_num_children_social,0.21747519499167278,F +coef_num_full_time_workers_escort,0.26498806847910095,F +coef_num_full_time_workers_othmaint,-0.1802355063278875,F +coef_hhsize_escort,0.11111794157280563,F +coef_hhsize_othmaint,-0.10130464850722279,F +coef_hhsize_eatout,-0.13935663394655493,F +coef_age_18to24_social,1.1448624101673186,F +coef_age_24to35_escort,-0.5608881691528708,F +coef_age_24to35_shopping,-0.3108414980140646,F +coef_age_24to35_othdiscr,-0.34338639441727353,F +coef_age_50to79_escort,-0.3812786064549372,F +coef_age_18to24_and_iNM_tour_freq_2,-0.9472458465963635,F +coef_log_total_hours_available_7am_to_10pm_escort,3.2260518038123336,F +coef_consecutive_hours_available_7am_to_10pm_shopping,0.06806700796118008,F +coef_consecutive_hours_available_7am_to_10pm_othmaint,0.05307614489617537,F +coef_consecutive_hours_available_7am_to_10pm_othdiscr,0.062136717913049824,F +coef_shopping_accessibility_shopping,0.05776720283340465,F +coef_othdiscr_accessibility_othdiscr,0.026534922230928487,F +coef_othdiscr_accessibility_social,0.04531077718755425,F diff --git a/resident/configs/non_mandatory_tour_frequency_coefficients_PTYPE_PART.csv b/resident/configs/non_mandatory_tour_frequency_coefficients_PTYPE_PART.csv new file mode 100644 index 0000000..e44cb57 --- /dev/null +++ b/resident/configs/non_mandatory_tour_frequency_coefficients_PTYPE_PART.csv @@ -0,0 +1,43 @@ +coefficient_name,value,constrain +coef_escorting_tour,-6.8444776692013,F +coef_discretionary_tour,-7.061093571650552,F +coef_shopping_tour,-7.265878398154104,F +coef_maintenance_tour,-10.631962973623128,F +coef_visiting_or_social_tour,-7.513076659640363,F +coef_eating_out_tour,-8.032068601942477,F +coef_total_indNM_tours_is_0_prior_tours,4.350891211480593,F +coef_total_indNM_tours_is_1,5.4208851654881824,F +coef_total_indNM_tours_is_2,5.8949683354189375,F +coef_total_indNM_tours_is_3p,5.94305016560457,F +coef_has_joint_tours_and_iNM_tour_freq_1,-0.29050043860982105,F +coef_prev_school_escort_tours,-0.611900397388031,F +coef_unavailable,-999.0,T +coef_low_income_escort,-0.41827164724106775,F +coef_low_income_othdiscr,-0.19464500351479955,F +coef_medium_low_income_escort,-0.37906865383167276,F +coef_zero_auto_iNM_tour_freq_1,-1.1380175469769664,F +coef_zero_auto_iNM_tour_freq_2p,-1.3659866939532648,F +coef_auto_deficient_iNM_tour_freq_1,-0.42631646773848203,F +coef_auto_deficient_iNM_tour_freq_2p,-0.7609412509478872,F +coef_num_children_escort,0.6491494416510694,F +coef_num_children_shopping,0.1479897628434831,F +coef_num_children_othdiscr,0.09237804825842522,F +coef_num_full_time_workers_eatout,-0.24419160458661263,F +coef_age_18to24_escort,-1.2806214268139127,F +coef_age_24to35_escort,-0.7100598101390896,F +coef_age_24to35_shopping,-0.4586169269694417,F +coef_age_24to35_othdiscr,-0.3230013853315813,F +coef_age_50to79_escort,-0.8046532853606215,F +coef_log_total_hours_available_7am_to_10pm_escort,1.457991506538752,F +coef_log_total_hours_available_7am_to_10pm_shopping,1.0540599454856179,F +coef_log_total_hours_available_7am_to_10pm_othmaint,3.1263153979195755,F +coef_log_total_hours_available_7am_to_10pm_eatout,1.450002894756668,F +coef_log_total_hours_available_7am_to_10pm_social,1.0022229262201703,F +coef_log_total_hours_available_7am_to_10pm_othdiscr,1.592166386021326,F +coef_consecutive_hours_available_7am_to_10pm_shopping,0.08487848115827784,F +coef_shopping_accessibility_escorting,0.034914955261943735,F +coef_shopping_accessibility_shopping,0.05379740509782797,F +coef_othdiscr_accessibility_eating_out,0.06844529127625759,F +coef_othdiscr_accessibility_othdiscr,0.024135548666842303,F +coef_othdiscr_accessibility_social,0.034748385315176565,F +coef_telecommute_4_days_week_and_iNM_tour_freq_1,-1.0215421163576748,F diff --git a/resident/configs/non_mandatory_tour_frequency_coefficients_PTYPE_PRESCHOOL.csv b/resident/configs/non_mandatory_tour_frequency_coefficients_PTYPE_PRESCHOOL.csv new file mode 100644 index 0000000..6fe7800 --- /dev/null +++ b/resident/configs/non_mandatory_tour_frequency_coefficients_PTYPE_PRESCHOOL.csv @@ -0,0 +1,22 @@ +coefficient_name,value,constrain +coef_escorting_tour,-11.051836936413467,F +coef_discretionary_tour,-9.634146897194343,F +coef_shopping_tour,-9.036614322521848,F +coef_maintenance_tour,-19.37699515494972,F +coef_visiting_or_social_tour,-6.595936622418915,F +coef_eating_out_tour,-17.102175352222606,F +coef_total_indNM_tours_is_0_prior_tours,3.24758823306399,F +coef_total_indNM_tours_is_1,2.4595057378149705,F +coef_total_indNM_tours_is_2p,2.679341375562811,F +coef_num_children_escort,0.3449608531280371,F +coef_num_full_time_workers_social,-0.7989076159896628,F +coef_hhsize_othdiscr,0.10761055792559372,F +coef_log_total_hours_available_7am_to_10pm_escort,3.138584054552107,F +coef_log_total_hours_available_7am_to_10pm_shopping,1.6577579483896183,F +coef_log_total_hours_available_7am_to_10pm_othmaint,4.7337852273390135,F +coef_log_total_hours_available_7am_to_10pm_eatout,4.921758449660736,F +coef_log_total_hours_available_7am_to_10pm_othdiscr,2.055556483820614,F +coef_shopping_accessibility_shopping,0.1461117250482414,F +coef_shopping_accessibility_maintenance,0.26356027630549783,F +coef_othdiscr_accessibility_othdiscr,0.0632885406604063,F +coef_othdiscr_accessibility_social,0.20216296220808352,F diff --git a/resident/configs/non_mandatory_tour_frequency_coefficients_PTYPE_RETIRED.csv b/resident/configs/non_mandatory_tour_frequency_coefficients_PTYPE_RETIRED.csv new file mode 100644 index 0000000..7379051 --- /dev/null +++ b/resident/configs/non_mandatory_tour_frequency_coefficients_PTYPE_RETIRED.csv @@ -0,0 +1,41 @@ +coefficient_name,value,constrain +coef_escorting_tour,-13.324718928099896,F +coef_discretionary_tour,-5.033358573683751,F +coef_shopping_tour,-9.13631330258294,F +coef_maintenance_tour,-9.945067353298386,F +coef_visiting_or_social_tour,-4.189801745365118,F +coef_eating_out_tour,-4.445717330935003,F +coef_total_indNM_tours_is_1,6.00131407260292,F +coef_total_indNM_tours_is_2,5.883565852357907,F +coef_total_indNM_tours_is_3,5.828445878907544,F +coef_total_indNM_tours_is_4p,5.934428647072386,F +coef_has_joint_tours_and_iNM_tour_freq_1,-5.7952837722578465,F +coef_has_joint_tours_and_iNM_tour_freq_2p,-5.76,F +coef_unavailable,-999.0,T +coef_low_income_escort,-0.6839180517907705,F +coef_low_income_shopping,-0.36787550353215537,F +coef_low_income_othmaint,-0.7732373479965099,F +coef_low_income_eatout,-0.6616337553879514,F +coef_low_income_othdiscr,-0.5329988579150085,F +coef_medium_low_income_othmaint,-0.21589267232884296,F +coef_high_income_shopping,-0.25087649228047343,F +coef_auto_deficient_iNM_tour_freq_1,-0.7868235054750288,F +coef_auto_deficient_iNM_tour_freq_2,-1.0455559653756108,F +coef_auto_deficient_iNM_tour_freq_3p,-1.1157343188741007,F +coef_num_children_escort,0.754137688219863,F +coef_num_full_time_workers_escort,0.3339757816794557,F +coef_hhsize_shopping,-0.1425515978162942,F +coef_hhsize_othmaint,-0.20501266076301913,F +coef_hhsize_eatout,-0.29701211013117823,F +coef_hhsize_othdiscr,-0.17093648977342044,F +coef_retired_hh_escort,-0.3294828681385463,F +coef_age_80p_escort,-0.5906910988839469,F +coef_age_80p_eatout,-0.40828362020671494,F +coef_age_80p_and_iNM_tour_freq_2,-0.2537832085563407,F +coef_log_total_hours_available_7am_to_10pm_escort,4.560726446110003,F +coef_log_total_hours_available_7am_to_10pm_shopping,2.7270167242212993,F +coef_log_total_hours_available_7am_to_10pm_othmaint,3.1187871560709866,F +coef_log_total_hours_available_7am_to_10pm_othdiscr,1.4177528382551887,F +coef_consecutive_hours_available_7am_to_10pm_escort,-0.14521052349224925,F +coef_consecutive_hours_available_7am_to_10pm_eatout,0.07669930567537751,F +coef_othdiscr_accessibility_eating_out,0.0878608354323988,F diff --git a/resident/configs/non_mandatory_tour_frequency_coefficients_PTYPE_SCHOOL.csv b/resident/configs/non_mandatory_tour_frequency_coefficients_PTYPE_SCHOOL.csv new file mode 100644 index 0000000..c11b3c4 --- /dev/null +++ b/resident/configs/non_mandatory_tour_frequency_coefficients_PTYPE_SCHOOL.csv @@ -0,0 +1,24 @@ +coefficient_name,value,constrain +coef_unavailable,-999.0,T +coef_escorting_tour,-9.690966426250665,F +coef_discretionary_tour,-7.5690966961253565,F +coef_shopping_tour,-8.975997622510478,F +coef_maintenance_tour,-13.246253630607148,F +coef_visiting_or_social_tour,-14.6311575770962,F +coef_eating_out_tour,-6.7325400979612295,F +coef_total_indNM_tours_is_0_prior_tours,1.106305308197831,F +coef_total_indNM_tours_is_1,1.747170199655668,F +coef_total_indNM_tours_is_2p,1.3703636107226296,F +coef_has_joint_tours_and_iNM_tour_freq_1,-0.9122450724032953,F +coef_num_full_time_workers_escort,-0.1578338449120939,F +coef_hhsize_escort,0.1551946995851052,F +coef_log_total_hours_available_7am_to_10pm_escort,2.0528332539104683,F +coef_log_total_hours_available_7am_to_10pm_shopping,1.960668017568013,F +coef_log_total_hours_available_7am_to_10pm_othmaint,3.377458574954797,F +coef_log_total_hours_available_7am_to_10pm_eatout,0.9922950368954243,F +coef_log_total_hours_available_7am_to_10pm_social,2.494008974014185,F +coef_log_total_hours_available_7am_to_10pm_othdiscr,1.7579258741166293,F +coef_consecutive_hours_available_7am_to_10pm_othmaint,-0.14726873682504554,F +coef_shopping_accessibility_escorting,0.10532543446648758,F +coef_shopping_accessibility_maintenance,0.1770030247224446,F +coef_othdiscr_accessibility_social,0.20759071360015494,F diff --git a/resident/configs/non_mandatory_tour_frequency_coefficients_PTYPE_UNIVERSITY.csv b/resident/configs/non_mandatory_tour_frequency_coefficients_PTYPE_UNIVERSITY.csv new file mode 100644 index 0000000..1cc9f79 --- /dev/null +++ b/resident/configs/non_mandatory_tour_frequency_coefficients_PTYPE_UNIVERSITY.csv @@ -0,0 +1,33 @@ +coefficient_name,value,constrain +coef_escorting_tour,-8.498374870237669,F +coef_discretionary_tour,-4.812463904304644,F +coef_shopping_tour,-12.693463829895798,F +coef_maintenance_tour,-11.058840934154171,F +coef_visiting_or_social_tour,-4.459306542169845,F +coef_eating_out_tour,-6.157924697688962,F +coef_total_indNM_tours_is_0_prior_tours,4.999764921270562,F +coef_total_indNM_tours_is_1,5.705610555681865,F +coef_total_indNM_tours_is_2,6.832163735527388,F +coef_total_indNM_tours_is_3p,7.208937046301491,F +coef_unavailable,-999.0,T +coef_zero_auto_iNM_tour_freq_1,-1.4891145894268167,F +coef_zero_auto_iNM_tour_freq_2p,-2.1435322461710236,F +coef_auto_deficient_iNM_tour_freq_1,-0.4607257760453242,F +coef_auto_deficient_iNM_tour_freq_2,-0.69349273445043,F +coef_auto_deficient_iNM_tour_freq_3p,-1.2029607161678721,F +coef_num_children_escort,0.7701459374658138,F +coef_num_full_time_workers_eatout,0.42257286721446335,F +coef_hhsize_eatout,-0.38977424932597926,F +coef_age_18to24_shopping,-0.404262220153878,F +coef_age_18to24_eatout,0.6249447012564978,F +coef_age_24to35_shopping,-0.28923438967158355,F +coef_age_50to79_othdiscr,0.3076225296007352,F +coef_log_total_hours_available_7am_to_10pm_escort,1.8792910621880181,F +coef_log_total_hours_available_7am_to_10pm_shopping,3.0367849074959543,F +coef_log_total_hours_available_7am_to_10pm_othmaint,2.880344751247897,F +coef_log_total_hours_available_7am_to_10pm_eatout,0.8062448674910451,F +coef_consecutive_hours_available_7am_to_10pm_othdiscr,0.11890563179293542,F +coef_shopping_accessibility_shopping,0.1616020726995222,F +coef_shopping_accessibility_maintenance,0.07126681192848552,F +coef_othdiscr_accessibility_eating_out,0.08890904673851951,F +coef_othdiscr_accessibility_othdiscr,0.04477521803529839,F diff --git a/resident/configs/non_mandatory_tour_frequency_extension_probs.csv b/resident/configs/non_mandatory_tour_frequency_extension_probs.csv new file mode 100644 index 0000000..8308852 --- /dev/null +++ b/resident/configs/non_mandatory_tour_frequency_extension_probs.csv @@ -0,0 +1,193 @@ +ptype,has_mandatory_tour,has_joint_tour,nonmandatory_tour_type,0_tours,1_tours,2_tours +1,0,0,1,0.71087271,0.886861358,1 +2,0,0,1,0.640712953,0.863555773,1 +3,0,0,1,0.726456335,0.726456335,1 +4,0,0,1,0.665679012,0.903816044,1 +5,0,0,1,0.861430335,0.92284115,1 +6,0,0,1,1,1,1 +7,0,0,1,0,1,1 +8,0,0,1,0.754632385,0.918132897,1 +1,1,0,1,0.824975545,1,1 +2,1,0,1,0.881285721,0.964131273,1 +3,1,0,1,0.856158692,1,1 +4,1,0,1,1,1,1 +5,1,0,1,1,1,1 +6,1,0,1,1,1,1 +7,1,0,1,1,1,1 +8,1,0,1,1,1,1 +1,0,0,2,0.918802841,1,1 +2,0,0,2,1,1,1 +3,0,0,2,1,1,1 +4,0,0,2,1,1,1 +5,0,0,2,0.911351921,0.964395496,1 +6,0,0,2,1,1,1 +7,0,0,2,1,1,1 +8,0,0,2,0.91707294,1,1 +1,1,0,2,1,1,1 +2,1,0,2,1,1,1 +3,1,0,2,1,1,1 +4,1,0,2,1,1,1 +5,1,0,2,1,1,1 +6,1,0,2,1,1,1 +7,1,0,2,1,1,1 +8,1,0,2,1,1,1 +1,0,0,3,0.885080064,0.942540032,1 +2,0,0,3,0.832615585,1,1 +3,0,0,3,1,1,1 +4,0,0,3,1,1,1 +5,0,0,3,0.874364173,0.972270724,1 +6,0,0,3,1,1,1 +7,0,0,3,1,1,1 +8,0,0,3,1,1,1 +1,1,0,3,1,1,1 +2,1,0,3,1,1,1 +3,1,0,3,1,1,1 +4,1,0,3,1,1,1 +5,1,0,3,1,1,1 +6,1,0,3,1,1,1 +7,1,0,3,1,1,1 +8,1,0,3,1,1,1 +1,0,0,4,1,1,1 +2,0,0,4,0.99296599,1,1 +3,0,0,4,1,1,1 +4,0,0,4,0.976072814,1,1 +5,0,0,4,0.956476086,0.993572522,1 +6,0,0,4,1,1,1 +7,0,0,4,1,1,1 +8,0,0,4,0.991144007,1,1 +1,1,0,4,0.99740908,1,1 +2,1,0,4,0.939184617,1,1 +3,1,0,4,1,1,1 +4,1,0,4,0.749851433,1,1 +5,1,0,4,1,1,1 +6,1,0,4,1,1,1 +7,1,0,4,1,1,1 +8,1,0,4,0.949874016,1,1 +1,0,0,5,1,1,1 +2,0,0,5,1,1,1 +3,0,0,5,1,1,1 +4,0,0,5,0.954541082,1,1 +5,0,0,5,0.955216408,1,1 +6,0,0,5,1,1,1 +7,0,0,5,1,1,1 +8,0,0,5,0.931183243,1,1 +1,1,0,5,0.970742777,1,1 +2,1,0,5,0.882901912,1,1 +3,1,0,5,1,1,1 +4,1,0,5,0.334132623,1,1 +5,1,0,5,1,1,1 +6,1,0,5,0.934804611,0.934804611,1 +7,1,0,5,0.965932685,1,1 +8,1,0,5,1,1,1 +1,0,0,6,1,1,1 +2,0,0,6,1,1,1 +3,0,0,6,1,1,1 +4,0,0,6,0.86514867,1,1 +5,0,0,6,0.896321835,1,1 +6,0,0,6,1,1,1 +7,0,0,6,1,1,1 +8,0,0,6,1,1,1 +1,1,0,6,1,1,1 +2,1,0,6,1,1,1 +3,1,0,6,1,1,1 +4,1,0,6,1,1,1 +5,1,0,6,1,1,1 +6,1,0,6,1,1,1 +7,1,0,6,1,1,1 +8,1,0,6,1,1,1 +1,0,1,1,1,1,1 +2,0,1,1,1,1,1 +3,0,1,1,1,1,1 +4,0,1,1,1,1,1 +5,0,1,1,1,1,1 +6,0,1,1,1,1,1 +7,0,1,1,1,1,1 +8,0,1,1,1,1,1 +1,1,1,1,1,1,1 +2,1,1,1,1,1,1 +3,1,1,1,1,1,1 +4,1,1,1,1,1,1 +5,1,1,1,1,1,1 +6,1,1,1,1,1,1 +7,1,1,1,1,1,1 +8,1,1,1,1,1,1 +1,0,1,2,1,1,1 +2,0,1,2,1,1,1 +3,0,1,2,1,1,1 +4,0,1,2,1,1,1 +5,0,1,2,1,1,1 +6,0,1,2,1,1,1 +7,0,1,2,1,1,1 +8,0,1,2,1,1,1 +1,1,1,2,1,1,1 +2,1,1,2,1,1,1 +3,1,1,2,1,1,1 +4,1,1,2,1,1,1 +5,1,1,2,1,1,1 +6,1,1,2,1,1,1 +7,1,1,2,1,1,1 +8,1,1,2,1,1,1 +1,0,1,3,1,1,1 +2,0,1,3,1,1,1 +3,0,1,3,1,1,1 +4,0,1,3,1,1,1 +5,0,1,3,1,1,1 +6,0,1,3,1,1,1 +7,0,1,3,1,1,1 +8,0,1,3,1,1,1 +1,1,1,3,1,1,1 +2,1,1,3,1,1,1 +3,1,1,3,1,1,1 +4,1,1,3,1,1,1 +5,1,1,3,1,1,1 +6,1,1,3,1,1,1 +7,1,1,3,1,1,1 +8,1,1,3,1,1,1 +1,0,1,4,1,1,1 +2,0,1,4,1,1,1 +3,0,1,4,1,1,1 +4,0,1,4,1,1,1 +5,0,1,4,1,1,1 +6,0,1,4,1,1,1 +7,0,1,4,1,1,1 +8,0,1,4,1,1,1 +1,1,1,4,1,1,1 +2,1,1,4,1,1,1 +3,1,1,4,1,1,1 +4,1,1,4,1,1,1 +5,1,1,4,1,1,1 +6,1,1,4,1,1,1 +7,1,1,4,1,1,1 +8,1,1,4,1,1,1 +1,0,1,5,1,1,1 +2,0,1,5,1,1,1 +3,0,1,5,1,1,1 +4,0,1,5,1,1,1 +5,0,1,5,1,1,1 +6,0,1,5,1,1,1 +7,0,1,5,1,1,1 +8,0,1,5,1,1,1 +1,1,1,5,1,1,1 +2,1,1,5,1,1,1 +3,1,1,5,1,1,1 +4,1,1,5,1,1,1 +5,1,1,5,1,1,1 +6,1,1,5,1,1,1 +7,1,1,5,1,1,1 +8,1,1,5,1,1,1 +1,0,1,6,1,1,1 +2,0,1,6,1,1,1 +3,0,1,6,1,1,1 +4,0,1,6,1,1,1 +5,0,1,6,1,1,1 +6,0,1,6,1,1,1 +7,0,1,6,1,1,1 +8,0,1,6,1,1,1 +1,1,1,6,1,1,1 +2,1,1,6,1,1,1 +3,1,1,6,1,1,1 +4,1,1,6,1,1,1 +5,1,1,6,1,1,1 +6,1,1,6,1,1,1 +7,1,1,6,1,1,1 +8,1,1,6,1,1,1 diff --git a/resident/configs/non_mandatory_tour_scheduling.yaml b/resident/configs/non_mandatory_tour_scheduling.yaml new file mode 100644 index 0000000..1850c12 --- /dev/null +++ b/resident/configs/non_mandatory_tour_scheduling.yaml @@ -0,0 +1,92 @@ +LOGIT_TYPE: MNL + +preprocessor: + SPEC: non_mandatory_tour_scheduling_annotate_tours_preprocessor + DF: non_mandatory_tours + TABLES: + - land_use + - joint_tour_participants + - school_escort_tours + +SIMULATE_CHOOSER_COLUMNS: + - home_zone_id + - age + - female + - adult + - ptype + - has_pre_school_child_with_mandatory + - has_driving_age_child_with_mandatory + - is_income_less25K + - is_income_25K_to_60K + - is_income_60K_to_120K + - auto_ownership + - num_children + - num_adults + - num_retired_adults + - hhsize + - num_mand + - num_joint_tours + - num_escort_tours + - num_non_escort_tours + - num_add_shop_maint_tours + - num_add_soc_discr_tours + +LOGSUM_SETTINGS: tour_mode_choice.yaml + +# map : : +# segmentation of tours for processing may differ from segmentation of spec +# vectorize_tour_scheduling iterates over +# using to choose spec for scheduling alt chice +# using for logsums (if enabled) +# tour_mode_choice segmentation for logsums is: eatout,escort,othdiscr,othmaint,shopping,social,school,univ,work,atwork +TOUR_SPEC_SEGMENTS: + escort: escort + shopping: shopping + eatout: eatout + othdiscr: othdiscr + othmaint: othmaint + social: social + +# spec keyed by +SPEC_SEGMENTS: + escort: + 'SPEC': tour_scheduling_nonmandatory_escort.csv + 'COEFFICIENTS': tour_scheduling_nonmandatory_escort_coefficients.csv + shopping: + 'SPEC': tour_scheduling_nonmandatory_shopping.csv + 'COEFFICIENTS': tour_scheduling_nonmandatory_shopping_coefficients.csv + eatout: + 'SPEC': tour_scheduling_nonmandatory_eatout.csv + 'COEFFICIENTS': tour_scheduling_nonmandatory_eatout_coefficients.csv + othdiscr: + 'SPEC': tour_scheduling_nonmandatory_othdiscr.csv + 'COEFFICIENTS': tour_scheduling_nonmandatory_othdiscr_coefficients.csv + othmaint: + 'SPEC': tour_scheduling_nonmandatory_othmaint.csv + 'COEFFICIENTS': tour_scheduling_nonmandatory_othmaint_coefficients.csv + social: + 'SPEC': tour_scheduling_nonmandatory_social.csv + 'COEFFICIENTS': tour_scheduling_nonmandatory_social_coefficients.csv + +## alts preprocessor keyed by +#ALTS_PREPROCESSOR: +# escort: +# SPEC: tour_scheduling_nonmandatory_escort_annotate_alts_preprocessor.csv +# DF: alt_tdd +# shopping: +# SPEC: non_mandatory_tour_scheduling_shopping_annotate_alts_preprocessor.csv +# DF: alt_tdd +# eatout: +# SPEC: non_mandatory_tour_scheduling_annotate_alts_preprocessor.csv +# DF: alt_tdd +# othdiscr: +# SPEC: non_mandatory_tour_scheduling_annotate_alts_preprocessor.csv +# DF: alt_tdd +# othmaint: +# SPEC: non_mandatory_tour_scheduling_annotate_alts_preprocessor.csv +# DF: alt_tdd +# social: +# SPEC: non_mandatory_tour_scheduling_annotate_alts_preprocessor.csv +# DF: alt_tdd +# +DESTINATION_FOR_TOUR_PURPOSE: destination diff --git a/resident/configs/non_mandatory_tour_scheduling_annotate_tours_preprocessor.csv b/resident/configs/non_mandatory_tour_scheduling_annotate_tours_preprocessor.csv new file mode 100644 index 0000000..fb82384 --- /dev/null +++ b/resident/configs/non_mandatory_tour_scheduling_annotate_tours_preprocessor.csv @@ -0,0 +1,11 @@ +Description,Target,Expression +#,, +number of person joint tours,num_person_joint_tours,"reindex_i(joint_tour_participants.groupby('person_id').size(), non_mandatory_tours.person_id)" +#,, +,origin_to_destination_distance,"skim_dict.lookup(non_mandatory_tours.origin, non_mandatory_tours.destination, ('SOV_M_DIST', 'MD'))" +# included for school escorting model,, +flag to denote outbound school escort tours,is_outbound_school_escort_tour,"non_mandatory_tours.index.isin(school_escort_tours[school_escort_tours['school_escort_direction'] == 'outbound'].index)" +flag to denote inbound school escort tours,is_inbound_school_escort_tour,"non_mandatory_tours.index.isin(school_escort_tours[school_escort_tours['school_escort_direction'] == 'inbound'].index)" +school escort tour start time,school_escort_tour_start,"reindex(school_escort_tours.start.astype(int), non_mandatory_tours.index)" +school escort tour next start time,school_escort_tour_next_start,"reindex(school_escort_tours.next_pure_escort_start.astype(int), non_mandatory_tours.index)" +school escort tour end time,school_escort_tour_end,"reindex(school_escort_tours.end.astype(int), non_mandatory_tours.index)" \ No newline at end of file diff --git a/resident/configs/park_and_ride_lot_choice.csv b/resident/configs/park_and_ride_lot_choice.csv new file mode 100644 index 0000000..592de66 --- /dev/null +++ b/resident/configs/park_and_ride_lot_choice.csv @@ -0,0 +1,16 @@ +Label,Description,Expression,coefficient +# Driving utilities,,, +util_drive_time,drive time from origin to lot,@(olt_skims['SOV_M_TIME'] + lot_skims['SOV_M_TIME']),coef_ivt_work +util_drive_cost,auto operating cost,@df.sov_auto_op_cost * (olt_skims['SOV_M_DIST'] + lot_skims['SOV_M_DIST']) / df.cost_sensitivity,coef_income_work +# Transit utilties,,, +util_first_wait,first wait time,@(ldt_skims['WTW_FWT'] + dlt_skims['WTW_FWT']),coef_wait_work +util_xfer_wait,transfer wait time,@(ldt_skims['WTW_XWT'] + dlt_skims['WTW_XWT']),coef_xwait_work +util_walk_time,walk time,@(ldt_skims['WTW_AUX'] + dlt_skims['WTW_AUX']),coef_xwalk_work +util_ivtt,in vehcile transit time,@(ldt_skims['WTW_TIV'] + dlt_skims['WTW_TIV']),coef_ivt_work +util_xfer_penalty,transfer penalty,"@(-23+23*np.exp(0.414*np.clip(ldt_skims['WTW_XFR'], a_min=None,a_max=4))) + (-23+23*np.exp(0.414*np.clip(dlt_skims['WTW_XFR'], a_min=None,a_max=4)))",coef_xfer_work +# FIXME need fares,,, +#util_fare,transit fare,"@df.transitSubsidyPassDiscount * (odt_skims['WTW_FARE'] + dot_skims['WTW_FARE']) * 100*df.num_participants/df.cost_sensitivity",coef_income_work +util_parking_cost,parking cost,@df['PNR_PRKCST'],coef_income_work +# Size Term,,, +util_size_variable,Size variable,@df['PNR_CAP_TOTAL'].apply(np.log1p),1 +util_capacity,lot capacity availability,@df['pnr_lot_full'],coef_unavailable diff --git a/resident/configs/park_and_ride_lot_choice.yaml b/resident/configs/park_and_ride_lot_choice.yaml new file mode 100644 index 0000000..ad721e5 --- /dev/null +++ b/resident/configs/park_and_ride_lot_choice.yaml @@ -0,0 +1,35 @@ +SPEC: park_and_ride_lot_choice.csv + +COEFFICIENTS: park_and_ride_lot_choice_coeffs.csv + +preprocessor: + SPEC: park_and_ride_lot_choice_preprocessor.csv + DF: df + TABLES: + - land_use + - vehicles + - tours + + +LANDUSE_PNR_SPACES_COLUMN: PNR_CAP_TOTAL + +# will check the tour destination to make sure transit access is available +# if no transit access, then no PNR lot will be available and could get +# zero probabilities error +TRANSIT_SKIMS_FOR_ELIGIBILITY: + - WTW_TIV__PM + +# Precompute destination eligibility for tours +# LANDUSE_COL_FOR_PNR_ELIGIBLE_DEST: is_pnr_eligible_destination + +ITERATE_WITH_TOUR_MODE_CHOICE: True +MAX_ITERATIONS: 5 +ACCEPTED_TOLERANCE: 0.95 +RESAMPLE_STRATEGY: latest +PARK_AND_RIDE_MODES: # should match tour_mode_choice configs + - PNR_TRANSIT + +compute_settings: + protect_columns: + - out_period + - in_period diff --git a/resident/configs/park_and_ride_lot_choice_coeffs.csv b/resident/configs/park_and_ride_lot_choice_coeffs.csv new file mode 100644 index 0000000..54457d7 --- /dev/null +++ b/resident/configs/park_and_ride_lot_choice_coeffs.csv @@ -0,0 +1,10 @@ +coefficient_name,value,constrain +coef_zero,0.0,T +# took work coefficients from tour mode choice model,, +coef_unavailable,-999,F +coef_ivt_work,-0.016,F +coef_income_work,-0.625,F +coef_xwalk_work,-0.04,F +coef_wait_work,-0.024,F +coef_xfer_work,-0.024,F +coef_xwait_work,-0.032,F diff --git a/resident/configs/park_and_ride_lot_choice_preprocessor.csv b/resident/configs/park_and_ride_lot_choice_preprocessor.csv new file mode 100644 index 0000000..f3063eb --- /dev/null +++ b/resident/configs/park_and_ride_lot_choice_preprocessor.csv @@ -0,0 +1,16 @@ +Description,Target,Expression, +local,_DF_IS_TOUR,'tour_type' in df.columns, +,is_atwork_subtour,(df.tour_category == 'atwork') if _DF_IS_TOUR else False, +,_parent_tour_veh,"reindex(tours['selected_vehicle'], df['parent_tour_id']) if 'selected_vehicle' in df.columns else np.nan", +,_parent_tour_mode,"reindex(tours['tour_mode'], df['parent_tour_id']) if 'tour_mode' in df.columns else np.nan", +,_sov_veh_option,"np.where(is_atwork_subtour, _parent_tour_veh, df.get('vehicle_occup_1', np.nan))", +,num_participants,df.number_of_participants if _DF_IS_TOUR else 1, +# Auto operating costs,,, +,sov_auto_op_cost,"reindex(vehicles.groupby('vehicle_type')['auto_operating_cost'].mean(), pd.Series(_sov_veh_option, df.index)) if 'vehicle_occup_1' in df.columns else np.nan", +,sov_auto_op_cost,"np.where(sov_auto_op_cost.isna() | (pd.Series(_sov_veh_option, df.index) == 'non_hh_veh'), costPerMile, sov_auto_op_cost)", +# cost coef,,, +,_income_exponent,"np.where(df.get('tour_type') == 'work', 0.6, 0.5) if _DF_IS_TOUR else 0.5", +,cost_sensitivity,"np.maximum(df.income,1000).pow(_income_exponent)", +transit pass subsidy,_transit_pass_subsidy,"df.get('transit_pass_subsidy', False)", +transit pass ownership,_transit_pass_ownership,"df.get('transit_pass_ownership', False)", +transit subsidy pass discount, transitSubsidyPassDiscount,"np.where(_transit_pass_subsidy | _transit_pass_ownership,0,1)", \ No newline at end of file diff --git a/resident/configs/parking_location_choice.csv b/resident/configs/parking_location_choice.csv new file mode 100644 index 0000000..9d65aa2 --- /dev/null +++ b/resident/configs/parking_location_choice.csv @@ -0,0 +1,12 @@ +Description,Expression,segment_1,segment_2,segment_3 +Person is worker,_person_is_worker@((df.ptype == 1) | (df.ptype == 2)),1,1,1 +Trip is work purpose,_work_trip@(df.purpose == 'work'),1,1,1 +Parking to destination distance skim,_walk_distance@pd_skims['DISTWALK'],1,1,1 +#,,,, +# actual utility terms starts from here,,,, +Log of parking size,"@np.where(df.PRKSPACES>0,np.log(df.PRKSPACES),-999)",1,1,1 +Cost work trip,"@np.where(df.purpose == 'work', df.cost_parking, 0)",coef_cost_work_trip,coef_cost_work_trip,coef_cost_work_trip +Cost other trip,"@np.where(df.purpose != 'work', df.cost_parking, 0)",coef_cost_other_trip,coef_cost_other_trip,coef_cost_other_trip +Walk distance,@_walk_distance,coef_walk_distance,coef_walk_distance,coef_walk_distance +Walk Unavailability,"@np.where(_walk_distance == 0, 1, 0)",coef_unavailable,coef_unavailable,coef_unavailable +Require parking zone to be less than 0.75 mile from dest,"@np.where(_walk_distance > 0.75, 1, 0) * (df.destination != df.parking_zone)",coef_unavailable,coef_unavailable,coef_unavailable diff --git a/resident/configs/parking_location_choice.yaml b/resident/configs/parking_location_choice.yaml new file mode 100644 index 0000000..625f4ea --- /dev/null +++ b/resident/configs/parking_location_choice.yaml @@ -0,0 +1,28 @@ +SPECIFICATION: parking_location_choice.csv + +COEFFICIENTS: parking_location_choice_coeffs.csv + +PREPROCESSOR: + SPEC: parking_location_choice_annotate_trips_preprocessor + DF: trips_merged + TABLES: + - land_use + - persons + - tours + +# boolean column to filter choosers (True means keep) +CHOOSER_FILTER_COLUMN_NAME: is_park_eligible +CHOOSER_SEGMENT_COLUMN_NAME: parking_segment + +ALTERNATIVE_FILTER_COLUMN_NAME: is_parking_zone +ALT_DEST_COL_NAME: parking_zone + +TRIP_DEPARTURE_PERIOD: depart + +TRIP_ORIGIN: origin +TRIP_DESTINATION: destination + +AUTO_MODES: + - DRIVEALONE + - SHARED2 + - SHARED3 diff --git a/resident/configs/parking_location_choice_annotate_trips_preprocessor.csv b/resident/configs/parking_location_choice_annotate_trips_preprocessor.csv new file mode 100644 index 0000000..24dd814 --- /dev/null +++ b/resident/configs/parking_location_choice_annotate_trips_preprocessor.csv @@ -0,0 +1,14 @@ +Description,Target,Expression +Person Type,ptype,"reindex(persons.ptype, df.person_id)" +Trip mode is drive,drive_trip,"df.trip_mode.isin(['DRIVEALONE', 'SHARED2', 'SHARED3'])" +# putting all trips into the same parking segment,, +Parking segment,parking_segment,"np.where(df['tour_id'] % 3 == 0, 'segment_1',np.where(df['tour_id'] % 3 == 1, 'segment_2','segment_3'))" +Parking eligible trip,is_park_eligible,"(drive_trip & (df.purpose != 'home') & (reindex(land_use.parking_type,df.destination)==1))" +Next Trip Departure,_next_trip_depart,df.groupby('tour_id')['depart'].shift() +Activity duration,activity_duration,"np.where(_next_trip_depart.isna(), 0, _next_trip_depart - df.depart)" +,_tour_participants,df.tour_id.map(tours.number_of_participants) +,_is_joint,(_tour_participants > 1) +,_cost_parking,0 +,_cost_parking,"_cost_parking + df.trip_mode.isin(['DRIVEALONE']) * (df.parkingCost * df.autoParkingCostFactor)" +,_cost_parking,"_cost_parking + df.trip_mode.isin(['SHARED2']) * (df.parkingCost * df.autoParkingCostFactor)/np.where(_is_joint,_tour_participants,OCC_SHARED2)" +,cost_parking,"_cost_parking + df.trip_mode.isin(['SHARED3']) * (df.parkingCost * df.autoParkingCostFactor)/np.where(_is_joint,_tour_participants,OCC_SHARED3)" \ No newline at end of file diff --git a/resident/configs/parking_location_choice_coeffs.csv b/resident/configs/parking_location_choice_coeffs.csv new file mode 100644 index 0000000..0edccfb --- /dev/null +++ b/resident/configs/parking_location_choice_coeffs.csv @@ -0,0 +1,5 @@ +coefficient_name,value,constrain +coef_unavailable,-9999,T +coef_walk_distance,-11.8,F +coef_cost_work_trip,-0.0072,F +coef_cost_other_trip,-0.0041,F diff --git a/resident/configs/school_escorting.yaml b/resident/configs/school_escorting.yaml new file mode 100644 index 0000000..b4524a9 --- /dev/null +++ b/resident/configs/school_escorting.yaml @@ -0,0 +1,49 @@ + +OUTBOUND_SPEC: school_escorting_outbound.csv +OUTBOUND_COEFFICIENTS: school_escorting_coefficients_outbound.csv + +INBOUND_SPEC: school_escorting_inbound.csv +INBOUND_COEFFICIENTS: school_escorting_coefficients_inbound.csv + +OUTBOUND_COND_SPEC: school_escorting_outbound_cond.csv +OUTBOUND_COND_COEFFICIENTS: school_escorting_coefficients_outbound_cond.csv + +ALTS: school_escorting_alts.csv + +LOGIT_TYPE: MNL + +NUM_ESCORTEES: 3 +NUM_CHAPERONES: 2 + +GENDER_COLUMN: SEX + +preprocessor_outbound: + SPEC: school_escorting_preprocessor_outbound + DF: df + TABLES: + - persons + - tours + +preprocessor_inbound: + SPEC: school_escorting_preprocessor_inbound + DF: df + TABLES: + - persons + - tours + +preprocessor_outbound_cond: + SPEC: school_escorting_preprocessor_outbound + DF: df + TABLES: + - persons + - tours + +SIMULATE_CHOOSER_COLUMNS: + - home_zone_id + - income + - auto_ownership + - num_workers + +CONSTANTS: + max_bin_difference_between_departure_times: 1 + mins_per_time_bin: 30 diff --git a/resident/configs/school_escorting_alts.csv b/resident/configs/school_escorting_alts.csv new file mode 100644 index 0000000..eca6617 --- /dev/null +++ b/resident/configs/school_escorting_alts.csv @@ -0,0 +1,158 @@ +Alt,bundle1,bundle2,bundle3,chauf1,chauf2,chauf3,nbund1,nbund2,nbundles,nrs1,npe1,nrs2,npe2,Description +1,0,0,0,0,0,0,0,0,0,0,0,0,0,no one is escorted +2,1,0,0,1,0,0,1,0,1,1,0,0,0,child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing +3,1,0,0,2,0,0,1,0,1,0,1,0,0,child 1 is escorted in bundle 1 by chauffeur 1 as pure escort +4,1,0,0,3,0,0,0,1,1,0,0,1,0,child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing +5,1,0,0,4,0,0,0,1,1,0,0,0,1,child 1 is escorted in bundle 1 by chauffeur 2 as pure escort +6,0,1,0,0,1,0,1,0,1,1,0,0,0,child 2 is escorted in bundle 1 by chauffeur 1 as ride sharing +7,0,1,0,0,2,0,1,0,1,0,1,0,0,child 2 is escorted in bundle 1 by chauffeur 1 as pure escort +8,0,1,0,0,3,0,0,1,1,0,0,1,0,child 2 is escorted in bundle 1 by chauffeur 2 as ride sharing +9,0,1,0,0,4,0,0,1,1,0,0,0,1,child 2 is escorted in bundle 1 by chauffeur 2 as pure escort +10,0,0,1,0,0,1,1,0,1,1,0,0,0,child 3 is escorted in bundle 1 by chauffeur 1 as ride sharing +11,0,0,1,0,0,2,1,0,1,0,1,0,0,child 3 is escorted in bundle 1 by chauffeur 1 as pure escort +12,0,0,1,0,0,3,0,1,1,0,0,1,0,child 3 is escorted in bundle 1 by chauffeur 2 as ride sharing +13,0,0,1,0,0,4,0,1,1,0,0,0,1,child 3 is escorted in bundle 1 by chauffeur 2 as pure escort +14,1,1,0,1,1,0,1,0,1,1,0,0,0,child 1 and 2 are escorted in bundle 1 by chauffeur 1 as ride sharing +15,1,1,0,2,2,0,1,0,1,0,1,0,0,child 1 and 2 are escorted in bundle 1 by chauffeur 1 as pure escort +16,1,1,0,3,3,0,0,1,1,0,0,1,0,child 1 and 2 are escorted in bundle 1 by chauffeur 2 as ride sharing +17,1,1,0,4,4,0,0,1,1,0,0,0,1,child 1 and 2 are escorted in bundle 1 by chauffeur 2 as pure escort +18,1,0,1,1,0,1,1,0,1,1,0,0,0,child 1 and 3 are escorted in bundle 1 by chauffeur 1 as ride sharing +19,1,0,1,2,0,2,1,0,1,0,1,0,0,child 1 and 3 are escorted in bundle 1 by chauffeur 1 as pure escort +20,1,0,1,3,0,3,0,1,1,0,0,1,0,child 1 and 3 are escorted in bundle 1 by chauffeur 2 as ride sharing +21,1,0,1,4,0,4,0,1,1,0,0,0,1,child 1 and 3 are escorted in bundle 1 by chauffeur 2 as pure escort +22,0,1,1,0,1,1,1,0,1,1,0,0,0,child 2 and 3 are escorted in bundle 1 by chauffeur 1 as ride sharing +23,0,1,1,0,2,2,1,0,1,0,1,0,0,child 2 and 3 are escorted in bundle 1 by chauffeur 1 as pure escort +24,0,1,1,0,3,3,0,1,1,0,0,1,0,child 2 and 3 are escorted in bundle 1 by chauffeur 2 as ride sharing +25,0,1,1,0,4,4,0,1,1,0,0,0,1,child 2 and 3 are escorted in bundle 1 by chauffeur 2 as pure escort +26,1,2,0,1,2,0,2,0,2,1,1,0,0,child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing and child 2 is escorted in bundle 2 by chauffeur 1 as pure escort +27,1,2,0,1,3,0,1,1,2,1,0,1,0,child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing and child 2 is escorted in bundle 2 by chauffeur 2 as ride sharing +28,1,2,0,1,4,0,1,1,2,1,0,0,1,child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing and child 2 is escorted in bundle 2 by chauffeur 2 as pure escort +29,1,2,0,2,1,0,2,0,2,1,1,0,0,child 1 is escorted in bundle 1 by chauffeur 1 as pure escort and child 2 is escorted in bundle 2 by chauffeur 1 as ride sharing +30,1,2,0,2,2,0,2,0,2,0,2,0,0,child 1 is escorted in bundle 1 by chauffeur 1 as pure escort and child 2 is escorted in bundle 2 by chauffeur 1 as pure escort +31,1,2,0,2,3,0,1,1,2,0,1,1,0,child 1 is escorted in bundle 1 by chauffeur 1 as pure escort and child 2 is escorted in bundle 2 by chauffeur 2 as ride sharing +32,1,2,0,2,4,0,1,1,2,0,1,0,1,child 1 is escorted in bundle 1 by chauffeur 1 as pure escort and child 2 is escorted in bundle 2 by chauffeur 2 as pure escort +33,1,2,0,3,1,0,1,1,2,1,0,1,0,child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing and child 2 is escorted in bundle 2 by chauffeur 1 as ride sharing +34,1,2,0,3,2,0,1,1,2,0,1,1,0,child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing and child 2 is escorted in bundle 2 by chauffeur 1 as pure escort +35,1,2,0,3,4,0,0,2,2,0,0,1,1,child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing and child 2 is escorted in bundle 2 by chauffeur 2 as pure escort +36,1,2,0,4,1,0,1,1,2,1,0,0,1,child 1 is escorted in bundle 1 by chauffeur 2 as pure escort and child 2 is escorted in bundle 2 by chauffeur 1 as ride sharing +37,1,2,0,4,2,0,1,1,2,0,1,0,1,child 1 is escorted in bundle 1 by chauffeur 2 as pure escort and child 2 is escorted in bundle 2 by chauffeur 1 as pure escort +38,1,2,0,4,3,0,0,2,2,0,0,1,1,child 1 is escorted in bundle 1 by chauffeur 2 as pure escort and child 2 is escorted in bundle 2 by chauffeur 2 as ride sharing +39,1,2,0,4,4,0,0,2,2,0,0,0,2,child 1 is escorted in bundle 1 by chauffeur 2 as pure escort and child 2 is escorted in bundle 2 by chauffeur 2 as pure escort +40,1,0,2,1,0,2,2,0,2,1,1,0,0,child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 1 as pure escorting +41,1,0,2,1,0,3,1,1,2,1,0,1,0,child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 2 as ride sharing +42,1,0,2,1,0,4,1,1,2,1,0,0,1,child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 2 as pure escorting +43,1,0,2,2,0,1,2,0,2,1,1,0,0,child 1 is escorted in bundle 1 by chauffeur 1 as pure escort and child 3 is escorted in bundle 2 by chauffeur 1 as ride sharing +44,1,0,2,2,0,2,2,0,2,0,2,0,0,child 1 is escorted in bundle 1 by chauffeur 1 as pure escort and child 3 is escorted in bundle 2 by chauffeur 1 as pure escorting +45,1,0,2,2,0,3,1,1,2,0,1,1,0,child 1 is escorted in bundle 1 by chauffeur 1 as pure escort and child 3 is escorted in bundle 2 by chauffeur 2 as ride sharing +46,1,0,2,2,0,4,1,1,2,0,1,0,1,child 1 is escorted in bundle 1 by chauffeur 1 as pure escort and child 3 is escorted in bundle 2 by chauffeur 2 as pure escorting +47,1,0,2,3,0,1,1,1,2,1,0,1,0,child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 1 as ride sharing +48,1,0,2,3,0,2,1,1,2,0,1,1,0,child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 1 as pure escorting +49,1,0,2,3,0,4,0,2,2,0,0,1,1,child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 2 as pure escorting +50,1,0,2,4,0,1,1,1,2,1,0,0,1,child 1 is escorted in bundle 1 by chauffeur 2 as pure escort and child 3 is escorted in bundle 2 by chauffeur 1 as ride sharing +51,1,0,2,4,0,2,1,1,2,0,1,0,1,child 1 is escorted in bundle 1 by chauffeur 2 as pure escort and child 3 is escorted in bundle 2 by chauffeur 1 as pure escorting +52,1,0,2,4,0,3,0,2,2,0,0,1,1,child 1 is escorted in bundle 1 by chauffeur 2 as pure escort and child 3 is escorted in bundle 2 by chauffeur 2 as ride sharing +53,1,0,2,4,0,4,0,2,2,0,0,0,2,child 1 is escorted in bundle 1 by chauffeur 2 as pure escort and child 3 is escorted in bundle 2 by chauffeur 2 as pure escorting +54,0,1,2,0,1,2,2,0,2,1,1,0,0,child 2 is escorted in bundle 1 by chauffeur 1 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 1 as pure escorting +55,0,1,2,0,1,3,1,1,2,1,0,1,0,child 2 is escorted in bundle 1 by chauffeur 1 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 2 as ride sharing +56,0,1,2,0,1,4,1,1,2,1,0,0,1,child 2 is escorted in bundle 1 by chauffeur 1 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 2 as pure escorting +57,0,1,2,0,2,1,2,0,2,1,1,0,0,child 2 is escorted in bundle 1 by chauffeur 1 as pure escort and child 3 is escorted in bundle 2 by chauffeur 1 as ride sharing +58,0,1,2,0,2,2,2,0,2,0,2,0,0,child 2 is escorted in bundle 1 by chauffeur 1 as pure escort and child 3 is escorted in bundle 2 by chauffeur 1 as pure escorting +59,0,1,2,0,2,3,1,1,2,0,1,1,0,child 2 is escorted in bundle 1 by chauffeur 1 as pure escort and child 3 is escorted in bundle 2 by chauffeur 2 as ride sharing +60,0,1,2,0,2,4,1,1,2,0,1,0,1,child 2 is escorted in bundle 1 by chauffeur 1 as pure escort and child 3 is escorted in bundle 2 by chauffeur 2 as pure escorting +61,0,1,2,0,3,1,1,1,2,1,0,1,0,child 2 is escorted in bundle 1 by chauffeur 2 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 1 as ride sharing +62,0,1,2,0,3,2,1,1,2,0,1,1,0,child 2 is escorted in bundle 1 by chauffeur 2 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 1 as pure escorting +63,0,1,2,0,3,4,0,2,2,0,0,1,1,child 2 is escorted in bundle 1 by chauffeur 2 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 2 as pure escorting +64,0,1,2,0,4,1,1,1,2,1,0,0,1,child 2 is escorted in bundle 1 by chauffeur 2 as pure escort and child 3 is escorted in bundle 2 by chauffeur 1 as ride sharing +65,0,1,2,0,4,2,1,1,2,0,1,0,1,child 2 is escorted in bundle 1 by chauffeur 2 as pure escort and child 3 is escorted in bundle 2 by chauffeur 1 as pure escorting +66,0,1,2,0,4,3,0,2,2,0,0,1,1,child 2 is escorted in bundle 1 by chauffeur 2 as pure escort and child 3 is escorted in bundle 2 by chauffeur 2 as ride sharing +67,0,1,2,0,4,4,0,2,2,0,0,0,2,child 2 is escorted in bundle 1 by chauffeur 2 as pure escort and child 3 is escorted in bundle 2 by chauffeur 2 as pure escorting +68,1,1,1,1,1,1,1,0,1,1,0,0,0,"child 1, 2 and 3 are escorted in bundle 1 by chauffeur 1 as ride sharing" +69,1,1,1,2,2,2,1,0,1,0,1,0,0,"child 1, 2 and 3 are escorted in bundle 1 by chauffeur 1 as pure escort" +70,1,1,1,3,3,3,0,1,1,0,0,1,0,"child 1, 2 and 3 are escorted in bundle 1 by chauffeur 2 as ride sharing" +71,1,1,1,4,4,4,0,1,1,0,0,0,1,"child 1, 2 and 3 are escorted in bundle 1 by chauffeur 2 as pure escort" +72,1,2,3,1,2,2,3,0,3,1,2,0,0,"child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing, child 2 is escorted in bundle 2 by chauffeur 1 as pure escort and child 3 is escorted in bundle 3 by chauffeur 1 as pure escort" +73,1,2,3,1,2,3,2,1,3,1,1,1,0,"child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing, child 2 is escorted in bundle 2 by chauffeur 1 as pure escort and child 3 is escorted in bundle 3 by chauffeur 2 as ride sharing" +74,1,2,3,1,2,4,2,1,3,1,1,0,1,"child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing, child 2 is escorted in bundle 2 by chauffeur 1 as pure escort and child 3 is escorted in bundle 3 by chauffeur 2 as pure escort" +75,1,2,3,1,3,2,2,1,3,1,1,1,0,"child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing, child 2 is escorted in bundle 2 by chauffeur 2 as ride sharing, and child 3 is escorted in bundle 3 by chauffeur 1 as pure escort" +76,1,2,3,1,3,4,1,2,3,1,0,1,1,"child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing, child 2 is escorted in bundle 2 by chauffeur 2 as ride sharing, and child 3 is escorted in bundle 3 by chauffeur 2 as pure escort" +77,1,2,3,1,4,2,2,1,3,1,1,0,1,"child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing, child 2 is escorted in bundle 2 by chauffeur 2 as pure escort and child 3 is escorted in bundle 3 by chauffeur 1 as pure escort" +78,1,2,3,1,4,3,1,2,3,1,0,1,1,"child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing, child 2 is escorted in bundle 2 by chauffeur 2 as pure escort and child 3 is escorted in bundle 3 by chauffeur 2 as ride sharing" +79,1,2,3,1,4,4,1,2,3,1,0,0,2,"child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing, child 2 is escorted in bundle 2 by chauffeur 2 as pure escort and child 3 is escorted in bundle 3 by chauffeur 2 as pure escort" +80,1,2,3,2,1,2,3,0,3,1,2,0,0,"child 1 is escorted in bundle 1 by chauffeur 1 as pure escort, child 2 is escorted in bundle 2 by chauffeur 1 as ride sharing, and child 3 is escorted in bundle 3 by chauffeur 1 as pure escort" +81,1,2,3,2,1,3,2,1,3,1,1,1,0,"child 1 is escorted in bundle 1 by chauffeur 1 as pure escort, child 2 is escorted in bundle 2 by chauffeur 1 as ride sharing, and child 3 is escorted in bundle 3 by chauffeur 2 as ride sharing" +82,1,2,3,2,1,4,2,1,3,1,1,0,1,"child 1 is escorted in bundle 1 by chauffeur 1 as pure escort, child 2 is escorted in bundle 2 by chauffeur 1 as ride sharing, and child 3 is escorted in bundle 3 by chauffeur 2 as pure escort" +83,1,2,3,2,2,1,3,0,3,1,2,0,0,"child 1 is escorted in bundle 1 by chauffeur 1 as pure escort, child 2 is escorted in bundle 2 by chauffeur 1 as pure escort and child 3 is escorted in bundle 3 by chauffeur 1 as ride sharing" +84,1,2,3,2,2,2,3,0,3,0,3,0,0,"child 1 is escorted in bundle 1 by chauffeur 1 as pure escort, child 2 is escorted in bundle 2 by chauffeur 1 as pure escort and child 3 is escorted in bundle 3 by chauffeur 1 as pure escort" +85,1,2,3,2,2,3,2,1,3,0,2,1,0,"child 1 is escorted in bundle 1 by chauffeur 1 as pure escort, child 2 is escorted in bundle 2 by chauffeur 1 as pure escort and child 3 is escorted in bundle 3 by chauffeur 2 as ride sharing" +86,1,2,3,2,2,4,2,1,3,0,2,0,1,"child 1 is escorted in bundle 1 by chauffeur 1 as pure escort, child 2 is escorted in bundle 2 by chauffeur 1 as pure escort and child 3 is escorted in bundle 3 by chauffeur 2 as pure escort" +87,1,2,3,2,3,1,2,1,3,1,1,1,0,"child 1 is escorted in bundle 1 by chauffeur 1 as pure escort, child 2 is escorted in bundle 2 by chauffeur 2 as ride sharing, and child 3 is escorted in bundle 3 by chauffeur 1 as ride sharing" +88,1,2,3,2,3,2,2,1,3,0,2,1,0,"child 1 is escorted in bundle 1 by chauffeur 1 as pure escort, child 2 is escorted in bundle 2 by chauffeur 2 as ride sharing, and child 3 is escorted in bundle 3 by chauffeur 1 as pure escort" +89,1,2,3,2,3,4,1,2,3,0,1,1,1,"child 1 is escorted in bundle 1 by chauffeur 1 as pure escort, child 2 is escorted in bundle 2 by chauffeur 2 as ride sharing, and child 3 is escorted in bundle 3 by chauffeur 2 as pure escort" +90,1,2,3,2,4,1,2,1,3,1,1,0,1,"child 1 is escorted in bundle 1 by chauffeur 1 as pure escort, child 2 is escorted in bundle 2 by chauffeur 2 as pure escort and child 3 is escorted in bundle 3 by chauffeur 1 as ride sharing" +91,1,2,3,2,4,2,2,1,3,0,2,0,1,"child 1 is escorted in bundle 1 by chauffeur 1 as pure escort, child 2 is escorted in bundle 2 by chauffeur 2 as pure escort and child 3 is escorted in bundle 3 by chauffeur 1 as pure escort" +92,1,2,3,2,4,3,1,2,3,0,1,1,1,"child 1 is escorted in bundle 1 by chauffeur 1 as pure escort, child 2 is escorted in bundle 2 by chauffeur 2 as pure escort and child 3 is escorted in bundle 3 by chauffeur 2 as ride sharing" +93,1,2,3,2,4,4,1,2,3,0,1,0,2,"child 1 is escorted in bundle 1 by chauffeur 1 as pure escort, child 2 is escorted in bundle 2 by chauffeur 2 as pure escort and child 3 is escorted in bundle 3 by chauffeur 2 as pure escort" +94,1,2,3,3,1,2,2,1,3,1,1,1,0,"child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing, child 2 is escorted in bundle 2 by chauffeur 1 as ride sharing, and child 3 is escorted in bundle 3 by chauffeur 1 as pure escort" +95,1,2,3,3,1,4,1,2,3,1,0,1,1,"child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing, child 2 is escorted in bundle 2 by chauffeur 1 as ride sharing, and child 3 is escorted in bundle 3 by chauffeur 2 as pure escort" +96,1,2,3,3,2,1,2,1,3,1,1,1,0,"child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing, child 2 is escorted in bundle 2 by chauffeur 1 as pure escort and child 3 is escorted in bundle 3 by chauffeur 1 as ride sharing" +97,1,2,3,3,2,2,2,1,3,0,2,1,0,"child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing, child 2 is escorted in bundle 2 by chauffeur 1 as pure escort and child 3 is escorted in bundle 3 by chauffeur 1 as pure escort" +98,1,2,3,3,2,4,1,2,3,0,1,1,1,"child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing, child 2 is escorted in bundle 2 by chauffeur 1 as pure escort and child 3 is escorted in bundle 3 by chauffeur 2 as pure escort" +99,1,2,3,3,4,1,1,2,3,1,0,1,1,"child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing, child 2 is escorted in bundle 2 by chauffeur 2 as pure escort and child 3 is escorted in bundle 3 by chauffeur 1 as ride sharing" +100,1,2,3,3,4,2,1,2,3,0,1,1,1,"child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing, child 2 is escorted in bundle 2 by chauffeur 2 as pure escort and child 3 is escorted in bundle 3 by chauffeur 1 as pure escort" +101,1,2,3,3,4,4,0,3,3,0,0,1,2,"child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing, child 2 is escorted in bundle 2 by chauffeur 2 as pure escort and child 3 is escorted in bundle 3 by chauffeur 2 as pure escort" +102,1,2,3,4,1,2,2,1,3,1,1,0,1,"child 1 is escorted in bundle 1 by chauffeur 2 as pure escort, child 2 is escorted in bundle 2 by chauffeur 1 as ride sharing, and child 3 is escorted in bundle 3 by chauffeur 1 as pure escort" +103,1,2,3,4,1,3,1,2,3,1,0,1,1,"child 1 is escorted in bundle 1 by chauffeur 2 as pure escort, child 2 is escorted in bundle 2 by chauffeur 1 as ride sharing, and child 3 is escorted in bundle 3 by chauffeur 2 as ride sharing" +104,1,2,3,4,1,4,1,2,3,1,0,0,2,"child 1 is escorted in bundle 1 by chauffeur 2 as pure escort, child 2 is escorted in bundle 2 by chauffeur 1 as ride sharing, and child 3 is escorted in bundle 3 by chauffeur 2 as pure escort" +105,1,2,3,4,2,1,2,1,3,1,1,0,1,"child 1 is escorted in bundle 1 by chauffeur 2 as pure escort, child 2 is escorted in bundle 2 by chauffeur 1 as pure escort and child 3 is escorted in bundle 3 by chauffeur 1 as ride sharing" +106,1,2,3,4,2,2,2,1,3,0,2,0,1,"child 1 is escorted in bundle 1 by chauffeur 2 as pure escort, child 2 is escorted in bundle 2 by chauffeur 1 as pure escort and child 3 is escorted in bundle 3 by chauffeur 1 as pure escort" +107,1,2,3,4,2,3,1,2,3,0,1,1,1,"child 1 is escorted in bundle 1 by chauffeur 2 as pure escort, child 2 is escorted in bundle 2 by chauffeur 1 as pure escort and child 3 is escorted in bundle 3 by chauffeur 2 as ride sharing" +108,1,2,3,4,2,4,1,2,3,0,1,0,2,"child 1 is escorted in bundle 1 by chauffeur 2 as pure escort, child 2 is escorted in bundle 2 by chauffeur 1 as pure escort and child 3 is escorted in bundle 3 by chauffeur 2 as pure escort" +109,1,2,3,4,3,1,1,2,3,1,0,1,1,"child 1 is escorted in bundle 1 by chauffeur 2 as pure escort, child 2 is escorted in bundle 2 by chauffeur 2 as ride sharing, and child 3 is escorted in bundle 3 by chauffeur 1 as ride sharing" +110,1,2,3,4,3,2,1,2,3,0,1,1,1,"child 1 is escorted in bundle 1 by chauffeur 2 as pure escort, child 2 is escorted in bundle 2 by chauffeur 2 as ride sharing, and child 3 is escorted in bundle 3 by chauffeur 1 as pure escort" +111,1,2,3,4,3,4,0,3,3,0,0,1,2,"child 1 is escorted in bundle 1 by chauffeur 2 as pure escort, child 2 is escorted in bundle 2 by chauffeur 2 as ride sharing, and child 3 is escorted in bundle 3 by chauffeur 2 as pure escort" +112,1,2,3,4,4,1,1,2,3,1,0,0,2,"child 1 is escorted in bundle 1 by chauffeur 2 as pure escort, child 2 is escorted in bundle 2 by chauffeur 2 as pure escort and child 3 is escorted in bundle 3 by chauffeur 1 as ride sharing" +113,1,2,3,4,4,2,1,2,3,0,1,0,2,"child 1 is escorted in bundle 1 by chauffeur 2 as pure escort, child 2 is escorted in bundle 2 by chauffeur 2 as pure escort and child 3 is escorted in bundle 3 by chauffeur 1 as pure escort" +114,1,2,3,4,4,3,0,3,3,0,0,1,2,"child 1 is escorted in bundle 1 by chauffeur 2 as pure escort, child 2 is escorted in bundle 2 by chauffeur 2 as pure escort and child 3 is escorted in bundle 3 by chauffeur 2 as ride sharing" +115,1,2,3,4,4,4,0,3,3,0,0,0,3,"child 1 is escorted in bundle 1 by chauffeur 2 as pure escort, child 2 is escorted in bundle 2 by chauffeur 2 as pure escort and child 3 is escorted in bundle 3 by chauffeur 2 as pure escort" +116,1,1,2,1,1,2,2,0,2,1,1,0,0,child 1 and 2 are escorted in bundle 1 by chauffeur 1 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 1 as pure escort +117,1,1,2,1,1,3,1,1,2,1,0,1,0,child 1 and 2 are escorted in bundle 1 by chauffeur 1 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 2 as ride sharing +118,1,1,2,1,1,4,1,1,2,1,0,0,1,child 1 and 2 are escorted in bundle 1 by chauffeur 1 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 2 as pure escort +119,1,1,2,2,2,1,2,0,2,1,1,0,0,child 1 and 2 are escorted in bundle 1 by chauffeur 1 as pure escort and child 3 is escorted in bundle 2 by chauffeur 1 as ride sharing +120,1,1,2,2,2,2,2,0,2,0,2,0,0,child 1 and 2 are escorted in bundle 1 by chauffeur 1 as pure escort and child 3 is escorted in bundle 2 by chauffeur 1 as pure escort +121,1,1,2,2,2,3,1,1,2,0,1,1,0,child 1 and 2 are escorted in bundle 1 by chauffeur 1 as pure escort and child 3 is escorted in bundle 2 by chauffeur 2 as ride sharing +122,1,1,2,2,2,4,1,1,2,0,1,0,1,child 1 and 2 are escorted in bundle 1 by chauffeur 1 as pure escort and child 3 is escorted in bundle 2 by chauffeur 2 as pure escort +123,1,1,2,3,3,1,1,1,2,1,0,1,0,child 1 and 2 are escorted in bundle 1 by chauffeur 2 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 1 as ride sharing +124,1,1,2,3,3,2,1,1,2,0,1,1,0,child 1 and 2 are escorted in bundle 1 by chauffeur 2 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 1 as pure escort +125,1,1,2,3,3,4,0,2,2,0,0,1,1,child 1 and 2 are escorted in bundle 1 by chauffeur 2 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 2 as pure escort +126,1,1,2,4,4,1,1,1,2,1,0,0,1,child 1 and 2 are escorted in bundle 1 by chauffeur 2 as pure escort and child 3 is escorted in bundle 2 by chauffeur 1 as ride sharing +127,1,1,2,4,4,2,1,1,2,0,1,0,1,child 1 and 2 are escorted in bundle 1 by chauffeur 2 as pure escort and child 3 is escorted in bundle 2 by chauffeur 1 as pure escort +128,1,1,2,4,4,3,0,2,2,0,0,1,1,child 1 and 2 are escorted in bundle 1 by chauffeur 2 as pure escort and child 3 is escorted in bundle 2 by chauffeur 2 as ride sharing +129,1,1,2,4,4,4,0,2,2,0,0,0,2,child 1 and 2 are escorted in bundle 1 by chauffeur 2 as pure escort and child 3 is escorted in bundle 2 by chauffeur 2 as pure escort +130,1,2,1,1,2,1,2,0,2,1,1,0,0,child 1 and 3 are escorted in bundle 1 by chauffeur 1 as ride sharing and child 2 is escorted in bundle 2 by chauffeur 1 as pure escort +131,1,2,1,1,3,1,1,1,2,1,0,1,0,child 1 and 3 are escorted in bundle 1 by chauffeur 1 as ride sharing and child 2 is escorted in bundle 2 by chauffeur 2 as ride sharing +132,1,2,1,1,4,1,1,1,2,1,0,0,1,child 1 and 3 are escorted in bundle 1 by chauffeur 1 as ride sharing and child 2 is escorted in bundle 2 by chauffeur 2 as pure escort +133,1,2,1,2,1,2,2,0,2,1,1,0,0,child 1 and 3 are escorted in bundle 1 by chauffeur 1 as pure escort and child 2 is escorted in bundle 2 by chauffeur 1 as ride sharing +134,1,2,1,2,2,2,2,0,2,0,2,0,0,child 1 and 3 are escorted in bundle 1 by chauffeur 1 as pure escort and child 2 is escorted in bundle 2 by chauffeur 1 as pure escort +135,1,2,1,2,3,2,1,1,2,0,1,1,0,child 1 and 3 are escorted in bundle 1 by chauffeur 1 as pure escort and child 2 is escorted in bundle 2 by chauffeur 2 as ride sharing +136,1,2,1,2,4,2,1,1,2,0,1,0,1,child 1 and 3 are escorted in bundle 1 by chauffeur 1 as pure escort and child 2 is escorted in bundle 2 by chauffeur 2 as pure escort +137,1,2,1,3,1,3,1,1,2,1,0,1,0,child 1 and 3 are escorted in bundle 1 by chauffeur 2 as ride sharing and child 2 is escorted in bundle 2 by chauffeur 1 as ride sharing +138,1,2,1,3,2,3,1,1,2,0,1,1,0,child 1 and 3 are escorted in bundle 1 by chauffeur 2 as ride sharing and child 2 is escorted in bundle 2 by chauffeur 1 as pure escort +139,1,2,1,3,4,3,0,2,2,0,0,1,1,child 1 and 3 are escorted in bundle 1 by chauffeur 2 as ride sharing and child 2 is escorted in bundle 2 by chauffeur 2 as pure escort +140,1,2,1,4,1,4,1,1,2,1,0,0,1,child 1 and 3 are escorted in bundle 1 by chauffeur 2 as pure escort and child 2 is escorted in bundle 2 by chauffeur 1 as ride sharing +141,1,2,1,4,2,4,1,1,2,0,1,0,1,child 1 and 3 are escorted in bundle 1 by chauffeur 2 as pure escort and child 2 is escorted in bundle 2 by chauffeur 1 as pure escort +142,1,2,1,4,3,4,0,2,2,0,0,1,1,child 1 and 3 are escorted in bundle 1 by chauffeur 2 as pure escort and child 2 is escorted in bundle 2 by chauffeur 2 as ride sharing +143,1,2,1,4,4,4,0,2,2,0,0,0,2,child 1 and 3 are escorted in bundle 1 by chauffeur 2 as pure escort and child 2 is escorted in bundle 2 by chauffeur 2 as pure escort +144,1,2,2,1,2,2,2,0,2,1,1,0,0,child 2 and 3 are escorted in bundle 2 by chauffeur 1 as ride sharing and child 1 is escorted in bundle 1 by chauffeur 1 as pure escort +145,1,2,2,1,3,3,1,1,2,1,0,1,0,child 2 and 3 are escorted in bundle 2 by chauffeur 1 as ride sharing and child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing +146,1,2,2,1,4,4,1,1,2,1,0,0,1,child 2 and 3 are escorted in bundle 2 by chauffeur 1 as ride sharing and child 1 is escorted in bundle 1 by chauffeur 2 as pure escort +147,1,2,2,2,1,1,2,0,2,1,1,0,0,child 2 and 3 are escorted in bundle 2 by chauffeur 1 as pure escort and child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing +148,1,2,2,2,2,2,2,0,2,0,2,0,0,child 2 and 3 are escorted in bundle 2 by chauffeur 1 as pure escort and child 1 is escorted in bundle 1 by chauffeur 1 as pure escort +149,1,2,2,2,3,3,1,1,2,0,1,1,0,child 2 and 3 are escorted in bundle 2 by chauffeur 1 as pure escort and child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing +150,1,2,2,2,4,4,1,1,2,0,1,0,1,child 2 and 3 are escorted in bundle 2 by chauffeur 1 as pure escort and child 1 is escorted in bundle 1 by chauffeur 2 as pure escort +151,1,2,2,3,1,1,1,1,2,1,0,1,0,child 2 and 3 are escorted in bundle 2 by chauffeur 2 as ride sharing and child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing +152,1,2,2,3,2,2,1,1,2,0,1,1,0,child 2 and 3 are escorted in bundle 2 by chauffeur 2 as ride sharing and child 1 is escorted in bundle 1 by chauffeur 1 as pure escort +153,1,2,2,3,4,4,0,2,2,0,0,1,1,child 2 and 3 are escorted in bundle 2 by chauffeur 2 as ride sharing and child 1 is escorted in bundle 1 by chauffeur 2 as pure escort +154,1,2,2,4,1,1,1,1,2,1,0,0,1,child 2 and 3 are escorted in bundle 2 by chauffeur 2 as pure escort and child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing +155,1,2,2,4,2,2,1,1,2,0,1,0,1,child 2 and 3 are escorted in bundle 2 by chauffeur 2 as pure escort and child 1 is escorted in bundle 1 by chauffeur 1 as pure escort +156,1,2,2,4,3,3,0,2,2,0,0,1,1,child 2 and 3 are escorted in bundle 2 by chauffeur 2 as pure escort and child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing +157,1,2,2,4,4,4,0,2,2,0,0,0,2,child 2 and 3 are escorted in bundle 2 by chauffeur 2 as pure escort and child 1 is escorted in bundle 1 by chauffeur 2 as pure escort diff --git a/resident/configs/school_escorting_coefficients_inbound.csv b/resident/configs/school_escorting_coefficients_inbound.csv new file mode 100644 index 0000000..d6bc194 --- /dev/null +++ b/resident/configs/school_escorting_coefficients_inbound.csv @@ -0,0 +1,41 @@ +coefficient_name,value,constrain +coef_unavail,-999,T +coef_child_age_16p_noes,0.17488,F +coef_child_age_6_to_15_noes,-0.41751,F +coef_child_age_u5_noes,-1.36718,F +coef_ln_dist_from_school_noes,-0.01787,F +coef_ln_dist_from_school_u6_noes,-0.23304,F +coef_ln_dist_from_school_6to9_noes,-0.07286,F +coef_child_age_16p_rs,1.97184 +coef_child_age_10to15_rs,1.73544 +coef_child_age_6to9_rs,1.73544 +coef_child_age_u6_rs,1.34996 +coef_hh_inc_u25k_noes,0.0,F +coef_hh_inc_25to50k_noes,0.0,F +coef_zero_auto_hh_noes,0.13165,F +coef_cars_lt_workers_rs,-3.35586,F +coef_cars_lt_workers_pe,-1.59062,F +coef_chauf_female_rs,-0.44827,F +coef_chauf_male_rs,-0.90832,F +coef_chauf_female_pe,-0.68399,F +coef_chauf_male_pe,-1.01783,F +coef_chauf_pt_worker_rs,0.51244,F +coef_chauf_pt_worker_pe,0.23496,F +coef_chauf_non_worker_pe,0.69245,F +coef_chauf_univ_stud_re,0.47395,F +coef_chauf_age_u35_pe,0.00000,F +coef_chauf_time_to_work_or_univ_rs,-0.01974,F +coef_chauf_walk_dist_to_work_or_univ_rs,-0.73155,F +coef_chauf_walk_dist_to_work_or_univ_pe,0.00000,F +coef_abs_dev_distance,-0.08011,F +coef_rel_dev_distance,0.0,F +coef_same_taz_escort,1.44053,F +coef_same_taz_no_escort,1.96760,F +coef_no_escort_outbound,2.04553,F +coef_outbound_rs,0.0,F +coef_same_chauf,1.14533,F +coef_calib_child_age_u6_rs,8.75011,F +coef_calib_child_age_16p_rs,-0.20,F +coef_calib_child_age_6to15_noes,-3.46362,F +coef_calib_child_age_u6_noes,-0.36565,F +coef_calib_child_age_6to15_rs,-1.45344,F diff --git a/resident/configs/school_escorting_coefficients_outbound.csv b/resident/configs/school_escorting_coefficients_outbound.csv new file mode 100644 index 0000000..14f1e7d --- /dev/null +++ b/resident/configs/school_escorting_coefficients_outbound.csv @@ -0,0 +1,40 @@ +coefficient_name,value,constrain +coef_unavail,-999,T +coef_child_age_16p_noes,1.07391,F +coef_child_age_10_to_15_noes,0.46127,F +coef_child_age_u9_noes,0.13590,F +coef_ln_dist_to_school_noes,-0.33583,F +coef_ln_dist_to_school_u6_noes,-1.00920,F +coef_ln_dist_to_school_6to9_noes,-0.11156,F +coef_child_age_16p_rs,0.34244,F +coef_child_age_10to15_rs,.27494,F +coef_child_age_6to9_rs,0.20757,F +coef_child_age_u6_rs,0.33051,F +coef_child_dist_pe,-0.04593,F +coef_hh_inc_u25k_noes,0.18901,F +coef_hh_inc_25to50k_noes,0.12172,F +coef_zero_auto_hh_noes,9.0,F +coef_cars_lt_workers_rs,-0.88274,F +coef_cars_lt_workers_pe,-0.55291,F +coef_chauf_female_rs,-0.27828,F +coef_chauf_male_rs,-0.67320,F +coef_chauf_female_pe,-0.84859,F +coef_chauf_male_pe,-0.80965,F +coef_chauf_pt_worker_rs,-0.11422,F +coef_chauf_pt_worker_pe,0.51792,F +coef_chauf_non_worker_pe,0.51577,F +coef_chauf_univ_stud_re,0.0,F +coef_chauf_age_u35_pe,-0.33715,F +coef_chauf_time_to_work_or_univ_rs,0.0,F +coef_chauf_walk_dist_to_work_or_univ_rs,0.77326,F +coef_chauf_walk_dist_to_work_or_univ_pe,0.0,F +coef_abs_dev_distance,-0.07136,F +coef_rel_dev_distance,-0.09951,F +coef_same_taz_escort,2.77591,F +coef_same_taz_no_escort,2.62969,F +coef_same_taz_no_escort_23,2.21201,F +coef_calib_child_age_u6_rs,8.55400,F +coef_calib_child_age_16p_rs,-0.2000,F +coef_calib_child_age_6to15_noes,-4.53067,F +coef_calib_child_age_6to15_rs,-2.41923,F +coef_calib_child_age_u6_noes,-0.67902,F diff --git a/resident/configs/school_escorting_coefficients_outbound_cond.csv b/resident/configs/school_escorting_coefficients_outbound_cond.csv new file mode 100644 index 0000000..079d1e8 --- /dev/null +++ b/resident/configs/school_escorting_coefficients_outbound_cond.csv @@ -0,0 +1,41 @@ +coefficient_name,value,constrain +coef_unavail,-999,T +coef_child_age_16p_noes,-0.33924,F +coef_child_age_10_to_15_noes,-0.78691,F +coef_child_age_u9_noes,-0.97142,F +coef_ln_dist_to_school_noes,-0.02112,F +coef_ln_dist_to_school_u6_noes,-0.81356,F +coef_ln_dist_to_school_6to9_noes,-0.12910,F +coef_child_age_16p_rs,0.93518,F +coef_child_age_10to15_rs,0.58009,F +coef_child_age_6to9_rs,0.36698,F +coef_child_age_u6_rs,0.29043,F +coef_child_dist_pe,-0.03624,F +coef_hh_inc_u25k_noes,0.33839,F +coef_hh_inc_25to50k_noes,0.05888,F +coef_zero_auto_hh_noes,9.00000,F +coef_cars_lt_workers_rs,-0.38588,F +coef_cars_lt_workers_pe,-0.01213,F +coef_chauf_female_rs,-0.49717,F +coef_chauf_male_rs,-0.94654,F +coef_chauf_female_pe,-0.98546,F +coef_chauf_male_pe,-1.05266,F +coef_chauf_pt_worker_rs,-0.74807,F +coef_chauf_pt_worker_pe,0.31729,F +coef_chauf_non_worker_pe,0.19211,F +coef_chauf_univ_stud_re,0.0,F +coef_chauf_age_u35_pe,-0.41194,F +coef_chauf_time_to_work_or_univ_rs,0.0,F +coef_chauf_walk_dist_to_work_or_univ_rs,0.38819,F +coef_chauf_walk_dist_to_work_or_univ_pe,0.0,F +coef_abs_dev_distance,-0.04497,F +coef_rel_dev_distance,-0.09067,F +coef_same_taz_escort,2.56855,F +coef_same_taz_no_escort,2.21201,F +coef_no_escort_inbound,1.72902,F +coef_same_chauf,0.99276,F +coef_calib_child_age_u6_rs,8.55400,F +coef_calib_child_age_16p_rs,-0.2000,F +coef_calib_child_age_6to15_noes,-4.53067,F +coef_calib_child_age_6to15_rs,-2.41923,F +coef_calib_child_age_u6_noes,-0.67902,F diff --git a/resident/configs/school_escorting_inbound.csv b/resident/configs/school_escorting_inbound.csv new file mode 100644 index 0000000..2912752 --- /dev/null +++ b/resident/configs/school_escorting_inbound.csv @@ -0,0 +1,175 @@ +Label,Description,Expression,Coefficient +# ,Availability Conditions,, +util_one_child_to_school,Availability based on number of eligible children,((bundle2 + bundle3) > 0) & (num_children_going_to_school==1),coef_unavail +util_two_children_to_school,Availability based on number of eligible children,(bundle3 > 0) & (num_children_going_to_school == 2),coef_unavail +util_one_potential_chauffeur,Availability based on number of eligible chauffeurs,((bundle1 + bundle2 + bundle3) > 0) & (num_potential_chauffeurs == 0),coef_unavail +util_two_potential_chauffeurs,Availability based on number of eligible chauffeurs,((chauf1 > 2) | (chauf2 > 2) | (chauf3 > 2)) & (num_potential_chauffeurs == 1),coef_unavail +util_avail_rs_cdap_child1_chauf1,Availability for RideSharing by daily pattern - Child 1 and Chauffeur 1,(bundle1 > 0) & (chauf1 == 1) & (cdap_chauf1 != 'M'),coef_unavail +util_avail_rs_cdap_child1_chauf2,Availability for RideSharing by daily pattern - Child 1 and Chauffeur 2,(bundle1 > 0) & (chauf1 == 3) & (cdap_chauf2 != 'M'),coef_unavail +util_avail_rs_cdap_child2_chauf1,Availability for RideSharing by daily pattern - Child 2 and Chauffeur 1,(bundle2 > 0) & (chauf2 == 1) & (cdap_chauf1 != 'M'),coef_unavail +util_avail_rs_cdap_child2_chauf2,Availability for RideSharing by daily pattern - Child 2 and Chauffeur 2,(bundle2 > 0) & (chauf2 == 3) & (cdap_chauf2 != 'M'),coef_unavail +util_avail_rs_cdap_child3_chauf1,Availability for RideSharing by daily pattern - Child 3 and Chauffeur 1,(bundle3 > 0) & (chauf3 == 1) & (cdap_chauf1 != 'M'),coef_unavail +util_avail_rs_cdap_child3_chauf2,Availability for RideSharing by daily pattern - Child 3 and Chauffeur 2,(bundle3 > 0) & (chauf3 == 3) & (cdap_chauf2 != 'M'),coef_unavail +util_avail_rs_sync_child1_chauf1,Availability for RideSharing by synchronization - Child 1 and Chauffeur 1,@(df.bundle1 > 0) & (df.chauf1 == 1) & (np.abs(df.pref_depart_time_chauf1 + (df.time_mand1_to_school1 / mins_per_time_bin) - df.pref_depart_time_school1) > max_bin_difference_between_departure_times),coef_unavail +util_avail_rs_sync_child1_chauf2,Availability for RideSharing by synchronization - Child 1 and Chauffeur 2,@(df.bundle1 > 0) & (df.chauf1 == 3) & (np.abs(df.pref_depart_time_chauf2 + (df.time_mand2_to_school1 / mins_per_time_bin) - df.pref_depart_time_school1) > max_bin_difference_between_departure_times),coef_unavail +util_avail_rs_sync_child2_chauf1,Availability for RideSharing by synchronization - Child 2 and Chauffeur 1,@(df.bundle2 > 0) & (df.chauf2 == 1) & (np.abs(df.pref_depart_time_chauf1 + (df.time_mand1_to_school2 / mins_per_time_bin) - df.pref_depart_time_school2) > max_bin_difference_between_departure_times),coef_unavail +util_avail_rs_sync_child2_chauf2,Availability for RideSharing by synchronization - Child 2 and Chauffeur 2,@(df.bundle2 > 0) & (df.chauf2 == 3) & (np.abs(df.pref_depart_time_chauf2 + (df.time_mand2_to_school2 / mins_per_time_bin) - df.pref_depart_time_school2) > max_bin_difference_between_departure_times),coef_unavail +util_avail_rs_sync_child3_chauf1,Availability for RideSharing by synchronization - Child 3 and Chauffeur 1,@(df.bundle3 > 0) & (df.chauf3 == 1) & (np.abs(df.pref_depart_time_chauf1 + (df.time_mand1_to_school3 / mins_per_time_bin) - df.pref_depart_time_school3) > max_bin_difference_between_departure_times),coef_unavail +util_avail_rs_sync_child3_chauf2,Availability for RideSharing by synchronization - Child 3 and Chauffeur 2,@(df.bundle3 > 0) & (df.chauf3 == 3) & (np.abs(df.pref_depart_time_chauf2 + (df.time_mand2_to_school3 / mins_per_time_bin) - df.pref_depart_time_school3) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_cdap_child1_chauf1,Availability for Pure-Escorting by daily pattern - Child 1 and Chauffeur 1,(bundle1 > 0) & (chauf1 == 2) & (ptype_chauf1 < 6) & (cdap_chauf1 == 'H'),coef_unavail +util_avail_pe_cdap_child1_chauf2,Availability for Pure-Escorting by daily pattern - Child 1 and Chauffeur 2,(bundle1 > 0) & (chauf1 == 4) & (ptype_chauf2 < 6) & (cdap_chauf2 == 'H'),coef_unavail +util_avail_pe_cdap_child2_chauf1,Availability for Pure-Escorting by daily pattern - Child 2 and Chauffeur 1,(bundle2 > 0) & (chauf2 == 2) & (ptype_chauf1 < 6) & (cdap_chauf1 == 'H'),coef_unavail +util_avail_pe_cdap_child2_chauf2,Availability for Pure-Escorting by daily pattern - Child 2 and Chauffeur 2,(bundle2 > 0) & (chauf2 == 4) & (ptype_chauf2 < 6) & (cdap_chauf2 == 'H'),coef_unavail +util_avail_pe_cdap_child3_chauf1,Availability for Pure-Escorting by daily pattern - Child 3 and Chauffeur 1,(bundle3 > 0) & (chauf3 == 2) & (ptype_chauf1 < 6) & (cdap_chauf1 == 'H'),coef_unavail +util_avail_pe_cdap_child3_chauf2,Availability for Pure-Escorting by daily pattern - Child 3 and Chauffeur 2,(bundle3 > 0) & (chauf3 == 4) & (ptype_chauf2 < 6) & (cdap_chauf2 == 'H'),coef_unavail +util_avail_pe_sync_child1_chauf1,Availability for Pure-Escorting by synchronization - Child 1 and Chauffeur 1,@(df.bundle1 > 0) & (df.chauf1 == 2) & (df.cdap_chauf1 == 'H') & (((df.pref_depart_time_chauf1 + (df.time_mand_to_home1 / mins_per_time_bin)) - (df.pref_depart_time_school1 - (df.time_home_to_school1 / mins_per_time_bin))) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_sync_child1_chauf2,Availability for Pure-Escorting by synchronization - Child 1 and Chauffeur 2,@(df.bundle1 > 0) & (df.chauf1 == 4) & (df.cdap_chauf2 == 'H') & (((df.pref_depart_time_chauf2 + (df.time_mand_to_home2 / mins_per_time_bin)) - (df.pref_depart_time_school1 - (df.time_home_to_school1 / mins_per_time_bin))) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_sync_child2_chauf1,Availability for Pure-Escorting by synchronization - Child 2 and Chauffeur 1,@(df.bundle2 > 0) & (df.chauf2 == 2) & (df.cdap_chauf1 == 'H') & (((df.pref_depart_time_chauf1 + (df.time_mand_to_home1 / mins_per_time_bin)) - (df.pref_depart_time_school2 - (df.time_home_to_school2 / mins_per_time_bin))) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_sync_child2_chauf2,Availability for Pure-Escorting by synchronization - Child 2 and Chauffeur 2,@(df.bundle2 > 0) & (df.chauf2 == 4) & (df.cdap_chauf2 == 'H') & (((df.pref_depart_time_chauf2 + (df.time_mand_to_home2 / mins_per_time_bin)) - (df.pref_depart_time_school2 - (df.time_home_to_school2 / mins_per_time_bin))) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_sync_child3_chauf1,Availability for Pure-Escorting by synchronization - Child 3 and Chauffeur 1,@(df.bundle3 > 0) & (df.chauf3 == 2) & (df.cdap_chauf1 == 'H') & (((df.pref_depart_time_chauf1 + (df.time_mand_to_home1 / mins_per_time_bin)) - (df.pref_depart_time_school3 - (df.time_home_to_school3 / mins_per_time_bin))) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_sync_child3_chauf2,Availability for Pure-Escorting by synchronization - Child 3 and Chauffeur 2,@(df.bundle3 > 0) & (df.chauf3 == 4) & (df.cdap_chauf2 == 'H') & (((df.pref_depart_time_chauf2 + (df.time_mand_to_home2 / mins_per_time_bin)) - (df.pref_depart_time_school3 - (df.time_home_to_school3 / mins_per_time_bin))) > max_bin_difference_between_departure_times),coef_unavail +util_avail_bundle_child_1and2,Availability for bundling child 1 and child 2,@(df.bundle1 == df.bundle2) & (df.bundle1 > 0) & (np.abs(df.pref_depart_time_school1 - df.pref_depart_time_school2) > max_bin_difference_between_departure_times),coef_unavail +util_avail_bundle_child_1and3,Availability for bundling child 1 and child 3,@(df.bundle1 == df.bundle3) & (df.bundle1 > 0) & (np.abs(df.pref_depart_time_school1 - df.pref_depart_time_school3) > max_bin_difference_between_departure_times),coef_unavail +util_avail_bundle_child_2and3,Availability for bundling child 2 and child 3,@(df.bundle2 == df.bundle3) & (df.bundle2 > 0) & (np.abs(df.pref_depart_time_school2 - df.pref_depart_time_school3) > max_bin_difference_between_departure_times),coef_unavail +util_avail_mult_bundles_outbound1,Availability Chauffeur 1 - Expected arrival from previous pure escort must be before departure for subsequent escort,((nrs1 + npe1) > 1) & ~avail_multiple_bundles,coef_unavail +util_avail_mult_bundles_outbound2,Availability Chauffeur 2 - Expected arrival from previous pure escort must be before departure for subsequent escort,((nrs2 + npe2) > 1) & ~avail_multiple_bundles,coef_unavail +util_time_span11,Chauffeur 1 and Child 1 - time span for this alternative must not overlap time span reserved from outbound conditional,(bundle1 > 0) & ((chauf1 == 1) | (chauf1 == 2)) & (return_bin_outbound_school_escorting1 > pref_depart_time_school1),coef_unavail +util_time_span12,Chauffeur 1 and Child 2 - time span for this alternative must not overlap time span reserved from outbound conditional,(bundle1 > 0) & ((chauf2 == 1) | (chauf2 == 2)) & (return_bin_outbound_school_escorting1 > pref_depart_time_school2),coef_unavail +util_time_span13,Chauffeur 1 and Child 3 - time span for this alternative must not overlap time span reserved from outbound conditional,(bundle1 > 0) & ((chauf3 == 1) | (chauf3 == 2)) & (return_bin_outbound_school_escorting1 > pref_depart_time_school3),coef_unavail +util_time_span21,Chauffeur 2 and Child 1 - time span for this alternative must not overlap time span reserved from outbound conditional,(bundle1 > 0) & ((chauf1 == 3) | (chauf1 == 4)) & (return_bin_outbound_school_escorting2 > pref_depart_time_school1),coef_unavail +util_time_span22,Chauffeur 2 and Child 2 - time span for this alternative must not overlap time span reserved from outbound conditional,(bundle1 > 0) & ((chauf2 == 3) | (chauf2 == 4)) & (return_bin_outbound_school_escorting2 > pref_depart_time_school2),coef_unavail +util_time_span23,Chauffeur 2 and Child 3 - time span for this alternative must not overlap time span reserved from outbound conditional,(bundle1 > 0) & ((chauf3 == 3) | (chauf3 == 4)) & (return_bin_outbound_school_escorting2 > pref_depart_time_school3),coef_unavail +util_avail_pe_during_mand_child1_chauf1,Availability pure escort tour must take place before mandatory tour - Child 1 and Chauffeur 1,@(df.bundle1 > 0) & (df.chauf1 == 2) & (df.cdap_chauf1 == 'M') & ((df.pref_depart_time_chauf1 + max_bin_difference_between_departure_times) >= df.pref_depart_time_school1),coef_unavail +util_avail_pe_during_mand_child1_chauf2,Availability pure escort tour must take place before mandatory tour - Child 1 and Chauffeur 2,@(df.bundle1 > 0) & (df.chauf1 == 4) & (df.cdap_chauf2 == 'M') & ((df.pref_depart_time_chauf2 + max_bin_difference_between_departure_times) >= df.pref_depart_time_school1),coef_unavail +util_avail_pe_during_mand_child2_chauf1,Availability pure escort tour must take place before mandatory tour - Child 2 and Chauffeur 1,@(df.bundle2 > 0) & (df.chauf2 == 2) & (df.cdap_chauf1 == 'M') & ((df.pref_depart_time_chauf1 + max_bin_difference_between_departure_times) >= df.pref_depart_time_school2),coef_unavail +util_avail_pe_during_mand_child2_chauf2,Availability pure escort tour must take place before mandatory tour - Child 2 and Chauffeur 2,@(df.bundle2 > 0) & (df.chauf2 == 4) & (df.cdap_chauf2 == 'M') & ((df.pref_depart_time_chauf2 + max_bin_difference_between_departure_times) >= df.pref_depart_time_school2),coef_unavail +util_avail_pe_during_mand_child3_chauf1,Availability pure escort tour must take place before mandatory tour - Child 3 and Chauffeur 1,@(df.bundle3 > 0) & (df.chauf3 == 2) & (df.cdap_chauf1 == 'M') & ((df.pref_depart_time_chauf1 + max_bin_difference_between_departure_times) >= df.pref_depart_time_school3),coef_unavail +util_avail_pe_during_mand_child3_chauf2,Availability pure escort tour must take place before mandatory tour - Child 3 and Chauffeur 2,@(df.bundle3 > 0) & (df.chauf3 == 4) & (df.cdap_chauf2 == 'M') & ((df.pref_depart_time_chauf2 + max_bin_difference_between_departure_times) >= df.pref_depart_time_school3),coef_unavail +# ,No escorting,, +util_child1_age_16p_noes,Child 1 age 16 years or older - No escort,(bundle1 == 0) & (child_id1 > 0) & (age_child1 > 15),coef_child_age_16p_noes +util_child2_age_16p_noes,Child 2 age 16 years or older - No escort,(bundle2 == 0) & (child_id2 > 0) & (age_child2 > 15),coef_child_age_16p_noes +util_child3_age_16p_noes,Child 3 age 16 years or older - No escort,(bundle3 == 0) & (child_id3 > 0) & (age_child3 > 15),coef_child_age_16p_noes +util_child1_age_6_to_15_noes,Child 1 age 6 to 15 years - No escort,(bundle1 == 0) & (child_id1 > 0) & (age_child1 > 5) & (age_child1 < 16),coef_child_age_6_to_15_noes +util_child2_age_6_to_15_noes,Child 2 age 6 to 15 years - No escort,(bundle2 == 0) & (child_id2 > 0) & (age_child2 > 5) & (age_child2 < 16),coef_child_age_6_to_15_noes +util_child3_age_6_to_15_noes,Child 3 age 6 to 15 years - No escort,(bundle3 == 0) & (child_id3 > 0) & (age_child3 > 5) & (age_child3 < 16),coef_child_age_6_to_15_noes +util_child1_age_u5_noes,Child 1 age 5 years or younger - No escort,(bundle1 == 0) & (child_id1 > 0) & (age_child1 < 6),coef_child_age_u5_noes +util_child2_age_u5_noes,Child 2 age 5 years or younger - No escort,(bundle2 == 0) & (child_id2 > 0) & (age_child2 < 6),coef_child_age_u5_noes +util_child3_age_u5_noes,Child 3 age 5 years or younger - No escort,(bundle3 == 0) & (child_id3 > 0) & (age_child3 < 6),coef_child_age_u5_noes +util_ln_dist_from_school_child1_noes,Logged distance to school for Child 1 - No escort,@(df.bundle1 == 0) * (df.child_id1 > 0) * np.log(1 + df.dist_school_to_home1),coef_ln_dist_from_school_noes +util_ln_dist_from_school_child2_noes,Logged distance to school for Child 2 - No escort,@(df.bundle2 == 0) * (df.child_id2 > 0) * np.log(1 + df.dist_school_to_home2),coef_ln_dist_from_school_noes +util_ln_dist_from_school_child3_noes,Logged distance to school for Child 3 - No escort,@(df.bundle3 == 0) * (df.child_id3 > 0) * np.log(1 + df.dist_school_to_home3),coef_ln_dist_from_school_noes +util_ln_dist_from_school_child1_u6_noes,Logged distance to school for Child 1 under 6 years old - No escort,@(df.bundle1 == 0) * (df.child_id1 > 0) * (df.age_child1 < 6) * np.log(1 + df.dist_school_to_home1),coef_ln_dist_from_school_u6_noes +util_ln_dist_from_school_child2_u6_noes,Logged distance to school for Child 2 under 6 years old - No escort,@(df.bundle2 == 0) * (df.child_id2 > 0) * (df.age_child2 < 6) * np.log(1 + df.dist_school_to_home2),coef_ln_dist_from_school_u6_noes +util_ln_dist_from_school_child3_u6_noes,Logged distance to school for Child 3 under 6 years old - No escort,@(df.bundle3 == 0) * (df.child_id3 > 0) * (df.age_child3 < 6) * np.log(1 + df.dist_school_to_home3),coef_ln_dist_from_school_u6_noes +util_ln_dist_from_school_child1_6to9_noes,Logged distance to school for Child 1 6 to 9 years old - No escort,@(df.bundle1 == 0) * (df.child_id1 > 0) * (df.age_child1 > 5) * (df.age_child1 < 10) * np.log(1 + df.dist_school_to_home1),coef_ln_dist_from_school_6to9_noes +util_ln_dist_from_school_child2_6to9_noes,Logged distance to school for Child 2 6 to 9 years old - No escort,@(df.bundle2 == 0) * (df.child_id2 > 0) * (df.age_child2 > 5) * (df.age_child2 < 10) * np.log(1 + df.dist_school_to_home2),coef_ln_dist_from_school_6to9_noes +util_ln_dist_from_school_child3_6to9_noes,Logged distance to school for Child 3 6 to 9 years old - No escort,@(df.bundle3 == 0) * (df.child_id3 > 0) * (df.age_child3 > 5) * (df.age_child3 < 10) * np.log(1 + df.dist_school_to_home3),coef_ln_dist_from_school_6to9_noes +# ,Ride Sharing,, +util_child1_age_16p_rs,Child 1 age 16 years or older - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 > 15),coef_child_age_16p_rs +util_child2_age_16p_rs,Child 2 age 16 years or older - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 > 15),coef_child_age_16p_rs +util_child3_age_16p_rs,Child 3 age 16 years or older - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 > 15),coef_child_age_16p_rs +util_child1_age_10to15_rs,Child 1 age 10 to 15 years - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 > 9) & (age_child1 < 16),coef_child_age_10to15_rs +util_child2_age_10to15_rs,Child 2 age 10 to 15 years - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 > 9) & (age_child2 < 16),coef_child_age_10to15_rs +util_child3_age_10to15_rs,Child 3 age 10 to 15 years - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 > 9) & (age_child3 < 16),coef_child_age_10to15_rs +util_child1_age_6to9_rs,Child 1 age 6 to 9 years - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 > 5) & (age_child1 < 10),coef_child_age_6to9_rs +util_child2_age_6to9_rs,Child 2 age 6 to 9 years - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 > 5) & (age_child2 < 10),coef_child_age_6to9_rs +util_child3_age_6to9_rs,Child 3 age 6 to 9 years - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 > 5) & (age_child3 < 10),coef_child_age_6to9_rs +util_child1_age_u6_rs,Child 1 age under 6 years old - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 < 6),coef_child_age_u6_rs +util_child2_age_u6_rs,Child 2 age under 6 years old - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 < 6),coef_child_age_u6_rs +util_child3_age_u6_rs,Child 3 age under 6 years old - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 < 6),coef_child_age_u6_rs +# ,Pure Escort Distance,, +util_child1_dist_pe_over30miles,Child 1 Pure escorting not allowed if over 30 miles,((chauf1 == 2) | (chauf1 == 4)) & (dist_school_to_home1 > 30),coef_unavail +util_child2_dist_pe_over30miles,Child 2 Pure escorting not allowed if over 30 miles,((chauf2 == 2) | (chauf2 == 4)) & (dist_school_to_home2 > 30),coef_unavail +util_child3_dist_pe_over30miles,Child 3 Pure escorting not allowed if over 30 miles,((chauf3 == 2) | (chauf3 == 4)) & (dist_school_to_home3 > 30),coef_unavail +# ,Household Interactions,, +util_hh_inc_u25k_noes,Household income less than 25k - No Escorting,(income <= 25000) & ((bundle1 == 0) | ((bundle2 == 0) & (num_children_going_to_school > 1)) | ((bundle3 == 0) & (num_children_going_to_school > 2))),coef_hh_inc_u25k_noes +util_hh_inc_25to50k_noes,Household income between 25 and 50k - No Escorting,((income > 25000) & (income <= 50000)) & ((bundle1 == 0) | ((bundle2 == 0) & (num_children_going_to_school > 1)) | ((bundle3 == 0) & (num_children_going_to_school > 2))),coef_hh_inc_25to50k_noes +util_zero_auto_hh_noes,Zero cars in the household - No Escorting,(auto_ownership == 0) & ((bundle1 == 0) | ((bundle2 == 0) & (num_children_going_to_school > 1)) | ((bundle3 == 0) & (num_children_going_to_school > 2))),coef_zero_auto_hh_noes +util_cars_lt_workers_rs,Cars fewer than household workers - Ride Share,(auto_ownership < num_workers) & ((chauf1 % 2 == 1) | (chauf2 % 2 == 1) | (chauf3 % 2 == 1)),coef_cars_lt_workers_rs +util_cars_lt_workers_pe,Cars fewer than household workers - Pure escort,(auto_ownership < num_workers) & ((chauf1 % 2 == 0) | (chauf2 % 2 == 0) | (chauf3 % 2 == 0)),coef_cars_lt_workers_pe +# ,Chauffer Interactions,, +util_chauf1_female_rs,Chauffeur 1 Female - Ride share,(gender_chauf1 == 2) & ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_female_rs +util_chauf1_male_rs,Chauffeur 1 Male - Ride share,(gender_chauf1 == 1) & ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_male_rs +util_chauf1_female_pe,Chauffeur 1 Female - Pure Escort,(gender_chauf1 == 2) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_female_pe +util_chauf1_male_pe,Chauffeur 1 Male - Pure Escort,(gender_chauf1 == 1) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_male_pe +util_chauf2_female_rs,Chauffeur 2 Female - Ride share,(gender_chauf2 == 2) & ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_female_rs +util_chauf2_male_rs,Chauffeur 2 Male - Ride share,(gender_chauf2 == 1) & ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_male_rs +util_chauf2_female_pe,Chauffeur 2 Female - Pure Escort,(gender_chauf2 == 2) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_female_pe +util_chauf2_male_pe,Chauffeur 2 Male - Pure Escort,(gender_chauf2 == 1) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_male_pe +util_chauf1_pt_worker_rs,Chauffer 1 part time worker - Ride share,(ptype_chauf1 == 2) & ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_pt_worker_rs +util_chauf1_pt_worker_pe,Chauffer 1 part time worker - Pure escort,(ptype_chauf1 == 2) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_pt_worker_pe +util_chauf2_pt_worker_rs,Chauffer 2 part time worker - Ride share,(ptype_chauf2 == 2) & ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_pt_worker_rs +util_chauf2_pt_worker_pe,Chauffer 2 part time worker - Pure escort,(ptype_chauf2 == 2) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_pt_worker_pe +util_chauf1_non_worker_pe,Chauffer 1 non worker - Pure escort,(ptype_chauf1 == 4) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_non_worker_pe +util_chauf2_non_worker_pe,Chauffer 2 non worker - Pure escort,(ptype_chauf2 == 4) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_non_worker_pe +util_chauf1_univ_stud_re,Chauffer 1 university student - Ride Share,(ptype_chauf1 == 3) & ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_univ_stud_re +util_chauf2_univ_stud_re,Chauffer 2 university student - Ride Share,(ptype_chauf2 == 3) & ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_univ_stud_re +util_chauf1_age_u35_pe,Chauffer 1 Age 35 years or younger - Pure escort,(ptype_chauf1 == 4) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_age_u35_pe +util_chauf2_age_u35_pe,Chauffer 2 Age 35 years or younger - Pure escort,(ptype_chauf2 == 4) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_age_u35_pe +util_chauf1_time_to_work_or_univ_rs,Chauffer 1 Auto time to work or university - Ride Share,time_mand_to_home1 * ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_time_to_work_or_univ_rs +util_chauf2_time_to_work_or_univ_rs,Chauffer 2 Auto time to work or university - Ride Share,time_mand_to_home2 * ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_time_to_work_or_univ_rs +util_chauf1_walk_dist_to_work_or_univ_rs,Chauffer 1 Walk dist to work or university - Ride Share,(dist_mand1_to_home < 3) & ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_walk_dist_to_work_or_univ_rs +util_chauf2_walk_dist_to_work_or_univ_rs,Chauffer 2 Walk dist to work or university - Ride Share,(dist_mand2_to_home < 3) & ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_walk_dist_to_work_or_univ_rs +util_chauf1_walk_dist_to_work_or_univ_pe,Chauffer 1 Walk dist to work or university - Pure Escort,(dist_home_to_mand1 < 3) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_walk_dist_to_work_or_univ_pe +util_chauf2_walk_dist_to_work_or_univ_pe,Chauffer 2 Walk dist to work or university - Pure Escort,(dist_home_to_mand2 < 3) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_walk_dist_to_work_or_univ_pe +# ,Chauffer deviation,, +util_chauf1_abs_dev_rs_child1only,Chauffer 1 Absolute deviation ride share - child 1 only,((bundle1 != bundle2) & (bundle1 != bundle3) & (chauf1 == 1)) * abs_dev_dist_in_child1_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child1only,Chauffer 2 Absolute deviation ride share - child 1 only,((bundle1 != bundle2) & (bundle1 != bundle3) & (chauf1 == 3)) * abs_dev_dist_in_child1_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child2only,Chauffer 1 Absolute deviation ride share - child 2 only,((bundle1 != bundle2) & (bundle2 != bundle3) & (chauf2 == 1)) * abs_dev_dist_in_child2_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child2only,Chauffer 2 Absolute deviation ride share - child 2 only,((bundle1 != bundle2) & (bundle2 != bundle3) & (chauf2 == 3)) * abs_dev_dist_in_child2_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child3only,Chauffer 1 Absolute deviation ride share - child 3 only,((bundle1 != bundle3) & (bundle2 != bundle3) & (chauf3 == 1)) * abs_dev_dist_in_child3_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child3only,Chauffer 2 Absolute deviation ride share - child 3 only,((bundle1 != bundle3) & (bundle2 != bundle3) & (chauf3 == 3)) * abs_dev_dist_in_child3_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child12,Chauffer 1 Absolute deviation ride share - child 1 & 2,((bundle1 == bundle2) & (bundle1 != bundle3) & (chauf1 == 1)) * abs_dev_dist_in_child12_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child12,Chauffer 2 Absolute deviation ride share - child 1 & 2,((bundle1 == bundle2) & (bundle1 != bundle3) & (chauf1 == 3)) * abs_dev_dist_in_child12_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child13,Chauffer 1 Absolute deviation ride share - child 1 & 3,((bundle1 != bundle2) & (bundle1 == bundle3) & (chauf1 == 1)) * abs_dev_dist_in_child13_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child13,Chauffer 2 Absolute deviation ride share - child 1 & 3,((bundle1 != bundle2) & (bundle1 == bundle3) & (chauf1 == 3)) * abs_dev_dist_in_child13_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child23,Chauffer 1 Absolute deviation ride share - child 2 & 3,((bundle1 != bundle2) & (bundle2 == bundle3) & (chauf2 == 1)) * abs_dev_dist_in_child23_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child23,Chauffer 2 Absolute deviation ride share - child 2 & 3,((bundle1 != bundle2) & (bundle2 == bundle3) & (chauf2 == 3)) * abs_dev_dist_in_child23_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child123,Chauffer 1 Absolute deviation ride share - child 1 & 2 & 3,((bundle1 == bundle2) & (bundle2 == bundle3) & (chauf1 == 1)) * abs_dev_dist_in_child123_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child123,Chauffer 2 Absolute deviation ride share - child 1 & 2 & 3,((bundle1 == bundle2) & (bundle2 == bundle3) & (chauf1 == 3)) * abs_dev_dist_in_child123_chauf2,coef_abs_dev_distance +util_chauf1_rel_dev_rs_child1only,Chauffer 1 Relative deviation ride share - child 1 only,((bundle1 != bundle2) & (bundle1 != bundle3) & (chauf1 == 1)) * abs_dev_dist_in_child1_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child1only,Chauffer 2 Relative deviation ride share - child 1 only,((bundle1 != bundle2) & (bundle1 != bundle3) & (chauf1 == 3)) * abs_dev_dist_in_child1_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child2only,Chauffer 1 Relative deviation ride share - child 2 only,((bundle1 != bundle2) & (bundle2 != bundle3) & (chauf2 == 1)) * abs_dev_dist_in_child2_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child2only,Chauffer 2 Relative deviation ride share - child 2 only,((bundle1 != bundle2) & (bundle2 != bundle3) & (chauf2 == 3)) * abs_dev_dist_in_child2_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child3only,Chauffer 1 Relative deviation ride share - child 3 only,((bundle1 != bundle3) & (bundle2 != bundle3) & (chauf3 == 1)) * abs_dev_dist_in_child3_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child3only,Chauffer 2 Relative deviation ride share - child 3 only,((bundle1 != bundle3) & (bundle2 != bundle3) & (chauf3 == 3)) * abs_dev_dist_in_child3_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child12,Chauffer 1 Relative deviation ride share - child 1 & 2,((bundle1 == bundle2) & (bundle1 != bundle3) & (chauf1 == 1)) * abs_dev_dist_in_child12_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child12,Chauffer 2 Relative deviation ride share - child 1 & 2,((bundle1 == bundle2) & (bundle1 != bundle3) & (chauf1 == 3)) * abs_dev_dist_in_child12_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child13,Chauffer 1 Relative deviation ride share - child 1 & 3,((bundle1 != bundle2) & (bundle1 == bundle3) & (chauf1 == 1)) * abs_dev_dist_in_child13_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child13,Chauffer 2 Relative deviation ride share - child 1 & 3,((bundle1 != bundle2) & (bundle1 == bundle3) & (chauf1 == 3)) * abs_dev_dist_in_child13_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child23,Chauffer 1 Relative deviation ride share - child 2 & 3,((bundle1 != bundle2) & (bundle2 == bundle3) & (chauf2 == 1)) * abs_dev_dist_in_child23_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child23,Chauffer 2 Relative deviation ride share - child 2 & 3,((bundle1 != bundle2) & (bundle2 == bundle3) & (chauf2 == 3)) * abs_dev_dist_in_child23_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child123,Chauffer 1 Relative deviation ride share - child 1 & 2 & 3,((bundle1 == bundle2) & (bundle2 == bundle3) & (chauf1 == 1)) * abs_dev_dist_in_child123_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child123,Chauffer 2 Relative deviation ride share - child 1 & 2 & 3,((bundle1 == bundle2) & (bundle2 == bundle3) & (chauf1 == 3)) * abs_dev_dist_in_child123_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +# ,Same TAZ,, +util_same_taz_child12_escort,Same school TAZ for child 1 and 2 - Escorting,(bundle1 == bundle2) & (bundle1 > 0) & (school_location_child1 == school_location_child2) & (school_location_child1 > 0),coef_same_taz_escort +util_same_taz_child13_escort,Same school TAZ for child 1 and 3 - Escorting,(bundle1 == bundle3) & (bundle1 > 0) & (school_location_child1 == school_location_child3) & (school_location_child1 > 0),coef_same_taz_escort +util_same_taz_child32_escort,Same school TAZ for child 3 and 2 - Escorting,(bundle3 == bundle2) & (bundle2 > 0) & (school_location_child3 == school_location_child2) & (school_location_child2 > 0),coef_same_taz_escort +util_same_taz_child12_no_escort,Same school TAZ for child 1 and 2 - No Escorting,(bundle1 == 0) & (bundle2 == 0) & (child_id1 > 0) & (child_id2 > 0) & (school_location_child1 == school_location_child2) & (school_location_child1 > 0),coef_same_taz_no_escort +util_same_taz_child13_no_escort,Same school TAZ for child 1 and 3 - No Escorting,(bundle1 == 0) & (bundle3 == 0) & (child_id1 > 0) & (child_id3 > 0) & (school_location_child1 == school_location_child3) & (school_location_child1 > 0),coef_same_taz_no_escort +util_same_taz_child32_no_escort,Same school TAZ for child 3 and 2 - No Escorting,(bundle3 == 0) & (bundle2 == 0) & (child_id3 > 0) & (child_id2 > 0) & (school_location_child3 == school_location_child2) & (school_location_child2 > 0),coef_same_taz_no_escort +# ,Outbound Terms,, +util_no_escort_outbound_child1,No escorting in outbound direction - Child 1,(bundle1 == 0) & (child_id1 > 0) & (bundle1_outbound == 0),coef_no_escort_outbound +util_no_escort_outbound_child2,No escorting in outbound direction - Child 2,(bundle2 == 0) & (child_id2 > 0) & (bundle2_outbound == 0),coef_no_escort_outbound +util_no_escort_outbound_child3,No escorting in outbound direction - Child 3,(bundle3 == 0) & (child_id3 > 0) & (bundle3_outbound == 0),coef_no_escort_outbound +util_outbound_rs_child1,Ride sharing in the outbound direction - Child 1,((chauf1 == 1) | (chauf1 == 3)) & ((chauf1_outbound == 1) | (chauf1_outbound == 3)),coef_outbound_rs +util_outbound_rs_child2,Ride sharing in the outbound direction - Child 2,((chauf2 == 1) | (chauf2 == 3)) & ((chauf2_outbound == 1) | (chauf2_outbound == 3)),coef_outbound_rs +util_outbound_rs_child3,Ride sharing in the outbound direction - Child 3,((chauf3 == 1) | (chauf3 == 3)) & ((chauf3_outbound == 1) | (chauf3_outbound == 3)),coef_outbound_rs +util_same_chauf1,Same chauffeur in both directions (not child specific) - chauf 1 inbound & outbound,((chauf1 == 1) | (chauf1 == 2) | (chauf2 == 1) | (chauf2 == 2) | (chauf3 == 1) | (chauf3 == 2)) & ((nbund1_outbound > 0)),coef_same_chauf +util_same_chauf2,Same chauffeur in both directions (not child specific) - chauf 2 inbound & outbound,((chauf1 == 3) | (chauf1 == 4) | (chauf2 == 3) | (chauf2 == 4) | (chauf3 == 3) | (chauf3 == 4)) & ((nbund2_outbound > 0)),coef_same_chauf +# ,Calibration Constants,, +util_calib_child1_age_u6_rs,Child 1 age under 6 - Calibration constant - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 < 6),coef_calib_child_age_u6_rs +util_calib_child2_age_u6_rs,Child 2 age under 6 - Calibration constant - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 < 6),coef_calib_child_age_u6_rs +util_calib_child3_age_u6_rs,Child 3 age under 6 - Calibration constant - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 < 6),coef_calib_child_age_u6_rs +util_calib_child1_age_16p_rs,Child 1 age 16 years or older - Calibration constant - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 > 15),coef_calib_child_age_16p_rs +util_calib_child2_age_16p_rs,Child 2 age 16 years or older - Calibration constant - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 > 15),coef_calib_child_age_16p_rs +util_calib_child3_age_16p_rs,Child 3 age 16 years or older - Calibration constant - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 > 15),coef_calib_child_age_16p_rs +util_calib_child1_age_6to15_noes,Child 1 age 6 to 15 years - Calibration constant - No Escort,(bundle1 == 0) & (child_id1 > 0) & (age_child1 > 5) & (age_child1 < 16),coef_calib_child_age_6to15_noes +util_calib_child2_age_6to15_noes,Child 2 age 6 to 15 years - Calibration constant - No Escort,(bundle2 == 0) & (child_id2 > 0) & (age_child2 > 5) & (age_child2 < 16),coef_calib_child_age_6to15_noes +util_calib_child3_age_6to15_noes,Child 3 age 6 to 15 years - Calibration constant - No Escort,(bundle3 == 0) & (child_id3 > 0) & (age_child3 > 5) & (age_child3 < 16),coef_calib_child_age_6to15_noes +util_calib_child1_age_u6_noes,Child 1 age 5 years or younger - Calibration constant - No escort,(bundle1 == 0) & (child_id1 > 0) & (age_child1 < 6),coef_calib_child_age_u6_noes +util_calib_child2_age_u6_noes,Child 2 age 5 years or younger - Calibration constant - No escort,(bundle2 == 0) & (child_id2 > 0) & (age_child2 < 6),coef_calib_child_age_u6_noes +util_calib_child3_age_u6_noes,Child 3 age 5 years or younger - Calibration constant - No escort,(bundle3 == 0) & (child_id3 > 0) & (age_child3 < 6),coef_calib_child_age_u6_noes +util_calib_child1_age_6to15_rs,Child 1 age 6 to 15 years - Calibration constant - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 > 5) & (age_child1 < 16),coef_calib_child_age_6to15_rs +util_calib_child2_age_6to15_rs,Child 2 age 6 to 15 years - Calibration constant - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 > 5) & (age_child2 < 16),coef_calib_child_age_6to15_rs +util_calib_child3_age_6to15_rs,Child 3 age 6 to 15 years - Calibration constant - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 > 5) & (age_child3 < 16),coef_calib_child_age_6to15_rs diff --git a/resident/configs/school_escorting_outbound.csv b/resident/configs/school_escorting_outbound.csv new file mode 100644 index 0000000..82a1c66 --- /dev/null +++ b/resident/configs/school_escorting_outbound.csv @@ -0,0 +1,163 @@ +Label,Description,Expression,Coefficient +# ,Availability Conditions,, +util_one_child_to_school,Availability based on number of eligible children,((bundle2 + bundle3) > 0) & (num_children_going_to_school==1),coef_unavail +util_two_children_to_school,Availability based on number of eligible children,(bundle3 > 0) & (num_children_going_to_school == 2),coef_unavail +util_one_potential_chauffeur,Availability based on number of eligible chauffeurs,((bundle1 + bundle2 + bundle3) > 0) & (num_potential_chauffeurs == 0),coef_unavail +util_two_potential_chauffeurs,Availability based on number of eligible chauffeurs,((chauf1 > 2) | (chauf2 > 2) | (chauf3 > 2)) & (num_potential_chauffeurs == 1),coef_unavail +util_avail_rs_cdap_child1_chauf1,Availability for RideSharing by daily pattern - Child 1 and Chauffeur 1,(bundle1 > 0) & (chauf1 == 1) & (cdap_chauf1 != 'M'),coef_unavail +util_avail_rs_cdap_child1_chauf2,Availability for RideSharing by daily pattern - Child 1 and Chauffeur 2,(bundle1 > 0) & (chauf1 == 3) & (cdap_chauf2 != 'M'),coef_unavail +util_avail_rs_cdap_child2_chauf1,Availability for RideSharing by daily pattern - Child 2 and Chauffeur 1,(bundle2 > 0) & (chauf2 == 1) & (cdap_chauf1 != 'M'),coef_unavail +util_avail_rs_cdap_child2_chauf2,Availability for RideSharing by daily pattern - Child 2 and Chauffeur 2,(bundle2 > 0) & (chauf2 == 3) & (cdap_chauf2 != 'M'),coef_unavail +util_avail_rs_cdap_child3_chauf1,Availability for RideSharing by daily pattern - Child 3 and Chauffeur 1,(bundle3 > 0) & (chauf3 == 1) & (cdap_chauf1 != 'M'),coef_unavail +util_avail_rs_cdap_child3_chauf2,Availability for RideSharing by daily pattern - Child 3 and Chauffeur 2,(bundle3 > 0) & (chauf3 == 3) & (cdap_chauf2 != 'M'),coef_unavail +util_avail_rs_sync_child1_chauf1,Availability for RideSharing by synchronization - Child 1 and Chauffeur 1,@(df.bundle1 > 0) & (df.chauf1 == 1) & (df.pref_depart_time_chauf1 > 0) & (np.abs(df.pref_depart_time_chauf1 - df.pref_depart_time_school1) > max_bin_difference_between_departure_times),coef_unavail +util_avail_rs_sync_child1_chauf2,Availability for RideSharing by synchronization - Child 1 and Chauffeur 2,@(df.bundle1 > 0) & (df.chauf1 == 3) & (df.pref_depart_time_chauf2 > 0) & (np.abs(df.pref_depart_time_chauf2 - df.pref_depart_time_school1) > max_bin_difference_between_departure_times),coef_unavail +util_avail_rs_sync_child2_chauf1,Availability for RideSharing by synchronization - Child 2 and Chauffeur 1,@(df.bundle2 > 0) & (df.chauf2 == 1) & (df.pref_depart_time_chauf1 > 0) & (np.abs(df.pref_depart_time_chauf1 - df.pref_depart_time_school2) > max_bin_difference_between_departure_times),coef_unavail +util_avail_rs_sync_child2_chauf2,Availability for RideSharing by synchronization - Child 2 and Chauffeur 2,@(df.bundle2 > 0) & (df.chauf2 == 3) & (df.pref_depart_time_chauf2 > 0) & (np.abs(df.pref_depart_time_chauf2 - df.pref_depart_time_school2) > max_bin_difference_between_departure_times),coef_unavail +util_avail_rs_sync_child3_chauf1,Availability for RideSharing by synchronization - Child 3 and Chauffeur 1,@(df.bundle3 > 0) & (df.chauf3 == 1) & (df.pref_depart_time_chauf1 > 0) & (np.abs(df.pref_depart_time_chauf1 - df.pref_depart_time_school3) > max_bin_difference_between_departure_times),coef_unavail +util_avail_rs_sync_child3_chauf2,Availability for RideSharing by synchronization - Child 3 and Chauffeur 2,@(df.bundle3 > 0) & (df.chauf3 == 3) & (df.pref_depart_time_chauf2 > 0) & (np.abs(df.pref_depart_time_chauf2 - df.pref_depart_time_school3) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_cdap_child1_chauf1,Availability for Pure-Escorting by daily pattern - Child 1 and Chauffeur 1,(bundle1 > 0) & (chauf1 == 2) & (ptype_chauf1 < 6) & (cdap_chauf1 == 'H'),coef_unavail +util_avail_pe_cdap_child1_chauf2,Availability for Pure-Escorting by daily pattern - Child 1 and Chauffeur 2,(bundle1 > 0) & (chauf1 == 4) & (ptype_chauf2 < 6) & (cdap_chauf2 == 'H'),coef_unavail +util_avail_pe_cdap_child2_chauf1,Availability for Pure-Escorting by daily pattern - Child 2 and Chauffeur 1,(bundle2 > 0) & (chauf2 == 2) & (ptype_chauf1 < 6) & (cdap_chauf1 == 'H'),coef_unavail +util_avail_pe_cdap_child2_chauf2,Availability for Pure-Escorting by daily pattern - Child 2 and Chauffeur 2,(bundle2 > 0) & (chauf2 == 4) & (ptype_chauf2 < 6) & (cdap_chauf2 == 'H'),coef_unavail +util_avail_pe_cdap_child3_chauf1,Availability for Pure-Escorting by daily pattern - Child 3 and Chauffeur 1,(bundle3 > 0) & (chauf3 == 2) & (ptype_chauf1 < 6) & (cdap_chauf1 == 'H'),coef_unavail +util_avail_pe_cdap_child3_chauf2,Availability for Pure-Escorting by daily pattern - Child 3 and Chauffeur 2,(bundle3 > 0) & (chauf3 == 4) & (ptype_chauf2 < 6) & (cdap_chauf2 == 'H'),coef_unavail +util_avail_pe_sync_child1_chauf1,Availability for Pure-Escorting by synchronization - Child 1 and Chauffeur 1,@(df.bundle1 > 0) & (df.chauf1 == 2) & (df.ptype_chauf1 < 4) & (df.cdap_chauf1 == 'H') & (np.abs(df.pref_depart_time_school1 + ((df.time_home_to_school1 + df.time_school_to_home1) / mins_per_time_bin) - df.pref_depart_time_chauf1) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_sync_child1_chauf2,Availability for Pure-Escorting by synchronization - Child 1 and Chauffeur 2,@(df.bundle1 > 0) & (df.chauf1 == 4) & (df.ptype_chauf2 < 4) & (df.cdap_chauf2 == 'H') & (np.abs(df.pref_depart_time_school1 + ((df.time_home_to_school1 + df.time_school_to_home1) / mins_per_time_bin) - df.pref_depart_time_chauf2) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_sync_child2_chauf1,Availability for Pure-Escorting by synchronization - Child 2 and Chauffeur 1,@(df.bundle2 > 0) & (df.chauf2 == 2) & (df.ptype_chauf1 < 4) & (df.cdap_chauf1 == 'H') & (np.abs(df.pref_depart_time_school2 + ((df.time_home_to_school2 + df.time_school_to_home2) / mins_per_time_bin) - df.pref_depart_time_chauf1) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_sync_child2_chauf2,Availability for Pure-Escorting by synchronization - Child 2 and Chauffeur 2,@(df.bundle2 > 0) & (df.chauf2 == 4) & (df.ptype_chauf2 < 4) & (df.cdap_chauf2 == 'H') & (np.abs(df.pref_depart_time_school2 + ((df.time_home_to_school2 + df.time_school_to_home2) / mins_per_time_bin) - df.pref_depart_time_chauf2) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_sync_child3_chauf1,Availability for Pure-Escorting by synchronization - Child 3 and Chauffeur 1,@(df.bundle3 > 0) & (df.chauf3 == 2) & (df.ptype_chauf1 < 4) & (df.cdap_chauf1 == 'H') & (np.abs(df.pref_depart_time_school3 + ((df.time_home_to_school3 + df.time_school_to_home3) / mins_per_time_bin) - df.pref_depart_time_chauf1) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_sync_child3_chauf2,Availability for Pure-Escorting by synchronization - Child 3 and Chauffeur 2,@(df.bundle3 > 0) & (df.chauf3 == 4) & (df.ptype_chauf2 < 4) & (df.cdap_chauf2 == 'H') & (np.abs(df.pref_depart_time_school3 + ((df.time_home_to_school3 + df.time_school_to_home3) / mins_per_time_bin) - df.pref_depart_time_chauf2) > max_bin_difference_between_departure_times),coef_unavail +util_avail_bundle_child_1and2,Availability for bundling child 1 and child 2,@(df.bundle1 == df.bundle2) & (df.bundle1 > 0) & (np.abs(df.pref_depart_time_school1 - df.pref_depart_time_school2) > max_bin_difference_between_departure_times),coef_unavail +util_avail_bundle_child_1and3,Availability for bundling child 1 and child 3,@(df.bundle1 == df.bundle3) & (df.bundle1 > 0) & (np.abs(df.pref_depart_time_school1 - df.pref_depart_time_school3) > max_bin_difference_between_departure_times),coef_unavail +util_avail_bundle_child_2and3,Availability for bundling child 2 and child 3,@(df.bundle2 == df.bundle3) & (df.bundle2 > 0) & (np.abs(df.pref_depart_time_school2 - df.pref_depart_time_school3) > max_bin_difference_between_departure_times),coef_unavail +util_avail_mult_bundles_outbound1,Availability Chauffeur 1 - Expected arrival from previous pure escort must be before departure for subsequent escort,((nrs1 + npe1) > 1) & ~avail_multiple_bundles,coef_unavail +util_avail_mult_bundles_outbound2,Availability Chauffeur 2 - Expected arrival from previous pure escort must be before departure for subsequent escort,((nrs2 + npe2) > 1) & ~avail_multiple_bundles,coef_unavail +util_avail_pe_during_mand_child1_chauf1,Availability pure escort tour must take place before mandatory tour - Child 1 and Chauffeur 1,@(df.bundle1 > 0) & (df.chauf1 == 2) & (df.cdap_chauf1 == 'M') & ((df.pref_depart_time_chauf1 - max_bin_difference_between_departure_times) <= df.pref_depart_time_school1),coef_unavail +util_avail_pe_during_mand_child1_chauf2,Availability pure escort tour must take place before mandatory tour - Child 1 and Chauffeur 2,@(df.bundle1 > 0) & (df.chauf1 == 4) & (df.cdap_chauf2 == 'M') & ((df.pref_depart_time_chauf2 - max_bin_difference_between_departure_times) <= df.pref_depart_time_school1),coef_unavail +util_avail_pe_during_mand_child2_chauf1,Availability pure escort tour must take place before mandatory tour - Child 2 and Chauffeur 1,@(df.bundle2 > 0) & (df.chauf2 == 2) & (df.cdap_chauf1 == 'M') & ((df.pref_depart_time_chauf1 - max_bin_difference_between_departure_times) <= df.pref_depart_time_school2),coef_unavail +util_avail_pe_during_mand_child2_chauf2,Availability pure escort tour must take place before mandatory tour - Child 2 and Chauffeur 2,@(df.bundle2 > 0) & (df.chauf2 == 4) & (df.cdap_chauf2 == 'M') & ((df.pref_depart_time_chauf2 - max_bin_difference_between_departure_times) <= df.pref_depart_time_school2),coef_unavail +util_avail_pe_during_mand_child3_chauf1,Availability pure escort tour must take place before mandatory tour - Child 3 and Chauffeur 1,@(df.bundle3 > 0) & (df.chauf3 == 2) & (df.cdap_chauf1 == 'M') & ((df.pref_depart_time_chauf1 - max_bin_difference_between_departure_times) <= df.pref_depart_time_school3),coef_unavail +util_avail_pe_during_mand_child3_chauf2,Availability pure escort tour must take place before mandatory tour - Child 3 and Chauffeur 2,@(df.bundle3 > 0) & (df.chauf3 == 4) & (df.cdap_chauf2 == 'M') & ((df.pref_depart_time_chauf2 - max_bin_difference_between_departure_times) <= df.pref_depart_time_school3),coef_unavail +# ,No escorting,, +util_child1_age_16p_noes,Child 1 age 16 years or older - No escort,(bundle1 == 0) & (child_id1 > 0) & (age_child1 > 15),coef_child_age_16p_noes +util_child2_age_16p_noes,Child 2 age 16 years or older - No escort,(bundle2 == 0) & (child_id2 > 0) & (age_child2 > 15),coef_child_age_16p_noes +util_child3_age_16p_noes,Child 3 age 16 years or older - No escort,(bundle3 == 0) & (child_id3 > 0) & (age_child3 > 15),coef_child_age_16p_noes +util_child1_age_10_to_15_noes,Child 1 age 10 to 15 years - No escort,(bundle1 == 0) & (child_id1 > 0) & (age_child1 > 9) & (age_child1 < 16),coef_child_age_10_to_15_noes +util_child2_age_10_to_15_noes,Child 2 age 10 to 15 years - No escort,(bundle2 == 0) & (child_id2 > 0) & (age_child2 > 9) & (age_child2 < 16),coef_child_age_10_to_15_noes +util_child3_age_10_to_15_noes,Child 3 age 10 to 15 years - No escort,(bundle3 == 0) & (child_id3 > 0) & (age_child3 > 9) & (age_child3 < 16),coef_child_age_10_to_15_noes +util_child1_age_u9_noes,Child 1 age 9 years or younger - No escort,(bundle1 == 0) & (child_id1 > 0) & (age_child1 < 10),coef_child_age_u9_noes +util_child2_age_u9_noes,Child 2 age 9 years or younger - No escort,(bundle2 == 0) & (child_id2 > 0) & (age_child2 < 10),coef_child_age_u9_noes +util_child3_age_u9_noes,Child 3 age 9 years or younger - No escort,(bundle3 == 0) & (child_id3 > 0) & (age_child3 < 10),coef_child_age_u9_noes +util_ln_dist_to_school_child1_noes,Logged distance to school for Child 1 - No escort,@(df.bundle1 == 0) * (df.child_id1 > 0) * np.log(1 + df.dist_home_to_school1),coef_ln_dist_to_school_noes +util_ln_dist_to_school_child2_noes,Logged distance to school for Child 2 - No escort,@(df.bundle2 == 0) * (df.child_id2 > 0) * np.log(1 + df.dist_home_to_school2),coef_ln_dist_to_school_noes +util_ln_dist_to_school_child3_noes,Logged distance to school for Child 3 - No escort,@(df.bundle3 == 0) * (df.child_id3 > 0) * np.log(1 + df.dist_home_to_school3),coef_ln_dist_to_school_noes +util_ln_dist_to_school_child1_u6_noes,Logged distance to school for Child 1 under 6 years old - No escort,@(df.bundle1 == 0) * (df.child_id1 > 0) * (df.age_child1 < 6) * np.log(1 + df.dist_home_to_school1),coef_ln_dist_to_school_u6_noes +util_ln_dist_to_school_child2_u6_noes,Logged distance to school for Child 2 under 6 years old - No escort,@(df.bundle2 == 0) * (df.child_id2 > 0) * (df.age_child2 < 6) * np.log(1 + df.dist_home_to_school2),coef_ln_dist_to_school_u6_noes +util_ln_dist_to_school_child3_u6_noes,Logged distance to school for Child 3 under 6 years old - No escort,@(df.bundle3 == 0) * (df.child_id3 > 0) * (df.age_child3 < 6) * np.log(1 + df.dist_home_to_school3),coef_ln_dist_to_school_u6_noes +util_ln_dist_to_school_child1_6to9_noes,Logged distance to school for Child 1 6 to 9 years old - No escort,@(df.bundle1 == 0) * (df.child_id1 > 0) * (df.age_child1 > 5) * (df.age_child1 < 10) * np.log(1 + df.dist_home_to_school1),coef_ln_dist_to_school_6to9_noes +util_ln_dist_to_school_child2_6to9_noes,Logged distance to school for Child 2 6 to 9 years old - No escort,@(df.bundle2 == 0) * (df.child_id2 > 0) * (df.age_child2 > 5) * (df.age_child2 < 10) * np.log(1 + df.dist_home_to_school2),coef_ln_dist_to_school_6to9_noes +util_ln_dist_to_school_child3_6to9_noes,Logged distance to school for Child 3 6 to 9 years old - No escort,@(df.bundle3 == 0) * (df.child_id3 > 0) * (df.age_child3 > 5) * (df.age_child3 < 10) * np.log(1 + df.dist_home_to_school3),coef_ln_dist_to_school_6to9_noes +# ,Ride Sharing,, +util_child1_age_16p_rs,Child 1 age 16 years or older - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 > 15),coef_child_age_16p_rs +util_child2_age_16p_rs,Child 2 age 16 years or older - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 > 15),coef_child_age_16p_rs +util_child3_age_16p_rs,Child 3 age 16 years or older - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 > 15),coef_child_age_16p_rs +util_child1_age_10to15_rs,Child 1 age 10 to 15 years - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 > 9) & (age_child1 < 16),coef_child_age_10to15_rs +util_child2_age_10to15_rs,Child 2 age 10 to 15 years - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 > 9) & (age_child2 < 16),coef_child_age_10to15_rs +util_child3_age_10to15_rs,Child 3 age 10 to 15 years - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 > 9) & (age_child3 < 16),coef_child_age_10to15_rs +util_child1_age_6to9_rs,Child 1 age 6 to 9 years - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 > 5) & (age_child1 < 10),coef_child_age_6to9_rs +util_child2_age_6to9_rs,Child 2 age 6 to 9 years - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 > 5) & (age_child2 < 10),coef_child_age_6to9_rs +util_child3_age_6to9_rs,Child 3 age 6 to 9 years - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 > 5) & (age_child3 < 10),coef_child_age_6to9_rs +util_child1_age_u6_rs,Child 1 age under 6 years old - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 < 6),coef_child_age_u6_rs +util_child2_age_u6_rs,Child 2 age under 6 years old - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 < 6),coef_child_age_u6_rs +util_child3_age_u6_rs,Child 3 age under 6 years old - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 < 6),coef_child_age_u6_rs +# ,Pure Escort Distance,, +util_child1_dist_pe,Child 1 distance to school - Pure escorting,((chauf1 == 2) | (chauf1 == 4)) * dist_home_to_school1,coef_child_dist_pe +util_child2_dist_pe,Child 2 distance to school - Pure escorting,((chauf2 == 2) | (chauf2 == 4)) * dist_home_to_school2,coef_child_dist_pe +util_child3_dist_pe,Child 3 distance to school - Pure escorting,((chauf3 == 2) | (chauf3 == 4)) * dist_home_to_school3,coef_child_dist_pe +util_child1_dist_pe_over30miles,Child 1 Pure escorting not allowed if over 30 miles,((chauf1 == 2) | (chauf1 == 4)) & (dist_home_to_school1 > 30),coef_unavail +util_child2_dist_pe_over30miles,Child 2 Pure escorting not allowed if over 30 miles,((chauf2 == 2) | (chauf2 == 4)) & (dist_home_to_school2 > 30),coef_unavail +util_child3_dist_pe_over30miles,Child 3 Pure escorting not allowed if over 30 miles,((chauf3 == 2) | (chauf3 == 4)) & (dist_home_to_school3 > 30),coef_unavail +# ,Household Interactions,, +util_hh_inc_u25k_noes,Household income less than 25k - No Escorting,(income <= 25000) & ((bundle1 == 0) | ((bundle2 == 0) & (num_children_going_to_school > 1)) | ((bundle3 == 0) & (num_children_going_to_school > 2))),coef_hh_inc_u25k_noes +util_hh_inc_25to50k_noes,Household income between 25 and 50k - No Escorting,((income > 25000) & (income <= 50000)) & ((bundle1 == 0) | ((bundle2 == 0) & (num_children_going_to_school > 1)) | ((bundle3 == 0) & (num_children_going_to_school > 2))),coef_hh_inc_25to50k_noes +util_zero_auto_hh_noes,Zero cars in the household - No Escorting,(auto_ownership == 0) & ((bundle1 == 0) | ((bundle2 == 0) & (num_children_going_to_school > 1)) | ((bundle3 == 0) & (num_children_going_to_school > 2))),coef_zero_auto_hh_noes +util_cars_lt_workers_rs,Cars fewer than household workers - Ride Share,(auto_ownership < num_workers) & ((chauf1 % 2 == 1) | (chauf2 % 2 == 1) | (chauf3 % 2 == 1)),coef_cars_lt_workers_rs +util_cars_lt_workers_pe,Cars fewer than household workers - Pure escort,(auto_ownership < num_workers) & ((chauf1 % 2 == 0) | (chauf2 % 2 == 0) | (chauf3 % 2 == 0)),coef_cars_lt_workers_pe +# ,Chauffer Interactions,, +util_chauf1_female_rs,Chauffeur 1 Female - Ride share,(gender_chauf1 == 2) & ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_female_rs +util_chauf1_male_rs,Chauffeur 1 Male - Ride share,(gender_chauf1 == 1) & ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_male_rs +util_chauf1_female_pe,Chauffeur 1 Female - Pure Escort,(gender_chauf1 == 2) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_female_pe +util_chauf1_male_pe,Chauffeur 1 Male - Pure Escort,(gender_chauf1 == 1) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_male_pe +util_chauf2_female_rs,Chauffeur 2 Female - Ride share,(gender_chauf2 == 2) & ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_female_rs +util_chauf2_male_rs,Chauffeur 2 Male - Ride share,(gender_chauf2 == 1) & ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_male_rs +util_chauf2_female_pe,Chauffeur 2 Female - Pure Escort,(gender_chauf2 == 2) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_female_pe +util_chauf2_male_pe,Chauffeur 2 Male - Pure Escort,(gender_chauf2 == 1) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_male_pe +util_chauf1_pt_worker_rs,Chauffer 1 part time worker - Ride share,(ptype_chauf1 == 2) & ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_pt_worker_rs +util_chauf1_pt_worker_pe,Chauffer 1 part time worker - Pure escort,(ptype_chauf1 == 2) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_pt_worker_pe +util_chauf2_pt_worker_rs,Chauffer 2 part time worker - Ride share,(ptype_chauf2 == 2) & ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_pt_worker_rs +util_chauf2_pt_worker_pe,Chauffer 2 part time worker - Pure escort,(ptype_chauf2 == 2) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_pt_worker_pe +util_chauf1_non_worker_pe,Chauffer 1 non worker - Pure escort,(ptype_chauf1 == 4) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_non_worker_pe +util_chauf2_non_worker_pe,Chauffer 2 non worker - Pure escort,(ptype_chauf2 == 4) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_non_worker_pe +util_chauf1_univ_stud_re,Chauffer 1 university student - Ride Share,(ptype_chauf1 == 3) & ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_univ_stud_re +util_chauf2_univ_stud_re,Chauffer 2 university student - Ride Share,(ptype_chauf2 == 3) & ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_univ_stud_re +util_chauf1_age_u35_pe,Chauffer 1 Age 35 years or younger - Pure escort,(ptype_chauf1 == 4) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_age_u35_pe +util_chauf2_age_u35_pe,Chauffer 2 Age 35 years or younger - Pure escort,(ptype_chauf2 == 4) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_age_u35_pe +util_chauf1_time_to_work_or_univ_rs,Chauffer 1 Auto time to work or university - Ride Share,time_home_to_mand1 * ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_time_to_work_or_univ_rs +util_chauf2_time_to_work_or_univ_rs,Chauffer 2 Auto time to work or university - Ride Share,time_home_to_mand2 * ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_time_to_work_or_univ_rs +util_chauf1_walk_dist_to_work_or_univ_rs,Chauffer 1 Walk dist to work or university - Ride Share,(dist_home_to_mand1 < 3) & ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_walk_dist_to_work_or_univ_rs +util_chauf2_walk_dist_to_work_or_univ_rs,Chauffer 2 Walk dist to work or university - Ride Share,(dist_home_to_mand2 < 3) & ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_walk_dist_to_work_or_univ_rs +util_chauf1_walk_dist_to_work_or_univ_pe,Chauffer 1 Walk dist to work or university - Pure Escort,(dist_home_to_mand1 < 3) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_walk_dist_to_work_or_univ_pe +util_chauf2_walk_dist_to_work_or_univ_pe,Chauffer 2 Walk dist to work or university - Pure Escort,(dist_home_to_mand2 < 3) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_walk_dist_to_work_or_univ_pe +# ,Chauffer deviation,, +util_chauf1_abs_dev_rs_child1only,Chauffer 1 Absolute deviation ride share - child 1 only,((bundle1 != bundle2) & (bundle1 != bundle3) & (chauf1 == 1)) * abs_dev_dist_out_child1_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child1only,Chauffer 2 Absolute deviation ride share - child 1 only,((bundle1 != bundle2) & (bundle1 != bundle3) & (chauf1 == 3)) * abs_dev_dist_out_child1_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child2only,Chauffer 1 Absolute deviation ride share - child 2 only,((bundle1 != bundle2) & (bundle2 != bundle3) & (chauf2 == 1)) * abs_dev_dist_out_child2_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child2only,Chauffer 2 Absolute deviation ride share - child 2 only,((bundle1 != bundle2) & (bundle2 != bundle3) & (chauf2 == 3)) * abs_dev_dist_out_child2_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child3only,Chauffer 1 Absolute deviation ride share - child 3 only,((bundle1 != bundle3) & (bundle2 != bundle3) & (chauf3 == 1)) * abs_dev_dist_out_child3_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child3only,Chauffer 2 Absolute deviation ride share - child 3 only,((bundle1 != bundle3) & (bundle2 != bundle3) & (chauf3 == 3)) * abs_dev_dist_out_child3_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child12,Chauffer 1 Absolute deviation ride share - child 1 & 2,((bundle1 == bundle2) & (bundle1 != bundle3) & (chauf1 == 1)) * abs_dev_dist_out_child12_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child12,Chauffer 2 Absolute deviation ride share - child 1 & 2,((bundle1 == bundle2) & (bundle1 != bundle3) & (chauf1 == 3)) * abs_dev_dist_out_child12_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child13,Chauffer 1 Absolute deviation ride share - child 1 & 3,((bundle1 != bundle2) & (bundle1 == bundle3) & (chauf1 == 1)) * abs_dev_dist_out_child13_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child13,Chauffer 2 Absolute deviation ride share - child 1 & 3,((bundle1 != bundle2) & (bundle1 == bundle3) & (chauf1 == 3)) * abs_dev_dist_out_child13_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child23,Chauffer 1 Absolute deviation ride share - child 2 & 3,((bundle1 != bundle2) & (bundle2 == bundle3) & (chauf2 == 1)) * abs_dev_dist_out_child23_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child23,Chauffer 2 Absolute deviation ride share - child 2 & 3,((bundle1 != bundle2) & (bundle2 == bundle3) & (chauf2 == 3)) * abs_dev_dist_out_child23_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child123,Chauffer 1 Absolute deviation ride share - child 1 & 2 & 3,((bundle1 == bundle2) & (bundle2 == bundle3) & (chauf1 == 1)) * abs_dev_dist_out_child123_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child123,Chauffer 2 Absolute deviation ride share - child 1 & 2 & 3,((bundle1 == bundle2) & (bundle2 == bundle3) & (chauf1 == 3)) * abs_dev_dist_out_child123_chauf2,coef_abs_dev_distance +util_chauf1_rel_dev_rs_child1only,Chauffer 1 Relative deviation ride share - child 1 only,((bundle1 != bundle2) & (bundle1 != bundle3) & (chauf1 == 1)) * abs_dev_dist_out_child1_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child1only,Chauffer 2 Relative deviation ride share - child 1 only,((bundle1 != bundle2) & (bundle1 != bundle3) & (chauf1 == 3)) * abs_dev_dist_out_child1_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child2only,Chauffer 1 Relative deviation ride share - child 2 only,((bundle1 != bundle2) & (bundle2 != bundle3) & (chauf2 == 1)) * abs_dev_dist_out_child2_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child2only,Chauffer 2 Relative deviation ride share - child 2 only,((bundle1 != bundle2) & (bundle2 != bundle3) & (chauf2 == 3)) * abs_dev_dist_out_child2_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child3only,Chauffer 1 Relative deviation ride share - child 3 only,((bundle1 != bundle3) & (bundle2 != bundle3) & (chauf3 == 1)) * abs_dev_dist_out_child3_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child3only,Chauffer 2 Relative deviation ride share - child 3 only,((bundle1 != bundle3) & (bundle2 != bundle3) & (chauf3 == 3)) * abs_dev_dist_out_child3_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child12,Chauffer 1 Relative deviation ride share - child 1 & 2,((bundle1 == bundle2) & (bundle1 != bundle3) & (chauf1 == 1)) * abs_dev_dist_out_child12_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child12,Chauffer 2 Relative deviation ride share - child 1 & 2,((bundle1 == bundle2) & (bundle1 != bundle3) & (chauf1 == 3)) * abs_dev_dist_out_child12_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child13,Chauffer 1 Relative deviation ride share - child 1 & 3,((bundle1 != bundle2) & (bundle1 == bundle3) & (chauf1 == 1)) * abs_dev_dist_out_child13_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child13,Chauffer 2 Relative deviation ride share - child 1 & 3,((bundle1 != bundle2) & (bundle1 == bundle3) & (chauf1 == 3)) * abs_dev_dist_out_child13_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child23,Chauffer 1 Relative deviation ride share - child 2 & 3,((bundle1 != bundle2) & (bundle2 == bundle3) & (chauf2 == 1)) * abs_dev_dist_out_child23_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child23,Chauffer 2 Relative deviation ride share - child 2 & 3,((bundle1 != bundle2) & (bundle2 == bundle3) & (chauf2 == 3)) * abs_dev_dist_out_child23_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child123,Chauffer 1 Relative deviation ride share - child 1 & 2 & 3,((bundle1 == bundle2) & (bundle2 == bundle3) & (chauf1 == 1)) * abs_dev_dist_out_child123_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child123,Chauffer 2 Relative deviation ride share - child 1 & 2 & 3,((bundle1 == bundle2) & (bundle2 == bundle3) & (chauf1 == 3)) * abs_dev_dist_out_child123_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +# ,Same TAZ,, +util_same_taz_child12_escort,Same school TAZ for child 1 and 2 - Escorting,(bundle1 == bundle2) & (bundle1 > 0) & (school_location_child1 == school_location_child2) & (school_location_child1 > 0),coef_same_taz_escort +util_same_taz_child13_escort,Same school TAZ for child 1 and 3 - Escorting,(bundle1 == bundle3) & (bundle1 > 0) & (school_location_child1 == school_location_child3) & (school_location_child1 > 0),coef_same_taz_escort +util_same_taz_child32_escort,Same school TAZ for child 3 and 2 - Escorting,(bundle3 == bundle2) & (bundle2 > 0) & (school_location_child3 == school_location_child2) & (school_location_child2 > 0),coef_same_taz_escort +util_same_taz_child12_no_escort,Same school TAZ for child 1 and 2 - No Escorting,(bundle1 == 0) & (bundle2 == 0) & (child_id1 > 0) & (child_id2 > 0) & (school_location_child1 == school_location_child2) & (school_location_child1 > 0),coef_same_taz_no_escort +util_same_taz_child13_no_escort,Same school TAZ for child 1 and 3 - No Escorting,(bundle1 == 0) & (bundle3 == 0) & (child_id1 > 0) & (child_id3 > 0) & (school_location_child1 == school_location_child3) & (school_location_child1 > 0),coef_same_taz_no_escort +util_same_taz_child32_no_escort,Same school TAZ for child 3 and 2 - No Escorting,(bundle3 == 0) & (bundle2 == 0) & (child_id3 > 0) & (child_id2 > 0) & (school_location_child3 == school_location_child2) & (school_location_child2 > 0),coef_same_taz_no_escort_23 +# ,Calibration constants,, +util_calib_child1_age_u6_rs,Child 1 age under 6 - Calibration constant - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 < 6),coef_calib_child_age_u6_rs +util_calib_child2_age_u6_rs,Child 2 age under 6 - Calibration constant - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 < 6),coef_calib_child_age_u6_rs +util_calib_child3_age_u6_rs,Child 3 age under 6 - Calibration constant - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 < 6),coef_calib_child_age_u6_rs +util_calib_child1_age_16p_rs,Child 1 age 16 years or older - Calibration constant - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 > 15),coef_calib_child_age_16p_rs +util_calib_child2_age_16p_rs,Child 2 age 16 years or older - Calibration constant - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 > 15),coef_calib_child_age_16p_rs +util_calib_child3_age_16p_rs,Child 3 age 16 years or older - Calibration constant - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 > 15),coef_calib_child_age_16p_rs +util_calib_child1_age_6to15_noes,Child 1 age 6 to 15 years - Calibration constant - No Escort,(bundle1 == 0) & (child_id1 > 0) & (age_child1 > 5) & (age_child1 < 16),coef_calib_child_age_6to15_noes +util_calib_child2_age_6to15_noes,Child 2 age 6 to 15 years - Calibration constant - No Escort,(bundle2 == 0) & (child_id2 > 0) & (age_child2 > 5) & (age_child2 < 16),coef_calib_child_age_6to15_noes +util_calib_child3_age_6to15_noes,Child 3 age 6 to 15 years - Calibration constant - No Escort,(bundle3 == 0) & (child_id3 > 0) & (age_child3 > 5) & (age_child3 < 16),coef_calib_child_age_6to15_noes +util_calib_child1_age_6to15_rs,Child 1 age 6 to 15 years - Calibration constant - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 > 5) & (age_child1 < 16),coef_calib_child_age_6to15_rs +util_calib_child2_age_6to15_rs,Child 2 age 6 to 15 years - Calibration constant - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 > 5) & (age_child2 < 16),coef_calib_child_age_6to15_rs +util_calib_child3_age_6to15_rs,Child 3 age 6 to 15 years - Calibration constant - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 > 5) & (age_child3 < 16),coef_calib_child_age_6to15_rs +util_calib_child1_age_u6_noes,Child 1 age 6 years or younger - Calibration constant - No escort,(bundle1 == 0) & (child_id1 > 0) & (age_child1 < 6),coef_calib_child_age_u6_noes +util_calib_child2_age_u6_noes,Child 2 age 6 years or younger - Calibration constant - No escort,(bundle2 == 0) & (child_id2 > 0) & (age_child2 < 6),coef_calib_child_age_u6_noes +util_calib_child3_age_u6_noes,Child 3 age 6 years or younger - Calibration constant - No escort,(bundle3 == 0) & (child_id3 > 0) & (age_child3 < 6),coef_calib_child_age_u6_noes diff --git a/resident/configs/school_escorting_outbound_cond.csv b/resident/configs/school_escorting_outbound_cond.csv new file mode 100644 index 0000000..5d890c7 --- /dev/null +++ b/resident/configs/school_escorting_outbound_cond.csv @@ -0,0 +1,169 @@ +Label,Description,Expression,Coefficient +# ,Availability Conditions,, +util_one_child_to_school,Availability based on number of eligible children,((bundle2 + bundle3) > 0) & (num_children_going_to_school==1),coef_unavail +util_two_children_to_school,Availability based on number of eligible children,(bundle3 > 0) & (num_children_going_to_school == 2),coef_unavail +util_one_potential_chauffeur,Availability based on number of eligible chauffeurs,((bundle1 + bundle2 + bundle3) > 0) & (num_potential_chauffeurs == 0),coef_unavail +util_two_potential_chauffeurs,Availability based on number of eligible chauffeurs,((chauf1 > 2) | (chauf2 > 2) | (chauf3 > 2)) & (num_potential_chauffeurs == 1),coef_unavail +util_avail_rs_cdap_child1_chauf1,Availability for RideSharing by daily pattern - Child 1 and Chauffeur 1,(bundle1 > 0) & (chauf1 == 1) & (cdap_chauf1 != 'M'),coef_unavail +util_avail_rs_cdap_child1_chauf2,Availability for RideSharing by daily pattern - Child 1 and Chauffeur 2,(bundle1 > 0) & (chauf1 == 3) & (cdap_chauf2 != 'M'),coef_unavail +util_avail_rs_cdap_child2_chauf1,Availability for RideSharing by daily pattern - Child 2 and Chauffeur 1,(bundle2 > 0) & (chauf2 == 1) & (cdap_chauf1 != 'M'),coef_unavail +util_avail_rs_cdap_child2_chauf2,Availability for RideSharing by daily pattern - Child 2 and Chauffeur 2,(bundle2 > 0) & (chauf2 == 3) & (cdap_chauf2 != 'M'),coef_unavail +util_avail_rs_cdap_child3_chauf1,Availability for RideSharing by daily pattern - Child 3 and Chauffeur 1,(bundle3 > 0) & (chauf3 == 1) & (cdap_chauf1 != 'M'),coef_unavail +util_avail_rs_cdap_child3_chauf2,Availability for RideSharing by daily pattern - Child 3 and Chauffeur 2,(bundle3 > 0) & (chauf3 == 3) & (cdap_chauf2 != 'M'),coef_unavail +util_avail_rs_sync_child1_chauf1,Availability for RideSharing by synchronization - Child 1 and Chauffeur 1,@(df.bundle1 > 0) & (df.chauf1 == 1) & (np.abs(df.pref_depart_time_chauf1 - df.pref_depart_time_school1) > max_bin_difference_between_departure_times),coef_unavail +util_avail_rs_sync_child1_chauf2,Availability for RideSharing by synchronization - Child 1 and Chauffeur 2,@(df.bundle1 > 0) & (df.chauf1 == 3) & (np.abs(df.pref_depart_time_chauf2 - df.pref_depart_time_school1) > max_bin_difference_between_departure_times),coef_unavail +util_avail_rs_sync_child2_chauf1,Availability for RideSharing by synchronization - Child 2 and Chauffeur 1,@(df.bundle2 > 0) & (df.chauf2 == 1) & (np.abs(df.pref_depart_time_chauf1 - df.pref_depart_time_school2) > max_bin_difference_between_departure_times),coef_unavail +util_avail_rs_sync_child2_chauf2,Availability for RideSharing by synchronization - Child 2 and Chauffeur 2,@(df.bundle2 > 0) & (df.chauf2 == 3) & (np.abs(df.pref_depart_time_chauf2 - df.pref_depart_time_school2) > max_bin_difference_between_departure_times),coef_unavail +util_avail_rs_sync_child3_chauf1,Availability for RideSharing by synchronization - Child 3 and Chauffeur 1,@(df.bundle3 > 0) & (df.chauf3 == 1) & (np.abs(df.pref_depart_time_chauf1 - df.pref_depart_time_school3) > max_bin_difference_between_departure_times),coef_unavail +util_avail_rs_sync_child3_chauf2,Availability for RideSharing by synchronization - Child 3 and Chauffeur 2,@(df.bundle3 > 0) & (df.chauf3 == 3) & (np.abs(df.pref_depart_time_chauf2 - df.pref_depart_time_school3) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_cdap_child1_chauf1,Availability for Pure-Escorting by daily pattern - Child 1 and Chauffeur 1,(bundle1 > 0) & (chauf1 == 2) & (ptype_chauf1 < 6) & (cdap_chauf1 == 'H'),coef_unavail +util_avail_pe_cdap_child1_chauf2,Availability for Pure-Escorting by daily pattern - Child 1 and Chauffeur 2,(bundle1 > 0) & (chauf1 == 4) & (ptype_chauf2 < 6) & (cdap_chauf2 == 'H'),coef_unavail +util_avail_pe_cdap_child2_chauf1,Availability for Pure-Escorting by daily pattern - Child 2 and Chauffeur 1,(bundle2 > 0) & (chauf2 == 2) & (ptype_chauf1 < 6) & (cdap_chauf1 == 'H'),coef_unavail +util_avail_pe_cdap_child2_chauf2,Availability for Pure-Escorting by daily pattern - Child 2 and Chauffeur 2,(bundle2 > 0) & (chauf2 == 4) & (ptype_chauf2 < 6) & (cdap_chauf2 == 'H'),coef_unavail +util_avail_pe_cdap_child3_chauf1,Availability for Pure-Escorting by daily pattern - Child 3 and Chauffeur 1,(bundle3 > 0) & (chauf3 == 2) & (ptype_chauf1 < 6) & (cdap_chauf1 == 'H'),coef_unavail +util_avail_pe_cdap_child3_chauf2,Availability for Pure-Escorting by daily pattern - Child 3 and Chauffeur 2,(bundle3 > 0) & (chauf3 == 4) & (ptype_chauf2 < 6) & (cdap_chauf2 == 'H'),coef_unavail +util_avail_pe_sync_child1_chauf1,Availability for Pure-Escorting by synchronization - Child 1 and Chauffeur 1,@(df.bundle1 > 0) & (df.chauf1 == 2) & (df.ptype_chauf1 < 4) & (df.cdap_chauf1 == 'H') & (np.abs(df.pref_depart_time_school1 + ((df.time_home_to_school1 + df.time_school_to_home1) / mins_per_time_bin) - df.pref_depart_time_chauf1) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_sync_child1_chauf2,Availability for Pure-Escorting by synchronization - Child 1 and Chauffeur 2,@(df.bundle1 > 0) & (df.chauf1 == 4) & (df.ptype_chauf2 < 4) & (df.cdap_chauf2 == 'H') & (np.abs(df.pref_depart_time_school1 + ((df.time_home_to_school1 + df.time_school_to_home1) / mins_per_time_bin) - df.pref_depart_time_chauf2) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_sync_child2_chauf1,Availability for Pure-Escorting by synchronization - Child 2 and Chauffeur 1,@(df.bundle2 > 0) & (df.chauf2 == 2) & (df.ptype_chauf1 < 4) & (df.cdap_chauf1 == 'H') & (np.abs(df.pref_depart_time_school2 + ((df.time_home_to_school2 + df.time_school_to_home2) / mins_per_time_bin) - df.pref_depart_time_chauf1) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_sync_child2_chauf2,Availability for Pure-Escorting by synchronization - Child 2 and Chauffeur 2,@(df.bundle2 > 0) & (df.chauf2 == 4) & (df.ptype_chauf2 < 4) & (df.cdap_chauf2 == 'H') & (np.abs(df.pref_depart_time_school2 + ((df.time_home_to_school2 + df.time_school_to_home2) / mins_per_time_bin) - df.pref_depart_time_chauf2) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_sync_child3_chauf1,Availability for Pure-Escorting by synchronization - Child 3 and Chauffeur 1,@(df.bundle3 > 0) & (df.chauf3 == 2) & (df.ptype_chauf1 < 4) & (df.cdap_chauf1 == 'H') & (np.abs(df.pref_depart_time_school3 + ((df.time_home_to_school3 + df.time_school_to_home3) / mins_per_time_bin) - df.pref_depart_time_chauf1) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_sync_child3_chauf2,Availability for Pure-Escorting by synchronization - Child 3 and Chauffeur 2,@(df.bundle3 > 0) & (df.chauf3 == 4) & (df.ptype_chauf2 < 4) & (df.cdap_chauf2 == 'H') & (np.abs(df.pref_depart_time_school3 + ((df.time_home_to_school3 + df.time_school_to_home3) / mins_per_time_bin) - df.pref_depart_time_chauf2) > max_bin_difference_between_departure_times),coef_unavail +util_avail_bundle_child_1and2,Availability for bundling child 1 and child 2,@(df.bundle1 == df.bundle2) & (df.bundle1 > 0) & (np.abs(df.pref_depart_time_school1 - df.pref_depart_time_school2) > max_bin_difference_between_departure_times),coef_unavail +util_avail_bundle_child_1and3,Availability for bundling child 1 and child 3,@(df.bundle1 == df.bundle3) & (df.bundle1 > 0) & (np.abs(df.pref_depart_time_school1 - df.pref_depart_time_school3) > max_bin_difference_between_departure_times),coef_unavail +util_avail_bundle_child_2and3,Availability for bundling child 2 and child 3,@(df.bundle2 == df.bundle3) & (df.bundle2 > 0) & (np.abs(df.pref_depart_time_school2 - df.pref_depart_time_school3) > max_bin_difference_between_departure_times),coef_unavail +util_avail_mult_bundles_outbound1,Availability Chauffeur 1 - Expected arrival from previous pure escort must be before departure for subsequent escort,((nrs1 + npe1) > 1) & ~avail_multiple_bundles,coef_unavail +util_avail_mult_bundles_outbound2,Availability Chauffeur 2 - Expected arrival from previous pure escort must be before departure for subsequent escort,((nrs2 + npe2) > 1) & ~avail_multiple_bundles,coef_unavail +util_avail_pe_during_mand_child1_chauf1,Availability pure escort tour must take place before mandatory tour - Child 1 and Chauffeur 1,@(df.bundle1 > 0) & (df.chauf1 == 2) & (df.cdap_chauf1 == 'M') & ((df.pref_depart_time_chauf1 - max_bin_difference_between_departure_times) <= df.pref_depart_time_school1),coef_unavail +util_avail_pe_during_mand_child1_chauf2,Availability pure escort tour must take place before mandatory tour - Child 1 and Chauffeur 2,@(df.bundle1 > 0) & (df.chauf1 == 4) & (df.cdap_chauf2 == 'M') & ((df.pref_depart_time_chauf2 - max_bin_difference_between_departure_times) <= df.pref_depart_time_school1),coef_unavail +util_avail_pe_during_mand_child2_chauf1,Availability pure escort tour must take place before mandatory tour - Child 2 and Chauffeur 1,@(df.bundle2 > 0) & (df.chauf2 == 2) & (df.cdap_chauf1 == 'M') & ((df.pref_depart_time_chauf1 - max_bin_difference_between_departure_times) <= df.pref_depart_time_school2),coef_unavail +util_avail_pe_during_mand_child2_chauf2,Availability pure escort tour must take place before mandatory tour - Child 2 and Chauffeur 2,@(df.bundle2 > 0) & (df.chauf2 == 4) & (df.cdap_chauf2 == 'M') & ((df.pref_depart_time_chauf2 - max_bin_difference_between_departure_times) <= df.pref_depart_time_school2),coef_unavail +util_avail_pe_during_mand_child3_chauf1,Availability pure escort tour must take place before mandatory tour - Child 3 and Chauffeur 1,@(df.bundle3 > 0) & (df.chauf3 == 2) & (df.cdap_chauf1 == 'M') & ((df.pref_depart_time_chauf1 - max_bin_difference_between_departure_times) <= df.pref_depart_time_school3),coef_unavail +util_avail_pe_during_mand_child3_chauf2,Availability pure escort tour must take place before mandatory tour - Child 3 and Chauffeur 2,@(df.bundle3 > 0) & (df.chauf3 == 4) & (df.cdap_chauf2 == 'M') & ((df.pref_depart_time_chauf2 - max_bin_difference_between_departure_times) <= df.pref_depart_time_school3),coef_unavail +# ,No escorting,, +util_child1_age_16p_noes,Child 1 age 16 years or older - No escort,(bundle1 == 0) & (child_id1 > 0) & (age_child1 > 15),coef_child_age_16p_noes +util_child2_age_16p_noes,Child 2 age 16 years or older - No escort,(bundle2 == 0) & (child_id2 > 0) & (age_child2 > 15),coef_child_age_16p_noes +util_child3_age_16p_noes,Child 3 age 16 years or older - No escort,(bundle3 == 0) & (child_id3 > 0) & (age_child3 > 15),coef_child_age_16p_noes +util_child1_age_10_to_15_noes,Child 1 age 10 to 15 years - No escort,(bundle1 == 0) & (child_id1 > 0) & (age_child1 > 9) & (age_child1 < 16),coef_child_age_10_to_15_noes +util_child2_age_10_to_15_noes,Child 2 age 10 to 15 years - No escort,(bundle2 == 0) & (child_id2 > 0) & (age_child2 > 9) & (age_child2 < 16),coef_child_age_10_to_15_noes +util_child3_age_10_to_15_noes,Child 3 age 10 to 15 years - No escort,(bundle3 == 0) & (child_id3 > 0) & (age_child3 > 9) & (age_child3 < 16),coef_child_age_10_to_15_noes +util_child1_age_u9_noes,Child 1 age 9 years or younger - No escort,(bundle1 == 0) & (child_id1 > 0) & (age_child1 < 10),coef_child_age_u9_noes +util_child2_age_u9_noes,Child 2 age 9 years or younger - No escort,(bundle2 == 0) & (child_id2 > 0) & (age_child2 < 10),coef_child_age_u9_noes +util_child3_age_u9_noes,Child 3 age 9 years or younger - No escort,(bundle3 == 0) & (child_id3 > 0) & (age_child3 < 10),coef_child_age_u9_noes +util_ln_dist_to_school_child1_noes,Logged distance to school for Child 1 - No escort,@(df.bundle1 == 0) * (df.child_id1 > 0) * np.log(1 + df.dist_home_to_school1),coef_ln_dist_to_school_noes +util_ln_dist_to_school_child2_noes,Logged distance to school for Child 2 - No escort,@(df.bundle2 == 0) * (df.child_id2 > 0) * np.log(1 + df.dist_home_to_school2),coef_ln_dist_to_school_noes +util_ln_dist_to_school_child3_noes,Logged distance to school for Child 3 - No escort,@(df.bundle3 == 0) * (df.child_id3 > 0) * np.log(1 + df.dist_home_to_school3),coef_ln_dist_to_school_noes +util_ln_dist_to_school_child1_u6_noes,Logged distance to school for Child 1 under 6 years old - No escort,@(df.bundle1 == 0) * (df.child_id1 > 0) * (df.age_child1 < 6) * np.log(1 + df.dist_home_to_school1),coef_ln_dist_to_school_u6_noes +util_ln_dist_to_school_child2_u6_noes,Logged distance to school for Child 2 under 6 years old - No escort,@(df.bundle2 == 0) * (df.child_id2 > 0) * (df.age_child2 < 6) * np.log(1 + df.dist_home_to_school2),coef_ln_dist_to_school_u6_noes +util_ln_dist_to_school_child3_u6_noes,Logged distance to school for Child 3 under 6 years old - No escort,@(df.bundle3 == 0) * (df.child_id3 > 0) * (df.age_child3 < 6) * np.log(1 + df.dist_home_to_school3),coef_ln_dist_to_school_u6_noes +util_ln_dist_to_school_child1_6to9_noes,Logged distance to school for Child 1 6 to 9 years old - No escort,@(df.bundle1 == 0) * (df.child_id1 > 0) * (df.age_child1 > 5) * (df.age_child1 < 10) * np.log(1 + df.dist_home_to_school1),coef_ln_dist_to_school_6to9_noes +util_ln_dist_to_school_child2_6to9_noes,Logged distance to school for Child 2 6 to 9 years old - No escort,@(df.bundle2 == 0) * (df.child_id2 > 0) * (df.age_child2 > 5) * (df.age_child2 < 10) * np.log(1 + df.dist_home_to_school2),coef_ln_dist_to_school_6to9_noes +util_ln_dist_to_school_child3_6to9_noes,Logged distance to school for Child 3 6 to 9 years old - No escort,@(df.bundle3 == 0) * (df.child_id3 > 0) * (df.age_child3 > 5) * (df.age_child3 < 10) * np.log(1 + df.dist_home_to_school3),coef_ln_dist_to_school_6to9_noes +# ,Ride Sharing,, +util_child1_age_16p_rs,Child 1 age 16 years or older - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 > 15),coef_child_age_16p_rs +util_child2_age_16p_rs,Child 2 age 16 years or older - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 > 15),coef_child_age_16p_rs +util_child3_age_16p_rs,Child 3 age 16 years or older - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 > 15),coef_child_age_16p_rs +util_child1_age_10to15_rs,Child 1 age 10 to 15 years - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 > 9) & (age_child1 < 16),coef_child_age_10to15_rs +util_child2_age_10to15_rs,Child 2 age 10 to 15 years - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 > 9) & (age_child2 < 16),coef_child_age_10to15_rs +util_child3_age_10to15_rs,Child 3 age 10 to 15 years - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 > 9) & (age_child3 < 16),coef_child_age_10to15_rs +util_child1_age_6to9_rs,Child 1 age 6 to 9 years - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 > 5) & (age_child1 < 10),coef_child_age_6to9_rs +util_child2_age_6to9_rs,Child 2 age 6 to 9 years - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 > 5) & (age_child2 < 10),coef_child_age_6to9_rs +util_child3_age_6to9_rs,Child 3 age 6 to 9 years - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 > 5) & (age_child3 < 10),coef_child_age_6to9_rs +util_child1_age_u6_rs,Child 1 age under 6 years old - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 < 6),coef_child_age_u6_rs +util_child2_age_u6_rs,Child 2 age under 6 years old - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 < 6),coef_child_age_u6_rs +util_child3_age_u6_rs,Child 3 age under 6 years old - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 < 6),coef_child_age_u6_rs +# ,Pure Escort Distance,, +util_child1_dist_pe,Child 1 distance to school - Pure escorting,((chauf1 == 2) | (chauf1 == 4)) * dist_home_to_school1,coef_child_dist_pe +util_child2_dist_pe,Child 2 distance to school - Pure escorting,((chauf2 == 2) | (chauf2 == 4)) * dist_home_to_school2,coef_child_dist_pe +util_child3_dist_pe,Child 3 distance to school - Pure escorting,((chauf3 == 2) | (chauf3 == 4)) * dist_home_to_school3,coef_child_dist_pe +util_child1_dist_pe_over30miles,Child 1 Pure escorting not allowed if over 30 miles,((chauf1 == 2) | (chauf1 == 4)) & (dist_home_to_school1 > 30),coef_unavail +util_child2_dist_pe_over30miles,Child 2 Pure escorting not allowed if over 30 miles,((chauf2 == 2) | (chauf2 == 4)) & (dist_home_to_school2 > 30),coef_unavail +util_child3_dist_pe_over30miles,Child 3 Pure escorting not allowed if over 30 miles,((chauf3 == 2) | (chauf3 == 4)) & (dist_home_to_school3 > 30),coef_unavail +# ,Household Interactions,, +util_hh_inc_u25k_noes,Household income less than 25k - No Escorting,(income <= 25000) & ((bundle1 == 0) | ((bundle2 == 0) & (num_children_going_to_school > 1)) | ((bundle3 == 0) & (num_children_going_to_school > 2))),coef_hh_inc_u25k_noes +util_hh_inc_25to50k_noes,Household income between 25 and 50k - No Escorting,((income > 25000) & (income <= 50000)) & ((bundle1 == 0) | ((bundle2 == 0) & (num_children_going_to_school > 1)) | ((bundle3 == 0) & (num_children_going_to_school > 2))),coef_hh_inc_25to50k_noes +util_zero_auto_hh_noes,Zero cars in the household - No Escorting,(auto_ownership == 0) & ((bundle1 == 0) | ((bundle2 == 0) & (num_children_going_to_school > 1)) | ((bundle3 == 0) & (num_children_going_to_school > 2))),coef_zero_auto_hh_noes +util_cars_lt_workers_rs,Cars fewer than household workers - Ride Share,(auto_ownership < num_workers) & ((chauf1 % 2 == 1) | (chauf2 % 2 == 1) | (chauf3 % 2 == 1)),coef_cars_lt_workers_rs +util_cars_lt_workers_pe,Cars fewer than household workers - Pure escort,(auto_ownership < num_workers) & ((chauf1 % 2 == 0) | (chauf2 % 2 == 0) | (chauf3 % 2 == 0)),coef_cars_lt_workers_pe +# ,Chauffer Interactions,, +util_chauf1_female_rs,Chauffeur 1 Female - Ride share,(gender_chauf1 == 2) & ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_female_rs +util_chauf1_male_rs,Chauffeur 1 Male - Ride share,(gender_chauf1 == 1) & ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_male_rs +util_chauf1_female_pe,Chauffeur 1 Female - Pure Escort,(gender_chauf1 == 2) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_female_pe +util_chauf1_male_pe,Chauffeur 1 Male - Pure Escort,(gender_chauf1 == 1) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_male_pe +util_chauf2_female_rs,Chauffeur 2 Female - Ride share,(gender_chauf2 == 2) & ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_female_rs +util_chauf2_male_rs,Chauffeur 2 Male - Ride share,(gender_chauf2 == 1) & ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_male_rs +util_chauf2_female_pe,Chauffeur 2 Female - Pure Escort,(gender_chauf2 == 2) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_female_pe +util_chauf2_male_pe,Chauffeur 2 Male - Pure Escort,(gender_chauf2 == 1) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_male_pe +util_chauf1_pt_worker_rs,Chauffer 1 part time worker - Ride share,(ptype_chauf1 == 2) & ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_pt_worker_rs +util_chauf1_pt_worker_pe,Chauffer 1 part time worker - Pure escort,(ptype_chauf1 == 2) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_pt_worker_pe +util_chauf2_pt_worker_rs,Chauffer 2 part time worker - Ride share,(ptype_chauf2 == 2) & ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_pt_worker_rs +util_chauf2_pt_worker_pe,Chauffer 2 part time worker - Pure escort,(ptype_chauf2 == 2) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_pt_worker_pe +util_chauf1_non_worker_pe,Chauffer 1 non worker - Pure escort,(ptype_chauf1 == 4) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_non_worker_pe +util_chauf2_non_worker_pe,Chauffer 2 non worker - Pure escort,(ptype_chauf2 == 4) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_non_worker_pe +util_chauf1_univ_stud_re,Chauffer 1 university student - Ride Share,(ptype_chauf1 == 3) & ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_univ_stud_re +util_chauf2_univ_stud_re,Chauffer 2 university student - Ride Share,(ptype_chauf2 == 3) & ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_univ_stud_re +util_chauf1_age_u35_pe,Chauffer 1 Age 35 years or younger - Pure escort,(ptype_chauf1 == 4) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_age_u35_pe +util_chauf2_age_u35_pe,Chauffer 2 Age 35 years or younger - Pure escort,(ptype_chauf2 == 4) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_age_u35_pe +util_chauf1_time_to_work_or_univ_rs,Chauffer 1 Auto time to work or university - Ride Share,time_home_to_mand1 * ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_time_to_work_or_univ_rs +util_chauf2_time_to_work_or_univ_rs,Chauffer 2 Auto time to work or university - Ride Share,time_home_to_mand2 * ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_time_to_work_or_univ_rs +util_chauf1_walk_dist_to_work_or_univ_rs,Chauffer 1 Walk dist to work or university - Ride Share,(dist_home_to_mand1 < 3) & ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_walk_dist_to_work_or_univ_rs +util_chauf2_walk_dist_to_work_or_univ_rs,Chauffer 2 Walk dist to work or university - Ride Share,(dist_home_to_mand2 < 3) & ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_walk_dist_to_work_or_univ_rs +util_chauf1_walk_dist_to_work_or_univ_pe,Chauffer 1 Walk dist to work or university - Pure Escort,(dist_home_to_mand1 < 3) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_walk_dist_to_work_or_univ_pe +util_chauf2_walk_dist_to_work_or_univ_pe,Chauffer 2 Walk dist to work or university - Pure Escort,(dist_home_to_mand2 < 3) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_walk_dist_to_work_or_univ_pe +# ,Chauffer deviation,, +util_chauf1_abs_dev_rs_child1only,Chauffer 1 Absolute deviation ride share - child 1 only,((bundle1 != bundle2) & (bundle1 != bundle3) & (chauf1 == 1)) * abs_dev_dist_out_child1_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child1only,Chauffer 2 Absolute deviation ride share - child 1 only,((bundle1 != bundle2) & (bundle1 != bundle3) & (chauf1 == 3)) * abs_dev_dist_out_child1_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child2only,Chauffer 1 Absolute deviation ride share - child 2 only,((bundle1 != bundle2) & (bundle2 != bundle3) & (chauf2 == 1)) * abs_dev_dist_out_child2_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child2only,Chauffer 2 Absolute deviation ride share - child 2 only,((bundle1 != bundle2) & (bundle2 != bundle3) & (chauf2 == 3)) * abs_dev_dist_out_child2_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child3only,Chauffer 1 Absolute deviation ride share - child 3 only,((bundle1 != bundle3) & (bundle2 != bundle3) & (chauf3 == 1)) * abs_dev_dist_out_child3_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child3only,Chauffer 2 Absolute deviation ride share - child 3 only,((bundle1 != bundle3) & (bundle2 != bundle3) & (chauf3 == 3)) * abs_dev_dist_out_child3_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child12,Chauffer 1 Absolute deviation ride share - child 1 & 2,((bundle1 == bundle2) & (bundle1 != bundle3) & (chauf1 == 1)) * abs_dev_dist_out_child12_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child12,Chauffer 2 Absolute deviation ride share - child 1 & 2,((bundle1 == bundle2) & (bundle1 != bundle3) & (chauf1 == 3)) * abs_dev_dist_out_child12_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child13,Chauffer 1 Absolute deviation ride share - child 1 & 3,((bundle1 != bundle2) & (bundle1 == bundle3) & (chauf1 == 1)) * abs_dev_dist_out_child13_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child13,Chauffer 2 Absolute deviation ride share - child 1 & 3,((bundle1 != bundle2) & (bundle1 == bundle3) & (chauf1 == 3)) * abs_dev_dist_out_child13_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child23,Chauffer 1 Absolute deviation ride share - child 2 & 3,((bundle1 != bundle2) & (bundle2 == bundle3) & (chauf2 == 1)) * abs_dev_dist_out_child23_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child23,Chauffer 2 Absolute deviation ride share - child 2 & 3,((bundle1 != bundle2) & (bundle2 == bundle3) & (chauf2 == 3)) * abs_dev_dist_out_child23_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child123,Chauffer 1 Absolute deviation ride share - child 1 & 2 & 3,((bundle1 == bundle2) & (bundle2 == bundle3) & (chauf1 == 1)) * abs_dev_dist_out_child123_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child123,Chauffer 2 Absolute deviation ride share - child 1 & 2 & 3,((bundle1 == bundle2) & (bundle2 == bundle3) & (chauf1 == 3)) * abs_dev_dist_out_child123_chauf2,coef_abs_dev_distance +util_chauf1_rel_dev_rs_child1only,Chauffer 1 Relative deviation ride share - child 1 only,((bundle1 != bundle2) & (bundle1 != bundle3) & (chauf1 == 1)) * abs_dev_dist_out_child1_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child1only,Chauffer 2 Relative deviation ride share - child 1 only,((bundle1 != bundle2) & (bundle1 != bundle3) & (chauf1 == 3)) * abs_dev_dist_out_child1_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child2only,Chauffer 1 Relative deviation ride share - child 2 only,((bundle1 != bundle2) & (bundle2 != bundle3) & (chauf2 == 1)) * abs_dev_dist_out_child2_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child2only,Chauffer 2 Relative deviation ride share - child 2 only,((bundle1 != bundle2) & (bundle2 != bundle3) & (chauf2 == 3)) * abs_dev_dist_out_child2_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child3only,Chauffer 1 Relative deviation ride share - child 3 only,((bundle1 != bundle3) & (bundle2 != bundle3) & (chauf3 == 1)) * abs_dev_dist_out_child3_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child3only,Chauffer 2 Relative deviation ride share - child 3 only,((bundle1 != bundle3) & (bundle2 != bundle3) & (chauf3 == 3)) * abs_dev_dist_out_child3_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child12,Chauffer 1 Relative deviation ride share - child 1 & 2,((bundle1 == bundle2) & (bundle1 != bundle3) & (chauf1 == 1)) * abs_dev_dist_out_child12_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child12,Chauffer 2 Relative deviation ride share - child 1 & 2,((bundle1 == bundle2) & (bundle1 != bundle3) & (chauf1 == 3)) * abs_dev_dist_out_child12_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child13,Chauffer 1 Relative deviation ride share - child 1 & 3,((bundle1 != bundle2) & (bundle1 == bundle3) & (chauf1 == 1)) * abs_dev_dist_out_child13_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child13,Chauffer 2 Relative deviation ride share - child 1 & 3,((bundle1 != bundle2) & (bundle1 == bundle3) & (chauf1 == 3)) * abs_dev_dist_out_child13_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child23,Chauffer 1 Relative deviation ride share - child 2 & 3,((bundle1 != bundle2) & (bundle2 == bundle3) & (chauf2 == 1)) * abs_dev_dist_out_child23_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child23,Chauffer 2 Relative deviation ride share - child 2 & 3,((bundle1 != bundle2) & (bundle2 == bundle3) & (chauf2 == 3)) * abs_dev_dist_out_child23_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child123,Chauffer 1 Relative deviation ride share - child 1 & 2 & 3,((bundle1 == bundle2) & (bundle2 == bundle3) & (chauf1 == 1)) * abs_dev_dist_out_child123_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child123,Chauffer 2 Relative deviation ride share - child 1 & 2 & 3,((bundle1 == bundle2) & (bundle2 == bundle3) & (chauf1 == 3)) * abs_dev_dist_out_child123_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +# ,Same TAZ,, +util_same_taz_child12_escort,Same school TAZ for child 1 and 2 - Escorting,(bundle1 == bundle2) & (bundle1 > 0) & (school_location_child1 == school_location_child2) & (school_location_child1 > 0),coef_same_taz_escort +util_same_taz_child13_escort,Same school TAZ for child 1 and 3 - Escorting,(bundle1 == bundle3) & (bundle1 > 0) & (school_location_child1 == school_location_child3) & (school_location_child1 > 0),coef_same_taz_escort +util_same_taz_child32_escort,Same school TAZ for child 3 and 2 - Escorting,(bundle3 == bundle2) & (bundle2 > 0) & (school_location_child3 == school_location_child2) & (school_location_child2 > 0),coef_same_taz_escort +util_same_taz_child12_no_escort,Same school TAZ for child 1 and 2 - No Escorting,(bundle1 == 0) & (bundle2 == 0) & (child_id1 > 0) & (child_id2 > 0) & (school_location_child1 == school_location_child2) & (school_location_child1 > 0),coef_same_taz_no_escort +util_same_taz_child13_no_escort,Same school TAZ for child 1 and 3 - No Escorting,(bundle1 == 0) & (bundle3 == 0) & (child_id1 > 0) & (child_id3 > 0) & (school_location_child1 == school_location_child3) & (school_location_child1 > 0),coef_same_taz_no_escort +util_same_taz_child32_no_escort,Same school TAZ for child 3 and 2 - No Escorting,(bundle3 == 0) & (bundle2 == 0) & (child_id3 > 0) & (child_id2 > 0) & (school_location_child3 == school_location_child2) & (school_location_child2 > 0),coef_same_taz_no_escort +# ,Constants related to inbound choice,, +util_no_escort_inbound_child1,No escorting in inbound direction - Child 1,(bundle1 == 0) & (child_id1 > 0) & (bundle1_inbound == 0),coef_no_escort_inbound +util_no_escort_inbound_child2,No escorting in inbound direction - Child 2,(bundle2 == 0) & (child_id2 > 0) & (bundle2_inbound == 0),coef_no_escort_inbound +util_no_escort_inbound_child3,No escorting in inbound direction - Child 3,(bundle3 == 0) & (child_id3 > 0) & (bundle3_inbound == 0),coef_no_escort_inbound +util_same_chauf1,Same chauffeur in both directions (not child specific) - chauf 1 inbound & inbound,((chauf1 == 1) | (chauf1 == 2) | (chauf2 == 1) | (chauf2 == 2) | (chauf3 == 1) | (chauf3 == 2)) & ((nbund1_inbound > 0)),coef_same_chauf +util_same_chauf2,Same chauffeur in both directions (not child specific) - chauf 2 inbound & inbound,((chauf1 == 3) | (chauf1 == 4) | (chauf2 == 3) | (chauf2 == 4) | (chauf3 == 3) | (chauf3 == 4)) & ((nbund2_inbound > 0)),coef_same_chauf +# ,Calibration constants,, +util_calib_child1_age_u6_rs,Child 1 age under 6 - Calibration constant - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 < 6),coef_calib_child_age_u6_rs +util_calib_child2_age_u6_rs,Child 2 age under 6 - Calibration constant - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 < 6),coef_calib_child_age_u6_rs +util_calib_child3_age_u6_rs,Child 3 age under 6 - Calibration constant - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 < 6),coef_calib_child_age_u6_rs +util_calib_child1_age_16p_rs,Child 1 age 16 years or older - Calibration constant - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 > 15),coef_calib_child_age_16p_rs +util_calib_child2_age_16p_rs,Child 2 age 16 years or older - Calibration constant - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 > 15),coef_calib_child_age_16p_rs +util_calib_child3_age_16p_rs,Child 3 age 16 years or older - Calibration constant - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 > 15),coef_calib_child_age_16p_rs +util_calib_child1_age_6to15_noes,Child 1 age 6 to 15 years - Calibration constant - No Escort,(bundle1 == 0) & (child_id1 > 0) & (age_child1 > 5) & (age_child1 < 16),coef_calib_child_age_6to15_noes +util_calib_child2_age_6to15_noes,Child 2 age 6 to 15 years - Calibration constant - No Escort,(bundle2 == 0) & (child_id2 > 0) & (age_child2 > 5) & (age_child2 < 16),coef_calib_child_age_6to15_noes +util_calib_child3_age_6to15_noes,Child 3 age 6 to 15 years - Calibration constant - No Escort,(bundle3 == 0) & (child_id3 > 0) & (age_child3 > 5) & (age_child3 < 16),coef_calib_child_age_6to15_noes +util_calib_child1_age_6to15_rs,Child 1 age 6 to 15 years - Calibration constant - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 > 5) & (age_child1 < 16),coef_calib_child_age_6to15_rs +util_calib_child2_age_6to15_rs,Child 2 age 6 to 15 years - Calibration constant - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 > 5) & (age_child2 < 16),coef_calib_child_age_6to15_rs +util_calib_child3_age_6to15_rs,Child 3 age 6 to 15 years - Calibration constant - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 > 5) & (age_child3 < 16),coef_calib_child_age_6to15_rs +util_calib_child1_age_u6_noes,Child 1 age 6 years or younger - Calibration constant - No escort,(bundle1 == 0) & (child_id1 > 0) & (age_child1 < 6),coef_calib_child_age_u6_noes +util_calib_child2_age_u6_noes,Child 2 age 6 years or younger - Calibration constant - No escort,(bundle2 == 0) & (child_id2 > 0) & (age_child2 < 6),coef_calib_child_age_u6_noes +util_calib_child3_age_u6_noes,Child 3 age 6 years or younger - Calibration constant - No escort,(bundle3 == 0) & (child_id3 > 0) & (age_child3 < 6),coef_calib_child_age_u6_noes diff --git a/resident/configs/school_escorting_preprocessor_inbound.csv b/resident/configs/school_escorting_preprocessor_inbound.csv new file mode 100644 index 0000000..4c174fc --- /dev/null +++ b/resident/configs/school_escorting_preprocessor_inbound.csv @@ -0,0 +1,148 @@ +Description,Target,Expression +Number of children going to school,num_children_going_to_school,"np.where(df['child_id1'] > 0, 1, 0) + np.where(df['child_id2'] > 0, 1, 0) + np.where(df['child_id3'] > 0,1,0)" +Number of potential chauffeurs,num_potential_chauffeurs,"np.where(df['chauf_id1'] > 0, 1, 0) + np.where(df['chauf_id2'] > 0, 1, 0)" +Person Type - chauffer 1,ptype_chauf1,"reindex(persons.ptype, df.chauf_id1)" +Person Type - chauffer 2,ptype_chauf2,"reindex(persons.ptype, df.chauf_id2)" +Gender - chauffer 1,gender_chauf1,"reindex(persons.SEX, df.chauf_id1)" +Gender - chauffer 2,gender_chauf2,"reindex(persons.SEX, df.chauf_id2)" +Age - chauffer 1,age_chauf1,"reindex(persons.age, df.chauf_id1)" +Age - chauffer 2,age_chauf2,"reindex(persons.age, df.chauf_id2)" +Daily activity pattern - chauffer 1,cdap_chauf1,"reindex(persons.cdap_activity, df.chauf_id1)" +Daily activity pattern - chauffer 2,cdap_chauf2,"reindex(persons.cdap_activity, df.chauf_id2)" +Age - child 1,age_child1,"reindex(persons.age, df.child_id1)" +Age - child 2,age_child2,"reindex(persons.age, df.child_id2)" +Age - child 3,age_child3,"reindex(persons.age, df.child_id3)" +# Departure times from school and work ,, +Preferred departure time from school - child 1,pref_depart_time_school1,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').end, df.child_id1)" +Preferred departure time from school - child 2,pref_depart_time_school2,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').end, df.child_id2)" +Preferred departure time from school - child 3,pref_depart_time_school3,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').end, df.child_id3)" +# setting preffered departure time for chauffer to the last mandatory tour of the day for inbound escorting ,, +Preferred departure time from work / univ - chauffer 1 - tour 1,pref_depart_time_chauf1_tour1,"reindex(tours[(tours.tour_category == 'mandatory') & (tours.tour_num == 1)].set_index('person_id').end, df.chauf_id1)" +Preferred departure time from work / univ - chauffer 2 - tour 1,pref_depart_time_chauf2_tour1,"reindex(tours[(tours.tour_category == 'mandatory') & (tours.tour_num == 1)].set_index('person_id').end, df.chauf_id2)" +Preferred departure time from work / univ - chauffer 1 - tour 2,pref_depart_time_chauf1_tour2,"reindex(tours[(tours.tour_category == 'mandatory') & (tours.tour_num == 2)].set_index('person_id').end, df.chauf_id1)" +Preferred departure time from work / univ - chauffer 2 - tour 2,pref_depart_time_chauf2_tour2,"reindex(tours[(tours.tour_category == 'mandatory') & (tours.tour_num == 2)].set_index('person_id').end, df.chauf_id2)" +Preferred departure time from work / univ - chauffer 1,pref_depart_time_chauf1,"np.where(pref_depart_time_chauf1_tour2 > pref_depart_time_chauf1_tour1, pref_depart_time_chauf1_tour2, pref_depart_time_chauf1_tour1)" +Preferred departure time from work / univ - chauffer 1,pref_depart_time_chauf2,"np.where(pref_depart_time_chauf2_tour2 > pref_depart_time_chauf2_tour1, pref_depart_time_chauf2_tour2, pref_depart_time_chauf2_tour1)" +# Distances and times to school and work ,, +School location - child 1,school_location_child1,"reindex(persons.school_zone_id, df.child_id1)" +School location - child 2,school_location_child2,"reindex(persons.school_zone_id, df.child_id2)" +School location - child 3,school_location_child3,"reindex(persons.school_zone_id, df.child_id3)" +School location - chauffer 1,_school_location_chauf1,"reindex(persons.workplace_zone_id, df.chauf_id1)" +School location - chauffer 2,_school_location_chauf2,"reindex(persons.workplace_zone_id, df.chauf_id2)" +Work location - chauffer 1,_work_location_chauf1,"reindex(persons.workplace_zone_id, df.chauf_id1)" +Work location - chauffer 2,_work_location_chauf2,"reindex(persons.workplace_zone_id, df.chauf_id2)" +Mandatory tour location - chauffer 1,_mandatory_location_chauf1,"_school_location_chauf1.where(ptype_chauf1 == 3, _work_location_chauf1)" +Mandatory tour location - chauffer 2,_mandatory_location_chauf2,"_school_location_chauf1.where(ptype_chauf2 == 3, _work_location_chauf2)" +# creating valid school locations to pass to skim_dict,, +,_valid_school_location_child1,school_location_child1.fillna(persons[persons.school_zone_id > 0].school_zone_id.mode()[0]) +,_valid_school_location_child2,school_location_child2.fillna(persons[persons.school_zone_id > 0].school_zone_id.mode()[0]) +,_valid_school_location_child3,school_location_child3.fillna(persons[persons.school_zone_id > 0].school_zone_id.mode()[0]) +,_valid_mandatory_location_chauf1,_mandatory_location_chauf1.fillna(persons[persons.workplace_zone_id > 0].workplace_zone_id.mode()[0]) +,_valid_mandatory_location_chauf2,_mandatory_location_chauf2.fillna(persons[persons.workplace_zone_id > 0].workplace_zone_id.mode()[0]) +Auto time home to school - child 1,time_home_to_school1,"np.where(school_location_child1 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child1, ('SR2_M_TIME', 'PM')), 0)" +Auto time home to school - child 2,time_home_to_school2,"np.where(school_location_child2 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child2, ('SR2_M_TIME', 'PM')), 0)" +Auto time home to school - child 3,time_home_to_school3,"np.where(school_location_child3 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child3, ('SR2_M_TIME', 'PM')), 0)" +Auto time school to home - child 1,time_school_to_home1,"np.where(school_location_child1 > 0, skim_dict.lookup(_valid_school_location_child1, df.home_zone_id, ('SR2_M_TIME', 'PM')), 0)" +Auto time school to home - child 2,time_school_to_home2,"np.where(school_location_child2 > 0, skim_dict.lookup(_valid_school_location_child2, df.home_zone_id, ('SR2_M_TIME', 'PM')), 0)" +Auto time school to home - child 3,time_school_to_home3,"np.where(school_location_child3 > 0, skim_dict.lookup(_valid_school_location_child3, df.home_zone_id, ('SR2_M_TIME', 'PM')), 0)" +Auto dist home to school - child 1,dist_school_to_home1,"np.where(school_location_child1 > 0, skim_dict.lookup(_valid_school_location_child1, df.home_zone_id, ('SR2_M_DIST', 'PM')), 0)" +Auto dist home to school - child 2,dist_school_to_home2,"np.where(school_location_child2 > 0, skim_dict.lookup(_valid_school_location_child2, df.home_zone_id, ('SR2_M_DIST', 'PM')), 0)" +Auto dist home to school - child 3,dist_school_to_home3,"np.where(school_location_child3 > 0, skim_dict.lookup(_valid_school_location_child3, df.home_zone_id, ('SR2_M_DIST', 'PM')), 0)" +Auto dist intra school taz - child 1,dist_intra_school1,"np.where(school_location_child1 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child1, ('SR2_M_DIST', 'PM')), 0)" +Auto dist intra school taz - child 2,dist_intra_school2,"np.where(school_location_child2 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child2, ('SR2_M_DIST', 'PM')), 0)" +Auto dist intra school taz - child 3,dist_intra_school3,"np.where(school_location_child3 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child3, ('SR2_M_DIST', 'PM')), 0)" +Auto time home to work or university - chauffer 1,time_home_to_mand1,"np.where(_mandatory_location_chauf1 > 0, skim_dict.lookup(df.home_zone_id, _valid_mandatory_location_chauf1, ('SR2_M_TIME', 'PM')), 0)" +Auto time home to work or university - chauffer 2,time_home_to_mand2,"np.where(_mandatory_location_chauf2 > 0, skim_dict.lookup(df.home_zone_id, _valid_mandatory_location_chauf2, ('SR2_M_TIME', 'PM')), 0)" +Auto time work or university to home - chauffer 1,time_mand_to_home1,"np.where(_mandatory_location_chauf1 > 0, skim_dict.lookup(_valid_mandatory_location_chauf1, df.home_zone_id, ('SR2_M_TIME', 'PM')), 0)" +Auto time work or university to home - chauffer 2,time_mand_to_home2,"np.where(_mandatory_location_chauf2 > 0, skim_dict.lookup(_valid_mandatory_location_chauf2, df.home_zone_id, ('SR2_M_TIME', 'PM')), 0)" +Auto dist home to work or university - chauffer 1,dist_home_to_mand1,"np.where(_mandatory_location_chauf1 > 0, skim_dict.lookup(df.home_zone_id, _valid_mandatory_location_chauf1, ('SR2_M_DIST', 'PM')), 0)" +Auto dist home to work or university - chauffer 2,dist_home_to_mand2,"np.where(_mandatory_location_chauf2 > 0, skim_dict.lookup(df.home_zone_id, _valid_mandatory_location_chauf2, ('SR2_M_DIST', 'PM')), 0)" +Auto dist work or university to home - chauffer 1,dist_mand1_to_home,"np.where(_mandatory_location_chauf1 > 0, skim_dict.lookup(_valid_mandatory_location_chauf1, df.home_zone_id, ('SR2_M_DIST', 'PM')), 0)" +Auto dist work or university to home - chauffer 2,dist_mand2_to_home,"np.where(_mandatory_location_chauf2 > 0, skim_dict.lookup(_valid_mandatory_location_chauf2, df.home_zone_id, ('SR2_M_DIST', 'PM')), 0)" +# inbound distance combinations between chauffeurs and children,, +Distance from chauffeur 1 mandatory location to child 1 school,time_mand1_to_school1,"np.where((school_location_child1 > 0) & (_mandatory_location_chauf1 > 0), skim_dict.lookup(_valid_school_location_child1, _valid_mandatory_location_chauf1, ('SR2_M_TIME', 'PM')), 0)" +Distance from chauffeur 1 mandatory location to child 2 school,time_mand1_to_school2,"np.where((school_location_child2 > 0) & (_mandatory_location_chauf1 > 0), skim_dict.lookup(_valid_school_location_child2, _valid_mandatory_location_chauf1, ('SR2_M_TIME', 'PM')), 0)" +Distance from chauffeur 1 mandatory location to child 3 school,time_mand1_to_school3,"np.where((school_location_child3 > 0) & (_mandatory_location_chauf1 > 0), skim_dict.lookup(_valid_school_location_child3, _valid_mandatory_location_chauf1, ('SR2_M_TIME', 'PM')), 0)" +Distance from chauffeur 2 mandatory location to child 1 school,time_mand2_to_school1,"np.where((school_location_child1 > 0) & (_mandatory_location_chauf2 > 0), skim_dict.lookup(_valid_school_location_child1, _valid_mandatory_location_chauf2, ('SR2_M_TIME', 'PM')), 0)" +Distance from chauffeur 2 mandatory location to child 2 school,time_mand2_to_school2,"np.where((school_location_child2 > 0) & (_mandatory_location_chauf2 > 0), skim_dict.lookup(_valid_school_location_child2, _valid_mandatory_location_chauf2, ('SR2_M_TIME', 'PM')), 0)" +Distance from chauffeur 2 mandatory location to child 3 school,time_mand2_to_school3,"np.where((school_location_child3 > 0) & (_mandatory_location_chauf2 > 0), skim_dict.lookup(_valid_school_location_child3, _valid_mandatory_location_chauf2, ('SR2_M_TIME', 'PM')), 0)" +Distance from chauffeur 1 mandatory location to child 1 school,_dist_mand1_to_school1,"np.where((school_location_child1 > 0) & (_mandatory_location_chauf1 > 0), skim_dict.lookup(_valid_mandatory_location_chauf1, _valid_school_location_child1, ('SR2_M_TIME', 'PM')), 0)" +Distance from chauffeur 2 mandatory location to child 1 school,_dist_mand2_to_school1,"np.where((school_location_child1 > 0) & (_mandatory_location_chauf2 > 0), skim_dict.lookup(_valid_mandatory_location_chauf2, _valid_school_location_child1, ('SR2_M_TIME', 'PM')), 0)" +Distance from chauffeur 1 mandatory location to child 2 school,_dist_mand1_to_school2,"np.where((school_location_child2 > 0) & (_mandatory_location_chauf1 > 0), skim_dict.lookup(_valid_mandatory_location_chauf1, _valid_school_location_child2, ('SR2_M_TIME', 'PM')), 0)" +Distance from chauffeur 2 mandatory location to child 2 school,_dist_mand2_to_school2,"np.where((school_location_child2 > 0) & (_mandatory_location_chauf2 > 0), skim_dict.lookup(_valid_mandatory_location_chauf2, _valid_school_location_child2, ('SR2_M_TIME', 'PM')), 0)" +Distance from chauffeur 1 mandatory location to child 3 school,_dist_mand1_to_school3,"np.where((school_location_child3 > 0) & (_mandatory_location_chauf1 > 0), skim_dict.lookup(_valid_mandatory_location_chauf1, _valid_school_location_child3, ('SR2_M_TIME', 'PM')), 0)" +Distance from chauffeur 2 mandatory location to child 3 school,_dist_mand2_to_school3,"np.where((school_location_child3 > 0) & (_mandatory_location_chauf2 > 0), skim_dict.lookup(_valid_mandatory_location_chauf2, _valid_school_location_child3, ('SR2_M_TIME', 'PM')), 0)" +Distance from child 1 school to child 2 school,_dist_school1_to_school2,"np.where((school_location_child1 > 0) & (school_location_child2 > 0), skim_dict.lookup(_valid_school_location_child1, _valid_school_location_child2, ('SR2_M_TIME', 'PM')), 0)" +Distance from child 1 school to child 3 school,_dist_school1_to_school3,"np.where((school_location_child1 > 0) & (school_location_child3 > 0), skim_dict.lookup(_valid_school_location_child1, _valid_school_location_child3, ('SR2_M_TIME', 'PM')), 0)" +Distance from child 2 school to child 3 school,_dist_school2_to_school3,"np.where((school_location_child2 > 0) & (school_location_child3 > 0), skim_dict.lookup(_valid_school_location_child2, _valid_school_location_child3, ('SR2_M_TIME', 'PM')), 0)" +Distance from child 2 school to child 1 school,_dist_school2_to_school1,"np.where((school_location_child2 > 0) & (school_location_child1 > 0), skim_dict.lookup(_valid_school_location_child2, _valid_school_location_child1, ('SR2_M_TIME', 'PM')), 0)" +Distance from child 3 school to child 1 school,_dist_school3_to_school1,"np.where((school_location_child3 > 0) & (school_location_child1 > 0), skim_dict.lookup(_valid_school_location_child3, _valid_school_location_child1, ('SR2_M_TIME', 'PM')), 0)" +Distance from child 3 school to child 2 school,_dist_school3_to_school2,"np.where((school_location_child3 > 0) & (school_location_child2 > 0), skim_dict.lookup(_valid_school_location_child3, _valid_school_location_child2, ('SR2_M_TIME', 'PM')), 0)" +,_return_min_taking_child1,"(pref_depart_time_school1 * mins_per_time_bin) + time_home_to_school1 + time_school_to_home1" +,_return_min_taking_child2,"(pref_depart_time_school2 * mins_per_time_bin) + time_home_to_school2 + time_school_to_home2" +,_return_min_taking_child3,"(pref_depart_time_school3 * mins_per_time_bin) + time_home_to_school3 + time_school_to_home3" +Availability of taking child 1 and then 2,_avail_child1_then_child2,"(_return_min_taking_child1 < (pref_depart_time_school2 * mins_per_time_bin))" +Availability of taking child 2 and then 1,_avail_child2_then_child1,"(_return_min_taking_child2 < (pref_depart_time_school1 * mins_per_time_bin))" +Availability of taking child 1 and then 3,_avail_child1_then_child3,"(_return_min_taking_child1 < (pref_depart_time_school3 * mins_per_time_bin))" +Availability of taking child 3 and then 1,_avail_child3_then_child1,"(_return_min_taking_child3 < (pref_depart_time_school1 * mins_per_time_bin))" +Availability of taking child 2 and then 3,_avail_child2_then_child3,"(_return_min_taking_child2 < (pref_depart_time_school3 * mins_per_time_bin))" +Availability of taking child 3 and then 2,_avail_child3_then_child2,"(_return_min_taking_child3 < (pref_depart_time_school2 * mins_per_time_bin))" +multiple_bundle_availability,avail_multiple_bundles,(_avail_child1_then_child2 | _avail_child2_then_child1 | _avail_child1_then_child3 | _avail_child3_then_child1 | _avail_child2_then_child3 | _avail_child3_then_child2) +# absolute deviation distance inbound,, +Absolute deviation inbound distance Child 1 Chauffer 1,abs_dev_dist_in_child1_chauf1,"np.maximum(_dist_mand1_to_school1 + dist_school_to_home1 - dist_mand1_to_home,0)" +Absolute deviation inbound distance Child 1 Chauffer 2,abs_dev_dist_in_child1_chauf2,"np.maximum(_dist_mand2_to_school1 + dist_school_to_home1 - dist_mand2_to_home,0)" +Absolute deviation inbound distance Child 2 Chauffer 1,abs_dev_dist_in_child2_chauf1,"np.maximum(_dist_mand1_to_school2 + dist_school_to_home2 - dist_mand1_to_home,0)" +Absolute deviation inbound distance Child 2 Chauffer 2,abs_dev_dist_in_child2_chauf2,"np.maximum(_dist_mand2_to_school2 + dist_school_to_home2 - dist_mand2_to_home,0)" +Absolute deviation inbound distance Child 3 Chauffer 1,abs_dev_dist_in_child3_chauf1,"np.maximum(_dist_mand1_to_school3 + dist_school_to_home3 - dist_mand1_to_home,0)" +Absolute deviation inbound distance Child 3 Chauffer 2,abs_dev_dist_in_child3_chauf2,"np.maximum(_dist_mand2_to_school3 + dist_school_to_home3 - dist_mand2_to_home,0)" +Absolute deviation inbound distance child12 Chauffer 1,abs_dev_dist_in_child12_chauf1,"np.maximum(np.minimum(_dist_mand1_to_school1 + _dist_school1_to_school2 + dist_school_to_home2, _dist_mand1_to_school2 + _dist_school2_to_school1 + dist_school_to_home1) - dist_mand1_to_home, 0)" +Absolute deviation inbound distance child12 Chauffer 2,abs_dev_dist_in_child12_chauf2,"np.maximum(np.minimum(_dist_mand2_to_school1 + _dist_school1_to_school2 + dist_school_to_home2, _dist_mand2_to_school2 + _dist_school2_to_school1 + dist_school_to_home1) - dist_mand2_to_home, 0)" +Absolute deviation inbound distance child13 Chauffer 1,abs_dev_dist_in_child13_chauf1,"np.maximum(np.minimum(_dist_mand1_to_school1 + _dist_school1_to_school3 + dist_school_to_home2, _dist_mand1_to_school3 + _dist_school3_to_school1 + dist_school_to_home3) - dist_mand1_to_home, 0)" +Absolute deviation inbound distance child13 Chauffer 2,abs_dev_dist_in_child13_chauf2,"np.maximum(np.minimum(_dist_mand2_to_school1 + _dist_school1_to_school3 + dist_school_to_home2, _dist_mand2_to_school3 + _dist_school3_to_school1 + dist_school_to_home3) - dist_mand2_to_home, 0)" +Absolute deviation inbound distance child23 Chauffer 1,abs_dev_dist_in_child23_chauf1,"np.maximum(np.minimum(_dist_mand1_to_school1 + _dist_school2_to_school3 + dist_school_to_home3, _dist_mand1_to_school3 + _dist_school3_to_school2 + dist_school_to_home2) - dist_mand1_to_home, 0)" +Absolute deviation inbound distance child23 Chauffer 2,abs_dev_dist_in_child23_chauf2,"np.maximum(np.minimum(_dist_mand2_to_school1 + _dist_school2_to_school3 + dist_school_to_home3, _dist_mand2_to_school3 + _dist_school3_to_school2 + dist_school_to_home2) - dist_mand2_to_home, 0)" +,_dist_mand1_school1_school2_school3,"_dist_mand1_to_school1 + _dist_school1_to_school2 + _dist_school2_to_school3 + dist_school_to_home1" +,_dist_mand1_school1_school3_school2,"_dist_mand1_to_school1 + _dist_school1_to_school3 + _dist_school3_to_school2 + dist_school_to_home1" +,_dist_mand1_school2_school1_school3,"_dist_mand1_to_school2 + _dist_school2_to_school1 + _dist_school1_to_school3 + dist_school_to_home2" +,_dist_mand1_school2_school3_school1,"_dist_mand1_to_school2 + _dist_school2_to_school3 + _dist_school3_to_school1 + dist_school_to_home2" +,_dist_mand1_school3_school1_school2,"_dist_mand1_to_school3 + _dist_school3_to_school1 + _dist_school1_to_school2 + dist_school_to_home3" +,_dist_mand1_school3_school2_school1,"_dist_mand1_to_school3 + _dist_school3_to_school2 + _dist_school2_to_school1 + dist_school_to_home3" +,_min_dist_dropoff_order_in_child123_chauf1,_dist_mand1_school1_school2_school3 +,_min_dist_dropoff_order_in_child123_chauf1,"np.where((_dist_mand1_school1_school3_school2 > 0) & (_dist_mand1_school1_school3_school2 < _min_dist_dropoff_order_in_child123_chauf1), _dist_mand1_school1_school3_school2, _min_dist_dropoff_order_in_child123_chauf1)" +,_min_dist_dropoff_order_in_child123_chauf1,"np.where((_dist_mand1_school2_school1_school3 > 0) & (_dist_mand1_school2_school1_school3 < _min_dist_dropoff_order_in_child123_chauf1), _dist_mand1_school2_school1_school3, _min_dist_dropoff_order_in_child123_chauf1)" +,_min_dist_dropoff_order_in_child123_chauf1,"np.where((_dist_mand1_school2_school3_school1 > 0) & (_dist_mand1_school2_school3_school1 < _min_dist_dropoff_order_in_child123_chauf1), _dist_mand1_school2_school3_school1, _min_dist_dropoff_order_in_child123_chauf1)" +,_min_dist_dropoff_order_in_child123_chauf1,"np.where((_dist_mand1_school3_school1_school2 > 0) & (_dist_mand1_school3_school1_school2 < _min_dist_dropoff_order_in_child123_chauf1), _dist_mand1_school3_school1_school2, _min_dist_dropoff_order_in_child123_chauf1)" +,_min_dist_dropoff_order_in_child123_chauf1,"np.where((_dist_mand1_school3_school2_school1 > 0) & (_dist_mand1_school3_school2_school1 < _min_dist_dropoff_order_in_child123_chauf1), _dist_mand1_school3_school2_school1, _min_dist_dropoff_order_in_child123_chauf1)" +Absolute deviation inbound distance child123 Chauffer 1,abs_dev_dist_in_child123_chauf1,"np.maximum(_min_dist_dropoff_order_in_child123_chauf1 - dist_home_to_mand1, 0)" +,_dist_mand2_school1_school2_school3,"_dist_mand2_to_school1 + _dist_school1_to_school2 + _dist_school2_to_school3 + dist_school_to_home1" +,_dist_mand2_school1_school3_school2,"_dist_mand2_to_school1 + _dist_school1_to_school3 + _dist_school3_to_school2 + dist_school_to_home1" +,_dist_mand2_school2_school1_school3,"_dist_mand2_to_school2 + _dist_school2_to_school1 + _dist_school1_to_school3 + dist_school_to_home2" +,_dist_mand2_school2_school3_school1,"_dist_mand2_to_school2 + _dist_school2_to_school3 + _dist_school3_to_school1 + dist_school_to_home2" +,_dist_mand2_school3_school1_school2,"_dist_mand2_to_school3 + _dist_school3_to_school1 + _dist_school1_to_school2 + dist_school_to_home3" +,_dist_mand2_school3_school2_school1,"_dist_mand2_to_school3 + _dist_school3_to_school2 + _dist_school2_to_school1 + dist_school_to_home3" +,_min_dist_dropoff_order_in_child123_chauf2,_dist_mand2_school1_school2_school3 +,_min_dist_dropoff_order_in_child123_chauf2,"np.where((_dist_mand2_school1_school3_school2 > 0) & (_dist_mand2_school1_school3_school2 < _min_dist_dropoff_order_in_child123_chauf2), _dist_mand2_school1_school3_school2, _min_dist_dropoff_order_in_child123_chauf2)" +,_min_dist_dropoff_order_in_child123_chauf2,"np.where((_dist_mand2_school2_school1_school3 > 0) & (_dist_mand2_school2_school1_school3 < _min_dist_dropoff_order_in_child123_chauf2), _dist_mand2_school2_school1_school3, _min_dist_dropoff_order_in_child123_chauf2)" +,_min_dist_dropoff_order_in_child123_chauf2,"np.where((_dist_mand2_school2_school3_school1 > 0) & (_dist_mand2_school2_school3_school1 < _min_dist_dropoff_order_in_child123_chauf2), _dist_mand2_school2_school3_school1, _min_dist_dropoff_order_in_child123_chauf2)" +,_min_dist_dropoff_order_in_child123_chauf2,"np.where((_dist_mand2_school3_school1_school2 > 0) & (_dist_mand2_school3_school1_school2 < _min_dist_dropoff_order_in_child123_chauf2), _dist_mand2_school3_school1_school2, _min_dist_dropoff_order_in_child123_chauf2)" +,_min_dist_dropoff_order_in_child123_chauf2,"np.where((_dist_mand2_school3_school2_school1 > 0) & (_dist_mand2_school3_school2_school1 < _min_dist_dropoff_order_in_child123_chauf2), _dist_mand2_school3_school2_school1, _min_dist_dropoff_order_in_child123_chauf2)" +Absolute deviation inbound distance child123 Chauffer 2,abs_dev_dist_in_child123_chauf2,"np.maximum(_min_dist_dropoff_order_in_child123_chauf2 - dist_home_to_mand1, 0)" +# overlapping time windows from outbound escorting,, +Preferred departure time to school - child 1,_pref_depart_time_to_school1,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').start, df.child_id1)" +Preferred departure time to school - child 2,_pref_depart_time_to_school2,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').start, df.child_id2)" +Preferred departure time to school - child 3,_pref_depart_time_to_school3,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').start, df.child_id3)" +,return_min_taking_outbound_child1_pe,"(_pref_depart_time_to_school1 * mins_per_time_bin) + time_home_to_school1 + time_school_to_home1" +,return_min_taking_outbound_child2_pe,"(_pref_depart_time_to_school2 * mins_per_time_bin) + time_home_to_school2 + time_school_to_home2" +,return_min_taking_outbound_child3_pe,"(_pref_depart_time_to_school3 * mins_per_time_bin) + time_home_to_school3 + time_school_to_home3" +finding latest time chauffer 1 returns from outbound pure escort tour,outbound_pe_return_time_home_chauf1,-1 +,outbound_pe_return_time_home_chauf1,"np.where((df.chauf1_outbound == 2) & (return_min_taking_outbound_child1_pe > outbound_pe_return_time_home_chauf1), return_min_taking_outbound_child1_pe, outbound_pe_return_time_home_chauf1)" +,outbound_pe_return_time_home_chauf1,"np.where((df.chauf2_outbound == 2) & (return_min_taking_outbound_child2_pe > outbound_pe_return_time_home_chauf1), return_min_taking_outbound_child2_pe, outbound_pe_return_time_home_chauf1)" +,outbound_pe_return_time_home_chauf1,"np.where((df.chauf3_outbound == 2) & (return_min_taking_outbound_child3_pe > outbound_pe_return_time_home_chauf1), return_min_taking_outbound_child3_pe, outbound_pe_return_time_home_chauf1)" +finding latest time chauffer 2 returns from outbound pure escort tour,outbound_pe_return_time_home_chauf2,-1 +,outbound_pe_return_time_home_chauf2,"np.where((df.chauf1_outbound == 4) & (return_min_taking_outbound_child1_pe > outbound_pe_return_time_home_chauf2), return_min_taking_outbound_child1_pe, outbound_pe_return_time_home_chauf2)" +,outbound_pe_return_time_home_chauf2,"np.where((df.chauf2_outbound == 4) & (return_min_taking_outbound_child2_pe > outbound_pe_return_time_home_chauf2), return_min_taking_outbound_child2_pe, outbound_pe_return_time_home_chauf2)" +,outbound_pe_return_time_home_chauf2,"np.where((df.chauf3_outbound == 4) & (return_min_taking_outbound_child3_pe > outbound_pe_return_time_home_chauf2), return_min_taking_outbound_child3_pe, outbound_pe_return_time_home_chauf2)" +finding latest time chauffer 1 returns from outbound ride share tour,outbound_rs_return_time_home_chauf1,pref_depart_time_chauf1 +finding latest time chauffer 2 returns from outbound ride share tour,outbound_rs_return_time_home_chauf2,pref_depart_time_chauf2 +return time of outbound school escoring tour - chauffeur 1,return_bin_outbound_school_escorting1,"np.where(df.nrs1_outbound > 0, pref_depart_time_chauf1, outbound_pe_return_time_home_chauf1 / mins_per_time_bin)" +return time of outbound school escoring tour - chauffeur 2,return_bin_outbound_school_escorting2,"np.where(df.nrs2_outbound > 0, pref_depart_time_chauf2, outbound_pe_return_time_home_chauf2 / mins_per_time_bin)" diff --git a/resident/configs/school_escorting_preprocessor_outbound.csv b/resident/configs/school_escorting_preprocessor_outbound.csv new file mode 100644 index 0000000..8997826 --- /dev/null +++ b/resident/configs/school_escorting_preprocessor_outbound.csv @@ -0,0 +1,122 @@ +Description,Target,Expression +Number of children going to school,num_children_going_to_school,"np.where(df['child_id1'] > 0, 1, 0) + np.where(df['child_id2'] > 0, 1, 0) + np.where(df['child_id3'] > 0,1,0)" +Number of potential chauffeurs,num_potential_chauffeurs,"np.where(df['chauf_id1'] > 0, 1, 0) + np.where(df['chauf_id2'] > 0, 1, 0)" +Person Type - chauffer 1,ptype_chauf1,"reindex(persons.ptype, df.chauf_id1)" +Person Type - chauffer 2,ptype_chauf2,"reindex(persons.ptype, df.chauf_id2)" +Gender - chauffer 1,gender_chauf1,"reindex(persons.SEX, df.chauf_id1)" +Gender - chauffer 2,gender_chauf2,"reindex(persons.SEX, df.chauf_id2)" +Age - chauffer 1,age_chauf1,"reindex(persons.age, df.chauf_id1)" +Age - chauffer 2,age_chauf2,"reindex(persons.age, df.chauf_id2)" +Daily activity pattern - chauffer 1,cdap_chauf1,"reindex(persons.cdap_activity, df.chauf_id1)" +Daily activity pattern - chauffer 2,cdap_chauf2,"reindex(persons.cdap_activity, df.chauf_id2)" +Age - child 1,age_child1,"reindex(persons.age, df.child_id1)" +Age - child 2,age_child2,"reindex(persons.age, df.child_id2)" +Age - child 3,age_child3,"reindex(persons.age, df.child_id3)" +# Departure times to school and work +Preferred departure time to school - child 1,pref_depart_time_school1,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').start, df.child_id1)" +Preferred departure time to school - child 2,pref_depart_time_school2,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').start, df.child_id2)" +Preferred departure time to school - child 3,pref_depart_time_school3,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').start, df.child_id3)" +Preferred departure time to work / univ - chauffer 1,pref_depart_time_chauf1,"reindex(tours[(tours.tour_category == 'mandatory') & (tours.tour_num == 1)].set_index('person_id').start, df.chauf_id1)" +Preferred departure time to work / univ - chauffer 2,pref_depart_time_chauf2,"reindex(tours[(tours.tour_category == 'mandatory') & (tours.tour_num == 1)].set_index('person_id').start, df.chauf_id2)" +# Distances and times to school and work +School location - child 1,school_location_child1,"reindex(persons.school_zone_id, df.child_id1)" +School location - child 2,school_location_child2,"reindex(persons.school_zone_id, df.child_id2)" +School location - child 3,school_location_child3,"reindex(persons.school_zone_id, df.child_id3)" +School location - chauffer 1,_school_location_chauf1,"reindex(persons.workplace_zone_id, df.chauf_id1)" +School location - chauffer 2,_school_location_chauf2,"reindex(persons.workplace_zone_id, df.chauf_id2)" +Work location - chauffer 1,_work_location_chauf1,"reindex(persons.workplace_zone_id, df.chauf_id1)" +Work location - chauffer 2,_work_location_chauf2,"reindex(persons.workplace_zone_id, df.chauf_id2)" +Mandatory tour location - chauffer 1,_mandatory_location_chauf1,"_school_location_chauf1.where(ptype_chauf1 == 3, _work_location_chauf1)" +Mandatory tour location - chauffer 2,_mandatory_location_chauf2,"_school_location_chauf1.where(ptype_chauf2 == 3, _work_location_chauf2)" +# creating valid school locations to pass to skim_dict,, +,_valid_school_location_child1,school_location_child1.fillna(persons[persons.school_zone_id > 0].school_zone_id.mode()[0]) +,_valid_school_location_child2,school_location_child2.fillna(persons[persons.school_zone_id > 0].school_zone_id.mode()[0]) +,_valid_school_location_child3,school_location_child3.fillna(persons[persons.school_zone_id > 0].school_zone_id.mode()[0]) +,_valid_mandatory_location_chauf1,_mandatory_location_chauf1.fillna(persons[persons.workplace_zone_id > 0].workplace_zone_id.mode()[0]) +,_valid_mandatory_location_chauf2,_mandatory_location_chauf2.fillna(persons[persons.workplace_zone_id > 0].workplace_zone_id.mode()[0]) +Auto time home to school - child 1,time_home_to_school1,"np.where(school_location_child1 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child1, ('SR2_M_TIME', 'AM')), 0)" +Auto time home to school - child 2,time_home_to_school2,"np.where(school_location_child2 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child2, ('SR2_M_TIME', 'AM')), 0)" +Auto time home to school - child 3,time_home_to_school3,"np.where(school_location_child3 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child3, ('SR2_M_TIME', 'AM')), 0)" +Auto time school to home - child 1,time_school_to_home1,"np.where(school_location_child1 > 0, skim_dict.lookup(_valid_school_location_child1, df.home_zone_id, ('SR2_M_TIME', 'AM')), 0)" +Auto time school to home - child 2,time_school_to_home2,"np.where(school_location_child2 > 0, skim_dict.lookup(_valid_school_location_child2, df.home_zone_id, ('SR2_M_TIME', 'AM')), 0)" +Auto time school to home - child 3,time_school_to_home3,"np.where(school_location_child3 > 0, skim_dict.lookup(_valid_school_location_child3, df.home_zone_id, ('SR2_M_TIME', 'AM')), 0)" +Auto dist home to school - child 1,dist_home_to_school1,"np.where(school_location_child1 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child1, ('SR2_M_DIST', 'AM')), 0)" +Auto dist home to school - child 2,dist_home_to_school2,"np.where(school_location_child2 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child2, ('SR2_M_DIST', 'AM')), 0)" +Auto dist home to school - child 3,dist_home_to_school3,"np.where(school_location_child3 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child3, ('SR2_M_DIST', 'AM')), 0)" +Auto dist intra school taz - child 1,dist_intra_school1,"np.where(school_location_child1 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child1, ('SR2_M_DIST', 'AM')), 0)" +Auto dist intra school taz - child 2,dist_intra_school2,"np.where(school_location_child2 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child2, ('SR2_M_DIST', 'AM')), 0)" +Auto dist intra school taz - child 3,dist_intra_school3,"np.where(school_location_child3 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child3, ('SR2_M_DIST', 'AM')), 0)" +Auto time home to work or university - chauffer 1,time_home_to_mand1,"np.where(_mandatory_location_chauf1 > 0, skim_dict.lookup(df.home_zone_id, _valid_mandatory_location_chauf1, ('SR2_M_TIME', 'AM')), 0)" +Auto time home to work or university - chauffer 2,time_home_to_mand2,"np.where(_mandatory_location_chauf2 > 0, skim_dict.lookup(df.home_zone_id, _valid_mandatory_location_chauf2, ('SR2_M_TIME', 'AM')), 0)" +Auto dist home to work or university - chauffer 1,dist_home_to_mand1,"np.where(_mandatory_location_chauf1 > 0, skim_dict.lookup(df.home_zone_id, _valid_mandatory_location_chauf1, ('SR2_M_DIST', 'AM')), 0)" +Auto dist home to work or university - chauffer 2,dist_home_to_mand2,"np.where(_mandatory_location_chauf2 > 0, skim_dict.lookup(df.home_zone_id, _valid_mandatory_location_chauf2, ('SR2_M_DIST', 'AM')), 0)" +# outbound distance combinations between chauffeurs and children,, +Distance from child 1 school to chauffeur 1 mandatory location,_dist_school1_to_mand1,"np.where((school_location_child1 > 0) & (_mandatory_location_chauf1 > 0), skim_dict.lookup(_valid_school_location_child1, _valid_mandatory_location_chauf1, ('SR2_M_TIME', 'AM')), 0)" +Distance from child 1 school to chauffeur 2 mandatory location,_dist_school1_to_mand2,"np.where((school_location_child1 > 0) & (_mandatory_location_chauf2 > 0), skim_dict.lookup(_valid_school_location_child1, _valid_mandatory_location_chauf2, ('SR2_M_TIME', 'AM')), 0)" +Distance from child 2 school to chauffeur 1 mandatory location,_dist_school2_to_mand1,"np.where((school_location_child2 > 0) & (_mandatory_location_chauf1 > 0), skim_dict.lookup(_valid_school_location_child2, _valid_mandatory_location_chauf1, ('SR2_M_TIME', 'AM')), 0)" +Distance from child 2 school to chauffeur 2 mandatory location,_dist_school2_to_mand2,"np.where((school_location_child2 > 0) & (_mandatory_location_chauf2 > 0), skim_dict.lookup(_valid_school_location_child2, _valid_mandatory_location_chauf2, ('SR2_M_TIME', 'AM')), 0)" +Distance from child 3 school to chauffeur 1 mandatory location,_dist_school3_to_mand1,"np.where((school_location_child3 > 0) & (_mandatory_location_chauf1 > 0), skim_dict.lookup(_valid_school_location_child3, _valid_mandatory_location_chauf1, ('SR2_M_TIME', 'AM')), 0)" +Distance from child 3 school to chauffeur 2 mandatory location,_dist_school3_to_mand2,"np.where((school_location_child3 > 0) & (_mandatory_location_chauf2 > 0), skim_dict.lookup(_valid_school_location_child3, _valid_mandatory_location_chauf2, ('SR2_M_TIME', 'AM')), 0)" +Distance from child 1 school to child 2 school,_dist_school1_to_school2,"np.where((school_location_child1 > 0) & (school_location_child2 > 0), skim_dict.lookup(_valid_school_location_child1, _valid_school_location_child2, ('SR2_M_TIME', 'AM')), 0)" +Distance from child 1 school to child 3 school,_dist_school1_to_school3,"np.where((school_location_child1 > 0) & (school_location_child3 > 0), skim_dict.lookup(_valid_school_location_child1, _valid_school_location_child3, ('SR2_M_TIME', 'AM')), 0)" +Distance from child 2 school to child 3 school,_dist_school2_to_school3,"np.where((school_location_child2 > 0) & (school_location_child3 > 0), skim_dict.lookup(_valid_school_location_child2, _valid_school_location_child3, ('SR2_M_TIME', 'AM')), 0)" +Distance from child 2 school to child 1 school,_dist_school2_to_school1,"np.where((school_location_child2 > 0) & (school_location_child1 > 0), skim_dict.lookup(_valid_school_location_child2, _valid_school_location_child1, ('SR2_M_TIME', 'AM')), 0)" +Distance from child 3 school to child 1 school,_dist_school3_to_school1,"np.where((school_location_child3 > 0) & (school_location_child1 > 0), skim_dict.lookup(_valid_school_location_child3, _valid_school_location_child1, ('SR2_M_TIME', 'AM')), 0)" +Distance from child 3 school to child 2 school,_dist_school3_to_school2,"np.where((school_location_child3 > 0) & (school_location_child2 > 0), skim_dict.lookup(_valid_school_location_child3, _valid_school_location_child2, ('SR2_M_TIME', 'AM')), 0)" +# absolute deviation distance outbound,, +Absolute deviation outbound distance Child 1 Chauffer 1,abs_dev_dist_out_child1_chauf1,"np.maximum(dist_home_to_school1 + _dist_school1_to_mand1 - dist_home_to_mand1,0)" +Absolute deviation outbound distance Child 1 Chauffer 2,abs_dev_dist_out_child1_chauf2,"np.maximum(dist_home_to_school1 + _dist_school1_to_mand2 - dist_home_to_mand2,0)" +Absolute deviation outbound distance Child 2 Chauffer 1,abs_dev_dist_out_child2_chauf1,"np.maximum(dist_home_to_school2 + _dist_school2_to_mand1 - dist_home_to_mand1,0)" +Absolute deviation outbound distance Child 2 Chauffer 2,abs_dev_dist_out_child2_chauf2,"np.maximum(dist_home_to_school2 + _dist_school2_to_mand2 - dist_home_to_mand2,0)" +Absolute deviation outbound distance Child 3 Chauffer 1,abs_dev_dist_out_child3_chauf1,"np.maximum(dist_home_to_school3 + _dist_school3_to_mand1 - dist_home_to_mand1,0)" +Absolute deviation outbound distance Child 3 Chauffer 2,abs_dev_dist_out_child3_chauf2,"np.maximum(dist_home_to_school3 + _dist_school3_to_mand2 - dist_home_to_mand2,0)" +Absolute deviation outbound distance child12 Chauffer 1,abs_dev_dist_out_child12_chauf1,"np.maximum(np.minimum(dist_home_to_school1 + _dist_school1_to_school2 + _dist_school2_to_mand1, dist_home_to_school2 + _dist_school2_to_school1 + _dist_school1_to_mand1) - dist_home_to_mand1, 0)" +Absolute deviation outbound distance child12 Chauffer 2,abs_dev_dist_out_child12_chauf2,"np.maximum(np.minimum(dist_home_to_school1 + _dist_school1_to_school2 + _dist_school2_to_mand2, dist_home_to_school2 + _dist_school2_to_school1 + _dist_school1_to_mand2) - dist_home_to_mand2, 0)" +Absolute deviation outbound distance child13 Chauffer 1,abs_dev_dist_out_child13_chauf1,"np.maximum(np.minimum(dist_home_to_school1 + _dist_school1_to_school3 + _dist_school3_to_mand1, dist_home_to_school3 + _dist_school3_to_school1 + _dist_school1_to_mand1) - dist_home_to_mand1, 0)" +Absolute deviation outbound distance child13 Chauffer 2,abs_dev_dist_out_child13_chauf2,"np.maximum(np.minimum(dist_home_to_school1 + _dist_school1_to_school3 + _dist_school3_to_mand2, dist_home_to_school3 + _dist_school3_to_school1 + _dist_school1_to_mand2) - dist_home_to_mand2, 0)" +Absolute deviation outbound distance child23 Chauffer 1,abs_dev_dist_out_child23_chauf1,"np.maximum(np.minimum(dist_home_to_school2 + _dist_school2_to_school3 + _dist_school3_to_mand1, dist_home_to_school3 + _dist_school3_to_school2 + _dist_school2_to_mand1) - dist_home_to_mand1, 0)" +Absolute deviation outbound distance child23 Chauffer 2,abs_dev_dist_out_child23_chauf2,"np.maximum(np.minimum(dist_home_to_school2 + _dist_school2_to_school3 + _dist_school3_to_mand2, dist_home_to_school3 + _dist_school3_to_school2 + _dist_school2_to_mand2) - dist_home_to_mand2, 0)" +,_dist_school1_school2_school3_mand1,"dist_home_to_school1 + _dist_school1_to_school2 + _dist_school2_to_school3 + _dist_school3_to_mand1" +,_dist_school1_school3_school2_mand1,"dist_home_to_school1 + _dist_school1_to_school3 + _dist_school3_to_school2 + _dist_school2_to_mand1" +,_dist_school2_school1_school3_mand1,"dist_home_to_school2 + _dist_school2_to_school1 + _dist_school1_to_school3 + _dist_school3_to_mand1" +,_dist_school2_school3_school1_mand1,"dist_home_to_school2 + _dist_school2_to_school3 + _dist_school3_to_school1 + _dist_school1_to_mand1" +,_dist_school3_school1_school2_mand1,"dist_home_to_school3 + _dist_school3_to_school1 + _dist_school1_to_school2 + _dist_school2_to_mand1" +,_dist_school3_school2_school1_mand1,"dist_home_to_school3 + _dist_school3_to_school2 + _dist_school2_to_school1 + _dist_school1_to_mand1" +,_min_dist_dropoff_order_out_child123_chauf1,_dist_school1_school2_school3_mand1 +,_min_dist_dropoff_order_out_child123_chauf1,"np.where((_dist_school1_school3_school2_mand1 > 0) & (_dist_school1_school3_school2_mand1 < _min_dist_dropoff_order_out_child123_chauf1), _dist_school1_school3_school2_mand1, _min_dist_dropoff_order_out_child123_chauf1)" +,_min_dist_dropoff_order_out_child123_chauf1,"np.where((_dist_school2_school1_school3_mand1 > 0) & (_dist_school2_school1_school3_mand1 < _min_dist_dropoff_order_out_child123_chauf1), _dist_school2_school1_school3_mand1, _min_dist_dropoff_order_out_child123_chauf1)" +,_min_dist_dropoff_order_out_child123_chauf1,"np.where((_dist_school2_school3_school1_mand1 > 0) & (_dist_school2_school3_school1_mand1 < _min_dist_dropoff_order_out_child123_chauf1), _dist_school2_school3_school1_mand1, _min_dist_dropoff_order_out_child123_chauf1)" +,_min_dist_dropoff_order_out_child123_chauf1,"np.where((_dist_school3_school1_school2_mand1 > 0) & (_dist_school3_school1_school2_mand1 < _min_dist_dropoff_order_out_child123_chauf1), _dist_school3_school1_school2_mand1, _min_dist_dropoff_order_out_child123_chauf1)" +,_min_dist_dropoff_order_out_child123_chauf1,"np.where((_dist_school3_school2_school1_mand1 > 0) & (_dist_school3_school2_school1_mand1 < _min_dist_dropoff_order_out_child123_chauf1), _dist_school3_school2_school1_mand1, _min_dist_dropoff_order_out_child123_chauf1)" +Absolute deviation outbound distance child123 Chauffer 1,abs_dev_dist_out_child123_chauf1,"np.maximum(_min_dist_dropoff_order_out_child123_chauf1 - dist_home_to_mand1, 0)" +,_dist_school1_school2_school3_mand2,"dist_home_to_school1 + _dist_school1_to_school2 + _dist_school2_to_school3 + _dist_school3_to_mand2" +,_dist_school1_school3_school2_mand2,"dist_home_to_school1 + _dist_school1_to_school3 + _dist_school3_to_school2 + _dist_school2_to_mand2" +,_dist_school2_school1_school3_mand2,"dist_home_to_school2 + _dist_school2_to_school1 + _dist_school1_to_school3 + _dist_school3_to_mand2" +,_dist_school2_school3_school1_mand2,"dist_home_to_school2 + _dist_school2_to_school3 + _dist_school3_to_school1 + _dist_school1_to_mand2" +,_dist_school3_school1_school2_mand2,"dist_home_to_school3 + _dist_school3_to_school1 + _dist_school1_to_school2 + _dist_school2_to_mand2" +,_dist_school3_school2_school1_mand2,"dist_home_to_school3 + _dist_school3_to_school2 + _dist_school2_to_school1 + _dist_school1_to_mand2" +,_min_dist_dropoff_order_out_child123_chauf2,_dist_school1_school2_school3_mand2 +,_min_dist_dropoff_order_out_child123_chauf2,"np.where((_dist_school1_school3_school2_mand2 > 0) & (_dist_school1_school3_school2_mand2 < _min_dist_dropoff_order_out_child123_chauf2), _dist_school1_school3_school2_mand2, _min_dist_dropoff_order_out_child123_chauf2)" +,_min_dist_dropoff_order_out_child123_chauf2,"np.where((_dist_school2_school1_school3_mand2 > 0) & (_dist_school2_school1_school3_mand2 < _min_dist_dropoff_order_out_child123_chauf2), _dist_school2_school1_school3_mand2, _min_dist_dropoff_order_out_child123_chauf2)" +,_min_dist_dropoff_order_out_child123_chauf2,"np.where((_dist_school2_school3_school1_mand2 > 0) & (_dist_school2_school3_school1_mand2 < _min_dist_dropoff_order_out_child123_chauf2), _dist_school2_school3_school1_mand2, _min_dist_dropoff_order_out_child123_chauf2)" +,_min_dist_dropoff_order_out_child123_chauf2,"np.where((_dist_school3_school1_school2_mand2 > 0) & (_dist_school3_school1_school2_mand2 < _min_dist_dropoff_order_out_child123_chauf2), _dist_school3_school1_school2_mand2, _min_dist_dropoff_order_out_child123_chauf2)" +,_min_dist_dropoff_order_out_child123_chauf2,"np.where((_dist_school3_school2_school1_mand2 > 0) & (_dist_school3_school2_school1_mand2 < _min_dist_dropoff_order_out_child123_chauf2), _dist_school3_school2_school1_mand2, _min_dist_dropoff_order_out_child123_chauf2)" +Absolute deviation outbound distance child123 Chauffer 1,abs_dev_dist_out_child123_chauf2,"np.maximum(_min_dist_dropoff_order_out_child123_chauf2 - dist_home_to_mand2, 0)" +# Availability for multiple bundles,, +,_return_min_taking_child1,"(pref_depart_time_school1 * mins_per_time_bin) + time_home_to_school1 + time_school_to_home1" +,_return_min_taking_child2,"(pref_depart_time_school2 * mins_per_time_bin) + time_home_to_school2 + time_school_to_home2" +,_return_min_taking_child3,"(pref_depart_time_school3 * mins_per_time_bin) + time_home_to_school3 + time_school_to_home3" +Availability of taking child 1 and then 2,_avail_child1_then_child2,"(_return_min_taking_child1 < (pref_depart_time_school2 * mins_per_time_bin))" +Availability of taking child 2 and then 1,_avail_child2_then_child1,"(_return_min_taking_child2 < (pref_depart_time_school1 * mins_per_time_bin))" +Availability of taking child 1 and then 3,_avail_child1_then_child3,"(_return_min_taking_child1 < (pref_depart_time_school3 * mins_per_time_bin))" +Availability of taking child 3 and then 1,_avail_child3_then_child1,"(_return_min_taking_child3 < (pref_depart_time_school1 * mins_per_time_bin))" +Availability of taking child 2 and then 3,_avail_child2_then_child3,"(_return_min_taking_child2 < (pref_depart_time_school3 * mins_per_time_bin))" +Availability of taking child 3 and then 2,_avail_child3_then_child2,"(_return_min_taking_child3 < (pref_depart_time_school2 * mins_per_time_bin))" +multiple_bundle_availability,avail_multiple_bundles,(_avail_child1_then_child2 | _avail_child2_then_child1 | _avail_child1_then_child3 | _avail_child3_then_child1 | _avail_child2_then_child3 | _avail_child3_then_child2) +# ,, +# Inbound specific terms ,, +Preferred return time from school - child 1,pref_return_time_school1,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').end, df.child_id1)" +Preferred return time from school - child 2,pref_return_time_school2,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').end, df.child_id2)" +Preferred return time from school - child 3,pref_return_time_school3,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').end, df.child_id3)" +Preferred return time from work / univ - chauffer 1,pref_return_time_chauf1,"reindex(tours[(tours.tour_category == 'mandatory') & (tours.tour_num == 1)].set_index('person_id').end, df.chauf_id1)" +Preferred return time from work / univ - chauffer 2,pref_return_time_chauf2,"reindex(tours[(tours.tour_category == 'mandatory') & (tours.tour_num == 1)].set_index('person_id').end, df.chauf_id2)" diff --git a/resident/configs/school_escorting_preprocessor_outbound_cond.csv b/resident/configs/school_escorting_preprocessor_outbound_cond.csv new file mode 100644 index 0000000..10a9811 --- /dev/null +++ b/resident/configs/school_escorting_preprocessor_outbound_cond.csv @@ -0,0 +1,138 @@ +Description,Target,Expression +Number of children going to school,num_children_going_to_school,"np.where(df['child_id1'] > 0, 1, 0) + np.where(df['child_id2'] > 0, 1, 0) + np.where(df['child_id3'] > 0,1,0)" +Number of potential chauffeurs,num_potential_chauffeurs,"np.where(df['chauf_id1'] > 0, 1, 0) + np.where(df['chauf_id2'] > 0, 1, 0)" +Person Type - chauffer 1,ptype_chauf1,"reindex(persons.ptype, df.chauf_id1)" +Person Type - chauffer 2,ptype_chauf2,"reindex(persons.ptype, df.chauf_id2)" +Gender - chauffer 1,gender_chauf1,"reindex(persons.SEX, df.chauf_id1)" +Gender - chauffer 2,gender_chauf2,"reindex(persons.SEX, df.chauf_id2)" +Age - chauffer 1,age_chauf1,"reindex(persons.age, df.chauf_id1)" +Age - chauffer 2,age_chauf2,"reindex(persons.age, df.chauf_id2)" +Daily activity pattern - chauffer 1,cdap_chauf1,"reindex(persons.cdap_activity, df.chauf_id1)" +Daily activity pattern - chauffer 2,cdap_chauf2,"reindex(persons.cdap_activity, df.chauf_id2)" +Age - child 1,age_child1,"reindex(persons.age, df.child_id1)" +Age - child 2,age_child2,"reindex(persons.age, df.child_id2)" +Age - child 3,age_child3,"reindex(persons.age, df.child_id3)" +# Departure times to school and work +Preferred departure time to school - child 1,pref_depart_time_school1,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').start, df.child_id1)" +Preferred departure time to school - child 2,pref_depart_time_school2,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').start, df.child_id2)" +Preferred departure time to school - child 3,pref_depart_time_school3,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').start, df.child_id3)" +Preferred departure time to work / univ - chauffer 1,pref_depart_time_chauf1,"reindex(tours[(tours.tour_category == 'mandatory') & (tours.tour_num == 1)].set_index('person_id').start, df.chauf_id1)" +Preferred departure time to work / univ - chauffer 2,pref_depart_time_chauf2,"reindex(tours[(tours.tour_category == 'mandatory') & (tours.tour_num == 1)].set_index('person_id').start, df.chauf_id2)" +# Distances and times to school and work +School location - child 1,school_location_child1,"reindex(persons.school_zone_id, df.child_id1)" +School location - child 2,school_location_child2,"reindex(persons.school_zone_id, df.child_id2)" +School location - child 3,school_location_child3,"reindex(persons.school_zone_id, df.child_id3)" +School location - chauffer 1,_school_location_chauf1,"reindex(persons.workplace_zone_id, df.chauf_id1)" +School location - chauffer 2,_school_location_chauf2,"reindex(persons.workplace_zone_id, df.chauf_id2)" +Work location - chauffer 1,_work_location_chauf1,"reindex(persons.workplace_zone_id, df.chauf_id1)" +Work location - chauffer 2,_work_location_chauf2,"reindex(persons.workplace_zone_id, df.chauf_id2)" +Mandatory tour location - chauffer 1,_mandatory_location_chauf1,"_school_location_chauf1.where(ptype_chauf1 == 3, _work_location_chauf1)" +Mandatory tour location - chauffer 2,_mandatory_location_chauf2,"_school_location_chauf1.where(ptype_chauf2 == 3, _work_location_chauf2)" +# creating valid school locations to pass to skim_dict,, +,_valid_school_location_child1,school_location_child1.fillna(persons[persons.school_zone_id > 0].school_zone_id.mode()[0]) +,_valid_school_location_child2,school_location_child2.fillna(persons[persons.school_zone_id > 0].school_zone_id.mode()[0]) +,_valid_school_location_child3,school_location_child3.fillna(persons[persons.school_zone_id > 0].school_zone_id.mode()[0]) +,_valid_mandatory_location_chauf1,_mandatory_location_chauf1.fillna(persons[persons.workplace_zone_id > 0].workplace_zone_id.mode()[0]) +,_valid_mandatory_location_chauf2,_mandatory_location_chauf2.fillna(persons[persons.workplace_zone_id > 0].workplace_zone_id.mode()[0]) +Auto time home to school - child 1,time_home_to_school1,"np.where(school_location_child1 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child1, ('SR2_M_TIME', 'AM')), 0)" +Auto time home to school - child 2,time_home_to_school2,"np.where(school_location_child2 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child2, ('SR2_M_TIME', 'AM')), 0)" +Auto time home to school - child 3,time_home_to_school3,"np.where(school_location_child3 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child3, ('SR2_M_TIME', 'AM')), 0)" +Auto time school to home - child 1,time_school_to_home1,"np.where(school_location_child1 > 0, skim_dict.lookup(_valid_school_location_child1, df.home_zone_id, ('SR2_M_TIME', 'AM')), 0)" +Auto time school to home - child 2,time_school_to_home2,"np.where(school_location_child2 > 0, skim_dict.lookup(_valid_school_location_child2, df.home_zone_id, ('SR2_M_TIME', 'AM')), 0)" +Auto time school to home - child 3,time_school_to_home3,"np.where(school_location_child3 > 0, skim_dict.lookup(_valid_school_location_child3, df.home_zone_id, ('SR2_M_TIME', 'AM')), 0)" +Auto dist home to school - child 1,dist_home_to_school1,"np.where(school_location_child1 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child1, ('SR2_M_DIST', 'AM')), 0)" +Auto dist home to school - child 2,dist_home_to_school2,"np.where(school_location_child2 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child2, ('SR2_M_DIST', 'AM')), 0)" +Auto dist home to school - child 3,dist_home_to_school3,"np.where(school_location_child3 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child3, ('SR2_M_DIST', 'AM')), 0)" +Auto dist intra school taz - child 1,dist_intra_school1,"np.where(school_location_child1 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child1, ('SR2_M_DIST', 'AM')), 0)" +Auto dist intra school taz - child 2,dist_intra_school2,"np.where(school_location_child2 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child2, ('SR2_M_DIST', 'AM')), 0)" +Auto dist intra school taz - child 3,dist_intra_school3,"np.where(school_location_child3 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child3, ('SR2_M_DIST', 'AM')), 0)" +Auto time home to work or university - chauffer 1,time_home_to_mand1,"np.where(_mandatory_location_chauf1 > 0, skim_dict.lookup(df.home_zone_id, _valid_mandatory_location_chauf1, ('SR2_M_TIME', 'AM')), 0)" +Auto time home to work or university - chauffer 2,time_home_to_mand2,"np.where(_mandatory_location_chauf2 > 0, skim_dict.lookup(df.home_zone_id, _valid_mandatory_location_chauf2, ('SR2_M_TIME', 'AM')), 0)" +Auto dist home to work or university - chauffer 1,dist_home_to_mand1,"np.where(_mandatory_location_chauf1 > 0, skim_dict.lookup(df.home_zone_id, _valid_mandatory_location_chauf1, ('SR2_M_DIST', 'AM')), 0)" +Auto dist home to work or university - chauffer 2,dist_home_to_mand2,"np.where(_mandatory_location_chauf2 > 0, skim_dict.lookup(df.home_zone_id, _valid_mandatory_location_chauf2, ('SR2_M_DIST', 'AM')), 0)" +# outbound distance combinations between chauffeurs and children,, +Distance from child 1 school to chauffeur 1 mandatory location,_dist_school1_to_mand1,"np.where((school_location_child1 > 0) & (_mandatory_location_chauf1 > 0), skim_dict.lookup(_valid_school_location_child1, _valid_mandatory_location_chauf1, ('SR2_M_TIME', 'AM')), 0)" +Distance from child 1 school to chauffeur 2 mandatory location,_dist_school1_to_mand2,"np.where((school_location_child1 > 0) & (_mandatory_location_chauf2 > 0), skim_dict.lookup(_valid_school_location_child1, _valid_mandatory_location_chauf2, ('SR2_M_TIME', 'AM')), 0)" +Distance from child 2 school to chauffeur 1 mandatory location,_dist_school2_to_mand1,"np.where((school_location_child2 > 0) & (_mandatory_location_chauf1 > 0), skim_dict.lookup(_valid_school_location_child2, _valid_mandatory_location_chauf1, ('SR2_M_TIME', 'AM')), 0)" +Distance from child 2 school to chauffeur 2 mandatory location,_dist_school2_to_mand2,"np.where((school_location_child2 > 0) & (_mandatory_location_chauf2 > 0), skim_dict.lookup(_valid_school_location_child2, _valid_mandatory_location_chauf2, ('SR2_M_TIME', 'AM')), 0)" +Distance from child 3 school to chauffeur 1 mandatory location,_dist_school3_to_mand1,"np.where((school_location_child3 > 0) & (_mandatory_location_chauf1 > 0), skim_dict.lookup(_valid_school_location_child3, _valid_mandatory_location_chauf1, ('SR2_M_TIME', 'AM')), 0)" +Distance from child 3 school to chauffeur 2 mandatory location,_dist_school3_to_mand2,"np.where((school_location_child3 > 0) & (_mandatory_location_chauf2 > 0), skim_dict.lookup(_valid_school_location_child3, _valid_mandatory_location_chauf2, ('SR2_M_TIME', 'AM')), 0)" +Distance from child 1 school to child 2 school,_dist_school1_to_school2,"np.where((school_location_child1 > 0) & (school_location_child2 > 0), skim_dict.lookup(_valid_school_location_child1, _valid_school_location_child2, ('SR2_M_TIME', 'AM')), 0)" +Distance from child 1 school to child 3 school,_dist_school1_to_school3,"np.where((school_location_child1 > 0) & (school_location_child3 > 0), skim_dict.lookup(_valid_school_location_child1, _valid_school_location_child3, ('SR2_M_TIME', 'AM')), 0)" +Distance from child 2 school to child 3 school,_dist_school2_to_school3,"np.where((school_location_child2 > 0) & (school_location_child3 > 0), skim_dict.lookup(_valid_school_location_child2, _valid_school_location_child3, ('SR2_M_TIME', 'AM')), 0)" +Distance from child 2 school to child 1 school,_dist_school2_to_school1,"np.where((school_location_child2 > 0) & (school_location_child1 > 0), skim_dict.lookup(_valid_school_location_child2, _valid_school_location_child1, ('SR2_M_TIME', 'AM')), 0)" +Distance from child 3 school to child 1 school,_dist_school3_to_school1,"np.where((school_location_child3 > 0) & (school_location_child1 > 0), skim_dict.lookup(_valid_school_location_child3, _valid_school_location_child1, ('SR2_M_TIME', 'AM')), 0)" +Distance from child 3 school to child 2 school,_dist_school3_to_school2,"np.where((school_location_child3 > 0) & (school_location_child2 > 0), skim_dict.lookup(_valid_school_location_child3, _valid_school_location_child2, ('SR2_M_TIME', 'AM')), 0)" +# absolute deviation distance outbound,, +Absolute deviation outbound distance Child 1 Chauffer 1,abs_dev_dist_out_child1_chauf1,"np.maximum(dist_home_to_school1 + _dist_school1_to_mand1 - dist_home_to_mand1,0)" +Absolute deviation outbound distance Child 1 Chauffer 2,abs_dev_dist_out_child1_chauf2,"np.maximum(dist_home_to_school1 + _dist_school1_to_mand2 - dist_home_to_mand2,0)" +Absolute deviation outbound distance Child 2 Chauffer 1,abs_dev_dist_out_child2_chauf1,"np.maximum(dist_home_to_school2 + _dist_school2_to_mand1 - dist_home_to_mand1,0)" +Absolute deviation outbound distance Child 2 Chauffer 2,abs_dev_dist_out_child2_chauf2,"np.maximum(dist_home_to_school2 + _dist_school2_to_mand2 - dist_home_to_mand2,0)" +Absolute deviation outbound distance Child 3 Chauffer 1,abs_dev_dist_out_child3_chauf1,"np.maximum(dist_home_to_school3 + _dist_school3_to_mand1 - dist_home_to_mand1,0)" +Absolute deviation outbound distance Child 3 Chauffer 2,abs_dev_dist_out_child3_chauf2,"np.maximum(dist_home_to_school3 + _dist_school3_to_mand2 - dist_home_to_mand2,0)" +Absolute deviation outbound distance child12 Chauffer 1,abs_dev_dist_out_child12_chauf1,"np.maximum(np.minimum(dist_home_to_school1 + _dist_school1_to_school2 + _dist_school2_to_mand1, dist_home_to_school2 + _dist_school2_to_school1 + _dist_school1_to_mand1) - dist_home_to_mand1, 0)" +Absolute deviation outbound distance child12 Chauffer 2,abs_dev_dist_out_child12_chauf2,"np.maximum(np.minimum(dist_home_to_school1 + _dist_school1_to_school2 + _dist_school2_to_mand2, dist_home_to_school2 + _dist_school2_to_school1 + _dist_school1_to_mand2) - dist_home_to_mand2, 0)" +Absolute deviation outbound distance child13 Chauffer 1,abs_dev_dist_out_child13_chauf1,"np.maximum(np.minimum(dist_home_to_school1 + _dist_school1_to_school3 + _dist_school3_to_mand1, dist_home_to_school3 + _dist_school3_to_school1 + _dist_school1_to_mand1) - dist_home_to_mand1, 0)" +Absolute deviation outbound distance child13 Chauffer 2,abs_dev_dist_out_child13_chauf2,"np.maximum(np.minimum(dist_home_to_school1 + _dist_school1_to_school3 + _dist_school3_to_mand2, dist_home_to_school3 + _dist_school3_to_school1 + _dist_school1_to_mand2) - dist_home_to_mand2, 0)" +Absolute deviation outbound distance child23 Chauffer 1,abs_dev_dist_out_child23_chauf1,"np.maximum(np.minimum(dist_home_to_school2 + _dist_school2_to_school3 + _dist_school3_to_mand1, dist_home_to_school3 + _dist_school3_to_school2 + _dist_school2_to_mand1) - dist_home_to_mand1, 0)" +Absolute deviation outbound distance child23 Chauffer 2,abs_dev_dist_out_child23_chauf2,"np.maximum(np.minimum(dist_home_to_school2 + _dist_school2_to_school3 + _dist_school3_to_mand2, dist_home_to_school3 + _dist_school3_to_school2 + _dist_school2_to_mand2) - dist_home_to_mand2, 0)" +,_dist_school1_school2_school3_mand1,"dist_home_to_school1 + _dist_school1_to_school2 + _dist_school2_to_school3 + _dist_school3_to_mand1" +,_dist_school1_school3_school2_mand1,"dist_home_to_school1 + _dist_school1_to_school3 + _dist_school3_to_school2 + _dist_school2_to_mand1" +,_dist_school2_school1_school3_mand1,"dist_home_to_school2 + _dist_school2_to_school1 + _dist_school1_to_school3 + _dist_school3_to_mand1" +,_dist_school2_school3_school1_mand1,"dist_home_to_school2 + _dist_school2_to_school3 + _dist_school3_to_school1 + _dist_school1_to_mand1" +,_dist_school3_school1_school2_mand1,"dist_home_to_school3 + _dist_school3_to_school1 + _dist_school1_to_school2 + _dist_school2_to_mand1" +,_dist_school3_school2_school1_mand1,"dist_home_to_school3 + _dist_school3_to_school2 + _dist_school2_to_school1 + _dist_school1_to_mand1" +,_min_dist_dropoff_order_out_child123_chauf1,_dist_school1_school2_school3_mand1 +,_min_dist_dropoff_order_out_child123_chauf1,"np.where((_dist_school1_school3_school2_mand1 > 0) & (_dist_school1_school3_school2_mand1 < _min_dist_dropoff_order_out_child123_chauf1), _dist_school1_school3_school2_mand1, _min_dist_dropoff_order_out_child123_chauf1)" +,_min_dist_dropoff_order_out_child123_chauf1,"np.where((_dist_school2_school1_school3_mand1 > 0) & (_dist_school2_school1_school3_mand1 < _min_dist_dropoff_order_out_child123_chauf1), _dist_school2_school1_school3_mand1, _min_dist_dropoff_order_out_child123_chauf1)" +,_min_dist_dropoff_order_out_child123_chauf1,"np.where((_dist_school2_school3_school1_mand1 > 0) & (_dist_school2_school3_school1_mand1 < _min_dist_dropoff_order_out_child123_chauf1), _dist_school2_school3_school1_mand1, _min_dist_dropoff_order_out_child123_chauf1)" +,_min_dist_dropoff_order_out_child123_chauf1,"np.where((_dist_school3_school1_school2_mand1 > 0) & (_dist_school3_school1_school2_mand1 < _min_dist_dropoff_order_out_child123_chauf1), _dist_school3_school1_school2_mand1, _min_dist_dropoff_order_out_child123_chauf1)" +,_min_dist_dropoff_order_out_child123_chauf1,"np.where((_dist_school3_school2_school1_mand1 > 0) & (_dist_school3_school2_school1_mand1 < _min_dist_dropoff_order_out_child123_chauf1), _dist_school3_school2_school1_mand1, _min_dist_dropoff_order_out_child123_chauf1)" +Absolute deviation outbound distance child123 Chauffer 1,abs_dev_dist_out_child123_chauf1,"np.maximum(_min_dist_dropoff_order_out_child123_chauf1 - dist_home_to_mand1, 0)" +,_dist_school1_school2_school3_mand2,"dist_home_to_school1 + _dist_school1_to_school2 + _dist_school2_to_school3 + _dist_school3_to_mand2" +,_dist_school1_school3_school2_mand2,"dist_home_to_school1 + _dist_school1_to_school3 + _dist_school3_to_school2 + _dist_school2_to_mand2" +,_dist_school2_school1_school3_mand2,"dist_home_to_school2 + _dist_school2_to_school1 + _dist_school1_to_school3 + _dist_school3_to_mand2" +,_dist_school2_school3_school1_mand2,"dist_home_to_school2 + _dist_school2_to_school3 + _dist_school3_to_school1 + _dist_school1_to_mand2" +,_dist_school3_school1_school2_mand2,"dist_home_to_school3 + _dist_school3_to_school1 + _dist_school1_to_school2 + _dist_school2_to_mand2" +,_dist_school3_school2_school1_mand2,"dist_home_to_school3 + _dist_school3_to_school2 + _dist_school2_to_school1 + _dist_school1_to_mand2" +,_min_dist_dropoff_order_out_child123_chauf2,_dist_school1_school2_school3_mand2 +,_min_dist_dropoff_order_out_child123_chauf2,"np.where((_dist_school1_school3_school2_mand2 > 0) & (_dist_school1_school3_school2_mand2 < _min_dist_dropoff_order_out_child123_chauf2), _dist_school1_school3_school2_mand2, _min_dist_dropoff_order_out_child123_chauf2)" +,_min_dist_dropoff_order_out_child123_chauf2,"np.where((_dist_school2_school1_school3_mand2 > 0) & (_dist_school2_school1_school3_mand2 < _min_dist_dropoff_order_out_child123_chauf2), _dist_school2_school1_school3_mand2, _min_dist_dropoff_order_out_child123_chauf2)" +,_min_dist_dropoff_order_out_child123_chauf2,"np.where((_dist_school2_school3_school1_mand2 > 0) & (_dist_school2_school3_school1_mand2 < _min_dist_dropoff_order_out_child123_chauf2), _dist_school2_school3_school1_mand2, _min_dist_dropoff_order_out_child123_chauf2)" +,_min_dist_dropoff_order_out_child123_chauf2,"np.where((_dist_school3_school1_school2_mand2 > 0) & (_dist_school3_school1_school2_mand2 < _min_dist_dropoff_order_out_child123_chauf2), _dist_school3_school1_school2_mand2, _min_dist_dropoff_order_out_child123_chauf2)" +,_min_dist_dropoff_order_out_child123_chauf2,"np.where((_dist_school3_school2_school1_mand2 > 0) & (_dist_school3_school2_school1_mand2 < _min_dist_dropoff_order_out_child123_chauf2), _dist_school3_school2_school1_mand2, _min_dist_dropoff_order_out_child123_chauf2)" +Absolute deviation outbound distance child123 Chauffer 1,abs_dev_dist_out_child123_chauf2,"np.maximum(_min_dist_dropoff_order_out_child123_chauf2 - dist_home_to_mand2, 0)" +# Availability for multiple bundles,, +,_return_min_taking_child1,"(pref_depart_time_school1 * mins_per_time_bin) + time_home_to_school1 + time_school_to_home1" +,_return_min_taking_child2,"(pref_depart_time_school2 * mins_per_time_bin) + time_home_to_school2 + time_school_to_home2" +,_return_min_taking_child3,"(pref_depart_time_school3 * mins_per_time_bin) + time_home_to_school3 + time_school_to_home3" +Availability of taking child 1 and then 2,_avail_child1_then_child2,"(_return_min_taking_child1 < (pref_depart_time_school2 * mins_per_time_bin))" +Availability of taking child 2 and then 1,_avail_child2_then_child1,"(_return_min_taking_child2 < (pref_depart_time_school1 * mins_per_time_bin))" +Availability of taking child 1 and then 3,_avail_child1_then_child3,"(_return_min_taking_child1 < (pref_depart_time_school3 * mins_per_time_bin))" +Availability of taking child 3 and then 1,_avail_child3_then_child1,"(_return_min_taking_child3 < (pref_depart_time_school1 * mins_per_time_bin))" +Availability of taking child 2 and then 3,_avail_child2_then_child3,"(_return_min_taking_child2 < (pref_depart_time_school3 * mins_per_time_bin))" +Availability of taking child 3 and then 2,_avail_child3_then_child2,"(_return_min_taking_child3 < (pref_depart_time_school2 * mins_per_time_bin))" +multiple_bundle_availability,avail_multiple_bundles,(_avail_child1_then_child2 | _avail_child2_then_child1 | _avail_child1_then_child3 | _avail_child3_then_child1 | _avail_child2_then_child3 | _avail_child3_then_child2) +# ,, +# Inbound specific terms ,, +Preferred return time from school - child 1,pref_return_time_school1,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').end, df.child_id1)" +Preferred return time from school - child 2,pref_return_time_school2,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').end, df.child_id2)" +Preferred return time from school - child 3,pref_return_time_school3,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').end, df.child_id3)" +Preferred return time from work / univ - chauffer 1,pref_return_time_chauf1,"reindex(tours[(tours.tour_category == 'mandatory') & (tours.tour_num == 1)].set_index('person_id').end, df.chauf_id1)" +Preferred return time from work / univ - chauffer 2,pref_return_time_chauf2,"reindex(tours[(tours.tour_category == 'mandatory') & (tours.tour_num == 1)].set_index('person_id').end, df.chauf_id2)" +# overlapping time windows from inbound escorting,, +# ,_return_min_taking_inbound_child1_pe,"(_pref_depart_time_to_school1 * mins_per_time_bin) + time_home_to_school1 + time_school_to_home1" +# ,_return_min_taking_inbound_child2_pe,"(_pref_depart_time_to_school2 * mins_per_time_bin) + time_home_to_school2 + time_school_to_home2" +# ,_return_min_taking_inbound_child3_pe,"(_pref_depart_time_to_school3 * mins_per_time_bin) + time_home_to_school3 + time_school_to_home3" +# finding earliest time chauffer 1 returns from inbound pure escort tour,inbound_pe_return_time_home_chauf1,-1 +# ,inbound_pe_return_time_home_chauf1,"np.where((df.chauf1_inbound == 2) & (_return_min_taking_inbound_child1_pe > inbound_pe_return_time_home_chauf1), _return_min_taking_inbound_child1_pe, inbound_pe_return_time_home_chauf1)" +# ,inbound_pe_return_time_home_chauf1,"np.where((df.chauf2_inbound == 2) & (_return_min_taking_inbound_child2_pe > inbound_pe_return_time_home_chauf1), _return_min_taking_inbound_child2_pe, inbound_pe_return_time_home_chauf1)" +# ,inbound_pe_return_time_home_chauf1,"np.where((df.chauf3_inbound == 2) & (_return_min_taking_inbound_child3_pe > inbound_pe_return_time_home_chauf1), _return_min_taking_inbound_child3_pe, inbound_pe_return_time_home_chauf1)" +# finding earliest time chauffer 2 returns from inbound pure escort tour,inbound_pe_return_time_home_chauf2,-1 +# ,inbound_pe_return_time_home_chauf2,"np.where((df.chauf1_inbound == 2) & (_return_min_taking_inbound_child1_pe > inbound_pe_return_time_home_chauf2), _return_min_taking_inbound_child1_pe, inbound_pe_return_time_home_chauf2)" +# ,inbound_pe_return_time_home_chauf2,"np.where((df.chauf2_inbound == 2) & (_return_min_taking_inbound_child2_pe > inbound_pe_return_time_home_chauf2), _return_min_taking_inbound_child2_pe, inbound_pe_return_time_home_chauf2)" +# ,inbound_pe_return_time_home_chauf2,"np.where((df.chauf3_inbound == 2) & (_return_min_taking_inbound_child3_pe > inbound_pe_return_time_home_chauf2), _return_min_taking_inbound_child3_pe, inbound_pe_return_time_home_chauf2)" +# finding latest time chauffer 1 returns from inbound ride share tour,inbound_rs_return_time_home_chauf1,pref_depart_time_chauf1 +# finding latest time chauffer 2 returns from inbound ride share tour,inbound_rs_return_time_home_chauf2,pref_depart_time_chauf2 +# return time of inbound school escoring tour - chauffeur 1,return_bin_inbound_school_escorting1,"np.where(df.nrs1_inbound > 0, pref_depart_time_chauf1, inbound_pe_return_time_home_chauf1)" +# return time of inbound school escoring tour - chauffeur 2,return_bin_inbound_school_escorting2,"np.where(df.nrs2_inbound > 0, pref_depart_time_chauf2, inbound_pe_return_time_home_chauf2)" diff --git a/resident/configs/school_location.csv b/resident/configs/school_location.csv new file mode 100644 index 0000000..8619b53 --- /dev/null +++ b/resident/configs/school_location.csv @@ -0,0 +1,31 @@ +Label,Description,Expression,university,highschool,gradeschool,preschool +local_dist,,"_DIST@skims[('SOV_M_DIST', 'MD')]",1,1,1,1 +util_mode_choice_logsum,Mode choice logsum,mode_choice_logsum,coef_mclogsum_univ,coef_mclogsum_hsch,coef_mclogsum_gs,coef_mclogsum_ps +util_Distance,Distance,@_DIST.clip(upper=50),coef_dist_univ,coef_dist_hsch,coef_dist_gs,coef_dist_ps +util_log_Distance,log_Distance,@np.log(_DIST.clip(upper=50)+1),coef_lndist_univ,coef_zero,coef_zero,coef_lndist_ps +util_Distance_squareroot,Squareroot of distance,@_DIST.clip(upper=50)**0.5,coef_sqrtdist_univ,coef_sqrtdist_hsch,coef_sqrtdist_gs,coef_sqrtdist_ps +util_Distance_squared,Distance_squared,@_DIST.clip(upper=50)**2,coef_sqrddist_univ,coef_sqrddist_hsch,coef_sqrddist_gs,coef_sqrddist_ps +util_Distance_cubed,Distance_cubed,@_DIST.clip(upper=50)**3,coef_cubeddist_univ,coef_cubeddist_hsch,coef_cubeddist_gs,coef_cubeddist_ps +util_Distance_worker_univ,Distance for a worker_university specific,"@np.where(df.ptype==1, _DIST.clip(upper=50), 0)",coef_workerdist_univ,coef_zero,coef_zero,coef_zero +# FIXME this ENROLL_COLL variable is looking at the number of enrollment in the origin zone not the destination zone,,,,,, +# need to merge onto the alternative destination,,,,,, +util_Distance_largeuniversity_univ,Distance for large university enrollment,"@np.where(df.ENROLL_COLL>5000, _DIST.clip(upper=50), 0)",coef_univenrol_dist_univ,coef_zero,coef_zero,coef_zero +util_Distance _lowincome_prek,Distance - low income,"@np.where(df.income<60000, _DIST.clip(upper=50), 0)",coef_zero,coef_zero,coef_zero,coef_lowincdist_ps +util_Distance - age03_prek,Distance - age 0 to 3,"@np.where(df.age<3,_DIST.clip(upper=50),0)",coef_zero,coef_zero,coef_zero,coef_age03dist_ps +util_LoggedSize,Logged Size variable - University specific,@df['size_term'].apply(np.log1p),coef_lnSize,coef_lnSize,coef_lnSize,coef_lnSize +util_no_attractions,no attractions if logged university size is zero,@df['size_term']==0,-999,-999,-999,-999 +util_sample_of_corrections_factor,Sample of alternatives correction factor,"@np.minimum(np.log(df.pick_count/df.prob), 60)",1,1,1,1 +util_sp_utility_adjustment,shadow price utility adjustment,@df['shadow_price_utility_adjustment'],1,1,1 +#,,,,,, +util_ABM2calibration_0-1miles,ABM2 calibration_0-1miles,@(_DIST<1),coef_zero,coef_abmcalib_01miles,coef_abmcalib_01miles,coef_abmcalib_01miles +util_ABM2calibration_1-2miles,ABM2 calibration_1-2miles,@(_DIST<2) * (_DIST>=1),coef_zero,coef_abmcalib_12miles,coef_abmcalib_12miles,coef_abmcalib_12miles +util_ABM2calibration_2-3miles,ABM2 calibration_2-3miles,@(_DIST<3) * (_DIST>=2),coef_zero,coef_abmcalib_23miles,coef_abmcalib_23miles,coef_abmcalib_23miles +util_ABM2calibration_0-20miles,ABM2 calibration_0-20miles,@(_DIST<20) * (_DIST),coef_zero,coef_abmcalib_20miles,coef_abmcalib_20miles,coef_abmcalib_20miles +#,,,,,, +#calibration constants,,, +util_Calibration 0-2 - miles,Calibration 0-2 - miles,@_DIST<=2,,coef_distance_0_2miles,coef_distance_2_5miles,coef_distance_5_10miles +util_Calibration 2-5 - miles,Calibration 2-5 - miles,@(_DIST>2) * (_DIST<=5),,coef_distance_0_2miles,coef_distance_2_5miles,coef_distance_5_10miles +util_Calibration 5-10 - miles,Calibration 5-10 - miles,@(_DIST>5) * (_DIST<=10),,coef_distance_0_2miles,coef_distance_2_5miles,coef_distance_5_10miles +util_ABM2 calibration 10-20 - miles,ABM3 calibration 10-20 - miles,@(_DIST>10) * (_DIST<=20),,coef_distance_0_2miles,coef_distance_2_5miles,coef_distance_5_10miles +util_ABM2 calibration 20-30 - miles,ABM3 calibration 20-30 - miles,@(_DIST>20) * (_DIST<=30),,coef_distance_0_2miles,coef_distance_2_5miles,coef_distance_5_10miles +util_ABM2 calibration >30 - miles,ABM3 calibration >30 - miles,@(_DIST>30),,coef_distance_0_2miles,coef_distance_2_5miles,coef_distance_5_10miles diff --git a/resident/configs/school_location.yaml b/resident/configs/school_location.yaml new file mode 100644 index 0000000..85cee6a --- /dev/null +++ b/resident/configs/school_location.yaml @@ -0,0 +1,74 @@ +SAMPLE_SIZE: 30 +ESTIMATION_SAMPLE_SIZE: 10 + +SIMULATE_CHOOSER_COLUMNS: + - home_zone_id + - school_segment + - household_id + - ptype + - ENROLL_COLL + - income + - age + +# model-specific logsum-related settings +CHOOSER_ORIG_COL_NAME: home_zone_id +ALT_DEST_COL_NAME: alt_dest +IN_PERIOD: 14 +OUT_PERIOD: 8 + +DEST_CHOICE_COLUMN_NAME: school_zone_id +# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if not desired in persons table +DEST_CHOICE_LOGSUM_COLUMN_NAME: school_location_logsum +# comment out MODE_CHOICE_LOGSUM_COLUMN_NAME if not desired in persons table +MODE_CHOICE_LOGSUM_COLUMN_NAME: school_modechoice_logsum + +# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if saved alt logsum table +DEST_CHOICE_SAMPLE_TABLE_NAME: school_location_sample + + +SAMPLE_SPEC: school_location_sample.csv +SPEC: school_location.csv +COEFFICIENTS: school_location_coefficients.csv + +LOGSUM_SETTINGS: tour_mode_choice.yaml +LOGSUM_PREPROCESSOR: nontour_preprocessor + +LOGSUM_TOUR_PURPOSE: + university: univ + highschool: school + gradeschool: school + preschool: school + +annotate_persons: + SPEC: annotate_persons_school + DF: persons + +# - shadow pricing + +# required by initialize_households when creating school_destination_size table +CHOOSER_TABLE_NAME: persons + +# size_terms model_selector +MODEL_SELECTOR: school + +# chooser column with segment_id for this segment type +CHOOSER_SEGMENT_COLUMN_NAME: school_segment + +# boolean column to filter choosers (True means keep) +CHOOSER_FILTER_COLUMN_NAME: is_student + + +# FIXME - these are assigned to persons in annotate_persons. we need a better way to manage this +SEGMENT_IDS: + university: 4 # SCHOOL_SEGMENT_UNIV DEFINED IN constants.yaml + highschool: 3 # SCHOOL_SEGMENT_HIGH + gradeschool: 2 # SCHOOL_SEGMENT_GRADE + preschool: 1 # SCHOOL_SEGMENT_PREK + + +# model adds these tables (informational - not added if commented out) +SHADOW_PRICE_TABLE: school_shadow_prices +MODELED_SIZE_TABLE: school_modeled_size + +# not loaded if commented out +SAVED_SHADOW_PRICE_TABLE_NAME: school_shadow_prices.csv diff --git a/resident/configs/school_location_coefficients.csv b/resident/configs/school_location_coefficients.csv new file mode 100644 index 0000000..06450ed --- /dev/null +++ b/resident/configs/school_location_coefficients.csv @@ -0,0 +1,43 @@ +coefficient_name,value,constrain +coef_zero,0,T +coef_lnSize,1,T +coef_abmcalib_01miles,-0.962352271,T +coef_abmcalib_12miles,-0.6403,T +coef_abmcalib_23miles,-0.1299,T +coef_abmcalib_20miles,0.0307,T +#,, +coef_mclogsum_univ,0.25,F +coef_dist_univ,1.279,F +coef_lndist_univ,10.412,F +coef_sqrtdist_univ,-13.774,F +coef_sqrddist_univ,-0.011,F +coef_cubeddist_univ,0,F +coef_workerdist_univ,0.013,F +coef_univenrol_dist_univ,0.028,F +#,, +coef_mclogsum_hsch,0.25,F +coef_dist_hsch,0.0241,F +coef_sqrtdist_hsch,-2.2566,F +coef_sqrddist_hsch,0.012,F +coef_cubeddist_hsch,-0.0002,F +#,, +coef_mclogsum_gs,0.34935,F +coef_dist_gs,1.4025,F +coef_sqrtdist_gs,-6.6929,F +coef_sqrddist_gs,-0.0245,F +coef_cubeddist_gs,0.0002,F +#,, +coef_mclogsum_ps,0.5,F +coef_dist_ps,0.4333,F +coef_lndist_ps,4.3415,F +coef_sqrtdist_ps,-7.38,F +coef_sqrddist_ps,0.0125,F +coef_cubeddist_ps,-0.0004,F +coef_lowincdist_ps,-0.0956,F +coef_age03dist_ps,-0.007,F +#,, +coef_distance_0_2miles,0.170,F +coef_distance_2_5miles,0.120,F +coef_distance_5_10miles,0.250,F +coef_distance_10_20miles,-0.400,F +coef_distance_20_30miles,-2.900,F diff --git a/resident/configs/school_location_sample.csv b/resident/configs/school_location_sample.csv new file mode 100644 index 0000000..c809fd8 --- /dev/null +++ b/resident/configs/school_location_sample.csv @@ -0,0 +1,16 @@ +Label,Description,Expression,university,highschool,gradeschool,preschool +local_dist,,"_DIST@skims[('SOV_M_DIST', 'MD')]",1,1,1,1 +util_Distance,Distance,@_DIST.clip(upper=50),coef_dist_univ,coef_dist_hsch,coef_dist_gs,coef_dist_ps +util_log_Distance,log_Distance,@np.log(_DIST.clip(upper=50)+1),coef_lndist_univ,coef_zero,coef_zero,coef_lndist_ps +util_Squareroot_distance,Squareroot_distance,@_DIST.clip(upper=50)**0.5,coef_sqrtdist_univ,coef_sqrtdist_hsch,coef_sqrtdist_gs,coef_sqrtdist_ps +util_Distance_squared,Distance_squared,@_DIST.clip(upper=50)**2,coef_sqrddist_univ,coef_sqrddist_hsch,coef_sqrddist_gs,coef_sqrddist_ps +util_Distance_cubed,Distance_cubed,@_DIST.clip(upper=50)**3,coef_cubeddist_univ,coef_cubeddist_hsch,coef_cubeddist_gs,coef_cubeddist_ps +util_Distance_worker_univspecific,Distance for a worker_university specific,"@np.where(df.ptype==1, _DIST.clip(upper=50), 0)",coef_workerdist_univ,coef_zero,coef_zero,coef_zero +# FIXME this ENROLL_COLL variable is looking at the number of enrollment in the origin zone not the destination zone,,,,,, +# need to merge onto the alternative destination,,,,,, +util_Distance_largeuniversity_univ,Distance for large university enrollment,"@np.where(df.ENROLL_COLL>5000, _DIST.clip(upper=50), 0)",coef_univenrol_dist_univ,coef_zero,coef_zero,coef_zero +util_Distance _lowincome_prek,Distance - low income,"@np.where(df.income<60000, _DIST.clip(upper=50), 0)",coef_zero,coef_zero,coef_zero,coef_lowincdist_ps +util_Distance - age03_prek,Distance - age 0 to 3,"@np.where(df.age<3,_DIST.clip(upper=50),0)",coef_zero,coef_zero,coef_zero,coef_age03dist_ps +util_LoggedSize,Logged Size variable - University specific,@df['size_term'].apply(np.log1p),coef_lnSize,coef_lnSize,coef_lnSize,coef_lnSize +util_no_attractions,no attractions if logged university size is zero,@df['size_term']==0,-999,-999,-999,-999 +util_sp_utility_adjustment,shadow price utility adjustment,@df['shadow_price_utility_adjustment'],1,1,1,1 \ No newline at end of file diff --git a/resident/configs/settings.yaml b/resident/configs/settings.yaml new file mode 100644 index 0000000..355a1f4 --- /dev/null +++ b/resident/configs/settings.yaml @@ -0,0 +1,163 @@ +model_name: resident + +#inherit_settings: True + +multiprocess: False +num_processes: 10 +chunk_training_mode: disabled + +# turn shadow_pricing on and off for all models (e.g. school and work) +# see shadow_pricing.yaml for additional settings +use_shadow_pricing: True + +memory_profile: False + +# Sample TAZ before MAZ in destination choice +want_dest_choice_presampling: True + +# turn writing of sample_tables on and off for all models +# (if True, tables will be written if DEST_CHOICE_SAMPLE_TABLE_NAME is specified in individual model settings) +want_dest_choice_sample_tables: False + +# trace household id; comment out or leave empty for no trace +# households with all tour types +trace_hh_id: + +# trace origin, destination in accessibility calculation; comment out or leave empty for no trace +#trace_od: + +# metro hhs 862973, so 20% = 172595 +households_sample_size: 172595 + +# input tables +input_table_list: + - tablename: households + filename: households.csv + index_col: household_id + rename_columns: + MAZ: home_zone_id + HHINCADJ: income + NP: hhsize + WORKERS: num_workers + - tablename: persons + filename: persons.csv + index_col: person_id + rename_columns: + AGEP: age + - tablename: land_use + filename: land_use.csv + index_col: zone_id + rename_columns: + MAZ: zone_id + +cleanup_pipeline_after_run: False + +output_tables: + h5_store: False + action: include + prefix: final_ + sort: True + tables: + - checkpoints + - accessibility + - land_use + - households + - persons + - tours + - trips + - joint_tour_participants + - vehicles + - proto_disaggregate_accessibility + - disaggregate_accessibility + +resume_after: + +models: + ### mp_init_proto_pop (single process) + - initialize_proto_population # Separate step so proto tables can be split for multiprocess. + ### mp_disaggregate_accessibility + - compute_disaggregate_accessibility + ### mp_initialize_hhs (single process) + - initialize_landuse + - initialize_households + ### mp_accessibility + - compute_accessibility + ### mp_households + # - license_holding_status + # - bike_comfort + - av_ownership + - auto_ownership_simulate + - work_from_home + - external_worker_identification + - external_workplace_location + - school_location + - workplace_location + - vehicle_type_choice + - adjust_auto_operating_cost + - transit_pass_subsidy + - transit_pass_ownership + - free_parking + - telecommute_frequency + - cdap_simulate + - mandatory_tour_frequency + - mandatory_tour_scheduling + - school_escorting + - joint_tour_frequency_composition + - external_joint_tour_identification + - joint_tour_participation + - joint_tour_destination + - external_joint_tour_destination + - joint_tour_scheduling + - non_mandatory_tour_frequency + - external_non_mandatory_identification + - non_mandatory_tour_destination + - external_non_mandatory_destination + - non_mandatory_tour_scheduling + - vehicle_allocation + - park_and_ride_lot_choice + - tour_mode_choice_simulate + - atwork_subtour_frequency + - atwork_subtour_destination + - atwork_subtour_scheduling + - atwork_subtour_mode_choice + - stop_frequency + - trip_purpose + - trip_destination + - trip_purpose_and_destination + - trip_scheduling + - trip_mode_choice + - parking_location + ### mp_summarize (single process) + - write_data_dictionary + - track_skim_usage + - write_trip_matrices + - write_tables + +multiprocess_steps: + - name: mp_init_proto_pop + begin: initialize_proto_population + - name: mp_disaggregate_accessibility + num_processes: 10 + begin: compute_disaggregate_accessibility + slice: + tables: + - proto_households + - proto_persons + - proto_tours + - name: mp_initialize_hhs + begin: initialize_landuse + - name: mp_accessibility + begin: compute_accessibility + num_processes: 10 + slice: + tables: + - accessibility + exclude: True # this is needed so landuse (i.e. destinations) doesn't get split + - name: mp_households + begin: av_ownership + slice: + tables: + - households + - persons + - name: mp_summarize + begin: write_data_dictionary \ No newline at end of file diff --git a/resident/configs/shadow_pricing.yaml b/resident/configs/shadow_pricing.yaml new file mode 100644 index 0000000..82fc3b9 --- /dev/null +++ b/resident/configs/shadow_pricing.yaml @@ -0,0 +1,50 @@ +shadow_pricing_models: + school: school_location + workplace: workplace_location + external_workplace: external_workplace_location + +# global switch to enable/disable loading of saved shadow prices +# (ignored if global use_shadow_pricing switch is False) +LOAD_SAVED_SHADOW_PRICES: False + +# write out choices by iteration to trace folder +WRITE_ITERATION_CHOICES: False + +# number of shadow price iterations for cold start +MAX_ITERATIONS: 10 + +# number of shadow price iterations for warm start (after loading saved shadow_prices) +MAX_ITERATIONS_SAVED: 1 + +## Shadow pricing method +# SHADOW_PRICE_METHOD: ctramp +# SHADOW_PRICE_METHOD: daysim +SHADOW_PRICE_METHOD: simulation + +# --- simulation method settings +# ignore criteria for zones smaller than size_threshold +SIZE_THRESHOLD: 10 +# ignore criteria for zones smaller than target_threshold (total employmnet or enrollment) +TARGET_THRESHOLD: 20 +# zone passes if modeled is within percent_tolerance of predicted_size +PERCENT_TOLERANCE: 5 +# max percentage of zones allowed to fail +FAIL_THRESHOLD: 1 +# apply different targets for each segment specified in destination_size_terms.csv +school_segmentation_targets: + # format is segment: land_use_column + university: ENROLL_COLL + highschool: ENROLL_K8 + gradeschool: ENROLL_912 + preschool: preschool_target + +# if target names are the same, they will be combined together +workplace_segmentation_targets: + constr_maint: EMP_TOT + health: EMP_TOT + military: EMP_TOT + mngt_busi_scic_arts: EMP_TOT + prod_trans_move: EMP_TOT + sales_office: EMP_TOT + services: EMP_TOT + diff --git a/resident/configs/stop_frequency.yaml b/resident/configs/stop_frequency.yaml new file mode 100644 index 0000000..6118976 --- /dev/null +++ b/resident/configs/stop_frequency.yaml @@ -0,0 +1,77 @@ +# inherit_settings: True +LOGIT_TYPE: MNL + +preprocessor: + SPEC: stop_frequency_annotate_tours_preprocessor + DF: tours_merged + TABLES: + - persons + - land_use + - accessibility + +SEGMENT_COL: primary_purpose + +SPEC_SEGMENTS: + - primary_purpose: work + SPEC: stop_frequency_work.csv + COEFFICIENTS: stop_frequency_coefficients_work.csv + - primary_purpose: school + SPEC: stop_frequency_school.csv + COEFFICIENTS: stop_frequency_coefficients_school.csv + - primary_purpose: univ + SPEC: stop_frequency_univ.csv + COEFFICIENTS: stop_frequency_coefficients_univ.csv + - primary_purpose: social + SPEC: stop_frequency_social.csv + COEFFICIENTS: stop_frequency_coefficients_social.csv + - primary_purpose: shopping + SPEC: stop_frequency_shopping.csv + COEFFICIENTS: stop_frequency_coefficients_shopping.csv + - primary_purpose: eatout + SPEC: stop_frequency_eatout.csv + COEFFICIENTS: stop_frequency_coefficients_eatout.csv + - primary_purpose: escort + SPEC: stop_frequency_escort.csv + COEFFICIENTS: stop_frequency_coefficients_escort.csv + - primary_purpose: othmaint + SPEC: stop_frequency_othmaint.csv + COEFFICIENTS: stop_frequency_coefficients_othmaint.csv + - primary_purpose: othdiscr + SPEC: stop_frequency_othdiscr.csv + COEFFICIENTS: stop_frequency_coefficients_othdiscr.csv + - primary_purpose: atwork + SPEC: stop_frequency_atwork.csv + COEFFICIENTS: stop_frequency_coefficients_atwork.csv + +CONSTANTS: + TRANSIT_MODES: + - WALK_TRANSIT + - PNR_TRANSIT + - KNR_TRANSIT + - TNC_TRANSIT + DRIVE_TO_TRANSIT_MODES: + - PNR_LOC + - KNR_LOC + - TNC_LOC + - PNR_PRM + - KNR_PRM + - TNC_PRM + - PNR_MIX + - KNR_MIX + - TNC_MIX + NONMOTORIZED_MODES: + - WALK + - BIKE + SHOP_TOUR: shopping + MAINT_TOUR: othmaint + SCHOOL_TOUR: school + EATOUT_TOUR: eatout + SOCIAL_TOUR: social + DISCR_TOUR: othdiscr + num_atwork_subtours_map: + no_subtours: 0 + eat: 1 + business1: 1 + maint: 1 + business2: 2 + eat_business: 2 diff --git a/resident/configs/stop_frequency_alternatives.csv b/resident/configs/stop_frequency_alternatives.csv new file mode 100644 index 0000000..72f49a7 --- /dev/null +++ b/resident/configs/stop_frequency_alternatives.csv @@ -0,0 +1,18 @@ +#,,alt file for building tours even though simulation is simple_simulate not interaction_simulate +alt,out,in +0out_0in,0,0 +0out_1in,0,1 +0out_2in,0,2 +0out_3in,0,3 +1out_0in,1,0 +1out_1in,1,1 +1out_2in,1,2 +1out_3in,1,3 +2out_0in,2,0 +2out_1in,2,1 +2out_2in,2,2 +2out_3in,2,3 +3out_0in,3,0 +3out_1in,3,1 +3out_2in,3,2 +3out_3in,3,3 diff --git a/resident/configs/stop_frequency_annotate_tours_preprocessor.csv b/resident/configs/stop_frequency_annotate_tours_preprocessor.csv new file mode 100644 index 0000000..bd2847c --- /dev/null +++ b/resident/configs/stop_frequency_annotate_tours_preprocessor.csv @@ -0,0 +1,60 @@ +Description,Target,Expression +#,, +# define primary_purpose to use for slicing choosers with a value that identifies the spec to be used ,, +# e.g. univ segment means there will be a spec called stop_frequency_univ.csv,, +# so the 'school' tour_type can treat univ and non-univ school tours differently,, +,primary_purpose,"df.tour_type.where((df.tour_type != 'school') | ~(df.pstudent == PSTUDENT_UNIVERSITY), 'univ')" +,primary_purpose,"primary_purpose.where(df.tour_category!='atwork', 'atwork')" +#,, +,distance_in_miles,"od_skims[('SOV_M_DIST', 'MD')]" +#,, +,is_joint,df.tour_category=='joint' +,_HH_PERSON_COUNT,"lambda exp, persons: persons.query(exp).groupby('household_id').size()" +,num_full,"reindex_i(_HH_PERSON_COUNT('ptype == %s' % PEMPLOY_FULL, persons), df.household_id)" +,num_part,"reindex_i(_HH_PERSON_COUNT('ptype == %s' % PEMPLOY_PART, persons), df.household_id)" +,num_student,"reindex_i(_HH_PERSON_COUNT('pstudent != %s' % PSTUDENT_NOT, persons), df.household_id)" +Num Kids between 0 and 4 (including) years old,num_age_0_4,"reindex_i(_HH_PERSON_COUNT('age < 5', persons), df.household_id)" +Num kids between 4 and 15 (including) years old,num_age_5_15,"reindex_i(_HH_PERSON_COUNT('(age >= 5) & (age <16)', persons), df.household_id)" +Number of Adults (>= 16 years old),num_adult,"reindex_i(_HH_PERSON_COUNT('age >= 16', persons), df.household_id)" +,more_cars_than_workers,df.auto_ownership >= (num_full + num_part) +,tour_mode_is_transit,df.tour_mode.isin(TRANSIT_MODES) +,tour_mode_is_drive_transit,df.tour_mode.isin(DRIVE_TO_TRANSIT_MODES) +,tour_mode_is_non_motorized,df.tour_mode.isin(NONMOTORIZED_MODES) +,tour_mode_is_schbus,df.tour_mode.isin(['SCH_BUS']) + +#,, +#num_work_tours already defined,, +,num_total_tours,"reindex_i(df.groupby('person_id').size(), df.person_id)" +school but not university,num_school_tours,"reindex_i(df[primary_purpose==SCHOOL_TOUR].groupby('person_id').size(), df.person_id)" +,num_univ_tours,(df.pstudent == PSTUDENT_UNIVERSITY) * num_school_tours +#num_escort_tours already defined,, +# indiv tour counts should not include joint tours by point_person,, +,num_shop_tours,"reindex_i(df[~is_joint & (df.tour_type==SHOP_TOUR)].groupby('person_id').size(), df.person_id)" +,num_maint_tours,"reindex_i(df[~is_joint & (df.tour_type==MAINT_TOUR)].groupby('person_id').size(), df.person_id)" +,num_eatout_tours,"reindex_i(df[~is_joint & (df.tour_type==EATOUT_TOUR)].groupby('person_id').size(), df.person_id)" +,num_social_tours,"reindex_i(df[~is_joint & (df.tour_type==SOCIAL_TOUR)].groupby('person_id').size(), df.person_id)" +,num_discr_tours,"reindex_i(df[~is_joint & (df.tour_type==DISCR_TOUR)].groupby('person_id').size(), df.person_id)" +#,, +Number of subtours in the tour,num_atwork_subtours,"df.atwork_subtour_frequency.map(num_atwork_subtours_map, na_action='ignore').fillna(0).astype(np.int8)" +#,, +,periods_per_hour,2 +,duration_hours,df.duration / periods_per_hour +Number of hh shop tours including joint,num_hh_shop_tours,"reindex_i(df[df.tour_type==SHOP_TOUR].groupby('household_id').size(), df.person_id)" +Number of hh maint tours including joint,num_hh_maint_tours,"reindex_i(df[df.tour_type==MAINT_TOUR].groupby('household_id').size(), df.person_id)" +tourStartsInPeakPeriod,_tour_starts_in_peak,(network_los.skim_time_period_label(df.start) == 'AM') | (network_los.skim_time_period_label(df.start) == 'PM') +AccesibilityAtOrigin fallback,hhacc,0 +AccesibilityAtOrigin if transit,hhacc,"hhacc.where(~tour_mode_is_transit, df.trPkRetail.where(_tour_starts_in_peak, df.trOpRetail))" +AccesibilityAtOrigin if non_motorized,hhacc,"hhacc.where(~tour_mode_is_non_motorized, df.nmRetail)" +AccesibilityADestination fallback,pracc,0 +AccesibilityADestination peak transit,_dest_trPkRetail,"reindex(accessibility.trPkRetail, df.destination)" +AccesibilityADestination off-peak transit,_dest_trOpRetail,"reindex(accessibility.trOpRetail, df.destination)" +AccesibilityAtDestination if transit,pracc,"pracc.where(~tour_mode_is_transit, _dest_trPkRetail.where(_tour_starts_in_peak, _dest_trOpRetail))" +AccesibilityAtDestination if non_motorized,pracc,"pracc.where(~tour_mode_is_non_motorized, reindex(accessibility.nmRetail, df.destination))" +,destination_area_type,"reindex(land_use.pseudomsa, df.destination)" +,tour_mode_is_sov,df.tour_mode == 'SOV' +,tour_mode_is_hov,"df.tour_mode.isin(['HOV2', 'HOV3'])" +,tour_mode_is_taxi,"df.tour_mode.isin(['TAXI', 'TNC_SINGLE', 'TNC_SHARED'])" +presence of non_worker other than self in household,has_non_worker,"df.person_id.map(other_than(persons.household_id, persons.ptype == PTYPE_NONWORK))" +presence of retiree other than self in household,has_retiree,"df.person_id.map(other_than(persons.household_id, persons.ptype == PTYPE_RETIRED))" +presence of preschooler other than self in household,has_preschool_kid,"df.person_id.map(other_than(persons.household_id, persons.ptype == PTYPE_PRESCHOOL))" +presence of school_kid other than self in household,has_school_kid,"df.person_id.map(other_than(persons.household_id, persons.ptype == PTYPE_SCHOOL))" \ No newline at end of file diff --git a/resident/configs/stop_frequency_atwork.csv b/resident/configs/stop_frequency_atwork.csv new file mode 100644 index 0000000..00880ca --- /dev/null +++ b/resident/configs/stop_frequency_atwork.csv @@ -0,0 +1,9 @@ +Label,Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in +util_alternative_specific_constants,Alternative specific constants,1,,coef_alternative_specific_constants_0out_1in_atwork,coef_alternative_specific_constants_0out_2in_atwork,coef_alternative_specific_constants_0out_3in_atwork,coef_alternative_specific_constants_1out_0in_atwork,coef_alternative_specific_constants_1out_1in_atwork,coef_alternative_specific_constants_1out_2in_atwork,coef_alternative_specific_constants_1out_3in_atwork,coef_alternative_specific_constants_2out_0in_atwork,coef_alternative_specific_constants_2out_1in_atwork,coef_alternative_specific_constants_2out_2in_atwork,coef_alternative_specific_constants_2out_3in_atwork,coef_alternative_specific_constants_3out_0in_atwork,coef_alternative_specific_constants_3out_1in_atwork,coef_alternative_specific_constants_3out_2in_atwork,coef_alternative_specific_constants_3out_3in_atwork +util_is_pre_covid,Tour happened before covid hit,@scenarioYear==2016,,coef_is_pre_covid_1plus_stops_atwork,coef_is_pre_covid_1plus_stops_atwork,coef_is_pre_covid_1plus_stops_atwork,coef_is_pre_covid_1plus_stops_atwork,coef_is_pre_covid_1plus_stops_atwork,coef_is_pre_covid_1plus_stops_atwork,coef_is_pre_covid_1plus_stops_atwork,coef_is_pre_covid_1plus_stops_atwork,coef_is_pre_covid_1plus_stops_atwork,coef_is_pre_covid_1plus_stops_atwork,coef_is_pre_covid_1plus_stops_atwork,coef_is_pre_covid_1plus_stops_atwork,coef_is_pre_covid_1plus_stops_atwork,coef_is_pre_covid_1plus_stops_atwork,coef_is_pre_covid_1plus_stops_atwork +util_num_age_5_15,Number of children in household age 5 to 15,num_age_5_15,,coef_num_age_5_15_1plus_stops_atwork,coef_num_age_5_15_1plus_stops_atwork,coef_num_age_5_15_1plus_stops_atwork,coef_num_age_5_15_1plus_stops_atwork,coef_num_age_5_15_1plus_stops_atwork,coef_num_age_5_15_1plus_stops_atwork,coef_num_age_5_15_1plus_stops_atwork,coef_num_age_5_15_1plus_stops_atwork,coef_num_age_5_15_1plus_stops_atwork,coef_num_age_5_15_1plus_stops_atwork,coef_num_age_5_15_1plus_stops_atwork,coef_num_age_5_15_1plus_stops_atwork,coef_num_age_5_15_1plus_stops_atwork,coef_num_age_5_15_1plus_stops_atwork,coef_num_age_5_15_1plus_stops_atwork +util_tour_mode_non_motorized,Tour mode is non-motorized,tour_mode_is_non_motorized,,coef_tour_mode_non_motorized_1plus_stops_atwork,coef_tour_mode_non_motorized_1plus_stops_atwork,coef_tour_mode_non_motorized_1plus_stops_atwork,coef_tour_mode_non_motorized_1plus_stops_atwork,coef_tour_mode_non_motorized_1plus_stops_atwork,coef_tour_mode_non_motorized_1plus_stops_atwork,coef_tour_mode_non_motorized_1plus_stops_atwork,coef_tour_mode_non_motorized_1plus_stops_atwork,coef_tour_mode_non_motorized_1plus_stops_atwork,coef_tour_mode_non_motorized_1plus_stops_atwork,coef_tour_mode_non_motorized_1plus_stops_atwork,coef_tour_mode_non_motorized_1plus_stops_atwork,coef_tour_mode_non_motorized_1plus_stops_atwork,coef_tour_mode_non_motorized_1plus_stops_atwork,coef_tour_mode_non_motorized_1plus_stops_atwork +util_tour_duration_in_hours,Tour duration in hours,(duration / 2),,coef_tour_duration_in_hours_1plus_stops_atwork,coef_tour_duration_in_hours_1plus_stops_atwork,coef_tour_duration_in_hours_1plus_stops_atwork,coef_tour_duration_in_hours_1plus_stops_atwork,coef_tour_duration_in_hours_1plus_stops_atwork,coef_tour_duration_in_hours_1plus_stops_atwork,coef_tour_duration_in_hours_1plus_stops_atwork,coef_tour_duration_in_hours_1plus_stops_atwork,coef_tour_duration_in_hours_1plus_stops_atwork,coef_tour_duration_in_hours_1plus_stops_atwork,coef_tour_duration_in_hours_1plus_stops_atwork,coef_tour_duration_in_hours_1plus_stops_atwork,coef_tour_duration_in_hours_1plus_stops_atwork,coef_tour_duration_in_hours_1plus_stops_atwork,coef_tour_duration_in_hours_1plus_stops_atwork +util_tour_type_eat,Atwork tour type is eat,(tour_type == 'eat'),,coef_tour_type_eat_1plus_stops_atwork,coef_tour_type_eat_1plus_stops_atwork,coef_tour_type_eat_1plus_stops_atwork,coef_tour_type_eat_1plus_stops_atwork,coef_tour_type_eat_1plus_stops_atwork,coef_tour_type_eat_1plus_stops_atwork,coef_tour_type_eat_1plus_stops_atwork,coef_tour_type_eat_1plus_stops_atwork,coef_tour_type_eat_1plus_stops_atwork,coef_tour_type_eat_1plus_stops_atwork,coef_tour_type_eat_1plus_stops_atwork,coef_tour_type_eat_1plus_stops_atwork,coef_tour_type_eat_1plus_stops_atwork,coef_tour_type_eat_1plus_stops_atwork,coef_tour_type_eat_1plus_stops_atwork +util_nostop_drive_transit,no stops on pnr knr tnc transit tours,"@(df.tour_mode.isin(['PNR_TRANSIT','KNR_TRANSIT']))",,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable +util_abm3_calibration_constants,calibration constants,is_joint==0,-0.3411831667549546,1.5644482135598068,-0.12240711424725262,-1.9592538168463436,1.1518382833625738,2.9268706664109896,-10.0,-0.8904060015251857,-1.2471095726908494,-10.0,-5.0,-0.8005791109300985,-10.0,-10.0,-5.0,2.613748359446448 diff --git a/resident/configs/stop_frequency_coefficients_atwork.csv b/resident/configs/stop_frequency_coefficients_atwork.csv new file mode 100644 index 0000000..141a532 --- /dev/null +++ b/resident/configs/stop_frequency_coefficients_atwork.csv @@ -0,0 +1,22 @@ +coefficient_name,value,constrain +coef_unavailable,-999.0,T +coef_alternative_specific_constants_0out_1in_atwork,-3.092158257967754,F +coef_alternative_specific_constants_0out_2in_atwork,-4.899871765683751,F +coef_alternative_specific_constants_0out_3in_atwork,-5.768845734049241,F +coef_alternative_specific_constants_1out_0in_atwork,-3.546439038144793,F +coef_alternative_specific_constants_1out_1in_atwork,-5.3893910748423615,F +coef_alternative_specific_constants_1out_2in_atwork,-6.542073629348957,F +coef_alternative_specific_constants_1out_3in_atwork,-6.724352088610809,F +coef_alternative_specific_constants_2out_0in_atwork,-5.561245279802787,F +coef_alternative_specific_constants_2out_1in_atwork,-6.254352130392174,F +coef_alternative_specific_constants_2out_2in_atwork,-7.640624157012655,F +coef_alternative_specific_constants_2out_3in_atwork,-7.235168765535057,F +coef_alternative_specific_constants_3out_0in_atwork,-5.768855987819784,F +coef_alternative_specific_constants_3out_1in_atwork,-6.947501714137311,F +coef_alternative_specific_constants_3out_2in_atwork,-8.333869615557205,F +coef_alternative_specific_constants_3out_3in_atwork,-6.542030510182685,F +coef_is_pre_covid_1plus_stops_atwork,0.5756937400481714,F +coef_num_age_5_15_1plus_stops_atwork,0.2843024724548341,F +coef_tour_mode_non_motorized_1plus_stops_atwork,-1.1394252090580002,F +coef_tour_duration_in_hours_1plus_stops_atwork,0.8337318380115536,F +coef_tour_type_eat_1plus_stops_atwork,-1.5126695768515324,F diff --git a/resident/configs/stop_frequency_coefficients_eatout.csv b/resident/configs/stop_frequency_coefficients_eatout.csv new file mode 100644 index 0000000..a32e9fa --- /dev/null +++ b/resident/configs/stop_frequency_coefficients_eatout.csv @@ -0,0 +1,26 @@ +coefficient_name,value,constrain +coef_unavailable,-999.0,T +coef_alternative_specific_constants_0out_1in_eatout,-2.719322587570536,F +coef_alternative_specific_constants_0out_2in_eatout,-4.04271514492949,F +coef_alternative_specific_constants_0out_3in_eatout,-4.838544989216345,F +coef_alternative_specific_constants_1out_0in_eatout,-2.8659262240861816,F +coef_alternative_specific_constants_1out_1in_eatout,-3.916842376183419,F +coef_alternative_specific_constants_1out_2in_eatout,-5.230563088163616,F +coef_alternative_specific_constants_1out_3in_eatout,-5.117238989358759,F +coef_alternative_specific_constants_2out_0in_eatout,-4.067401892944042,F +coef_alternative_specific_constants_2out_1in_eatout,-5.31395707633995,F +coef_alternative_specific_constants_2out_2in_eatout,-5.884510389772713,F +coef_alternative_specific_constants_2out_3in_eatout,-7.063117137546255,F +coef_alternative_specific_constants_3out_0in_eatout,-4.811844812901839,F +coef_alternative_specific_constants_3out_1in_eatout,-6.051577660315912,F +coef_alternative_specific_constants_3out_2in_eatout,-6.370029686002297,F +coef_alternative_specific_constants_3out_3in_eatout,-7.3509005055134,F +coef_is_joint_tour_1plus_stops_eatout,-0.7985681547806562,F +coef_number_of_nonwork_tours_1plus_stops_eatout,-0.1427263606567685,F +coef_age_65_79_1plus_stops_eatout,0.256692320705862,F +coef_age_80_plus_1plus_stops_eatout,0.6133960840207545,F +coef_non_mandatory_accessibility_1plus_stops_eatout,0.0385191271941809,F +coef_tour_mode_non_motorized_1plus_stops_eatout,-0.6439075033171243,F +coef_tour_mode_hov_1plus_stops_eatout,0.7283869214461836,F +coef_tour_duration_in_hours_1plus_stops_eatout,0.0711785217183164,F +coef_distance_to_tour_destination_1plus_stops_eatout,0.0419177818028756,F diff --git a/resident/configs/stop_frequency_coefficients_escort.csv b/resident/configs/stop_frequency_coefficients_escort.csv new file mode 100644 index 0000000..da22c76 --- /dev/null +++ b/resident/configs/stop_frequency_coefficients_escort.csv @@ -0,0 +1,28 @@ +coefficient_name,value,constrain +coef_unavailable,-998.9999999850988,T +coef_alternative_specific_constants_0out_1in_escort,-2.4949660926999164,F +coef_alternative_specific_constants_0out_2in_escort,-3.672015130183568,F +coef_alternative_specific_constants_0out_3in_escort,-4.317384022338293,F +coef_alternative_specific_constants_1out_0in_escort,-2.9339040180046654,F +coef_alternative_specific_constants_1out_1in_escort,-3.948109055444319,F +coef_alternative_specific_constants_1out_2in_escort,-4.78515783271987,F +coef_alternative_specific_constants_1out_3in_escort,-5.303964208013312,F +coef_alternative_specific_constants_2out_0in_escort,-4.186671117779487,F +coef_alternative_specific_constants_2out_1in_escort,-5.057105742551855,F +coef_alternative_specific_constants_2out_2in_escort,-6.576948604682893,F +coef_alternative_specific_constants_2out_3in_escort,-6.443397350305856,F +coef_alternative_specific_constants_3out_0in_escort,-4.421518979725427,F +coef_alternative_specific_constants_3out_1in_escort,-5.088849493880071,F +coef_alternative_specific_constants_3out_2in_escort,-6.124959873262966,F +coef_alternative_specific_constants_3out_3in_escort,-6.220285587804925,F +coef_is_student_1plus_stops_escort,0.2161986201032582,F +coef_age_5_18_1plus_stops_escort,0.3727005469727215,F +coef_age_65_79_1plus_stops_escort,0.389659640407008,F +coef_hhinc_less_15_1plus_stops_escort,-0.3864753297575288,F +coef_hhsize_is_3_1plus_stops_escort,-0.6055607644471578,F +coef_hhsize_is_4p_1plus_stops_escort,-0.5839400121244439,F +coef_non_mandatory_accessibility_1plus_stops_escort,0.0692389635383912,F +coef_tour_mode_non_motorized_1plus_stops_escort,-1.2439664098905787,F +coef_start_time_is_afternoon_1plus_stops_escort,0.1764927977315441,F +coef_distance_to_tour_destination_1plus_stops_escort,0.0353903674319862,F +coef_no_stops_to_school_escorting_1plus_stops_escort,0.5908677898396228,F diff --git a/resident/configs/stop_frequency_coefficients_othdiscr.csv b/resident/configs/stop_frequency_coefficients_othdiscr.csv new file mode 100644 index 0000000..b7e0257 --- /dev/null +++ b/resident/configs/stop_frequency_coefficients_othdiscr.csv @@ -0,0 +1,30 @@ +coefficient_name,value,constrain +coef_unavailable,-999.0,T +coef_alternative_specific_constants_0out_1in_othdiscr,-2.4864652946738204,F +coef_alternative_specific_constants_0out_2in_othdiscr,-3.630516428961318,F +coef_alternative_specific_constants_0out_3in_othdiscr,-4.214343839043677,F +coef_alternative_specific_constants_1out_0in_othdiscr,-2.754048091372477,F +coef_alternative_specific_constants_1out_1in_othdiscr,-3.521188010752997,F +coef_alternative_specific_constants_1out_2in_othdiscr,-4.612681926260593,F +coef_alternative_specific_constants_1out_3in_othdiscr,-4.851923018137574,F +coef_alternative_specific_constants_2out_0in_othdiscr,-4.020178130616265,F +coef_alternative_specific_constants_2out_1in_othdiscr,-4.7492677704752095,F +coef_alternative_specific_constants_2out_2in_othdiscr,-5.897904534647468,F +coef_alternative_specific_constants_2out_3in_othdiscr,-6.020509324357631,F +coef_alternative_specific_constants_3out_0in_othdiscr,-4.428598584691764,F +coef_alternative_specific_constants_3out_1in_othdiscr,-5.2241670713521176,F +coef_alternative_specific_constants_3out_2in_othdiscr,-5.754805074140499,F +coef_alternative_specific_constants_3out_3in_othdiscr,-6.064960698334528,F +coef_is_joint_tour_1plusstops_othdiscr,-0.8985020250009612,F +coef_number_of_nonwork_tours_1plusstops_othdiscr,-0.2643315301718771,F +coef_age_5_18_1plusstops_othdiscr,-0.5660532740496371,F +coef_age_19_35_1plusstops_othdiscr,-0.2845268067760428,F +coef_age_65_79_1plusstops_othdiscr,0.1695756068531331,F +coef_preschool_child_in_hh_1plusstops_othdiscr,0.2693515451224259,F +coef_has_retiree_in_hh_1plusstops_othdiscr,-0.2169572360551797,F +coef_non_mandatory_accessibility_1plusstops_othdiscr,0.0690683633584472,F +coef_tour_mode_non_motorized_1plusstops_othdiscr,-1.508573161584664,F +coef_tour_mode_hov_1plusstops_othdiscr,0.9061054262736302,F +coef_start_time_is_morning_1plusstops_othdiscr,0.2982113854685047,F +coef_start_time_is_afternoon_1plusstops_othdiscr,0.1819176043755117,F +coef_distance_to_tour_destination_1plusstops_othdiscr,0.0375087318106341,F diff --git a/resident/configs/stop_frequency_coefficients_othmaint.csv b/resident/configs/stop_frequency_coefficients_othmaint.csv new file mode 100644 index 0000000..ac32ba7 --- /dev/null +++ b/resident/configs/stop_frequency_coefficients_othmaint.csv @@ -0,0 +1,27 @@ +coefficient_name,value,constrain +coef_unavailable,-999.0,T +coef_alternative_specific_constants_0out_1in_othmaint,-1.5602924404450391,F +coef_alternative_specific_constants_0out_2in_othmaint,-2.384779306812381,F +coef_alternative_specific_constants_0out_3in_othmaint,-2.613794769617,F +coef_alternative_specific_constants_1out_0in_othmaint,-2.307883473311813,F +coef_alternative_specific_constants_1out_1in_othmaint,-2.938077675037544,F +coef_alternative_specific_constants_1out_2in_othmaint,-3.556152461131553,F +coef_alternative_specific_constants_1out_3in_othmaint,-3.3623723352749835,F +coef_alternative_specific_constants_2out_0in_othmaint,-3.1913289639656037,F +coef_alternative_specific_constants_2out_1in_othmaint,-3.8047760031,F +coef_alternative_specific_constants_2out_2in_othmaint,-4.390949985891435,F +coef_alternative_specific_constants_2out_3in_othmaint,-4.092462208845949,F +coef_alternative_specific_constants_3out_0in_othmaint,-3.568806711128329,F +coef_alternative_specific_constants_3out_1in_othmaint,-4.029936250472073,F +coef_alternative_specific_constants_3out_2in_othmaint,-4.599707691979066,F +coef_alternative_specific_constants_3out_3in_othmaint,-4.159153508767797,F +coef_is_joint_tour_1plusstops_othmaint,-1.1415368904713223,F +coef_number_of_children_in_hh_1plusstops_othmaint,0.2355862776646383,F +coef_number_of_nonwork_tours_1plusstops_othmaint,-0.2334086312449428,F +coef_hhsize_is_4p_1plusstops_othmaint,-0.3807340945310137,F +coef_tour_mode_non_motorized_1plusstops_othmaint,-0.6198408634766976,F +coef_tour_mode_hov_1plusstops_othmaint,1.082794990210597,F +coef_tour_duration_in_hours_1plusstops_othmaint,0.0305033478198989,F +coef_start_time_is_morning_1plusstops_othmaint,0.4176418075950055,F +coef_start_time_is_afternoon_1plusstops_othmaint,0.3736907177152264,F +coef_distance_to_tour_destination_1plusstops_othmaint,0.0292475180940679,F diff --git a/resident/configs/stop_frequency_coefficients_school.csv b/resident/configs/stop_frequency_coefficients_school.csv new file mode 100644 index 0000000..946c3a5 --- /dev/null +++ b/resident/configs/stop_frequency_coefficients_school.csv @@ -0,0 +1,32 @@ +coefficient_name,value,constrain +coef_unavailable,-999.0,T +coef_alternative_specific_constants_0out_1in_school,-2.270036976078339,F +coef_alternative_specific_constants_0out_2in_school,-3.145965730364082,F +coef_alternative_specific_constants_0out_3in_school,-3.817363247655941,F +coef_alternative_specific_constants_1out_0in_school,-3.2274948701101858,F +coef_alternative_specific_constants_1out_1in_school,-4.844409089801835,F +coef_alternative_specific_constants_1out_2in_school,-5.650069018317617,F +coef_alternative_specific_constants_1out_3in_school,-6.05550780788901,F +coef_alternative_specific_constants_2out_0in_school,-4.928299261107959,F +coef_alternative_specific_constants_2out_1in_school,-7.308311886880356,F +coef_alternative_specific_constants_2out_2in_school,-7.44173289877394,F +coef_alternative_specific_constants_2out_3in_school,-8.28896797269094,F +coef_alternative_specific_constants_3out_0in_school,-5.984327657398892,F +coef_alternative_specific_constants_3out_1in_school,-8.001275620584247,F +coef_alternative_specific_constants_3out_2in_school,-8.693945624974088,F +coef_alternative_specific_constants_3out_3in_school,-8.694474791173255,F +coef_is_pre_covid_0out_1plusin_school,0.7125702463447188,F +coef_number_of_nonwork_tours_0out_1plusin_school,-0.3498536779228892,F +coef_school_child_in_hh_0out_1plusin_school,0.266793257512214,F +coef_non_mandatory_accessibility_0out_1plusin_school,0.0853214914318699,F +coef_tour_mode_non_motorized_0out_1plusin_school,-1.1253582283051773,F +coef_is_pre_covid_1plusout_1plusin_school,1.002629718374584,F +coef_number_of_nonwork_tours_1plusout_1plusin_school,-0.6057535513686793,F +coef_school_child_in_hh_1plusout_1plusin_school,0.8706807230114831,F +coef_non_worker_in_hh_1plusout_1plusin_school,0.3680982964801776,F +coef_non_mandatory_accessibility_1plusout_1plusin_school,0.1310875479177641,F +coef_tour_mode_non_motorized_1plusout_1plusin_school,-3.1117693140257034,F +coef_is_pre_covid_1plusout_0in_school,0.7698140702438344,F +coef_school_child_in_hh_1plusout_0in_school,0.7989949430082903,F +coef_tour_mode_non_motorized_1plusout_0in_school,-2.226875040638454,F +coef_tour_mode_schbus_0out_1plusin_school,-1.719166651447957,F diff --git a/resident/configs/stop_frequency_coefficients_shopping.csv b/resident/configs/stop_frequency_coefficients_shopping.csv new file mode 100644 index 0000000..84fb051 --- /dev/null +++ b/resident/configs/stop_frequency_coefficients_shopping.csv @@ -0,0 +1,28 @@ +coefficient_name,value,constrain +coef_unavailable,-999.0,T +coef_alternative_specific_constants_0out_1in_shopping,-1.99478486659318,F +coef_alternative_specific_constants_0out_2in_shopping,-3.0029547936212144,F +coef_alternative_specific_constants_0out_3in_shopping,-3.8162472442792046,F +coef_alternative_specific_constants_1out_0in_shopping,-1.9298112667501943,F +coef_alternative_specific_constants_1out_1in_shopping,-3.081424428417236,F +coef_alternative_specific_constants_1out_2in_shopping,-3.952826067704117,F +coef_alternative_specific_constants_1out_3in_shopping,-4.447523137843972,F +coef_alternative_specific_constants_2out_0in_shopping,-2.899949450341273,F +coef_alternative_specific_constants_2out_1in_shopping,-3.882204113550045,F +coef_alternative_specific_constants_2out_2in_shopping,-5.063709523400166,F +coef_alternative_specific_constants_2out_3in_shopping,-5.063705408442639,F +coef_alternative_specific_constants_3out_0in_shopping,-3.4691848835291794,F +coef_alternative_specific_constants_3out_1in_shopping,-4.042050191513082,F +coef_alternative_specific_constants_3out_2in_shopping,-5.363812433746681,F +coef_alternative_specific_constants_3out_3in_shopping,-5.794596035666434,F +coef_is_joint_tour_1plusstops_shopping,-0.9376746632102424,F +coef_is_pre_covid_1plusstops_shopping,0.1880597722233851,F +coef_is_worker_1plusstops_shopping,-0.2984125841057903,F +coef_number_of_nonwork_tours_1plusstops_shopping,-0.2795952331923437,F +coef_school_child_in_hh_1plusstops_shopping,0.2755159915996485,F +coef_non_mandatory_accessibility_1plusstops_shopping,0.0357694082510162,F +coef_tour_mode_non_motorized_1plusstops_shopping,-1.073202207993033,F +coef_tour_mode_hov_1plusstops_shopping,0.8575809701489939,F +coef_tour_mode_transit_1plusstops_shopping,-0.4963693102721252,F +coef_tour_duration_in_hours_1plusstops_shopping,0.091893516673009,F +coef_distance_to_tour_destination_1plusstops_shopping,0.0911619700854451,F diff --git a/resident/configs/stop_frequency_coefficients_social.csv b/resident/configs/stop_frequency_coefficients_social.csv new file mode 100644 index 0000000..cd075f0 --- /dev/null +++ b/resident/configs/stop_frequency_coefficients_social.csv @@ -0,0 +1,24 @@ +coefficient_name,value,constrain +coef_unavailable,-999.0,T +coef_alternative_specific_constants_0out_1in_social,-2.669744941267482,F +coef_alternative_specific_constants_0out_2in_social,-3.948157548888865,F +coef_alternative_specific_constants_0out_3in_social,-5.093324105648871,F +coef_alternative_specific_constants_1out_0in_social,-2.4747854649511694,F +coef_alternative_specific_constants_1out_1in_social,-3.820302803735448,F +coef_alternative_specific_constants_1out_2in_social,-4.842135917765225,F +coef_alternative_specific_constants_1out_3in_social,-5.0934431568988,F +coef_alternative_specific_constants_2out_0in_social,-3.350273969073528,F +coef_alternative_specific_constants_2out_1in_social,-5.429849829581639,F +coef_alternative_specific_constants_2out_2in_social,-6.346124542226189,F +coef_alternative_specific_constants_2out_3in_social,-5.652975455372254,F +coef_alternative_specific_constants_3out_0in_social,-4.84199879613982,F +coef_alternative_specific_constants_3out_1in_social,-5.093366632426675,F +coef_alternative_specific_constants_3out_2in_social,-7.039224828803537,F +coef_alternative_specific_constants_3out_3in_social,-7.039372547075768,F +coef_is_joint_tour_1plus_stops_social,-0.776560245920542,F +coef_number_of_nonwork_tours_1plus_stops_social,-0.2094030767278961,F +coef_age_5_18_1plus_stops_social,-0.7707681157165264,F +coef_non_mandatory_accessibility_1plus_stops_social,0.0399803049577818,F +coef_tour_mode_hov_1plus_stops_social,0.5244598307982651,F +coef_tour_duration_in_hours_1plus_stops_social,0.0731344275787003,F +coef_distance_to_tour_destination_1plus_stops_social,0.0431956613779886,F diff --git a/resident/configs/stop_frequency_coefficients_univ.csv b/resident/configs/stop_frequency_coefficients_univ.csv new file mode 100644 index 0000000..d82f6e1 --- /dev/null +++ b/resident/configs/stop_frequency_coefficients_univ.csv @@ -0,0 +1,20 @@ +coefficient_name,value,constrain +coef_unavailable,-999.0,T +coef_alternative_specific_constants_1out_0in_univ,-2.9606821036239173,F +coef_alternative_specific_constants_1out_1in_univ,-2.994590126632717,F +coef_alternative_specific_constants_1out_2in_univ,-3.964059230970261,F +coef_alternative_specific_constants_1out_3in_univ,-4.110765202022077,F +coef_alternative_specific_constants_2out_0in_univ,-3.653878365193867,F +coef_alternative_specific_constants_2out_1in_univ,-4.570265363130149,F +coef_alternative_specific_constants_2out_2in_univ,-5.109276446867356,F +coef_alternative_specific_constants_2out_3in_univ,-5.109225424254904,F +coef_alternative_specific_constants_3out_0in_univ,-3.836259451583652,F +coef_alternative_specific_constants_3out_1in_univ,-4.416165484960151,F +coef_alternative_specific_constants_3out_2in_univ,-4.975726090153633,F +coef_alternative_specific_constants_3out_3in_univ,-4.858027864893195,F +coef_alternative_specific_constants_0out_1plusin_univ,-2.9441205060555693,F +coef_preschool_child_in_hh_1plusstops_univ,0.7353602959988254,F +coef_tour_mode_non_motorized_1plusstops_univ,-0.7149209302739166,F +coef_tour_mode_hov_1plusstops_univ,0.621498744143073,F +coef_tour_mode_transit_1plusstops_univ,0.4356060774156569,F +coef_tour_duration_in_hours_1plusstops_univ,0.1838966207472205,F diff --git a/resident/configs/stop_frequency_coefficients_work.csv b/resident/configs/stop_frequency_coefficients_work.csv new file mode 100644 index 0000000..2ab4ccf --- /dev/null +++ b/resident/configs/stop_frequency_coefficients_work.csv @@ -0,0 +1,67 @@ +coefficient_name,value,constrain +coef_unavailable,-999.0,T +coef_alternative_specific_constants_0out_1in_work,-2.8447418642103286,F +coef_alternative_specific_constants_0out_2in_work,-5.074469232999513,F +coef_alternative_specific_constants_0out_3in_work,-7.356348978101946,F +coef_alternative_specific_constants_1out_0in_work,-3.998672291447317,F +coef_alternative_specific_constants_1out_1in_work,-5.340917046762127,F +coef_alternative_specific_constants_1out_2in_work,-7.293230160779633,F +coef_alternative_specific_constants_1out_3in_work,-8.644343958007005,F +coef_alternative_specific_constants_2out_0in_work,-5.706113217470375,F +coef_alternative_specific_constants_2out_1in_work,-7.153817173561018,F +coef_alternative_specific_constants_3out_0in_work,-6.7780536846962365,F +coef_alternative_specific_constants_3out_1in_work,-8.192357372172,F +coef_preschool_child_in_hh_1out_0in_work,0.4918316153479145,F +coef_school_child_in_hh_1out_0in_work,0.5231248472492089,F +coef_school_child_in_hh_1out_1in_work,0.281812148150233,F +coef_school_child_in_hh_1out_2in_work,0.5109837440498461,F +coef_school_child_in_hh_1out_3in_work,0.5146518187296193,F +coef_non_worker_in_hh_0out_1in_work,-0.2499539606769114,F +coef_non_worker_in_hh_0out_2in_work,-0.3895812012559245,F +coef_non_worker_in_hh_0out_3in_work,-0.7028004655351838,F +coef_tour_mode_sov_0out_1in_work,0.6663968126601825,F +coef_tour_mode_sov_0out_2in_work,0.8490983927253895,F +coef_tour_mode_sov_0out_3in_work,1.019470744148101,F +coef_tour_mode_hov_0out_1in_work,1.753765085617916,F +coef_tour_mode_hov_0out_2in_work,2.425463128045174,F +coef_tour_mode_hov_0out_3in_work,3.083984260465549,F +coef_tour_mode_hov_1out_0in_work,1.8810986245847765,F +coef_tour_mode_hov_1out_1in_work,2.903102643291588,F +coef_tour_mode_hov_1out_2in_work,3.0104904515059983,F +coef_tour_mode_hov_1out_3in_work,3.4462649672615124,F +coef_tour_mode_hov_2out_0in_work,2.3579589023621974,F +coef_tour_mode_hov_2out_1in_work,2.896537717160822,F +coef_tour_mode_hov_3out_0in_work,2.367639329734867,F +coef_tour_mode_hov_3out_1in_work,3.1434258750404256,F +coef_tour_duration_in_hours_0out_1in_work,0.0968419497302259,F +coef_tour_duration_in_hours_0out_2in_work,0.1904355233462784,F +coef_tour_duration_in_hours_0out_3in_work,0.3367138795904639,F +coef_tour_duration_in_hours_1out_0in_work,0.066650821483439,F +coef_tour_duration_in_hours_1out_1in_work,0.1646547411229061,F +coef_tour_duration_in_hours_1out_2in_work,0.2551530816688599,F +coef_tour_duration_in_hours_1out_3in_work,0.3252548498168742,F +coef_tour_duration_in_hours_2out_1in_work,0.1961700400992169,F +coef_tour_duration_in_hours_3out_1in_work,0.1501823000654714,F +coef_alternative_specific_constants_2plusout_2plusin_work,-9.526330213144162,F +coef_tour_mode_hov_2plusout_2plusin_work,3.7049086577656944,F +coef_tour_duration_in_hours_2plusout_2plusin_work,0.2609768272005556,F +coef_is_parttime_worker_1plusout_1plusin_work,0.3300187195960226,F +coef_is_parttime_worker_0out_1plusin_work,0.2855160872348049,F +coef_non_worker_in_hh_1plusout_1plusin_work,-0.4641234561873718,F +coef_preschool_child_in_hh_2plusout_0in_work,0.5136768446923758,F +coef_school_child_in_hh_2plusout_0in_work,0.715230225338151,F +coef_tour_mode_sov_1plusout_0in_work,0.3208347112170486,F +coef_tour_mode_sov_1plusout_1plusin_work,0.4540272237487077,F +coef_tour_duration_in_hours_2plusout_0in_work,0.0594361573692384,F +coef_preschool_child_in_hh_1plusout_1plusin_work,0.4336604925767433,F +coef_school_child_in_hh_2plusout_1plusin_work,0.3953971625740665,F +coef_age_35_to_44_1plusout_1plusin_work,0.2864843022250448,F +coef_age_55_to_64_0out_1plusin_work,0.173801196295945,F +coef_age_55_to_64_1plusout_0in_work,0.2187960088158339,F +coef_age_45_to_54_1plusout_1plusin_work,0.3495514458182455,F +coef_age_55_to_64_1plusout_1plusin_work,0.3141273930142538,F +coef_non_mandatory_accessibility_1plusout_0in_work,0.0555899473533157,F +coef_distance_to_tour_destination_0out_1plusin_work,0.0075317302289467,F +coef_distance_to_tour_destination_1plusout_1plusin_work,0.0103740905129598,F +coef_distance_to_tour_destination_1plusout_0in_work,0.0122085315777465,F +coef_telecommute_2_4_days_week_1plusout_1plusin_work,0.6007761919718061,F diff --git a/resident/configs/stop_frequency_eatout.csv b/resident/configs/stop_frequency_eatout.csv new file mode 100644 index 0000000..fa9103a --- /dev/null +++ b/resident/configs/stop_frequency_eatout.csv @@ -0,0 +1,14 @@ +Label,Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in +util_alternative_specific_constants,Alternative specific constants,1,,coef_alternative_specific_constants_0out_1in_eatout,coef_alternative_specific_constants_0out_2in_eatout,coef_alternative_specific_constants_0out_3in_eatout,coef_alternative_specific_constants_1out_0in_eatout,coef_alternative_specific_constants_1out_1in_eatout,coef_alternative_specific_constants_1out_2in_eatout,coef_alternative_specific_constants_1out_3in_eatout,coef_alternative_specific_constants_2out_0in_eatout,coef_alternative_specific_constants_2out_1in_eatout,coef_alternative_specific_constants_2out_2in_eatout,coef_alternative_specific_constants_2out_3in_eatout,coef_alternative_specific_constants_3out_0in_eatout,coef_alternative_specific_constants_3out_1in_eatout,coef_alternative_specific_constants_3out_2in_eatout,coef_alternative_specific_constants_3out_3in_eatout +util_is_joint_tour,Is joint tour,is_joint,,coef_is_joint_tour_1plus_stops_eatout,coef_is_joint_tour_1plus_stops_eatout,coef_is_joint_tour_1plus_stops_eatout,coef_is_joint_tour_1plus_stops_eatout,coef_is_joint_tour_1plus_stops_eatout,coef_is_joint_tour_1plus_stops_eatout,coef_is_joint_tour_1plus_stops_eatout,coef_is_joint_tour_1plus_stops_eatout,coef_is_joint_tour_1plus_stops_eatout,coef_is_joint_tour_1plus_stops_eatout,coef_is_joint_tour_1plus_stops_eatout,coef_is_joint_tour_1plus_stops_eatout,coef_is_joint_tour_1plus_stops_eatout,coef_is_joint_tour_1plus_stops_eatout,coef_is_joint_tour_1plus_stops_eatout +util_number_of_nonwork_tours,Number of non-work tours,(num_total_tours - num_work_tours),,coef_number_of_nonwork_tours_1plus_stops_eatout,coef_number_of_nonwork_tours_1plus_stops_eatout,coef_number_of_nonwork_tours_1plus_stops_eatout,coef_number_of_nonwork_tours_1plus_stops_eatout,coef_number_of_nonwork_tours_1plus_stops_eatout,coef_number_of_nonwork_tours_1plus_stops_eatout,coef_number_of_nonwork_tours_1plus_stops_eatout,coef_number_of_nonwork_tours_1plus_stops_eatout,coef_number_of_nonwork_tours_1plus_stops_eatout,coef_number_of_nonwork_tours_1plus_stops_eatout,coef_number_of_nonwork_tours_1plus_stops_eatout,coef_number_of_nonwork_tours_1plus_stops_eatout,coef_number_of_nonwork_tours_1plus_stops_eatout,coef_number_of_nonwork_tours_1plus_stops_eatout,coef_number_of_nonwork_tours_1plus_stops_eatout +util_age_65_79,Age Group - 65 yrs to 79 yrs,(age >= 65) & (age < 80),,coef_age_65_79_1plus_stops_eatout,coef_age_65_79_1plus_stops_eatout,coef_age_65_79_1plus_stops_eatout,coef_age_65_79_1plus_stops_eatout,coef_age_65_79_1plus_stops_eatout,coef_age_65_79_1plus_stops_eatout,coef_age_65_79_1plus_stops_eatout,coef_age_65_79_1plus_stops_eatout,coef_age_65_79_1plus_stops_eatout,coef_age_65_79_1plus_stops_eatout,coef_age_65_79_1plus_stops_eatout,coef_age_65_79_1plus_stops_eatout,coef_age_65_79_1plus_stops_eatout,coef_age_65_79_1plus_stops_eatout,coef_age_65_79_1plus_stops_eatout +util_age_80_plus,Age Group - 80 yrs and older,(age >= 80),,coef_age_80_plus_1plus_stops_eatout,coef_age_80_plus_1plus_stops_eatout,coef_age_80_plus_1plus_stops_eatout,coef_age_80_plus_1plus_stops_eatout,coef_age_80_plus_1plus_stops_eatout,coef_age_80_plus_1plus_stops_eatout,coef_age_80_plus_1plus_stops_eatout,coef_age_80_plus_1plus_stops_eatout,coef_age_80_plus_1plus_stops_eatout,coef_age_80_plus_1plus_stops_eatout,coef_age_80_plus_1plus_stops_eatout,coef_age_80_plus_1plus_stops_eatout,coef_age_80_plus_1plus_stops_eatout,coef_age_80_plus_1plus_stops_eatout,coef_age_80_plus_1plus_stops_eatout +util_non_mandatory_accessibility,Disaggreage accessibility at home location to non-mandatory location,(othdiscr_accessibility + shopping_accessibility) / 2,,coef_non_mandatory_accessibility_1plus_stops_eatout,coef_non_mandatory_accessibility_1plus_stops_eatout,coef_non_mandatory_accessibility_1plus_stops_eatout,coef_non_mandatory_accessibility_1plus_stops_eatout,coef_non_mandatory_accessibility_1plus_stops_eatout,coef_non_mandatory_accessibility_1plus_stops_eatout,coef_non_mandatory_accessibility_1plus_stops_eatout,coef_non_mandatory_accessibility_1plus_stops_eatout,coef_non_mandatory_accessibility_1plus_stops_eatout,coef_non_mandatory_accessibility_1plus_stops_eatout,coef_non_mandatory_accessibility_1plus_stops_eatout,coef_non_mandatory_accessibility_1plus_stops_eatout,coef_non_mandatory_accessibility_1plus_stops_eatout,coef_non_mandatory_accessibility_1plus_stops_eatout,coef_non_mandatory_accessibility_1plus_stops_eatout +util_tour_mode_non_motorized,Tour mode is non-motorized,tour_mode_is_non_motorized,,coef_tour_mode_non_motorized_1plus_stops_eatout,coef_tour_mode_non_motorized_1plus_stops_eatout,coef_tour_mode_non_motorized_1plus_stops_eatout,coef_tour_mode_non_motorized_1plus_stops_eatout,coef_tour_mode_non_motorized_1plus_stops_eatout,coef_tour_mode_non_motorized_1plus_stops_eatout,coef_tour_mode_non_motorized_1plus_stops_eatout,coef_tour_mode_non_motorized_1plus_stops_eatout,coef_tour_mode_non_motorized_1plus_stops_eatout,coef_tour_mode_non_motorized_1plus_stops_eatout,coef_tour_mode_non_motorized_1plus_stops_eatout,coef_tour_mode_non_motorized_1plus_stops_eatout,coef_tour_mode_non_motorized_1plus_stops_eatout,coef_tour_mode_non_motorized_1plus_stops_eatout,coef_tour_mode_non_motorized_1plus_stops_eatout +util_tour_mode_hov,Tour mode is hov,tour_mode_is_hov,,coef_tour_mode_hov_1plus_stops_eatout,coef_tour_mode_hov_1plus_stops_eatout,coef_tour_mode_hov_1plus_stops_eatout,coef_tour_mode_hov_1plus_stops_eatout,coef_tour_mode_hov_1plus_stops_eatout,coef_tour_mode_hov_1plus_stops_eatout,coef_tour_mode_hov_1plus_stops_eatout,coef_tour_mode_hov_1plus_stops_eatout,coef_tour_mode_hov_1plus_stops_eatout,coef_tour_mode_hov_1plus_stops_eatout,coef_tour_mode_hov_1plus_stops_eatout,coef_tour_mode_hov_1plus_stops_eatout,coef_tour_mode_hov_1plus_stops_eatout,coef_tour_mode_hov_1plus_stops_eatout,coef_tour_mode_hov_1plus_stops_eatout +util_tour_duration_in_hours,Tour duration in hours,(duration / 2),,coef_tour_duration_in_hours_1plus_stops_eatout,coef_tour_duration_in_hours_1plus_stops_eatout,coef_tour_duration_in_hours_1plus_stops_eatout,coef_tour_duration_in_hours_1plus_stops_eatout,coef_tour_duration_in_hours_1plus_stops_eatout,coef_tour_duration_in_hours_1plus_stops_eatout,coef_tour_duration_in_hours_1plus_stops_eatout,coef_tour_duration_in_hours_1plus_stops_eatout,coef_tour_duration_in_hours_1plus_stops_eatout,coef_tour_duration_in_hours_1plus_stops_eatout,coef_tour_duration_in_hours_1plus_stops_eatout,coef_tour_duration_in_hours_1plus_stops_eatout,coef_tour_duration_in_hours_1plus_stops_eatout,coef_tour_duration_in_hours_1plus_stops_eatout,coef_tour_duration_in_hours_1plus_stops_eatout +util_distance_to_tour_destination,Distance from origin to tour destination in miles,distance_in_miles,,coef_distance_to_tour_destination_1plus_stops_eatout,coef_distance_to_tour_destination_1plus_stops_eatout,coef_distance_to_tour_destination_1plus_stops_eatout,coef_distance_to_tour_destination_1plus_stops_eatout,coef_distance_to_tour_destination_1plus_stops_eatout,coef_distance_to_tour_destination_1plus_stops_eatout,coef_distance_to_tour_destination_1plus_stops_eatout,coef_distance_to_tour_destination_1plus_stops_eatout,coef_distance_to_tour_destination_1plus_stops_eatout,coef_distance_to_tour_destination_1plus_stops_eatout,coef_distance_to_tour_destination_1plus_stops_eatout,coef_distance_to_tour_destination_1plus_stops_eatout,coef_distance_to_tour_destination_1plus_stops_eatout,coef_distance_to_tour_destination_1plus_stops_eatout,coef_distance_to_tour_destination_1plus_stops_eatout +util_nostop_drive_transit,no stops on pnr knr tnc transit tours,"@(df.tour_mode.isin(['PNR_TRANSIT','KNR_TRANSIT']))",,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable +util_abm3_calibration_constants,calibration constants,is_joint==0,-0.10349,-0.5178,1.3724,-2.4904,0.10798,-0.03633,0.8058,-1.9377,0.5571,-2.5862,-0.7659,-0.1902,0.0788,-1.9865,-10.0,3.70084 +util_abm3_calibration_constants_joint,joint tour calibration constants,is_joint,-0.12758,-0.6326,-1.13646,-1.9196,1.4714,0.7681,-10.0,-0.4150,-0.5229,-5.0,-0.04229,-10.0,-5.0,-5.0,1.3845,-5.0 diff --git a/resident/configs/stop_frequency_escort.csv b/resident/configs/stop_frequency_escort.csv new file mode 100644 index 0000000..8558c1d --- /dev/null +++ b/resident/configs/stop_frequency_escort.csv @@ -0,0 +1,16 @@ +Label,Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in +util_alternative_specific_constants,Alternative specific constants,1,,coef_alternative_specific_constants_0out_1in_escort,coef_alternative_specific_constants_0out_2in_escort,coef_alternative_specific_constants_0out_3in_escort,coef_alternative_specific_constants_1out_0in_escort,coef_alternative_specific_constants_1out_1in_escort,coef_alternative_specific_constants_1out_2in_escort,coef_alternative_specific_constants_1out_3in_escort,coef_alternative_specific_constants_2out_0in_escort,coef_alternative_specific_constants_2out_1in_escort,coef_alternative_specific_constants_2out_2in_escort,coef_alternative_specific_constants_2out_3in_escort,coef_alternative_specific_constants_3out_0in_escort,coef_alternative_specific_constants_3out_1in_escort,coef_alternative_specific_constants_3out_2in_escort,coef_alternative_specific_constants_3out_3in_escort +util_is_student,person is student,is_student,,coef_is_student_1plus_stops_escort,coef_is_student_1plus_stops_escort,coef_is_student_1plus_stops_escort,coef_is_student_1plus_stops_escort,coef_is_student_1plus_stops_escort,coef_is_student_1plus_stops_escort,coef_is_student_1plus_stops_escort,coef_is_student_1plus_stops_escort,coef_is_student_1plus_stops_escort,coef_is_student_1plus_stops_escort,coef_is_student_1plus_stops_escort,coef_is_student_1plus_stops_escort,coef_is_student_1plus_stops_escort,coef_is_student_1plus_stops_escort,coef_is_student_1plus_stops_escort +util_age_5_18,Age Group - 5 yrs to 18 yrs,(age >= 5) & (age < 19),,coef_age_5_18_1plus_stops_escort,coef_age_5_18_1plus_stops_escort,coef_age_5_18_1plus_stops_escort,coef_age_5_18_1plus_stops_escort,coef_age_5_18_1plus_stops_escort,coef_age_5_18_1plus_stops_escort,coef_age_5_18_1plus_stops_escort,coef_age_5_18_1plus_stops_escort,coef_age_5_18_1plus_stops_escort,coef_age_5_18_1plus_stops_escort,coef_age_5_18_1plus_stops_escort,coef_age_5_18_1plus_stops_escort,coef_age_5_18_1plus_stops_escort,coef_age_5_18_1plus_stops_escort,coef_age_5_18_1plus_stops_escort +util_age_65_79,Age Group - 65 yrs to 79 yrs,(age >= 65) & (age < 80),,coef_age_65_79_1plus_stops_escort,coef_age_65_79_1plus_stops_escort,coef_age_65_79_1plus_stops_escort,coef_age_65_79_1plus_stops_escort,coef_age_65_79_1plus_stops_escort,coef_age_65_79_1plus_stops_escort,coef_age_65_79_1plus_stops_escort,coef_age_65_79_1plus_stops_escort,coef_age_65_79_1plus_stops_escort,coef_age_65_79_1plus_stops_escort,coef_age_65_79_1plus_stops_escort,coef_age_65_79_1plus_stops_escort,coef_age_65_79_1plus_stops_escort,coef_age_65_79_1plus_stops_escort,coef_age_65_79_1plus_stops_escort +util_hhinc_less_15,Income less than 15k,(income < 15000),,coef_hhinc_less_15_1plus_stops_escort,coef_hhinc_less_15_1plus_stops_escort,coef_hhinc_less_15_1plus_stops_escort,coef_hhinc_less_15_1plus_stops_escort,coef_hhinc_less_15_1plus_stops_escort,coef_hhinc_less_15_1plus_stops_escort,coef_hhinc_less_15_1plus_stops_escort,coef_hhinc_less_15_1plus_stops_escort,coef_hhinc_less_15_1plus_stops_escort,coef_hhinc_less_15_1plus_stops_escort,coef_hhinc_less_15_1plus_stops_escort,coef_hhinc_less_15_1plus_stops_escort,coef_hhinc_less_15_1plus_stops_escort,coef_hhinc_less_15_1plus_stops_escort,coef_hhinc_less_15_1plus_stops_escort +util_hhsize_is_3,Household size is 3,(hhsize == 3),,coef_hhsize_is_3_1plus_stops_escort,coef_hhsize_is_3_1plus_stops_escort,coef_hhsize_is_3_1plus_stops_escort,coef_hhsize_is_3_1plus_stops_escort,coef_hhsize_is_3_1plus_stops_escort,coef_hhsize_is_3_1plus_stops_escort,coef_hhsize_is_3_1plus_stops_escort,coef_hhsize_is_3_1plus_stops_escort,coef_hhsize_is_3_1plus_stops_escort,coef_hhsize_is_3_1plus_stops_escort,coef_hhsize_is_3_1plus_stops_escort,coef_hhsize_is_3_1plus_stops_escort,coef_hhsize_is_3_1plus_stops_escort,coef_hhsize_is_3_1plus_stops_escort,coef_hhsize_is_3_1plus_stops_escort +util_hhsize_is_4p,Household size is 4p,(hhsize >= 4),,coef_hhsize_is_4p_1plus_stops_escort,coef_hhsize_is_4p_1plus_stops_escort,coef_hhsize_is_4p_1plus_stops_escort,coef_hhsize_is_4p_1plus_stops_escort,coef_hhsize_is_4p_1plus_stops_escort,coef_hhsize_is_4p_1plus_stops_escort,coef_hhsize_is_4p_1plus_stops_escort,coef_hhsize_is_4p_1plus_stops_escort,coef_hhsize_is_4p_1plus_stops_escort,coef_hhsize_is_4p_1plus_stops_escort,coef_hhsize_is_4p_1plus_stops_escort,coef_hhsize_is_4p_1plus_stops_escort,coef_hhsize_is_4p_1plus_stops_escort,coef_hhsize_is_4p_1plus_stops_escort,coef_hhsize_is_4p_1plus_stops_escort +util_non_mandatory_accessibility,Disaggreage accessibility at home location to non-mandatory location,(othdiscr_accessibility + shopping_accessibility) / 2,,coef_non_mandatory_accessibility_1plus_stops_escort,coef_non_mandatory_accessibility_1plus_stops_escort,coef_non_mandatory_accessibility_1plus_stops_escort,coef_non_mandatory_accessibility_1plus_stops_escort,coef_non_mandatory_accessibility_1plus_stops_escort,coef_non_mandatory_accessibility_1plus_stops_escort,coef_non_mandatory_accessibility_1plus_stops_escort,coef_non_mandatory_accessibility_1plus_stops_escort,coef_non_mandatory_accessibility_1plus_stops_escort,coef_non_mandatory_accessibility_1plus_stops_escort,coef_non_mandatory_accessibility_1plus_stops_escort,coef_non_mandatory_accessibility_1plus_stops_escort,coef_non_mandatory_accessibility_1plus_stops_escort,coef_non_mandatory_accessibility_1plus_stops_escort,coef_non_mandatory_accessibility_1plus_stops_escort +util_tour_mode_non_motorized,Tour mode is non-motorized,tour_mode_is_non_motorized,,coef_tour_mode_non_motorized_1plus_stops_escort,coef_tour_mode_non_motorized_1plus_stops_escort,coef_tour_mode_non_motorized_1plus_stops_escort,coef_tour_mode_non_motorized_1plus_stops_escort,coef_tour_mode_non_motorized_1plus_stops_escort,coef_tour_mode_non_motorized_1plus_stops_escort,coef_tour_mode_non_motorized_1plus_stops_escort,coef_tour_mode_non_motorized_1plus_stops_escort,coef_tour_mode_non_motorized_1plus_stops_escort,coef_tour_mode_non_motorized_1plus_stops_escort,coef_tour_mode_non_motorized_1plus_stops_escort,coef_tour_mode_non_motorized_1plus_stops_escort,coef_tour_mode_non_motorized_1plus_stops_escort,coef_tour_mode_non_motorized_1plus_stops_escort,coef_tour_mode_non_motorized_1plus_stops_escort +util_start_time_is_afternoon,Tour start time between noon and 5pm,(start >= 19) & (start < 29),,coef_start_time_is_afternoon_1plus_stops_escort,coef_start_time_is_afternoon_1plus_stops_escort,coef_start_time_is_afternoon_1plus_stops_escort,coef_start_time_is_afternoon_1plus_stops_escort,coef_start_time_is_afternoon_1plus_stops_escort,coef_start_time_is_afternoon_1plus_stops_escort,coef_start_time_is_afternoon_1plus_stops_escort,coef_start_time_is_afternoon_1plus_stops_escort,coef_start_time_is_afternoon_1plus_stops_escort,coef_start_time_is_afternoon_1plus_stops_escort,coef_start_time_is_afternoon_1plus_stops_escort,coef_start_time_is_afternoon_1plus_stops_escort,coef_start_time_is_afternoon_1plus_stops_escort,coef_start_time_is_afternoon_1plus_stops_escort,coef_start_time_is_afternoon_1plus_stops_escort +util_distance_to_tour_destination,Distance from origin to tour destination in miles,distance_in_miles,,coef_distance_to_tour_destination_1plus_stops_escort,coef_distance_to_tour_destination_1plus_stops_escort,coef_distance_to_tour_destination_1plus_stops_escort,coef_distance_to_tour_destination_1plus_stops_escort,coef_distance_to_tour_destination_1plus_stops_escort,coef_distance_to_tour_destination_1plus_stops_escort,coef_distance_to_tour_destination_1plus_stops_escort,coef_distance_to_tour_destination_1plus_stops_escort,coef_distance_to_tour_destination_1plus_stops_escort,coef_distance_to_tour_destination_1plus_stops_escort,coef_distance_to_tour_destination_1plus_stops_escort,coef_distance_to_tour_destination_1plus_stops_escort,coef_distance_to_tour_destination_1plus_stops_escort,coef_distance_to_tour_destination_1plus_stops_escort,coef_distance_to_tour_destination_1plus_stops_escort +util_no_stops_to_school_escorting,Do not allow stops for school escort half-tour -- outbound,"(school_esc_outbound.isin(['ride_share', 'pure_escort']))",,coef_no_stops_to_school_escorting_1plus_stops_escort,coef_no_stops_to_school_escorting_1plus_stops_escort,coef_no_stops_to_school_escorting_1plus_stops_escort,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable +util_no_stops_from_school_escorting,Do not allow stops for school escort half-tour -- inbound,"(school_esc_inbound.isin(['ride_share', 'pure_escort']))",,coef_unavailable,coef_unavailable,coef_unavailable,coef_no_stops_to_school_escorting_1plus_stops_escort,coef_unavailable,coef_unavailable,coef_unavailable,coef_no_stops_to_school_escorting_1plus_stops_escort,coef_unavailable,coef_unavailable,coef_unavailable,coef_no_stops_to_school_escorting_1plus_stops_escort,coef_unavailable,coef_unavailable,coef_unavailable +util_nostop_drive_transit,no stops on pnr knr tnc transit tours,"@(df.tour_mode.isin(['PNR_TRANSIT','KNR_TRANSIT']))",,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable +util_abm3_calibration_constants,calibration constants,is_joint==0,0.01523,0.1329,-0.2217,-0.5655,-1.06194,-0.9030,0.7036,-2.4331,-0.6172,0.7406,1.7755,-4.1892,0.2658,-4.7708,-0.9725,3.5835 diff --git a/resident/configs/stop_frequency_othdiscr.csv b/resident/configs/stop_frequency_othdiscr.csv new file mode 100644 index 0000000..d8164c7 --- /dev/null +++ b/resident/configs/stop_frequency_othdiscr.csv @@ -0,0 +1,18 @@ +Label,Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in +util_alternative_specific_constants,Alternative specific constants,1,,coef_alternative_specific_constants_0out_1in_othdiscr,coef_alternative_specific_constants_0out_2in_othdiscr,coef_alternative_specific_constants_0out_3in_othdiscr,coef_alternative_specific_constants_1out_0in_othdiscr,coef_alternative_specific_constants_1out_1in_othdiscr,coef_alternative_specific_constants_1out_2in_othdiscr,coef_alternative_specific_constants_1out_3in_othdiscr,coef_alternative_specific_constants_2out_0in_othdiscr,coef_alternative_specific_constants_2out_1in_othdiscr,coef_alternative_specific_constants_2out_2in_othdiscr,coef_alternative_specific_constants_2out_3in_othdiscr,coef_alternative_specific_constants_3out_0in_othdiscr,coef_alternative_specific_constants_3out_1in_othdiscr,coef_alternative_specific_constants_3out_2in_othdiscr,coef_alternative_specific_constants_3out_3in_othdiscr +util_is_joint_tour,Is joint tour,is_joint,,coef_is_joint_tour_1plusstops_othdiscr,coef_is_joint_tour_1plusstops_othdiscr,coef_is_joint_tour_1plusstops_othdiscr,coef_is_joint_tour_1plusstops_othdiscr,coef_is_joint_tour_1plusstops_othdiscr,coef_is_joint_tour_1plusstops_othdiscr,coef_is_joint_tour_1plusstops_othdiscr,coef_is_joint_tour_1plusstops_othdiscr,coef_is_joint_tour_1plusstops_othdiscr,coef_is_joint_tour_1plusstops_othdiscr,coef_is_joint_tour_1plusstops_othdiscr,coef_is_joint_tour_1plusstops_othdiscr,coef_is_joint_tour_1plusstops_othdiscr,coef_is_joint_tour_1plusstops_othdiscr,coef_is_joint_tour_1plusstops_othdiscr +util_number_of_nonwork_tours,Number of non-work tours,(num_total_tours - num_work_tours),,coef_number_of_nonwork_tours_1plusstops_othdiscr,coef_number_of_nonwork_tours_1plusstops_othdiscr,coef_number_of_nonwork_tours_1plusstops_othdiscr,coef_number_of_nonwork_tours_1plusstops_othdiscr,coef_number_of_nonwork_tours_1plusstops_othdiscr,coef_number_of_nonwork_tours_1plusstops_othdiscr,coef_number_of_nonwork_tours_1plusstops_othdiscr,coef_number_of_nonwork_tours_1plusstops_othdiscr,coef_number_of_nonwork_tours_1plusstops_othdiscr,coef_number_of_nonwork_tours_1plusstops_othdiscr,coef_number_of_nonwork_tours_1plusstops_othdiscr,coef_number_of_nonwork_tours_1plusstops_othdiscr,coef_number_of_nonwork_tours_1plusstops_othdiscr,coef_number_of_nonwork_tours_1plusstops_othdiscr,coef_number_of_nonwork_tours_1plusstops_othdiscr +util_age_5_18,Age Group - 5 yrs to 18 yrs,(age >= 5) & (age < 19),,coef_age_5_18_1plusstops_othdiscr,coef_age_5_18_1plusstops_othdiscr,coef_age_5_18_1plusstops_othdiscr,coef_age_5_18_1plusstops_othdiscr,coef_age_5_18_1plusstops_othdiscr,coef_age_5_18_1plusstops_othdiscr,coef_age_5_18_1plusstops_othdiscr,coef_age_5_18_1plusstops_othdiscr,coef_age_5_18_1plusstops_othdiscr,coef_age_5_18_1plusstops_othdiscr,coef_age_5_18_1plusstops_othdiscr,coef_age_5_18_1plusstops_othdiscr,coef_age_5_18_1plusstops_othdiscr,coef_age_5_18_1plusstops_othdiscr,coef_age_5_18_1plusstops_othdiscr +util_age_19_35,Age Group - 19 yrs to 34 yrs,(age >= 19) & (age < 35),,coef_age_19_35_1plusstops_othdiscr,coef_age_19_35_1plusstops_othdiscr,coef_age_19_35_1plusstops_othdiscr,coef_age_19_35_1plusstops_othdiscr,coef_age_19_35_1plusstops_othdiscr,coef_age_19_35_1plusstops_othdiscr,coef_age_19_35_1plusstops_othdiscr,coef_age_19_35_1plusstops_othdiscr,coef_age_19_35_1plusstops_othdiscr,coef_age_19_35_1plusstops_othdiscr,coef_age_19_35_1plusstops_othdiscr,coef_age_19_35_1plusstops_othdiscr,coef_age_19_35_1plusstops_othdiscr,coef_age_19_35_1plusstops_othdiscr,coef_age_19_35_1plusstops_othdiscr +util_age_65_79,Age Group - 65 yrs to 79 yrs,(age >= 65) & (age < 80),,coef_age_65_79_1plusstops_othdiscr,coef_age_65_79_1plusstops_othdiscr,coef_age_65_79_1plusstops_othdiscr,coef_age_65_79_1plusstops_othdiscr,coef_age_65_79_1plusstops_othdiscr,coef_age_65_79_1plusstops_othdiscr,coef_age_65_79_1plusstops_othdiscr,coef_age_65_79_1plusstops_othdiscr,coef_age_65_79_1plusstops_othdiscr,coef_age_65_79_1plusstops_othdiscr,coef_age_65_79_1plusstops_othdiscr,coef_age_65_79_1plusstops_othdiscr,coef_age_65_79_1plusstops_othdiscr,coef_age_65_79_1plusstops_othdiscr,coef_age_65_79_1plusstops_othdiscr +util_preschool_child_in_hh,Presence of preschooler in household other than self,has_preschool_kid,,coef_preschool_child_in_hh_1plusstops_othdiscr,coef_preschool_child_in_hh_1plusstops_othdiscr,coef_preschool_child_in_hh_1plusstops_othdiscr,coef_preschool_child_in_hh_1plusstops_othdiscr,coef_preschool_child_in_hh_1plusstops_othdiscr,coef_preschool_child_in_hh_1plusstops_othdiscr,coef_preschool_child_in_hh_1plusstops_othdiscr,coef_preschool_child_in_hh_1plusstops_othdiscr,coef_preschool_child_in_hh_1plusstops_othdiscr,coef_preschool_child_in_hh_1plusstops_othdiscr,coef_preschool_child_in_hh_1plusstops_othdiscr,coef_preschool_child_in_hh_1plusstops_othdiscr,coef_preschool_child_in_hh_1plusstops_othdiscr,coef_preschool_child_in_hh_1plusstops_othdiscr,coef_preschool_child_in_hh_1plusstops_othdiscr +util_has_retiree_in_hh,Presence of reitred peopl in household other than self,has_retiree,,coef_has_retiree_in_hh_1plusstops_othdiscr,coef_has_retiree_in_hh_1plusstops_othdiscr,coef_has_retiree_in_hh_1plusstops_othdiscr,coef_has_retiree_in_hh_1plusstops_othdiscr,coef_has_retiree_in_hh_1plusstops_othdiscr,coef_has_retiree_in_hh_1plusstops_othdiscr,coef_has_retiree_in_hh_1plusstops_othdiscr,coef_has_retiree_in_hh_1plusstops_othdiscr,coef_has_retiree_in_hh_1plusstops_othdiscr,coef_has_retiree_in_hh_1plusstops_othdiscr,coef_has_retiree_in_hh_1plusstops_othdiscr,coef_has_retiree_in_hh_1plusstops_othdiscr,coef_has_retiree_in_hh_1plusstops_othdiscr,coef_has_retiree_in_hh_1plusstops_othdiscr,coef_has_retiree_in_hh_1plusstops_othdiscr +util_non_mandatory_accessibility,Disaggreage accessibility at home location to non-mandatory location,(othdiscr_accessibility + shopping_accessibility) / 2,,coef_non_mandatory_accessibility_1plusstops_othdiscr,coef_non_mandatory_accessibility_1plusstops_othdiscr,coef_non_mandatory_accessibility_1plusstops_othdiscr,coef_non_mandatory_accessibility_1plusstops_othdiscr,coef_non_mandatory_accessibility_1plusstops_othdiscr,coef_non_mandatory_accessibility_1plusstops_othdiscr,coef_non_mandatory_accessibility_1plusstops_othdiscr,coef_non_mandatory_accessibility_1plusstops_othdiscr,coef_non_mandatory_accessibility_1plusstops_othdiscr,coef_non_mandatory_accessibility_1plusstops_othdiscr,coef_non_mandatory_accessibility_1plusstops_othdiscr,coef_non_mandatory_accessibility_1plusstops_othdiscr,coef_non_mandatory_accessibility_1plusstops_othdiscr,coef_non_mandatory_accessibility_1plusstops_othdiscr,coef_non_mandatory_accessibility_1plusstops_othdiscr +util_tour_mode_non_motorized,Tour mode is non-motorized,tour_mode_is_non_motorized,,coef_tour_mode_non_motorized_1plusstops_othdiscr,coef_tour_mode_non_motorized_1plusstops_othdiscr,coef_tour_mode_non_motorized_1plusstops_othdiscr,coef_tour_mode_non_motorized_1plusstops_othdiscr,coef_tour_mode_non_motorized_1plusstops_othdiscr,coef_tour_mode_non_motorized_1plusstops_othdiscr,coef_tour_mode_non_motorized_1plusstops_othdiscr,coef_tour_mode_non_motorized_1plusstops_othdiscr,coef_tour_mode_non_motorized_1plusstops_othdiscr,coef_tour_mode_non_motorized_1plusstops_othdiscr,coef_tour_mode_non_motorized_1plusstops_othdiscr,coef_tour_mode_non_motorized_1plusstops_othdiscr,coef_tour_mode_non_motorized_1plusstops_othdiscr,coef_tour_mode_non_motorized_1plusstops_othdiscr,coef_tour_mode_non_motorized_1plusstops_othdiscr +util_tour_mode_hov,Tour mode is hov,tour_mode_is_hov,,coef_tour_mode_hov_1plusstops_othdiscr,coef_tour_mode_hov_1plusstops_othdiscr,coef_tour_mode_hov_1plusstops_othdiscr,coef_tour_mode_hov_1plusstops_othdiscr,coef_tour_mode_hov_1plusstops_othdiscr,coef_tour_mode_hov_1plusstops_othdiscr,coef_tour_mode_hov_1plusstops_othdiscr,coef_tour_mode_hov_1plusstops_othdiscr,coef_tour_mode_hov_1plusstops_othdiscr,coef_tour_mode_hov_1plusstops_othdiscr,coef_tour_mode_hov_1plusstops_othdiscr,coef_tour_mode_hov_1plusstops_othdiscr,coef_tour_mode_hov_1plusstops_othdiscr,coef_tour_mode_hov_1plusstops_othdiscr,coef_tour_mode_hov_1plusstops_othdiscr +util_start_time_is_morning,Tour start time is before noon,(start < 19),,coef_start_time_is_morning_1plusstops_othdiscr,coef_start_time_is_morning_1plusstops_othdiscr,coef_start_time_is_morning_1plusstops_othdiscr,coef_start_time_is_morning_1plusstops_othdiscr,coef_start_time_is_morning_1plusstops_othdiscr,coef_start_time_is_morning_1plusstops_othdiscr,coef_start_time_is_morning_1plusstops_othdiscr,coef_start_time_is_morning_1plusstops_othdiscr,coef_start_time_is_morning_1plusstops_othdiscr,coef_start_time_is_morning_1plusstops_othdiscr,coef_start_time_is_morning_1plusstops_othdiscr,coef_start_time_is_morning_1plusstops_othdiscr,coef_start_time_is_morning_1plusstops_othdiscr,coef_start_time_is_morning_1plusstops_othdiscr,coef_start_time_is_morning_1plusstops_othdiscr +util_start_time_is_afternoon,Tour start time between noon and 5pm,(start >= 19) & (start < 29),,coef_start_time_is_afternoon_1plusstops_othdiscr,coef_start_time_is_afternoon_1plusstops_othdiscr,coef_start_time_is_afternoon_1plusstops_othdiscr,coef_start_time_is_afternoon_1plusstops_othdiscr,coef_start_time_is_afternoon_1plusstops_othdiscr,coef_start_time_is_afternoon_1plusstops_othdiscr,coef_start_time_is_afternoon_1plusstops_othdiscr,coef_start_time_is_afternoon_1plusstops_othdiscr,coef_start_time_is_afternoon_1plusstops_othdiscr,coef_start_time_is_afternoon_1plusstops_othdiscr,coef_start_time_is_afternoon_1plusstops_othdiscr,coef_start_time_is_afternoon_1plusstops_othdiscr,coef_start_time_is_afternoon_1plusstops_othdiscr,coef_start_time_is_afternoon_1plusstops_othdiscr,coef_start_time_is_afternoon_1plusstops_othdiscr +util_distance_to_tour_destination,Distance from origin to tour destination in miles,distance_in_miles,,coef_distance_to_tour_destination_1plusstops_othdiscr,coef_distance_to_tour_destination_1plusstops_othdiscr,coef_distance_to_tour_destination_1plusstops_othdiscr,coef_distance_to_tour_destination_1plusstops_othdiscr,coef_distance_to_tour_destination_1plusstops_othdiscr,coef_distance_to_tour_destination_1plusstops_othdiscr,coef_distance_to_tour_destination_1plusstops_othdiscr,coef_distance_to_tour_destination_1plusstops_othdiscr,coef_distance_to_tour_destination_1plusstops_othdiscr,coef_distance_to_tour_destination_1plusstops_othdiscr,coef_distance_to_tour_destination_1plusstops_othdiscr,coef_distance_to_tour_destination_1plusstops_othdiscr,coef_distance_to_tour_destination_1plusstops_othdiscr,coef_distance_to_tour_destination_1plusstops_othdiscr,coef_distance_to_tour_destination_1plusstops_othdiscr +util_nostop_drive_transit,no stops on pnr knr tnc transit tours,"@(df.tour_mode.isin(['PNR_TRANSIT','KNR_TRANSIT']))",,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable +util_abm3_calibration_constants,calibration constants,is_joint==0,-0.7203,1.5385,1.3997,1.5604,-0.2187,-1.6251,0.4121,-5.1473,-2.0148,-3.6239,-3.7608,-10.0,-1.2510,-2.3088,-2.5209,2.5864 +util_abm3_calibration_constants_joint,joint tour calibration constants,is_joint,-25.0,-25.0,-25.0,-25.0,-25.0,-25.0,-25.0,-25.0,-25.0,-25.0,-25.0,-25.0,-25.0,-25.0,-25.0,-25.0 diff --git a/resident/configs/stop_frequency_othmaint.csv b/resident/configs/stop_frequency_othmaint.csv new file mode 100644 index 0000000..fa90f39 --- /dev/null +++ b/resident/configs/stop_frequency_othmaint.csv @@ -0,0 +1,15 @@ +Label,Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in +util_alternative_specific_constants,Alternative specific constants,1,,coef_alternative_specific_constants_0out_1in_othmaint,coef_alternative_specific_constants_0out_2in_othmaint,coef_alternative_specific_constants_0out_3in_othmaint,coef_alternative_specific_constants_1out_0in_othmaint,coef_alternative_specific_constants_1out_1in_othmaint,coef_alternative_specific_constants_1out_2in_othmaint,coef_alternative_specific_constants_1out_3in_othmaint,coef_alternative_specific_constants_2out_0in_othmaint,coef_alternative_specific_constants_2out_1in_othmaint,coef_alternative_specific_constants_2out_2in_othmaint,coef_alternative_specific_constants_2out_3in_othmaint,coef_alternative_specific_constants_3out_0in_othmaint,coef_alternative_specific_constants_3out_1in_othmaint,coef_alternative_specific_constants_3out_2in_othmaint,coef_alternative_specific_constants_3out_3in_othmaint +util_is_joint_tour,Is joint tour,is_joint,,coef_is_joint_tour_1plusstops_othmaint,coef_is_joint_tour_1plusstops_othmaint,coef_is_joint_tour_1plusstops_othmaint,coef_is_joint_tour_1plusstops_othmaint,coef_is_joint_tour_1plusstops_othmaint,coef_is_joint_tour_1plusstops_othmaint,coef_is_joint_tour_1plusstops_othmaint,coef_is_joint_tour_1plusstops_othmaint,coef_is_joint_tour_1plusstops_othmaint,coef_is_joint_tour_1plusstops_othmaint,coef_is_joint_tour_1plusstops_othmaint,coef_is_joint_tour_1plusstops_othmaint,coef_is_joint_tour_1plusstops_othmaint,coef_is_joint_tour_1plusstops_othmaint,coef_is_joint_tour_1plusstops_othmaint +util_number_of_children_in_hh,Number of children in HH,num_children,,coef_number_of_children_in_hh_1plusstops_othmaint,coef_number_of_children_in_hh_1plusstops_othmaint,coef_number_of_children_in_hh_1plusstops_othmaint,coef_number_of_children_in_hh_1plusstops_othmaint,coef_number_of_children_in_hh_1plusstops_othmaint,coef_number_of_children_in_hh_1plusstops_othmaint,coef_number_of_children_in_hh_1plusstops_othmaint,coef_number_of_children_in_hh_1plusstops_othmaint,coef_number_of_children_in_hh_1plusstops_othmaint,coef_number_of_children_in_hh_1plusstops_othmaint,coef_number_of_children_in_hh_1plusstops_othmaint,coef_number_of_children_in_hh_1plusstops_othmaint,coef_number_of_children_in_hh_1plusstops_othmaint,coef_number_of_children_in_hh_1plusstops_othmaint,coef_number_of_children_in_hh_1plusstops_othmaint +util_number_of_nonwork_tours,Number of non-work tours,(num_total_tours - num_work_tours),,coef_number_of_nonwork_tours_1plusstops_othmaint,coef_number_of_nonwork_tours_1plusstops_othmaint,coef_number_of_nonwork_tours_1plusstops_othmaint,coef_number_of_nonwork_tours_1plusstops_othmaint,coef_number_of_nonwork_tours_1plusstops_othmaint,coef_number_of_nonwork_tours_1plusstops_othmaint,coef_number_of_nonwork_tours_1plusstops_othmaint,coef_number_of_nonwork_tours_1plusstops_othmaint,coef_number_of_nonwork_tours_1plusstops_othmaint,coef_number_of_nonwork_tours_1plusstops_othmaint,coef_number_of_nonwork_tours_1plusstops_othmaint,coef_number_of_nonwork_tours_1plusstops_othmaint,coef_number_of_nonwork_tours_1plusstops_othmaint,coef_number_of_nonwork_tours_1plusstops_othmaint,coef_number_of_nonwork_tours_1plusstops_othmaint +util_hhsize_is_4p,Household size is 4p,(hhsize >= 4),,coef_hhsize_is_4p_1plusstops_othmaint,coef_hhsize_is_4p_1plusstops_othmaint,coef_hhsize_is_4p_1plusstops_othmaint,coef_hhsize_is_4p_1plusstops_othmaint,coef_hhsize_is_4p_1plusstops_othmaint,coef_hhsize_is_4p_1plusstops_othmaint,coef_hhsize_is_4p_1plusstops_othmaint,coef_hhsize_is_4p_1plusstops_othmaint,coef_hhsize_is_4p_1plusstops_othmaint,coef_hhsize_is_4p_1plusstops_othmaint,coef_hhsize_is_4p_1plusstops_othmaint,coef_hhsize_is_4p_1plusstops_othmaint,coef_hhsize_is_4p_1plusstops_othmaint,coef_hhsize_is_4p_1plusstops_othmaint,coef_hhsize_is_4p_1plusstops_othmaint +util_tour_mode_non_motorized,Tour mode is non-motorized,tour_mode_is_non_motorized,,coef_tour_mode_non_motorized_1plusstops_othmaint,coef_tour_mode_non_motorized_1plusstops_othmaint,coef_tour_mode_non_motorized_1plusstops_othmaint,coef_tour_mode_non_motorized_1plusstops_othmaint,coef_tour_mode_non_motorized_1plusstops_othmaint,coef_tour_mode_non_motorized_1plusstops_othmaint,coef_tour_mode_non_motorized_1plusstops_othmaint,coef_tour_mode_non_motorized_1plusstops_othmaint,coef_tour_mode_non_motorized_1plusstops_othmaint,coef_tour_mode_non_motorized_1plusstops_othmaint,coef_tour_mode_non_motorized_1plusstops_othmaint,coef_tour_mode_non_motorized_1plusstops_othmaint,coef_tour_mode_non_motorized_1plusstops_othmaint,coef_tour_mode_non_motorized_1plusstops_othmaint,coef_tour_mode_non_motorized_1plusstops_othmaint +util_tour_mode_hov,Tour mode is hov,tour_mode_is_hov,,coef_tour_mode_hov_1plusstops_othmaint,coef_tour_mode_hov_1plusstops_othmaint,coef_tour_mode_hov_1plusstops_othmaint,coef_tour_mode_hov_1plusstops_othmaint,coef_tour_mode_hov_1plusstops_othmaint,coef_tour_mode_hov_1plusstops_othmaint,coef_tour_mode_hov_1plusstops_othmaint,coef_tour_mode_hov_1plusstops_othmaint,coef_tour_mode_hov_1plusstops_othmaint,coef_tour_mode_hov_1plusstops_othmaint,coef_tour_mode_hov_1plusstops_othmaint,coef_tour_mode_hov_1plusstops_othmaint,coef_tour_mode_hov_1plusstops_othmaint,coef_tour_mode_hov_1plusstops_othmaint,coef_tour_mode_hov_1plusstops_othmaint +util_tour_duration_in_hours,Tour duration in hours,(duration / 2),,coef_tour_duration_in_hours_1plusstops_othmaint,coef_tour_duration_in_hours_1plusstops_othmaint,coef_tour_duration_in_hours_1plusstops_othmaint,coef_tour_duration_in_hours_1plusstops_othmaint,coef_tour_duration_in_hours_1plusstops_othmaint,coef_tour_duration_in_hours_1plusstops_othmaint,coef_tour_duration_in_hours_1plusstops_othmaint,coef_tour_duration_in_hours_1plusstops_othmaint,coef_tour_duration_in_hours_1plusstops_othmaint,coef_tour_duration_in_hours_1plusstops_othmaint,coef_tour_duration_in_hours_1plusstops_othmaint,coef_tour_duration_in_hours_1plusstops_othmaint,coef_tour_duration_in_hours_1plusstops_othmaint,coef_tour_duration_in_hours_1plusstops_othmaint,coef_tour_duration_in_hours_1plusstops_othmaint +util_start_time_is_morning,Tour start time is before noon,(start < 19),,coef_start_time_is_morning_1plusstops_othmaint,coef_start_time_is_morning_1plusstops_othmaint,coef_start_time_is_morning_1plusstops_othmaint,coef_start_time_is_morning_1plusstops_othmaint,coef_start_time_is_morning_1plusstops_othmaint,coef_start_time_is_morning_1plusstops_othmaint,coef_start_time_is_morning_1plusstops_othmaint,coef_start_time_is_morning_1plusstops_othmaint,coef_start_time_is_morning_1plusstops_othmaint,coef_start_time_is_morning_1plusstops_othmaint,coef_start_time_is_morning_1plusstops_othmaint,coef_start_time_is_morning_1plusstops_othmaint,coef_start_time_is_morning_1plusstops_othmaint,coef_start_time_is_morning_1plusstops_othmaint,coef_start_time_is_morning_1plusstops_othmaint +util_start_time_is_afternoon,Tour start time between noon and 5pm,(start >= 19) & (start < 29),,coef_start_time_is_afternoon_1plusstops_othmaint,coef_start_time_is_afternoon_1plusstops_othmaint,coef_start_time_is_afternoon_1plusstops_othmaint,coef_start_time_is_afternoon_1plusstops_othmaint,coef_start_time_is_afternoon_1plusstops_othmaint,coef_start_time_is_afternoon_1plusstops_othmaint,coef_start_time_is_afternoon_1plusstops_othmaint,coef_start_time_is_afternoon_1plusstops_othmaint,coef_start_time_is_afternoon_1plusstops_othmaint,coef_start_time_is_afternoon_1plusstops_othmaint,coef_start_time_is_afternoon_1plusstops_othmaint,coef_start_time_is_afternoon_1plusstops_othmaint,coef_start_time_is_afternoon_1plusstops_othmaint,coef_start_time_is_afternoon_1plusstops_othmaint,coef_start_time_is_afternoon_1plusstops_othmaint +util_distance_to_tour_destination,Distance from origin to tour destination in miles,distance_in_miles,,coef_distance_to_tour_destination_1plusstops_othmaint,coef_distance_to_tour_destination_1plusstops_othmaint,coef_distance_to_tour_destination_1plusstops_othmaint,coef_distance_to_tour_destination_1plusstops_othmaint,coef_distance_to_tour_destination_1plusstops_othmaint,coef_distance_to_tour_destination_1plusstops_othmaint,coef_distance_to_tour_destination_1plusstops_othmaint,coef_distance_to_tour_destination_1plusstops_othmaint,coef_distance_to_tour_destination_1plusstops_othmaint,coef_distance_to_tour_destination_1plusstops_othmaint,coef_distance_to_tour_destination_1plusstops_othmaint,coef_distance_to_tour_destination_1plusstops_othmaint,coef_distance_to_tour_destination_1plusstops_othmaint,coef_distance_to_tour_destination_1plusstops_othmaint,coef_distance_to_tour_destination_1plusstops_othmaint +util_nostop_drive_transit,no stops on pnr knr tnc transit tours,"@(df.tour_mode.isin(['PNR_TRANSIT','KNR_TRANSIT']))",,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable +util_abm3_calibration_constants,calibration constants,is_joint==0,-0.2656123444029792,0.15348593472541502,-0.3788741163797237,0.123283919762546,0.22954639797160692,0.5755515179272356,-0.21495880583644267,-0.6257626266582341,1.003382155880165,0.28582196724350073,0.0002870161481544698,-1.718504862855789,-2.145372675786179,-2.2250799871962363,1.1522061332946996,1.8970899388634406 +util_abm3_calibration_constants_joint,joint tour calibration constants,is_joint,-1.3355,0.9021,0.5683,-1.7962,1.50029,-0.9014,-1.3765,-10.0,-3.4045,0.7563,-10.0,-1.7586,-0.04717,-0.5280,-10.0,3.9129 diff --git a/resident/configs/stop_frequency_school.csv b/resident/configs/stop_frequency_school.csv new file mode 100644 index 0000000..4142df3 --- /dev/null +++ b/resident/configs/stop_frequency_school.csv @@ -0,0 +1,12 @@ +Label,Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in +util_alternative_specific_constants,Alternative specific constants,1,,coef_alternative_specific_constants_0out_1in_school,coef_alternative_specific_constants_0out_2in_school,coef_alternative_specific_constants_0out_3in_school,coef_alternative_specific_constants_1out_0in_school,coef_alternative_specific_constants_1out_1in_school,coef_alternative_specific_constants_1out_2in_school,coef_alternative_specific_constants_1out_3in_school,coef_alternative_specific_constants_2out_0in_school,coef_alternative_specific_constants_2out_1in_school,coef_alternative_specific_constants_2out_2in_school,coef_alternative_specific_constants_2out_3in_school,coef_alternative_specific_constants_3out_0in_school,coef_alternative_specific_constants_3out_1in_school,coef_alternative_specific_constants_3out_2in_school,coef_alternative_specific_constants_3out_3in_school +util_is_pre_covid,Tour happened before covid hit,@scenarioYear==2016,,coef_is_pre_covid_0out_1plusin_school,coef_is_pre_covid_0out_1plusin_school,coef_is_pre_covid_0out_1plusin_school,coef_is_pre_covid_1plusout_0in_school,coef_is_pre_covid_1plusout_1plusin_school,coef_is_pre_covid_1plusout_1plusin_school,coef_is_pre_covid_1plusout_1plusin_school,coef_is_pre_covid_1plusout_0in_school,coef_is_pre_covid_1plusout_1plusin_school,coef_is_pre_covid_1plusout_1plusin_school,coef_is_pre_covid_1plusout_1plusin_school,coef_is_pre_covid_1plusout_0in_school,coef_is_pre_covid_1plusout_1plusin_school,coef_is_pre_covid_1plusout_1plusin_school,coef_is_pre_covid_1plusout_1plusin_school +util_number_of_nonwork_tours,Number of non-work tours,(num_total_tours - num_work_tours),,coef_number_of_nonwork_tours_0out_1plusin_school,coef_number_of_nonwork_tours_0out_1plusin_school,coef_number_of_nonwork_tours_0out_1plusin_school,,coef_number_of_nonwork_tours_1plusout_1plusin_school,coef_number_of_nonwork_tours_1plusout_1plusin_school,coef_number_of_nonwork_tours_1plusout_1plusin_school,,coef_number_of_nonwork_tours_1plusout_1plusin_school,coef_number_of_nonwork_tours_1plusout_1plusin_school,coef_number_of_nonwork_tours_1plusout_1plusin_school,,coef_number_of_nonwork_tours_1plusout_1plusin_school,coef_number_of_nonwork_tours_1plusout_1plusin_school,coef_number_of_nonwork_tours_1plusout_1plusin_school +util_school_child_in_hh,Presence of school age child in household other than self,has_school_kid,,coef_school_child_in_hh_0out_1plusin_school,coef_school_child_in_hh_0out_1plusin_school,coef_school_child_in_hh_0out_1plusin_school,coef_school_child_in_hh_1plusout_0in_school,coef_school_child_in_hh_1plusout_1plusin_school,coef_school_child_in_hh_1plusout_1plusin_school,coef_school_child_in_hh_1plusout_1plusin_school,coef_school_child_in_hh_1plusout_0in_school,coef_school_child_in_hh_1plusout_1plusin_school,coef_school_child_in_hh_1plusout_1plusin_school,coef_school_child_in_hh_1plusout_1plusin_school,coef_school_child_in_hh_1plusout_0in_school,coef_school_child_in_hh_1plusout_1plusin_school,coef_school_child_in_hh_1plusout_1plusin_school,coef_school_child_in_hh_1plusout_1plusin_school +util_non_worker_in_hh,Presence of non workers in household other than self,has_non_worker,,,,,,coef_non_worker_in_hh_1plusout_1plusin_school,coef_non_worker_in_hh_1plusout_1plusin_school,coef_non_worker_in_hh_1plusout_1plusin_school,,coef_non_worker_in_hh_1plusout_1plusin_school,coef_non_worker_in_hh_1plusout_1plusin_school,coef_non_worker_in_hh_1plusout_1plusin_school,,coef_non_worker_in_hh_1plusout_1plusin_school,coef_non_worker_in_hh_1plusout_1plusin_school,coef_non_worker_in_hh_1plusout_1plusin_school +util_non_mandatory_accessibility,Disaggreage accessibility at home location to non-mandatory location,(othdiscr_accessibility + shopping_accessibility) / 2,,coef_non_mandatory_accessibility_0out_1plusin_school,coef_non_mandatory_accessibility_0out_1plusin_school,coef_non_mandatory_accessibility_0out_1plusin_school,,coef_non_mandatory_accessibility_1plusout_1plusin_school,coef_non_mandatory_accessibility_1plusout_1plusin_school,coef_non_mandatory_accessibility_1plusout_1plusin_school,,coef_non_mandatory_accessibility_1plusout_1plusin_school,coef_non_mandatory_accessibility_1plusout_1plusin_school,coef_non_mandatory_accessibility_1plusout_1plusin_school,,coef_non_mandatory_accessibility_1plusout_1plusin_school,coef_non_mandatory_accessibility_1plusout_1plusin_school,coef_non_mandatory_accessibility_1plusout_1plusin_school +util_tour_mode_non_motorized,Tour mode is non-motorized,tour_mode_is_non_motorized,,coef_tour_mode_non_motorized_0out_1plusin_school,coef_tour_mode_non_motorized_0out_1plusin_school,coef_tour_mode_non_motorized_0out_1plusin_school,coef_tour_mode_non_motorized_1plusout_0in_school,coef_tour_mode_non_motorized_1plusout_1plusin_school,coef_tour_mode_non_motorized_1plusout_1plusin_school,coef_tour_mode_non_motorized_1plusout_1plusin_school,coef_tour_mode_non_motorized_1plusout_0in_school,coef_tour_mode_non_motorized_1plusout_1plusin_school,coef_tour_mode_non_motorized_1plusout_1plusin_school,coef_tour_mode_non_motorized_1plusout_1plusin_school,coef_tour_mode_non_motorized_1plusout_0in_school,coef_tour_mode_non_motorized_1plusout_1plusin_school,coef_tour_mode_non_motorized_1plusout_1plusin_school,coef_tour_mode_non_motorized_1plusout_1plusin_school +util_tour_mode_schoolbus,Tour mode is schoolbus,tour_mode_is_schbus,,coef_tour_mode_schbus_0out_1plusin_school,coef_tour_mode_schbus_0out_1plusin_school,coef_tour_mode_schbus_0out_1plusin_school,,,,,,,,,,,, +util_nostopschooltour,no stops on school tours,tour_mode_is_schbus,,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable +util_nostop_drive_transit,no stops on pnr knr tnc transit tours,"@(df.tour_mode.isin(['PNR_TRANSIT','KNR_TRANSIT']))",,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable +util_abm3_calibration_constants,calibration constants,is_joint==0,-4.1165,1.3926,0.5790,0.9793,4.6426,2.2195,3.4805,2.7292,2.4262,3.10758,-25.0,-25.0,-2.7718,0.2813,-10.0,3.9258 diff --git a/resident/configs/stop_frequency_shopping.csv b/resident/configs/stop_frequency_shopping.csv new file mode 100644 index 0000000..bd0d72f --- /dev/null +++ b/resident/configs/stop_frequency_shopping.csv @@ -0,0 +1,16 @@ +Label,Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in +util_alternative_specific_constants,Alternative specific constants,1,,coef_alternative_specific_constants_0out_1in_shopping,coef_alternative_specific_constants_0out_2in_shopping,coef_alternative_specific_constants_0out_3in_shopping,coef_alternative_specific_constants_1out_0in_shopping,coef_alternative_specific_constants_1out_1in_shopping,coef_alternative_specific_constants_1out_2in_shopping,coef_alternative_specific_constants_1out_3in_shopping,coef_alternative_specific_constants_2out_0in_shopping,coef_alternative_specific_constants_2out_1in_shopping,coef_alternative_specific_constants_2out_2in_shopping,coef_alternative_specific_constants_2out_3in_shopping,coef_alternative_specific_constants_3out_0in_shopping,coef_alternative_specific_constants_3out_1in_shopping,coef_alternative_specific_constants_3out_2in_shopping,coef_alternative_specific_constants_3out_3in_shopping +util_is_joint_tour,Is joint tour,is_joint,,coef_is_joint_tour_1plusstops_shopping,coef_is_joint_tour_1plusstops_shopping,coef_is_joint_tour_1plusstops_shopping,coef_is_joint_tour_1plusstops_shopping,coef_is_joint_tour_1plusstops_shopping,coef_is_joint_tour_1plusstops_shopping,coef_is_joint_tour_1plusstops_shopping,coef_is_joint_tour_1plusstops_shopping,coef_is_joint_tour_1plusstops_shopping,coef_is_joint_tour_1plusstops_shopping,coef_is_joint_tour_1plusstops_shopping,coef_is_joint_tour_1plusstops_shopping,coef_is_joint_tour_1plusstops_shopping,coef_is_joint_tour_1plusstops_shopping,coef_is_joint_tour_1plusstops_shopping +util_is_pre_covid,Tour happened before covid hit,@scenarioYear==2016,,coef_is_pre_covid_1plusstops_shopping,coef_is_pre_covid_1plusstops_shopping,coef_is_pre_covid_1plusstops_shopping,coef_is_pre_covid_1plusstops_shopping,coef_is_pre_covid_1plusstops_shopping,coef_is_pre_covid_1plusstops_shopping,coef_is_pre_covid_1plusstops_shopping,coef_is_pre_covid_1plusstops_shopping,coef_is_pre_covid_1plusstops_shopping,coef_is_pre_covid_1plusstops_shopping,coef_is_pre_covid_1plusstops_shopping,coef_is_pre_covid_1plusstops_shopping,coef_is_pre_covid_1plusstops_shopping,coef_is_pre_covid_1plusstops_shopping,coef_is_pre_covid_1plusstops_shopping +util_is_worker,Person is worker,is_worker,,coef_is_worker_1plusstops_shopping,coef_is_worker_1plusstops_shopping,coef_is_worker_1plusstops_shopping,coef_is_worker_1plusstops_shopping,coef_is_worker_1plusstops_shopping,coef_is_worker_1plusstops_shopping,coef_is_worker_1plusstops_shopping,coef_is_worker_1plusstops_shopping,coef_is_worker_1plusstops_shopping,coef_is_worker_1plusstops_shopping,coef_is_worker_1plusstops_shopping,coef_is_worker_1plusstops_shopping,coef_is_worker_1plusstops_shopping,coef_is_worker_1plusstops_shopping,coef_is_worker_1plusstops_shopping +util_number_of_nonwork_tours,Number of non-work tours,(num_total_tours - num_work_tours),,coef_number_of_nonwork_tours_1plusstops_shopping,coef_number_of_nonwork_tours_1plusstops_shopping,coef_number_of_nonwork_tours_1plusstops_shopping,coef_number_of_nonwork_tours_1plusstops_shopping,coef_number_of_nonwork_tours_1plusstops_shopping,coef_number_of_nonwork_tours_1plusstops_shopping,coef_number_of_nonwork_tours_1plusstops_shopping,coef_number_of_nonwork_tours_1plusstops_shopping,coef_number_of_nonwork_tours_1plusstops_shopping,coef_number_of_nonwork_tours_1plusstops_shopping,coef_number_of_nonwork_tours_1plusstops_shopping,coef_number_of_nonwork_tours_1plusstops_shopping,coef_number_of_nonwork_tours_1plusstops_shopping,coef_number_of_nonwork_tours_1plusstops_shopping,coef_number_of_nonwork_tours_1plusstops_shopping +util_school_child_in_hh,Presence of school age child in household other than self,has_school_kid,,coef_school_child_in_hh_1plusstops_shopping,coef_school_child_in_hh_1plusstops_shopping,coef_school_child_in_hh_1plusstops_shopping,coef_school_child_in_hh_1plusstops_shopping,coef_school_child_in_hh_1plusstops_shopping,coef_school_child_in_hh_1plusstops_shopping,coef_school_child_in_hh_1plusstops_shopping,coef_school_child_in_hh_1plusstops_shopping,coef_school_child_in_hh_1plusstops_shopping,coef_school_child_in_hh_1plusstops_shopping,coef_school_child_in_hh_1plusstops_shopping,coef_school_child_in_hh_1plusstops_shopping,coef_school_child_in_hh_1plusstops_shopping,coef_school_child_in_hh_1plusstops_shopping,coef_school_child_in_hh_1plusstops_shopping +util_non_mandatory_accessibility,Disaggreage accessibility at home location to non-mandatory location,(othdiscr_accessibility + shopping_accessibility) / 2,,coef_non_mandatory_accessibility_1plusstops_shopping,coef_non_mandatory_accessibility_1plusstops_shopping,coef_non_mandatory_accessibility_1plusstops_shopping,coef_non_mandatory_accessibility_1plusstops_shopping,coef_non_mandatory_accessibility_1plusstops_shopping,coef_non_mandatory_accessibility_1plusstops_shopping,coef_non_mandatory_accessibility_1plusstops_shopping,coef_non_mandatory_accessibility_1plusstops_shopping,coef_non_mandatory_accessibility_1plusstops_shopping,coef_non_mandatory_accessibility_1plusstops_shopping,coef_non_mandatory_accessibility_1plusstops_shopping,coef_non_mandatory_accessibility_1plusstops_shopping,coef_non_mandatory_accessibility_1plusstops_shopping,coef_non_mandatory_accessibility_1plusstops_shopping,coef_non_mandatory_accessibility_1plusstops_shopping +util_tour_mode_non_motorized,Tour mode is non-motorized,tour_mode_is_non_motorized,,coef_tour_mode_non_motorized_1plusstops_shopping,coef_tour_mode_non_motorized_1plusstops_shopping,coef_tour_mode_non_motorized_1plusstops_shopping,coef_tour_mode_non_motorized_1plusstops_shopping,coef_tour_mode_non_motorized_1plusstops_shopping,coef_tour_mode_non_motorized_1plusstops_shopping,coef_tour_mode_non_motorized_1plusstops_shopping,coef_tour_mode_non_motorized_1plusstops_shopping,coef_tour_mode_non_motorized_1plusstops_shopping,coef_tour_mode_non_motorized_1plusstops_shopping,coef_tour_mode_non_motorized_1plusstops_shopping,coef_tour_mode_non_motorized_1plusstops_shopping,coef_tour_mode_non_motorized_1plusstops_shopping,coef_tour_mode_non_motorized_1plusstops_shopping,coef_tour_mode_non_motorized_1plusstops_shopping +util_tour_mode_hov,Tour mode is hov,tour_mode_is_hov,,coef_tour_mode_hov_1plusstops_shopping,coef_tour_mode_hov_1plusstops_shopping,coef_tour_mode_hov_1plusstops_shopping,coef_tour_mode_hov_1plusstops_shopping,coef_tour_mode_hov_1plusstops_shopping,coef_tour_mode_hov_1plusstops_shopping,coef_tour_mode_hov_1plusstops_shopping,coef_tour_mode_hov_1plusstops_shopping,coef_tour_mode_hov_1plusstops_shopping,coef_tour_mode_hov_1plusstops_shopping,coef_tour_mode_hov_1plusstops_shopping,coef_tour_mode_hov_1plusstops_shopping,coef_tour_mode_hov_1plusstops_shopping,coef_tour_mode_hov_1plusstops_shopping,coef_tour_mode_hov_1plusstops_shopping +util_tour_mode_transit,Tour mode is transit,tour_mode_is_transit,,coef_tour_mode_transit_1plusstops_shopping,coef_tour_mode_transit_1plusstops_shopping,coef_tour_mode_transit_1plusstops_shopping,coef_tour_mode_transit_1plusstops_shopping,coef_tour_mode_transit_1plusstops_shopping,coef_tour_mode_transit_1plusstops_shopping,coef_tour_mode_transit_1plusstops_shopping,coef_tour_mode_transit_1plusstops_shopping,coef_tour_mode_transit_1plusstops_shopping,coef_tour_mode_transit_1plusstops_shopping,coef_tour_mode_transit_1plusstops_shopping,coef_tour_mode_transit_1plusstops_shopping,coef_tour_mode_transit_1plusstops_shopping,coef_tour_mode_transit_1plusstops_shopping,coef_tour_mode_transit_1plusstops_shopping +util_tour_duration_in_hours,Tour duration in hours,(duration / 2),,coef_tour_duration_in_hours_1plusstops_shopping,coef_tour_duration_in_hours_1plusstops_shopping,coef_tour_duration_in_hours_1plusstops_shopping,coef_tour_duration_in_hours_1plusstops_shopping,coef_tour_duration_in_hours_1plusstops_shopping,coef_tour_duration_in_hours_1plusstops_shopping,coef_tour_duration_in_hours_1plusstops_shopping,coef_tour_duration_in_hours_1plusstops_shopping,coef_tour_duration_in_hours_1plusstops_shopping,coef_tour_duration_in_hours_1plusstops_shopping,coef_tour_duration_in_hours_1plusstops_shopping,coef_tour_duration_in_hours_1plusstops_shopping,coef_tour_duration_in_hours_1plusstops_shopping,coef_tour_duration_in_hours_1plusstops_shopping,coef_tour_duration_in_hours_1plusstops_shopping +util_distance_to_tour_destination,Distance from origin to tour destination in miles,distance_in_miles,,coef_distance_to_tour_destination_1plusstops_shopping,coef_distance_to_tour_destination_1plusstops_shopping,coef_distance_to_tour_destination_1plusstops_shopping,coef_distance_to_tour_destination_1plusstops_shopping,coef_distance_to_tour_destination_1plusstops_shopping,coef_distance_to_tour_destination_1plusstops_shopping,coef_distance_to_tour_destination_1plusstops_shopping,coef_distance_to_tour_destination_1plusstops_shopping,coef_distance_to_tour_destination_1plusstops_shopping,coef_distance_to_tour_destination_1plusstops_shopping,coef_distance_to_tour_destination_1plusstops_shopping,coef_distance_to_tour_destination_1plusstops_shopping,coef_distance_to_tour_destination_1plusstops_shopping,coef_distance_to_tour_destination_1plusstops_shopping,coef_distance_to_tour_destination_1plusstops_shopping +util_nostop_drive_transit,no stops on pnr knr tnc transit tours,"@(df.tour_mode.isin(['PNR_TRANSIT','KNR_TRANSIT']))",,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable +util_abm3_calibration_constants,calibration constants,is_joint==0,-0.2500906598537606,0.057193649152341756,0.4804144838454415,-0.261395884078163,0.22894326452057048,0.5910095505581588,-0.8981616524559722,-4.199029251095443,0.09230772468214696,0.5169317692363429,-0.9915339032679815,-1.1533584002576693,-0.42731015383922333,-2.6985663726233104,1.9854294273438238,3.3854166649272117 +util_abm3_calibration_constants_joint,joint tour calibration constants,is_joint,-0.2533,1.3037,0.7569,-1.6520,-0.7370,0.8157,0.006162,-10.0,-1.1890,-10.0,-10.0,-5.0,-0.0569,-10.0,-5.0,3.9856 diff --git a/resident/configs/stop_frequency_social.csv b/resident/configs/stop_frequency_social.csv new file mode 100644 index 0000000..71fec05 --- /dev/null +++ b/resident/configs/stop_frequency_social.csv @@ -0,0 +1,12 @@ +Label,Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in +util_alternative_specific_constants,Alternative specific constants,1,,coef_alternative_specific_constants_0out_1in_social,coef_alternative_specific_constants_0out_2in_social,coef_alternative_specific_constants_0out_3in_social,coef_alternative_specific_constants_1out_0in_social,coef_alternative_specific_constants_1out_1in_social,coef_alternative_specific_constants_1out_2in_social,coef_alternative_specific_constants_1out_3in_social,coef_alternative_specific_constants_2out_0in_social,coef_alternative_specific_constants_2out_1in_social,coef_alternative_specific_constants_2out_2in_social,coef_alternative_specific_constants_2out_3in_social,coef_alternative_specific_constants_3out_0in_social,coef_alternative_specific_constants_3out_1in_social,coef_alternative_specific_constants_3out_2in_social,coef_alternative_specific_constants_3out_3in_social +util_is_joint_tour,Is joint tour,is_joint,,coef_is_joint_tour_1plus_stops_social,coef_is_joint_tour_1plus_stops_social,coef_is_joint_tour_1plus_stops_social,coef_is_joint_tour_1plus_stops_social,coef_is_joint_tour_1plus_stops_social,coef_is_joint_tour_1plus_stops_social,coef_is_joint_tour_1plus_stops_social,coef_is_joint_tour_1plus_stops_social,coef_is_joint_tour_1plus_stops_social,coef_is_joint_tour_1plus_stops_social,coef_is_joint_tour_1plus_stops_social,coef_is_joint_tour_1plus_stops_social,coef_is_joint_tour_1plus_stops_social,coef_is_joint_tour_1plus_stops_social,coef_is_joint_tour_1plus_stops_social +util_number_of_nonwork_tours,Number of non-work tours,(num_total_tours - num_work_tours),,coef_number_of_nonwork_tours_1plus_stops_social,coef_number_of_nonwork_tours_1plus_stops_social,coef_number_of_nonwork_tours_1plus_stops_social,coef_number_of_nonwork_tours_1plus_stops_social,coef_number_of_nonwork_tours_1plus_stops_social,coef_number_of_nonwork_tours_1plus_stops_social,coef_number_of_nonwork_tours_1plus_stops_social,coef_number_of_nonwork_tours_1plus_stops_social,coef_number_of_nonwork_tours_1plus_stops_social,coef_number_of_nonwork_tours_1plus_stops_social,coef_number_of_nonwork_tours_1plus_stops_social,coef_number_of_nonwork_tours_1plus_stops_social,coef_number_of_nonwork_tours_1plus_stops_social,coef_number_of_nonwork_tours_1plus_stops_social,coef_number_of_nonwork_tours_1plus_stops_social +util_age_5_18,Age Group - 5 yrs to 18 yrs,(age >= 5) & (age < 19),,coef_age_5_18_1plus_stops_social,coef_age_5_18_1plus_stops_social,coef_age_5_18_1plus_stops_social,coef_age_5_18_1plus_stops_social,coef_age_5_18_1plus_stops_social,coef_age_5_18_1plus_stops_social,coef_age_5_18_1plus_stops_social,coef_age_5_18_1plus_stops_social,coef_age_5_18_1plus_stops_social,coef_age_5_18_1plus_stops_social,coef_age_5_18_1plus_stops_social,coef_age_5_18_1plus_stops_social,coef_age_5_18_1plus_stops_social,coef_age_5_18_1plus_stops_social,coef_age_5_18_1plus_stops_social +util_non_mandatory_accessibility,Disaggreage accessibility at home location to non-mandatory location,(othdiscr_accessibility + shopping_accessibility) / 2,,coef_non_mandatory_accessibility_1plus_stops_social,coef_non_mandatory_accessibility_1plus_stops_social,coef_non_mandatory_accessibility_1plus_stops_social,coef_non_mandatory_accessibility_1plus_stops_social,coef_non_mandatory_accessibility_1plus_stops_social,coef_non_mandatory_accessibility_1plus_stops_social,coef_non_mandatory_accessibility_1plus_stops_social,coef_non_mandatory_accessibility_1plus_stops_social,coef_non_mandatory_accessibility_1plus_stops_social,coef_non_mandatory_accessibility_1plus_stops_social,coef_non_mandatory_accessibility_1plus_stops_social,coef_non_mandatory_accessibility_1plus_stops_social,coef_non_mandatory_accessibility_1plus_stops_social,coef_non_mandatory_accessibility_1plus_stops_social,coef_non_mandatory_accessibility_1plus_stops_social +util_tour_mode_hov,Tour mode is hov,tour_mode_is_hov,,coef_tour_mode_hov_1plus_stops_social,coef_tour_mode_hov_1plus_stops_social,coef_tour_mode_hov_1plus_stops_social,coef_tour_mode_hov_1plus_stops_social,coef_tour_mode_hov_1plus_stops_social,coef_tour_mode_hov_1plus_stops_social,coef_tour_mode_hov_1plus_stops_social,coef_tour_mode_hov_1plus_stops_social,coef_tour_mode_hov_1plus_stops_social,coef_tour_mode_hov_1plus_stops_social,coef_tour_mode_hov_1plus_stops_social,coef_tour_mode_hov_1plus_stops_social,coef_tour_mode_hov_1plus_stops_social,coef_tour_mode_hov_1plus_stops_social,coef_tour_mode_hov_1plus_stops_social +util_tour_duration_in_hours,Tour duration in hours,(duration / 2),,coef_tour_duration_in_hours_1plus_stops_social,coef_tour_duration_in_hours_1plus_stops_social,coef_tour_duration_in_hours_1plus_stops_social,coef_tour_duration_in_hours_1plus_stops_social,coef_tour_duration_in_hours_1plus_stops_social,coef_tour_duration_in_hours_1plus_stops_social,coef_tour_duration_in_hours_1plus_stops_social,coef_tour_duration_in_hours_1plus_stops_social,coef_tour_duration_in_hours_1plus_stops_social,coef_tour_duration_in_hours_1plus_stops_social,coef_tour_duration_in_hours_1plus_stops_social,coef_tour_duration_in_hours_1plus_stops_social,coef_tour_duration_in_hours_1plus_stops_social,coef_tour_duration_in_hours_1plus_stops_social,coef_tour_duration_in_hours_1plus_stops_social +util_distance_to_tour_destination,Distance from origin to tour destination in miles,distance_in_miles,,coef_distance_to_tour_destination_1plus_stops_social,coef_distance_to_tour_destination_1plus_stops_social,coef_distance_to_tour_destination_1plus_stops_social,coef_distance_to_tour_destination_1plus_stops_social,coef_distance_to_tour_destination_1plus_stops_social,coef_distance_to_tour_destination_1plus_stops_social,coef_distance_to_tour_destination_1plus_stops_social,coef_distance_to_tour_destination_1plus_stops_social,coef_distance_to_tour_destination_1plus_stops_social,coef_distance_to_tour_destination_1plus_stops_social,coef_distance_to_tour_destination_1plus_stops_social,coef_distance_to_tour_destination_1plus_stops_social,coef_distance_to_tour_destination_1plus_stops_social,coef_distance_to_tour_destination_1plus_stops_social,coef_distance_to_tour_destination_1plus_stops_social +util_nostop_drive_transit,no stops on pnr knr tnc transit tours,"@(df.tour_mode.isin(['PNR_TRANSIT','KNR_TRANSIT']))",,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable +util_abm3_calibration_constants,calibration constants,is_joint==0,0.11096680817187969,-0.5366374837582596,0.49515245428835686,-0.639202818836102,-0.6855014200561323,-0.10103468501475253,-1.2010426433613348,-3.4876529906722045,-0.5781845322079998,-0.7324835700455633,-2.204662008303293,-5.0,-0.21810513280268298,-0.4631769418491418,-4.458065289588038,3.811850876178379 +util_abm3_calibration_constants_joint,joint tour calibration constants,is_joint,-0.1786,0.3422,0.8914,-3.7763,-2.4629,-2.3188,-1.9052,3.2893,-10.0,-10.0,-5.0,-5.0,-0.6356,-5.0,-5.0,-5.0 diff --git a/resident/configs/stop_frequency_univ.csv b/resident/configs/stop_frequency_univ.csv new file mode 100644 index 0000000..3563bb5 --- /dev/null +++ b/resident/configs/stop_frequency_univ.csv @@ -0,0 +1,11 @@ +Label,Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in +util_alternative_specific_constants,Alternative specific constants,1,,coef_alternative_specific_constants_0out_1plusin_univ,coef_alternative_specific_constants_0out_1plusin_univ,coef_alternative_specific_constants_0out_1plusin_univ,coef_alternative_specific_constants_1out_0in_univ,coef_alternative_specific_constants_1out_1in_univ,coef_alternative_specific_constants_1out_2in_univ,coef_alternative_specific_constants_1out_3in_univ,coef_alternative_specific_constants_2out_0in_univ,coef_alternative_specific_constants_2out_1in_univ,coef_alternative_specific_constants_2out_2in_univ,coef_alternative_specific_constants_2out_3in_univ,coef_alternative_specific_constants_3out_0in_univ,coef_alternative_specific_constants_3out_1in_univ,coef_alternative_specific_constants_3out_2in_univ,coef_alternative_specific_constants_3out_3in_univ +util_preschool_child_in_hh,Presence of preschooler in household other than self,has_preschool_kid,,coef_preschool_child_in_hh_1plusstops_univ,coef_preschool_child_in_hh_1plusstops_univ,coef_preschool_child_in_hh_1plusstops_univ,coef_preschool_child_in_hh_1plusstops_univ,coef_preschool_child_in_hh_1plusstops_univ,coef_preschool_child_in_hh_1plusstops_univ,coef_preschool_child_in_hh_1plusstops_univ,coef_preschool_child_in_hh_1plusstops_univ,coef_preschool_child_in_hh_1plusstops_univ,coef_preschool_child_in_hh_1plusstops_univ,coef_preschool_child_in_hh_1plusstops_univ,coef_preschool_child_in_hh_1plusstops_univ,coef_preschool_child_in_hh_1plusstops_univ,coef_preschool_child_in_hh_1plusstops_univ,coef_preschool_child_in_hh_1plusstops_univ +util_tour_mode_non_motorized,Tour mode is non-motorized,tour_mode_is_non_motorized,,coef_tour_mode_non_motorized_1plusstops_univ,coef_tour_mode_non_motorized_1plusstops_univ,coef_tour_mode_non_motorized_1plusstops_univ,coef_tour_mode_non_motorized_1plusstops_univ,coef_tour_mode_non_motorized_1plusstops_univ,coef_tour_mode_non_motorized_1plusstops_univ,coef_tour_mode_non_motorized_1plusstops_univ,coef_tour_mode_non_motorized_1plusstops_univ,coef_tour_mode_non_motorized_1plusstops_univ,coef_tour_mode_non_motorized_1plusstops_univ,coef_tour_mode_non_motorized_1plusstops_univ,coef_tour_mode_non_motorized_1plusstops_univ,coef_tour_mode_non_motorized_1plusstops_univ,coef_tour_mode_non_motorized_1plusstops_univ,coef_tour_mode_non_motorized_1plusstops_univ +util_tour_mode_hov,Tour mode is hov,tour_mode_is_hov,,coef_tour_mode_hov_1plusstops_univ,coef_tour_mode_hov_1plusstops_univ,coef_tour_mode_hov_1plusstops_univ,coef_tour_mode_hov_1plusstops_univ,coef_tour_mode_hov_1plusstops_univ,coef_tour_mode_hov_1plusstops_univ,coef_tour_mode_hov_1plusstops_univ,coef_tour_mode_hov_1plusstops_univ,coef_tour_mode_hov_1plusstops_univ,coef_tour_mode_hov_1plusstops_univ,coef_tour_mode_hov_1plusstops_univ,coef_tour_mode_hov_1plusstops_univ,coef_tour_mode_hov_1plusstops_univ,coef_tour_mode_hov_1plusstops_univ,coef_tour_mode_hov_1plusstops_univ +util_tour_mode_transit,Tour mode is transit,tour_mode_is_transit,,coef_tour_mode_transit_1plusstops_univ,coef_tour_mode_transit_1plusstops_univ,coef_tour_mode_transit_1plusstops_univ,coef_tour_mode_transit_1plusstops_univ,coef_tour_mode_transit_1plusstops_univ,coef_tour_mode_transit_1plusstops_univ,coef_tour_mode_transit_1plusstops_univ,coef_tour_mode_transit_1plusstops_univ,coef_tour_mode_transit_1plusstops_univ,coef_tour_mode_transit_1plusstops_univ,coef_tour_mode_transit_1plusstops_univ,coef_tour_mode_transit_1plusstops_univ,coef_tour_mode_transit_1plusstops_univ,coef_tour_mode_transit_1plusstops_univ,coef_tour_mode_transit_1plusstops_univ +util_tour_duration_in_hours,Tour duration in hours,(duration / 2),,coef_tour_duration_in_hours_1plusstops_univ,coef_tour_duration_in_hours_1plusstops_univ,coef_tour_duration_in_hours_1plusstops_univ,coef_tour_duration_in_hours_1plusstops_univ,coef_tour_duration_in_hours_1plusstops_univ,coef_tour_duration_in_hours_1plusstops_univ,coef_tour_duration_in_hours_1plusstops_univ,coef_tour_duration_in_hours_1plusstops_univ,coef_tour_duration_in_hours_1plusstops_univ,coef_tour_duration_in_hours_1plusstops_univ,coef_tour_duration_in_hours_1plusstops_univ,coef_tour_duration_in_hours_1plusstops_univ,coef_tour_duration_in_hours_1plusstops_univ,coef_tour_duration_in_hours_1plusstops_univ,coef_tour_duration_in_hours_1plusstops_univ +util_no_stops_to_school_escorting,Do not allow stops for school escort half-tour -- outbound,"(school_esc_outbound.isin(['ride_share', 'pure_escort']))",,,,,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable +util_no_stops_from_school_escorting,Do not allow stops for school escort half-tour -- inbound,"(school_esc_inbound.isin(['ride_share', 'pure_escort']))",,coef_unavailable,coef_unavailable,coef_unavailable,,coef_unavailable,coef_unavailable,coef_unavailable,,coef_unavailable,coef_unavailable,coef_unavailable,,coef_unavailable,coef_unavailable,coef_unavailable +util_nostop_drive_transit,no stops on pnr knr tnc transit tours,"@(df.tour_mode.isin(['PNR_TRANSIT','KNR_TRANSIT']))",,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable +util_abm3_calibration_constants,calibration constants,is_joint==0,0.37144,-0.03316,-0.64012,-5.3232,0.6633,-1.8947,-3.6886,-3.3084,0.3413,-15.5376,-20.0,-10.0,-0.2133,-25.0,-25.0,1.4841 diff --git a/resident/configs/stop_frequency_work.csv b/resident/configs/stop_frequency_work.csv new file mode 100644 index 0000000..199bdd7 --- /dev/null +++ b/resident/configs/stop_frequency_work.csv @@ -0,0 +1,19 @@ +Label,Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in +util_alternative_specific_constants,Alternative specific constants,1,,coef_alternative_specific_constants_0out_1in_work,coef_alternative_specific_constants_0out_2in_work,coef_alternative_specific_constants_0out_3in_work,coef_alternative_specific_constants_1out_0in_work,coef_alternative_specific_constants_1out_1in_work,coef_alternative_specific_constants_1out_2in_work,coef_alternative_specific_constants_1out_3in_work,coef_alternative_specific_constants_2out_0in_work,coef_alternative_specific_constants_2out_1in_work,coef_alternative_specific_constants_2plusout_2plusin_work,coef_alternative_specific_constants_2plusout_2plusin_work,coef_alternative_specific_constants_3out_0in_work,coef_alternative_specific_constants_3out_1in_work,coef_alternative_specific_constants_2plusout_2plusin_work,coef_alternative_specific_constants_2plusout_2plusin_work +util_is_parttime_worker,Person is part-time worker,(ptype == 2),,coef_is_parttime_worker_0out_1plusin_work,coef_is_parttime_worker_0out_1plusin_work,coef_is_parttime_worker_0out_1plusin_work,,coef_is_parttime_worker_1plusout_1plusin_work,coef_is_parttime_worker_1plusout_1plusin_work,coef_is_parttime_worker_1plusout_1plusin_work,,coef_is_parttime_worker_1plusout_1plusin_work,coef_is_parttime_worker_1plusout_1plusin_work,coef_is_parttime_worker_1plusout_1plusin_work,,coef_is_parttime_worker_1plusout_1plusin_work,coef_is_parttime_worker_1plusout_1plusin_work,coef_is_parttime_worker_1plusout_1plusin_work +util_age_35_to_44,Age Group - 35 yrs to 44 yrs,(age >= 35) & (age < 45),,,,,,coef_age_35_to_44_1plusout_1plusin_work,coef_age_35_to_44_1plusout_1plusin_work,coef_age_35_to_44_1plusout_1plusin_work,,coef_age_35_to_44_1plusout_1plusin_work,coef_age_35_to_44_1plusout_1plusin_work,coef_age_35_to_44_1plusout_1plusin_work,,coef_age_35_to_44_1plusout_1plusin_work,coef_age_35_to_44_1plusout_1plusin_work,coef_age_35_to_44_1plusout_1plusin_work +util_age_45_to_54,Age Group - 45 yrs to 54 yrs,(age >= 45) & (age < 55),,,,,,coef_age_45_to_54_1plusout_1plusin_work,coef_age_45_to_54_1plusout_1plusin_work,coef_age_45_to_54_1plusout_1plusin_work,,coef_age_45_to_54_1plusout_1plusin_work,coef_age_45_to_54_1plusout_1plusin_work,coef_age_45_to_54_1plusout_1plusin_work,,coef_age_45_to_54_1plusout_1plusin_work,coef_age_45_to_54_1plusout_1plusin_work,coef_age_45_to_54_1plusout_1plusin_work +util_age_55_to_64,Age Group - 55 yrs to 64 yrs,(age >= 55) & (age < 65),,coef_age_55_to_64_0out_1plusin_work,coef_age_55_to_64_0out_1plusin_work,coef_age_55_to_64_0out_1plusin_work,coef_age_55_to_64_1plusout_0in_work,coef_age_55_to_64_1plusout_1plusin_work,coef_age_55_to_64_1plusout_1plusin_work,coef_age_55_to_64_1plusout_1plusin_work,coef_age_55_to_64_1plusout_0in_work,coef_age_55_to_64_1plusout_1plusin_work,coef_age_55_to_64_1plusout_1plusin_work,coef_age_55_to_64_1plusout_1plusin_work,coef_age_55_to_64_1plusout_0in_work,coef_age_55_to_64_1plusout_1plusin_work,coef_age_55_to_64_1plusout_1plusin_work,coef_age_55_to_64_1plusout_1plusin_work +util_preschool_child_in_hh,Presence of preschooler in household other than self,has_preschool_kid,,,,,coef_preschool_child_in_hh_1out_0in_work,coef_preschool_child_in_hh_1plusout_1plusin_work,coef_preschool_child_in_hh_1plusout_1plusin_work,coef_preschool_child_in_hh_1plusout_1plusin_work,coef_preschool_child_in_hh_2plusout_0in_work,coef_preschool_child_in_hh_1plusout_1plusin_work,coef_preschool_child_in_hh_1plusout_1plusin_work,coef_preschool_child_in_hh_1plusout_1plusin_work,coef_preschool_child_in_hh_2plusout_0in_work,coef_preschool_child_in_hh_1plusout_1plusin_work,coef_preschool_child_in_hh_1plusout_1plusin_work,coef_preschool_child_in_hh_1plusout_1plusin_work +util_school_child_in_hh,Presence of school age child in household other than self,has_school_kid,,,,,coef_school_child_in_hh_1out_0in_work,coef_school_child_in_hh_1out_1in_work,coef_school_child_in_hh_1out_2in_work,coef_school_child_in_hh_1out_3in_work,coef_school_child_in_hh_2plusout_0in_work,coef_school_child_in_hh_2plusout_1plusin_work,coef_school_child_in_hh_2plusout_1plusin_work,coef_school_child_in_hh_2plusout_1plusin_work,coef_school_child_in_hh_2plusout_0in_work,coef_school_child_in_hh_2plusout_1plusin_work,coef_school_child_in_hh_2plusout_1plusin_work,coef_school_child_in_hh_2plusout_1plusin_work +util_non_worker_in_hh,Presence of non workers in household other than self,has_non_worker,,coef_non_worker_in_hh_0out_1in_work,coef_non_worker_in_hh_0out_2in_work,coef_non_worker_in_hh_0out_3in_work,,coef_non_worker_in_hh_1plusout_1plusin_work,coef_non_worker_in_hh_1plusout_1plusin_work,coef_non_worker_in_hh_1plusout_1plusin_work,,coef_non_worker_in_hh_1plusout_1plusin_work,coef_non_worker_in_hh_1plusout_1plusin_work,coef_non_worker_in_hh_1plusout_1plusin_work,,coef_non_worker_in_hh_1plusout_1plusin_work,coef_non_worker_in_hh_1plusout_1plusin_work,coef_non_worker_in_hh_1plusout_1plusin_work +util_non_mandatory_accessibility,Disaggreage accessibility at home location to non-mandatory location,(othdiscr_accessibility + shopping_accessibility) / 2,,,,,coef_non_mandatory_accessibility_1plusout_0in_work,,,,coef_non_mandatory_accessibility_1plusout_0in_work,,,,coef_non_mandatory_accessibility_1plusout_0in_work,,, +util_tour_mode_sov,Tour mode is sov,tour_mode_is_sov,,coef_tour_mode_sov_0out_1in_work,coef_tour_mode_sov_0out_2in_work,coef_tour_mode_sov_0out_3in_work,coef_tour_mode_sov_1plusout_0in_work,coef_tour_mode_sov_1plusout_1plusin_work,coef_tour_mode_sov_1plusout_1plusin_work,coef_tour_mode_sov_1plusout_1plusin_work,coef_tour_mode_sov_1plusout_0in_work,coef_tour_mode_sov_1plusout_1plusin_work,coef_tour_mode_sov_1plusout_1plusin_work,coef_tour_mode_sov_1plusout_1plusin_work,coef_tour_mode_sov_1plusout_0in_work,coef_tour_mode_sov_1plusout_1plusin_work,coef_tour_mode_sov_1plusout_1plusin_work,coef_tour_mode_sov_1plusout_1plusin_work +util_tour_mode_hov,Tour mode is hov,tour_mode_is_hov,,coef_tour_mode_hov_0out_1in_work,coef_tour_mode_hov_0out_2in_work,coef_tour_mode_hov_0out_3in_work,coef_tour_mode_hov_1out_0in_work,coef_tour_mode_hov_1out_1in_work,coef_tour_mode_hov_1out_2in_work,coef_tour_mode_hov_1out_3in_work,coef_tour_mode_hov_2out_0in_work,coef_tour_mode_hov_2out_1in_work,coef_tour_mode_hov_2plusout_2plusin_work,coef_tour_mode_hov_2plusout_2plusin_work,coef_tour_mode_hov_3out_0in_work,coef_tour_mode_hov_3out_1in_work,coef_tour_mode_hov_2plusout_2plusin_work,coef_tour_mode_hov_2plusout_2plusin_work +util_tour_duration_in_hours,Tour duration in hours,(duration / 2),,coef_tour_duration_in_hours_0out_1in_work,coef_tour_duration_in_hours_0out_2in_work,coef_tour_duration_in_hours_0out_3in_work,coef_tour_duration_in_hours_1out_0in_work,coef_tour_duration_in_hours_1out_1in_work,coef_tour_duration_in_hours_1out_2in_work,coef_tour_duration_in_hours_1out_3in_work,coef_tour_duration_in_hours_2plusout_0in_work,coef_tour_duration_in_hours_2out_1in_work,coef_tour_duration_in_hours_2plusout_2plusin_work,coef_tour_duration_in_hours_2plusout_2plusin_work,coef_tour_duration_in_hours_2plusout_0in_work,coef_tour_duration_in_hours_3out_1in_work,coef_tour_duration_in_hours_2plusout_2plusin_work,coef_tour_duration_in_hours_2plusout_2plusin_work +util_distance_to_tour_destination,Distance from origin to tour destination in miles,distance_in_miles,,coef_distance_to_tour_destination_0out_1plusin_work,coef_distance_to_tour_destination_0out_1plusin_work,coef_distance_to_tour_destination_0out_1plusin_work,coef_distance_to_tour_destination_1plusout_0in_work,coef_distance_to_tour_destination_1plusout_1plusin_work,coef_distance_to_tour_destination_1plusout_1plusin_work,coef_distance_to_tour_destination_1plusout_1plusin_work,coef_distance_to_tour_destination_1plusout_0in_work,coef_distance_to_tour_destination_1plusout_1plusin_work,coef_distance_to_tour_destination_1plusout_1plusin_work,coef_distance_to_tour_destination_1plusout_1plusin_work,coef_distance_to_tour_destination_1plusout_0in_work,coef_distance_to_tour_destination_1plusout_1plusin_work,coef_distance_to_tour_destination_1plusout_1plusin_work,coef_distance_to_tour_destination_1plusout_1plusin_work +util_telecommute_2_4_days_week,Person telecommutes 2 to 4 days,(telecommute_frequency == '2_4_days_week'),,,,,,coef_telecommute_2_4_days_week_1plusout_1plusin_work,coef_telecommute_2_4_days_week_1plusout_1plusin_work,coef_telecommute_2_4_days_week_1plusout_1plusin_work,,coef_telecommute_2_4_days_week_1plusout_1plusin_work,coef_telecommute_2_4_days_week_1plusout_1plusin_work,coef_telecommute_2_4_days_week_1plusout_1plusin_work,,coef_telecommute_2_4_days_week_1plusout_1plusin_work,coef_telecommute_2_4_days_week_1plusout_1plusin_work,coef_telecommute_2_4_days_week_1plusout_1plusin_work +util_no_stops_to_school_escorting,Do not allow stops for school escort half-tour -- outbound,"(school_esc_outbound.isin(['ride_share', 'pure_escort']))",,,,,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable +util_no_stops_from_school_escorting,Do not allow stops for school escort half-tour -- inbound,"(school_esc_inbound.isin(['ride_share', 'pure_escort']))",,coef_unavailable,coef_unavailable,coef_unavailable,,coef_unavailable,coef_unavailable,coef_unavailable,,coef_unavailable,coef_unavailable,coef_unavailable,,coef_unavailable,coef_unavailable,coef_unavailable +util_nostop_drive_transit,no stops on pnr knr tnc transit tours,"@(df.tour_mode.isin(['PNR_TRANSIT','KNR_TRANSIT']))",,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable +util_abm3_calibration_constants,calibration constants,is_joint==0,-0.1946,0.5187,1.00488,0.29090,-0.46260,-0.40521,0.90284,0.009135,-1.9785,0.6807,2.2795,2.3279,-1.1649,0.7008,-7.2781,2.91850 diff --git a/resident/configs/telecommute_frequency.csv b/resident/configs/telecommute_frequency.csv new file mode 100644 index 0000000..871e065 --- /dev/null +++ b/resident/configs/telecommute_frequency.csv @@ -0,0 +1,32 @@ +Label,Description,Expression,No_Telecommute,1_day_week,2_3_days_week,4_days_week +util_HasChildren0to5,Has children 0 to 5 years old,@df.num_young_children>0,,coef_HasChildren0to5_1day,coef_HasChildren0to5_234day,coef_HasChildren0to5_234day +util_HasChildren6to12,Has children 6 to 12 years old,@df.num_children_6_to_12>0,,coef_HasChildren6to12_1day,coef_HasChildren6to12_23day,coef_HasChildren6to12_4day +util_OneAdultInHH,One adult in hh,@df.num_adults==1,,coef_OneAdultInHH_1day,coef_OneAdultInHH_23day,coef_OneAdultInHH_4day +util_Female,female,@df.female,,coef_Female_1234day,coef_Female_1234day,coef_Female_1234day +util_PartTimeWorker,Part-time worker,@df.pemploy==2,,coef_PartTimeWorker_1234day,coef_PartTimeWorker_1234day,coef_PartTimeWorker_1234day +util_Income60to100k,Income 60-100k,"@df.income.between(60000, 100000)",,coef_Income60to100k_1day,coef_Income60to100k_23day,coef_Income60to100k_4day +util_Income100to150k,Income 100-150k,"@df.income.between(100000, 150000)",,coef_Income100to150k_1day,coef_Income100to150k_234day,coef_Income100to150k_234day +util_Income150kplus,Income 150k+,@df.income > 150000,,coef_Income150kplus_1day,coef_Income150kplus_23day,coef_Income150kplus_4day +util_0Autos,0 Autos,@df.auto_ownership==0,,coef_0Autos_1day,coef_0Autos_234day,coef_0Autos_234day +util_1Auto,1 Auto,@df.auto_ownership==1,,coef_1Auto_1day,coef_1Auto_234day,coef_1Auto_234day +util_3plusAutos,3+ Autos,@df.auto_ownership>=3,,coef_3plusAutos_1day,coef_3plusAutos_23day,coef_3plusAutos_4day +util_DistanceToWork,Distance to work,@df.distance_to_work,,coef_DistanceToWork_1day,coef_DistanceToWork_234day,coef_DistanceToWork_234day +util_calib_2020,scenario year is 2020,@scenarioYear==2020,,coef_calib_2020_1day,coef_calib_2020_23day,coef_calib_2020_4day +util_calib_2025,scenario year is 2025,@scenarioYear==2025,,coef_calib_2025_1day,coef_calib_2025_23day,coef_calib_2025_4day +util_calib_2035,scenario year is 2035,@scenarioYear==2035,,coef_calib_2035_1day,coef_calib_2035_23day,coef_calib_2035_4day +util_calib_2050,scenario year is 2050,@scenarioYear==2050,,coef_calib_2050_1day,coef_calib_2050_23day,coef_calib_2050_4day +util_2016,Model year is 2016,@PRE_COVID,,coef_2016_1day,coef_2016_23day,coef_2016_4day +util_accomodation,Accomodation industry,@df.naics_code==721,,coef_accomodation_1234day,coef_accomodation_1234day,coef_accomodation_1234day +util_agriculture,Agriculture industry,@df.naics_code==11,,coef_agriculture_1234day,coef_agriculture_1234day,coef_agriculture_1234day +util_business_srv,Business services industry,@df.naics_code==54,,coef_business_srv_1day,coef_business_srv_23day,coef_business_srv_4day +util_construction,Construction industry,@df.naics_code==23,,coef_construction_1day,coef_construction_234day,coef_construction_234day +util_education,Education industry,@df.naics_code==61,,coef_education_1234day,coef_education_1234day,coef_education_1234day +util_entertainment,Entertainment industry,@df.naics_code==71,,coef_entertainment_1day,coef_entertainment_23day,coef_entertainment_4day +util_food_srv,Food services industry,@df.naics_code==722,,coef_food_srv_1234day,coef_food_srv_1234day,coef_food_srv_1234day +util_government,Government industry,@df.naics_code==92,,coef_government_1day,coef_government_234day,coef_government_234day +util_healthcare,Healthcare industry,@df.naics_code==62,,coef_healthcare_1234day,coef_healthcare_1234day,coef_healthcare_1234day +util_manufacturing,Manufacturing industry,"@df.naics_code.isin([31,32,33])",,coef_manufacturing_1day,coef_manufacturing_234day,coef_manufacturing_234day +util_mgmt_srv,Management services industry,@df.naics_code==55,,coef_mgmt_srv_1day,coef_mgmt_srv_23day,coef_mgmt_srv_4day +util_military,Miliary industry,@df.naics_code==9000,,coef_military_1day,coef_military_234day,coef_military_234day +util_retail,Retail industry,"@df.naics_code.isin([44,45])",,coef_retail_1day,coef_retail_23day,coef_retail_4day +util_asc,Alternative specific constant,1,,asc_1day,asc_23day,asc_4day diff --git a/resident/configs/telecommute_frequency.yaml b/resident/configs/telecommute_frequency.yaml new file mode 100644 index 0000000..162066e --- /dev/null +++ b/resident/configs/telecommute_frequency.yaml @@ -0,0 +1,9 @@ + +# borrowed from free parking model + +SPEC: telecommute_frequency.csv +COEFFICIENTS: telecommute_frequency_coeffs.csv + +#LOGIT_TYPE: NL +LOGIT_TYPE: MNL + diff --git a/resident/configs/telecommute_frequency_coeffs.csv b/resident/configs/telecommute_frequency_coeffs.csv new file mode 100644 index 0000000..089828f --- /dev/null +++ b/resident/configs/telecommute_frequency_coeffs.csv @@ -0,0 +1,100 @@ +coefficient_name,value,constrain +coef_Services_1day,0.0,F +coef_SalesOffice_1day,0.0,F +coef_ResourceConstruct_1day,0.0,F +coef_TransportMat_1day,0.0,F +coef_HasChildren0to5_1day,0.0,T +coef_HasChildren6to12_1day,0.0,T +coef_OneAdultInHH_1day,0.2307987638695857,F +coef_2plusAdultsInHH_1day,0.0,F +coef_Female_1234day,0.0,T +coef_PartTimeWorker_1234day,-0.1647657054647985,F +coef_PaysToPark_1day,0.0,F +coef_Income60to100k_1day,0.3549582138983352,F +coef_Income100to150k_1day,0.0,T +coef_Income150kplus_1day,0.0,T +coef_0Autos_1day,0.0,T +coef_1Auto_1day,-0.2037126296304744,F +coef_3plusAutos_1day,-0.2366548258122986,F +coef_DistanceToWork_1day,0.0065817284180437,F +coef_Services_23day,0.0,F +coef_SalesOffice_23day,0.0,F +coef_ResourceConstruct_23day,0.0,F +coef_TransportMat_23day,0.0,F +coef_HasChildren0to5_234day,-0.3282697175018726,F +coef_HasChildren6to12_23day,0.0,T +coef_OneAdultInHH_23day,0.0,T +coef_2plusAdultsInHH_23day,0.0,F +coef_PaysToPark_23day,0.0,F +coef_Income60to100k_23day,0.0,T +coef_Income100to150k_234day,0.0,T +coef_Income150kplus_23day,0.0,T +coef_0Autos_234day,0.0,T +coef_1Auto_234day,0.0,T +coef_3plusAutos_23day,-0.2673431713437322,F +coef_DistanceToWork_234day,0.011270292473366,F +coef_HasChildren6to12_4day,0.0,T +coef_OneAdultInHH_4day,0.0,T +coef_2plusAdultsInHH_4day,0.0,F +coef_PaysToPark_4day,0.0,F +coef_Income60to100k_4day,0.0,T +coef_Income100to150k_4day,0.0,T +coef_Income150kplus_4day,0.0,T +coef_3plusAutos_4day,-0.5086281851316395,F +coef_calib_2020_1day,0.0,T +coef_calib_2025_1day,0.0,T +coef_calib_2035_1day,0.0,T +coef_calib_2050_1day,0.0,T +coef_calib_2020_23day,0.0,T +coef_calib_2025_23day,0.0,T +coef_calib_2035_23day,0.0,T +coef_calib_2050_23day,0.0,T +coef_calib_2020_4day,0.0,T +coef_calib_2025_4day,0.0,T +coef_calib_2035_4day,0.0,T +coef_calib_2050_4day,0.0,T +coef_2016_1day,-0.1247850665609524,F +coef_accomodation_1234day,-1.1672033007413072,F +coef_agriculture_1234day,0.0,T +coef_business_srv_1day,0.8048779013830238,F +coef_construction_1day,0.0,T +coef_education_1234day,-0.0387511815872183,F +coef_entertainment_1day,0.0,T +coef_food_srv_1day,0.0,F +coef_government_1day,0.4503818768273094,F +coef_healthcare_1day,0.0,F +coef_manufacturing_1day,0.0,T +coef_mgmt_srv_1day,0.0,T +coef_military_1day,0.0,T +coef_retail_1day,-0.8320341753416274,F +coef_2016_23day,-1.138520687534541,F +coef_accomodation_23day,0.0,F +coef_agriculture_23day,0.0,F +coef_business_srv_23day,0.9228435480770156,F +coef_construction_234day,0.0,T +coef_education_23day,0.0,F +coef_entertainment_23day,0.0,T +coef_food_srv_1234day,-1.6057467385857538,F +coef_government_234day,0.0,T +coef_healthcare_1234day,-0.2529974908266847,F +coef_manufacturing_234day,0.0,T +coef_mgmt_srv_23day,0.5723496481424273,F +coef_military_234day,-0.512646790150494,F +coef_retail_23day,-0.7576291501959187,F +coef_2016_4day,-3.7059529398972826,F +coef_accomodation_4day,0.0,F +coef_agriculture_4day,0.0,F +coef_business_srv_4day,1.117777186998694,F +coef_construction_4day,0.0,F +coef_education_4day,0.0,F +coef_entertainment_4day,0.0,T +coef_food_srv_4day,0.0,F +coef_government_4day,0.0,F +coef_healthcare_4day,0.0,F +coef_manufacturing_4day,0.0,F +coef_mgmt_srv_4day,0.970302573177628,F +coef_military_4day,0.0,F +coef_retail_4day,-2.2338993784519388,F +asc_1day,-2.7588707215613267,F +asc_23day,-1.7439620734289143,F +asc_4day,-2.157902150190833,F diff --git a/resident/configs/tour_departure_and_duration_alternatives.csv b/resident/configs/tour_departure_and_duration_alternatives.csv new file mode 100644 index 0000000..30ff417 --- /dev/null +++ b/resident/configs/tour_departure_and_duration_alternatives.csv @@ -0,0 +1,1177 @@ +start,end +1,1 +1,2 +1,3 +1,4 +1,5 +1,6 +1,7 +1,8 +1,9 +1,10 +1,11 +1,12 +1,13 +1,14 +1,15 +1,16 +1,17 +1,18 +1,19 +1,20 +1,21 +1,22 +1,23 +1,24 +1,25 +1,26 +1,27 +1,28 +1,29 +1,30 +1,31 +1,32 +1,33 +1,34 +1,35 +1,36 +1,37 +1,38 +1,39 +1,40 +1,41 +1,42 +1,43 +1,44 +1,45 +1,46 +1,47 +1,48 +2,2 +2,3 +2,4 +2,5 +2,6 +2,7 +2,8 +2,9 +2,10 +2,11 +2,12 +2,13 +2,14 +2,15 +2,16 +2,17 +2,18 +2,19 +2,20 +2,21 +2,22 +2,23 +2,24 +2,25 +2,26 +2,27 +2,28 +2,29 +2,30 +2,31 +2,32 +2,33 +2,34 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+39,48 +40,40 +40,41 +40,42 +40,43 +40,44 +40,45 +40,46 +40,47 +40,48 +41,41 +41,42 +41,43 +41,44 +41,45 +41,46 +41,47 +41,48 +42,42 +42,43 +42,44 +42,45 +42,46 +42,47 +42,48 +43,43 +43,44 +43,45 +43,46 +43,47 +43,48 +44,44 +44,45 +44,46 +44,47 +44,48 +45,45 +45,46 +45,47 +45,48 +46,46 +46,47 +46,48 +47,47 +47,48 +48,48 diff --git a/resident/configs/tour_departure_and_duration_segments.csv b/resident/configs/tour_departure_and_duration_segments.csv new file mode 100644 index 0000000..03bb928 --- /dev/null +++ b/resident/configs/tour_departure_and_duration_segments.csv @@ -0,0 +1,60 @@ +tour_purpose,time_period,start,end +work,EA,1,6 +work,AM,9,10 +work,MD,13,24 +work,PM,25,27 +work,EV,33,35 +#,,, +school,EA,1,6 +school,AM,9,10 +school,MD,13,24 +school,PM,25,27 +school,EV,33,35 +#,,, +univ,EA,1,6 +univ,AM,9,10 +univ,MD,13,24 +univ,PM,25,27 +univ,EV,33,35 +#,,, +shopping,EA,1,6 +shopping,AM,9,10 +shopping,MD,13,24 +shopping,PM,25,27 +shopping,EV,33,35 +#,,, +escort,EA,1,6 +escort,AM,9,10 +escort,MD,13,24 +escort,PM,25,27 +escort,EV,33,35 +#,,, +othmaint,EA,1,6 +othmaint,AM,9,10 +othmaint,MD,13,24 +othmaint,PM,25,27 +othmaint,EV,33,35 +#,,, +othdiscr,EA,1,6 +othdiscr,AM,9,10 +othdiscr,MD,13,24 +othdiscr,PM,25,27 +othdiscr,EV,33,35 +#,,, +social,EA,1,6 +social,AM,9,10 +social,MD,13,24 +social,PM,25,27 +social,EV,33,35 +#,,, +eatout,EA,1,6 +eatout,AM,9,10 +eatout,MD,13,24 +eatout,PM,25,27 +eatout,EV,33,35 +#,,, +atwork,EA,1,6 +atwork,AM,9,10 +atwork,MD,13,24 +atwork,PM,25,27 +atwork,EV,33,35 diff --git a/resident/configs/tour_mode_choice.csv b/resident/configs/tour_mode_choice.csv new file mode 100644 index 0000000..11272f0 --- /dev/null +++ b/resident/configs/tour_mode_choice.csv @@ -0,0 +1,202 @@ +Label,Description,Expression,DRIVEALONE,SHARED2,SHARED3,WALK,BIKE,WALK_TRANSIT,PNR_TRANSIT,KNR_TRANSIT,BIKE_TRANSIT,TAXI,TNC_SINGLE,TNC_SHARED,SCH_BUS,EBIKE,ESCOOTER +#,drivealone,,,,,,,,,,,,,,,, +util_DRIVEALONE_Unavailable,DRIVEALONE - Unavailable,sov_available == False,-999,,,,,,,,,,,,,, +util_Drive alone - In-vehicle time,Drive alone - In-vehicle time,@(df.da_time_skims_inb + df.da_time_skims_out) * df.autoIVTFactor_DA * df.time_factor,coef_ivt,,,,,,,,,,,,,, +util_Drive alone - cost - inb,Drive alone - cost - inbound,@(df.sov_auto_op_cost * df.autoCPMFactor_DA * df.da_dist_skims_inb + df.da_cost_skims_inb)/df.cost_sensitivity,coef_income,,,,,,,,,,,,,, +util_Drive alone - cost - out,Drive alone - cost - outbound,@(df.sov_auto_op_cost * df.autoCPMFactor_DA * df.da_dist_skims_out + df.da_cost_skims_out)/df.cost_sensitivity,coef_income,,,,,,,,,,,,,, +util_Drive alone - Parking cost ,Drive alone - Parking cost ,@(df.parkingCost*df.autoParkingCostFactor_DA)/df.cost_sensitivity,coef_income,,,,,,,,,,,,,, +util_Drive alone - Terminal Time - acc,Drive alone - Terminal Time ,@(df.oTermTime + df.dTermTime) * df.autoTermTimeFactor_DA * df.time_factor,coef_acctime,,,,,,,,,,,,,, +#,Shared ride2,,,,,,,,,,,,,,,, +util_Shared2_Unavailable,Shared ride 2 - Unavailable,sr2_available == False,,-999,,,,,,,,,,,,, +util_Shared ride 2 - In -vehicle time,Shared ride 2 - In -vehicle time,@(df.s2_time_skims_inb + df.s2_time_skims_out) * df.autoIVTFactor_SR2 * df.time_factor,,coef_ivt,,,,,,,,,,,,, +util_Shared ride 2 - cost - inb,Shared ride 2 - cost - inbound,@(df.sr2_auto_op_cost * df.autoCPMFactor_SR2 * df.s2_dist_skims_inb + df.s2_cost_skims_inb)/df.cost_sensitivity,,coef_income,,,,,,,,,,,,, +util_Shared ride 2 - cost - out,Shared ride 2 - cost - outbound,@(df.sr2_auto_op_cost * df.autoCPMFactor_SR2* df.s2_dist_skims_out + df.s2_cost_skims_out)/df.cost_sensitivity,,coef_income,,,,,,,,,,,,, +util_Shared ride 2 - Parking cost ,Shared ride 2 - Parking cost ,"@(df.parkingCost * df.autoParkingCostFactor_SR2 * np.where(df.is_joint,1,df.costFactorS2))/df.cost_sensitivity",,coef_income,,,,,,,,,,,,, +util_Drive alone - Terminal Time - acc,Shared ride 2 - Terminal Time - access,@(df.oTermTime + df.dTermTime) * df.autoTermTimeFactor_SR2 * df.time_factor,,coef_acctime,,,,,,,,,,,,, +util_SHARED2_Two_person_household,Shared ride 2- One person household,@(df.hhsize == 2),,coef_hhsize2_sr2,,,,,,,,,,,,, +util_SHARED RIDE 2_Three_person_household,Shared ride 2 - Two person household,@(df.hhsize == 3),,coef_hhsize3_sr2,,,,,,,,,,,,, +util_SHARED RIDE 2_Three+_person_household,Shared ride 2 - Two person household,@(df.hhsize > 3),,coef_hhsize4p_sr2,,,,,,,,,,,,, +util_SHARED RIDE 2_Person_is_16_24_years_old_or_older,Shared ride 2- Person is 16 to 24 years old or older,@(df.age >= 16) & (df.age <= 24),,coef_age1624_sr2,,,,,,,,,,,,, +util_SHARED RIDE 2_Person_is_41_55_years_old_or_older,Shared ride 2- Person is 41 to 55 years old or older,@(df.age >= 41) & (df.age <= 55),,coef_age4155_sr2,,,,,,,,,,,,, +util_SHARED RIDE 2_Person_is_56_64_years_old_or_older,Shared ride 2- Person is 56 to 64 years old or older,@(df.age >= 56) & (df.age <= 64),,coef_age5664_sr2,,,,,,,,,,,,, +util_SHARED RIDE 2_Person_is_65+_years_old_or_older,Shared ride 2- Person is more than 65 years old or older,@(df.age >= 65),,coef_age65pl_sr2,,,,,,,,,,,,, +util_Shared ride 2 - Female,Shared ride 2 - female,@(df.female == 1),,coef_female_sr2,,,,,,,,,,,,, +#,shared ride 3,,,,,,,,,,,,,,,, +util_Shared ride 3 - In -vehicle time,Shared ride 3 - In -vehicle time,@(df.s3_time_skims_out + df.s3_time_skims_inb) * df.autoIVTFactor_SR3 * df.time_factor,,,coef_ivt,,,,,,,,,,,, +util_Shared ride 3 - cost - inb,Shared ride 3 - cost - inbound,@(df.sr3p_auto_op_cost * df.s3_dist_skims_inb * df.autoCPMFactor_SR3 + df.s3_cost_skims_inb)/df.cost_sensitivity,,,coef_income,,,,,,,,,,,, +util_Shared ride 3 - cost - out,Shared ride 3 - cost - outbound,@(df.sr3p_auto_op_cost * df.s3_dist_skims_out * df.autoCPMFactor_SR3 + df.s3_cost_skims_out)/df.cost_sensitivity,,,coef_income,,,,,,,,,,,, +util_Shared ride 3 - Parking cost ,Shared ride 3 - Parking cost ,"@(df.parkingCost * df.autoParkingCostFactor_SR3 * np.where(df.is_joint,1,df.costFactorS3))/df.cost_sensitivity",,,coef_income,,,,,,,,,,,, +util_Shared ride 3 - Terminal Time - acc,Shared ride 3 - Terminal Time - acc,@(df.oTermTime + df.dTermTime) * df.autoTermTimeFactor_SR3 * df.time_factor,,,coef_acctime,,,,,,,,,,,, +util_SHARED RIDe 3_Two_person_household,Shared ride 3- One person household,@(df.hhsize == 2),,,coef_hhsize2_sr3p,,,,,,,,,,,, +util_SHARED RIDe 3_Three_person_household,Shared ride 3 - Two person household,@(df.hhsize == 3),,,coef_hhsize3_sr3p,,,,,,,,,,,, +util_SHARED RIDe 3_Three+_person_household,Shared ride 3 - Two person household,@(df.hhsize > 3),,,coef_hhsize4p_sr3p,,,,,,,,,,,, +util_SHARED RIDe 3_Person_is_16_24_years_old_or_older,Shared ride 3 - Person is 16 to 24 years old or older,@(df.age >= 16) & (df.age <= 24),,,coef_age1624_sr3p,,,,,,,,,,,, +util_SHARED RIDe 3_Person_is_41_55_years_old_or_older,Shared ride 3 - Person is 41 to 55 years old or older,@(df.age >= 41) & (df.age <= 55),,,coef_age4155_sr3p,,,,,,,,,,,, +util_SHARED RIDe 3_Person_is_56_64_years_old_or_older,Shared ride 3 - Person is 56 to 64 years old or older,@(df.age >= 56) & (df.age <= 64),,,coef_age5664_sr3p,,,,,,,,,,,, +util_SHARED RIDe 3_Person_is_65+_years_old_or_older,Shared ride 3 - Person is more than 65 years old or older,@(df.age >= 65),,,coef_age65pl_sr3p,,,,,,,,,,,, +util_Shared ride 3 - Female,Shared ride 3 - female,@(df.female == 1),,,coef_female_sr3p,,,,,,,,,,,, +#,Walk,,,,,,,,,,,,,,,, +util_Walk - Unavailable,Walk - Unavailable,walkAvailable==0,,,,-999,,,,,,,,,,, +util_Walk - Age under 16,Walk - Age under 16,@(df.age < 16),,,,coef_ageund16_walk,,,,,,,,,,, +util_Walk - Age 16 to 24,Walk - Age 16 to 24,@(df.age > 15) & (df.age < 25) ,,,,coef_age1624_walk,,,,,,,,,,, +util_Walk - Age 41 to 55,Walk - Age 41 to 55,@(df.age > 40) & (df.age < 56),,,,coef_age4155_walk,,,,,,,,,,, +util_Walk - Age 56 to 64,Walk - Age 56 to 64,@(df.age > 55) & (df.age < 65),,,,coef_age5664_walk,,,,,,,,,,, +util_Walk - Age 65 plus,Walk - Age 65 plus,@(df.age > 64),,,,coef_age65pl_walk,,,,,,,,,,, +util_Walk - Female,Walk - Female,@df.female,,,,coef_female_walk,,,,,,,,,,, +util_Walk - Income 60k to 100k,Walk - Income 60k to 100k,@(df.income > 59999) * (df.income < 100000),,,,coef_inc60100_walk,,,,,,,,,,, +util_Walk - Normalized Landuse Variable Sum [Origin Intersection + DU],Walk - Normalized Landuse Variable Sum [Origin Intersection + DU],LUVarsNormalized_walk,,,,coef_LUnorm_walk,,,,,,,,,,, +"util_Walk - Normalized Destination Employment Density, walk","Walk - Normalized Destination Employment Density, walk",dMGRAEmpDenNorm_walk,,,,coef_dEmpNorm_walk,,,,,,,,,,, +util_Walk - time coefficient,Walk - time coefficient,@(df.walk_time_skims_inb + df.walk_time_skims_out) * df.time_factor,,,,coef_walktime,,,,,,,,,,, +#,Bike,,,,,,,,,,,,,,,, +util_BIKE_Mode unavailable,Bike - Mode unavailable,bikeAvailable == 0,,,,,-999,,,,,,,,,, +"util_BIKE_Age under 16, bike","Bike - Age under 16, bike",@(df.age < 16),,,,,coef_ageund16_bike,,,,,,,,,, +"util_BIKE_Age 16 to 24, bike","Bike - Age 16 to 24, bike",@(df.age > 15) & (df.age < 25),,,,,coef_age1624_bike,,,,,,,,,, +"util_BIKE_Age 41 to 55, bike","Bike - Age 41 to 55, bike",@(df.age > 40) & (df.age < 56),,,,,coef_age4155_bike,,,,,,,,,, +"util_BIKE_Age 56 to 64, bike","Bike - Age 56 to 64, bike",@(df.age > 55) & (df.age < 65),,,,,coef_age5664_bike,,,,,,,,,, +"util_BIKE_Age 65 plus, bike","Bike - Age 65 plus, bike",@(df.age > 64),,,,,coef_age65pl_bike,,,,,,,,,, +"util_BIKE_Female, bike","Bike - Female, bike",@df.female,,,,,coef_female_bike,,,,,,,,,, +"util_BIKE_Income 100k plus, bike","Bike - Income 100k plus, bike",@(df.income > 99999),,,,,coef_inc100plus_bike,,,,,,,,,, +util_BIKE_Normalized Landuse Variable [Origin Employment + DU],Bike - - Normalized Landuse Variable [Origin Employment + DU],LUVarsNormalized_bike,,,,,coef_LUnorm_bike,,,,,,,,,, +util_BIKE_Bike - logsum,Bike - logsum,bikeLSI + bikeLSO,,,,,coef_bikeLogsum,,,,,,,,,, +#,Walk_LOC,,,,,,,,,,,,,,,, +util_WALK_Unavailable,WalkTransit_Available,walk_local_available == 0,,,,,,-999,,,,,,,,, +util_WALK_In_vehicle_time,WALK_LOC - In-vehicle time,@(odt_skims['WTW_TIV'] + dot_skims['WTW_TIV']) * df.time_factor,,,,,,coef_ivt,,,,,,,,, +util_WALK_iwait_time,WALK_LOC - wait time,@(odt_skims['WTW_FWT'] + dot_skims['WTW_FWT']) * df.time_factor,,,,,,coef_wait,,,,,,,,, +util_WALK_transfer_wait_time,WALK_LOC - transfer wait time,@(odt_skims['WTW_XWT'] + dot_skims['WTW_XWT'])* df.time_factor,,,,,,coef_xwait,,,,,,,,, +util_WALK_transfer_waLK_time,WALK_LOC - transfer walk time,@(odt_skims['WTW_AUX'] + dot_skims['WTW_AUX'])* df.time_factor,,,,,,coef_xwalk,,,,,,,,, +util_WALK_transfers_penalty,WALK_LOC - number of transfers,"@(-23+23*np.exp(0.414*np.clip(odt_skims['WTW_XFR'] + df.mtnev_egr_xfer, a_min=None,a_max=4))) + (-23+23*np.exp(0.414*np.clip(dot_skims['WTW_XFR'] + df.mtnev_egr_xfer, a_min=None,a_max=4)))*df.time_factor",,,,,,coef_xfer,,,,,,,,, +util_WALK_Walk_access_time,WALK_LOC - Walk access,@2 * df.origin_local_time* df.time_factor,,,,,,coef_acctime,,,,,,,,, +util_WALK_Walk_egress_time,WALK_LOC - egress time,"@np.where(df.nev_local_egress_available, df.nev_local_egress_time, np.where(df.microtransit_local_egress_available, df.microtransit_local_egress_time, 2*df.dest_local_time)) * df.time_factor",,,,,,coef_acctime,,,,,,,,, +util_WALK_wait_egress_time,WALK_LOC - Egress MT/NEV wait time,"@np.where(df.nev_local_egress_available, 2*nevWaitTime, np.where(df.microtransit_local_egress_available, 2*microtransitWaitTime, 0)) * df.time_factor",,,,,,coef_wait,,,,,,,,, +# FIXME need transit fares,,,,,,,,,,,,,,,,, +#util_WTW_FARE,WALK_LOC - Fare,@df.transitSubsidyPassDiscount*(odt_skims['WTW_FARE'] + dot_skims['WTW_FARE'])*100*df.num_participants/df.cost_sensitivity,,,,,,coef_income,,,,,,,,, +util_WALK_LOC - Age 16 to 24,WALK_LOC - Age 16 to 24,@(df.age > 15) & (df.age < 25),,,,,,coef_age1624_tran,,,,,,,,, +util_WALK_LOC - Age 41 to 55,WALK_LOC - Age 41 to 55,@(df.age > 40) & (df.age < 56),,,,,,coef_age4155_tran,,,,,,,,, +util_WALK_LOC - Age 56 to 64,WALK_LOC - Age 56 to 64,@(df.age > 55) & (df.age < 65),,,,,,coef_age5664_tran,,,,,,,,, +util_WALK_LOC - Age 65+,WALK_LOC - Age 65+,@(df.age > 64),,,,,,coef_age65pl_tran,,,,,,,,, +util_WALK_LOC - Female,WALK_LOC - Female,@(df.female),,,,,,coef_female_tran,,,,,,,,, +util_WALK_LOC - Origin Mix,WALK_LOC - Origin Mix,oMGRAMix,,,,,,coef_oMix_wTran,,,,,,,,, +util_WALK_LOC - Origin Intersection Density,WALK_LOC - Origin Intersection Density,oMGRATotInt,,,,,,coef_oIntDen_wTran,,,,,,,,, +util_WALK_LOC - Destination Employment Density,WALK_LOC - Destination Employment Density,dMGRAEmpDen,,,,,,coef_dEmpDen_wTran,,,,,,,,, +#,PNR_LOC,,,,,,,,,,,,,,,, +util_PNR_Unavailable,PNR_LOC - Unavailable,"@(df.is_valid_pnr == False)|(df.get('num_escortees', 0)>0)",,,,,,,-999,,,,,,,, +util_PNRTransit_0Auto,PNRTransit_0Auto,@(df.auto_ownership==0),,,,,,,-999,,,,,,,, +util_PNR_Unavailable_for_persons_less_than_16,PNR_LOC - Unavailable for persons less than 16,age < 16,,,,,,,-999,,,,,,,, +util_PNR_In_vehicle_time,PNR_LOC - In-vehicle time,@df.transit_time_pnr * df.time_factor,,,,,,,coef_ivt,,,,,,,, +util_PNR_iwait_time,PNR_LOC - First iwait time,@df.iwait_pnr * df.time_factor,,,,,,,coef_wait,,,,,,,, +util_PNR_transfer_wait_time,PNR_LOC - transfer wait time,@df.xfer_wait_pnr * df.time_factor,,,,,,,coef_xwait,,,,,,,, +util_PNR_number_of_transfers,PNR_LOC - number of transfers,"@(-23+23*np.exp(0.414*np.clip(df.xfer_walk_pnr + df.mtnev_egr_xfer, a_min=None,a_max=4))) * df.time_factor",,,,,,,coef_xferdrive,,,,,,,, +util_PNR_PNR_time,PNR_LOC - PNR time,@(df.auto_time_pnr_out + df.auto_time_pnr_inb) * df.time_factor,,,,,,,coef_acctime,,,,,,,, +util_PNR_PNR_cost,PNR_LOC - PNR cost,@df.sov_auto_op_cost * (df.auto_dist_pnr_out + df.auto_dist_pnr_inb) / df.cost_sensitivity,,,,,,,coef_income,,,,,,,, +util_PNR_Walk_egress_time_(at_attraction_end),PNR_LOC - Walk egress time (at attraction end),"@df.walk_at_dest_end_pnr * df.time_factor",,,,,,,coef_acctime,,,,,,,, +util_PNR_wait_egress_time_(at_attraction_end),PNR_LOC - Egress mt/nev wait time (at attraction end),"@np.where(df.nev_local_egress_available, 2*nevWaitTime, np.where(df.microtransit_local_egress_available, 2*microtransitWaitTime, 0)) * df.time_factor",,,,,,,coef_wait,,,,,,,, +util_PNR_Walk_other_time,PNR_LOC - Walk other time,@df.xfer_walk_pnr * df.time_factor,,,,,,,coef_xwalk,,,,,,,, +util_PNR_Fare_and_operating_cost,PNR_LOC - Fare ,@df.transitSubsidyPassDiscount * df.fare_pnr * 100 * df.num_participants / df.cost_sensitivity,,,,,,,coef_income,,,,,,,, +util_PNR_LOC - Age 16 to 24,PNR_LOC - Age 16 to 24,@(df.age > 15) & (df.age < 25),,,,,,,coef_age1624_tran,,,,,,,, +util_PNR_LOC - Age 41 to 55,PNR_LOC - Age 41 to 55,@(df.age > 40) & (df.age < 56),,,,,,,coef_age4155_tran,,,,,,,, +util_PNR_LOC - Age 56 to 64,PNR_LOC - Age 56 to 64,@(df.age > 55) & (df.age < 65),,,,,,,coef_age5664_tran,,,,,,,, +util_PNR_LOC - Age 65+,PNR_LOC - Age 65+,@(df.age > 65),,,,,,,coef_age65pl_tran,,,,,,,, +util_PNR_LOC - Female,PNR_LOC - Female,@(df.female),,,,,,,coef_female_tran,,,,,,,, +util_PNR_LOC - Destination Employment Density,PNR_LOC - Destination Employment Density,dMGRAEmpDen,,,,,,,coef_dEmpDen_dTran,,,,,,,, +#,,,,,,,,,,,,,,,,, +util_KNR_Unavailable,KNR_LOC - Unavailable,"@(df.knr_local_available == False)|(df.get('num_escortees', 0)>0)",,,,,,,,-999,,,,,,, +util_KNR_In_vehicle_time,KNR_LOC - In-vehicle time,@(odt_skims['KTW_TIV'] + dot_skims['WTK_TIV']) *df.time_factor,,,,,,,,coef_ivt,,,,,,, +util_KNR_iwait_time,KNR_LOC - First iwait time,@(odt_skims['KTW_FWT']) + (dot_skims['WTK_FWT'])*df.time_factor,,,,,,,,coef_wait,,,,,,, +util_KNR_transfer_wait_time,KNR_LOC - transfer wait time,@(odt_skims['KTW_XWT'] + dot_skims['WTK_XWT'])*df.time_factor,,,,,,,,coef_xwait,,,,,,, +util_KNR_number_of_transfers,KNR_LOC - number of transfers,"@(-23+23*np.exp(0.414*np.clip(odt_skims['KTW_XFR'] + df.mtnev_egr_xfer, a_min=None,a_max=4))) + (-23+23*np.exp(0.414*np.clip(dot_skims['WTK_XFR'] + df.mtnev_egr_xfer, a_min=None,a_max=4))) *df.time_factor",,,,,,,,coef_xferdrive,,,,,,, +util_KNR_KNR_time,KNR_LOC - KNR time,@(odt_skims['KTW_ACC'] + dot_skims['WTK_EGR'])*df.time_factor,,,,,,,,coef_acctime,,,,,,, +util_KNR_KNR_cost,KNR_LOC - KNR cost,@(df.sr2_auto_op_cost * ((odt_skims['KTW_ACC'] + dot_skims['WTK_EGR'])/60) *driveSpeed )/df.cost_sensitivity,,,,,,,,coef_income,,,,,,, +util_KNR_Walk_egress_time_(at_attraction_end),KNR_LOC - Walk egress time (at attraction end),"@np.where(df.nev_local_egress_available, df.nev_local_egress_time, np.where(df.microtransit_local_egress_available, df.microtransit_local_egress_time, 2*df.dest_local_time)) * df.time_factor",,,,,,,,coef_acctime,,,,,,, +util_KNR_wait_egress_time_(at_attraction_end),KNR_LOC - Egress mt/nev wait time (at attraction end),"@np.where(df.nev_local_egress_available, 2*nevWaitTime, np.where(df.microtransit_local_egress_available, 2*microtransitWaitTime, 0)) * df.time_factor",,,,,,,,coef_wait,,,,,,, +util_KNR_Walk_other_time,KNR_LOC - Walk other time,@(odt_skims['KTW_AUX'] + dot_skims['WTK_AUX'])*df.time_factor,,,,,,,,coef_xwalk,,,,,,, +# FIXME need transit fares,,,,,,,,,,,,,,,,, +#util_KNR_Fare_and_operating_cost,KNR_LOC - Fare ,@df.transitSubsidyPassDiscount*(odt_skims['KTW_FARE'] + dot_skims['WTK_FARE'])*100*df.num_participants/df.cost_sensitivity,,,,,,,,coef_income,,,,,,, +util_KNR_LOC - Age 16 to 24,KNR_LOC - Age 16 to 24,@(df.age > 15) & (df.age < 25),,,,,,,,coef_age1624_tran,,,,,,, +util_KNR_LOC - Age 41 to 55,KNR_LOC - Age 41 to 55,@(df.age > 40) & (df.age < 56),,,,,,,,coef_age4155_tran,,,,,,, +util_KNR_LOC - Age 56 to 64,KNR_LOC - Age 56 to 64,@(df.age > 55) & (df.age < 65),,,,,,,,coef_age5664_tran,,,,,,, +util_KNR_LOC - Age 65+,KNR_LOC - Age 65+,@(df.age > 64),,,,,,,,coef_age65pl_tran,,,,,,, +util_KNR_LOC - Female,KNR_LOC - Female,@(df.female),,,,,,,,coef_female_tran,,,,,,, +util_KNR_LOC - Destination Employment Density,KNR_LOC - Destination Employment Density,dMGRAEmpDen,,,,,,,,coef_dEmpDen_dTran,,,,,,, +#,,,,,,,,,,,,,,,,, +util_TNC_Unavailable,TNC_LOC - Unavailable,"@(df.tnc_local_available == False)|(df.get('num_escortees', 0)>0)",,,,,,,,,-999,,,,,, +util_TNC_Unavailable_for_persons_less_than_16,TNC_LOC - Unavailable for persons less than 16,age < 12,,,,,,,,,-999,,,,,, +util_TNC_In_vehicle_time,TNC_LOC - In-vehicle time,@(odt_skims['KTW_TIV'] + dot_skims['WTK_TIV']) *df.time_factor,,,,,,,,,coef_ivt,,,,,, +util_TNC_iwait_time,TNC_LOC - First iwait time,@(odt_skims['KTW_FWT']) + (dot_skims['WTK_FWT'])*df.time_factor,,,,,,,,,coef_wait,,,,,, +util_TNC_transfer_wait_time,TNC_LOC - transfer wait time,@(odt_skims['KTW_XWT'] + dot_skims['WTK_XWT']) *df.time_factor,,,,,,,,,coef_xwait,,,,,, +util_TNC_number_of_transfers,TNC_LOC - number of transfers,"@(-23+23*np.exp(0.414*np.clip(odt_skims['KTW_XFR'] + df.mtnev_acc_xfer + df.mtnev_egr_xfer, a_min=None,a_max=4))) + (-23+23*np.exp(0.414*np.clip(dot_skims['WTK_XFR'] + df.mtnev_acc_xfer + df.mtnev_egr_xfer, a_min=None,a_max=4))) *df.time_factor",,,,,,,,,coef_xferdrive,,,,,, +util_TNC_TNC_time,TNC_LOC - TNC time,"@np.where(df.nev_local_access_available, df.nev_local_access_time, np.where(df.microtransit_local_access_available, df.microtransit_local_access_time, odt_skims['KTW_ACC'] + dot_skims['WTK_EGR'])) *df.time_factor",,,,,,,,,coef_acctime,,,,,, +util_TNC_TNC_wait_time,TNC_LOC - TNC wait time,"@np.where(df.nev_local_access_available, 2*nevWaitTime, np.where(df.microtransit_local_access_available, 2*microtransitWaitTime, 2*df.origSingleTNCWaitTime)) *df.time_factor",,,,,,,,,coef_wait,,,,,, +util_TNC_TNC_fare,TNC_LOC - TNC fare,"@(~df.nev_local_access_available) * (~df.microtransit_local_access_available) * ((np.maximum(TNC_single_baseFare*2 + ((odt_skims['KTW_ACC'] + dot_skims['WTK_EGR'])/60 * driveSpeed) * TNC_single_costPerMile + (odt_skims['KTW_ACC'] + dot_skims['WTK_EGR']) * TNC_single_costPerMinute, TNC_single_costMinimum*2)*100)/df.cost_sensitivity)",,,,,,,,,coef_income,,,,,, +util_TNC_Walk_egress_time_(at_attraction_end),TNC_LOC - Walk egress time (at attraction end),"@np.where(df.nev_local_egress_available, df.nev_local_egress_time, np.where(df.microtransit_local_egress_available, df.microtransit_local_egress_time, 2*df.dest_local_time)) * df.time_factor",,,,,,,,,coef_acctime,,,,,, +util_TNC_wait_egress_time_(at_attraction_end),TNC_LOC - Egress mt/nev wait time (at attraction end),"@np.where(df.nev_local_egress_available, 2*nevWaitTime, np.where(df.microtransit_local_egress_available, 2*microtransitWaitTime, 0)) * df.time_factor",,,,,,,,,coef_wait,,,,,, +util_TNC_Walk_other_time,TNC_LOC - Walk other time,@(odt_skims['KTW_AUX'] + dot_skims['WTK_AUX']) *df.time_factor,,,,,,,,,coef_xwalk,,,,,, +# FIXME need Fares,,,,,,,,,,,,,,,,, +#util_TNC_Fare_and_operating_cost,TNC_LOC - Fare ,@df.transitSubsidyPassDiscount*(odt_skims['KTW_FARE'] + dot_skims['WTK_FARE'])*100*df.num_participants/df.cost_sensitivity,,,,,,,,,coef_income,,,,,, +util_TNC_LOC - Age 16 to 24,TNC_LOC - Age 16 to 24,@(df.age > 15) & (df.age < 25),,,,,,,,,coef_age1624_tran,,,,,, +util_TNC_LOC - Age 41 to 55,TNC_LOC - Age 41 to 55,@(df.age > 40) & (df.age < 56),,,,,,,,,coef_age4155_tran,,,,,, +util_TNC_LOC - Age 56 to 64,TNC_LOC - Age 56 to 64,@(df.age > 55) & (df.age < 65),,,,,,,,,coef_age5664_tran,,,,,, +util_TNC_LOC - Age 65+,TNC_LOC - Age 65+,@(df.age > 64),,,,,,,,,coef_age65pl_tran,,,,,, +util_TNC_LOC - Female,TNC_LOC - Female,@(df.female),,,,,,,,,coef_female_tran,,,,,, +util_TNC_LOC - Destination Employment Density,TNC_LOC - Destination Employment Density,dMGRAEmpDen,,,,,,,,,coef_dEmpDen_dTran,,,,,, +#,Taxi,,,,,,,,,,,,,,,, +util_Taxi - In-vehicle time,Taxi - In-vehicle time,@(df.s2_time_skims_inb + df.s2_time_skims_out) * df.time_factor,,,,,,,,,,coef_ivt,,,,, +util_Taxi - Wait time,Taxi - Wait time,@df.totalWaitTaxi * df.time_factor,,,,,,,,,,coef_wait,,,,, +util_Taxi - Fare,Taxi - Fare,@(((Taxi_baseFare*2 + (df.s2_dist_skims_out + df.s2_dist_skims_inb) * Taxi_costPerMile + (df.s2_time_skims_out + df.s2_time_skims_inb) * Taxi_costPerMinute)*100 + df.s2_cost_skims_out + df.s2_cost_skims_inb))/df.cost_sensitivity,,,,,,,,,,coef_income,,,,, +#,TNC Single,,,,,,,,,,,,,,,, +util_TNC Single - In-vehicle time,TNC Single - In-vehicle time,@(df.s2_time_skims_out + df.s2_time_skims_inb) * df.time_factor,,,,,,,,,,,coef_ivt,,,, +util_TNC Single - Wait time,TNC Single - Wait time,@df.totalWaitSingleTNC * df.time_factor,,,,,,,,,,,coef_wait,,,, +util_TNC Single - Cost,TNC Single - Cost,"@((np.maximum(TNC_single_baseFare*2 + (df.s2_dist_skims_out + df.s2_dist_skims_inb) * TNC_single_costPerMile + (df.s2_time_skims_out + df.s2_time_skims_inb) * TNC_single_costPerMinute, TNC_single_costMinimum*2)*100 + df.s2_cost_skims_out + df.s2_cost_skims_inb))/df.cost_sensitivity",,,,,,,,,,,coef_income,,,, +#,TNC Shared,,,,,,,,,,,,,,,, +util_TNC Shared_switch,TNC Shared - switch turn-off (depends on data availability),@((~df.nev_available) & (~df.microtransit_available) & (scenarioYear==2022)),,,,,,,,,,,,-999,,, +util_TNC Shared - In-vehicle time,TNC Shared - In-vehicle time,"@(np.where(df.nev_available, df.nev_time, np.where(df.microtransit_available, df.microtransit_time, (df.s3_time_skims_out + df.s3_time_skims_inb) * TNC_shared_IVTFactor))) * df.time_factor",,,,,,,,,,,,coef_ivt,,, +util_TNC Shared - Wait time,TNC Shared - Wait time,"@np.where(df.nev_available, 2*nevWaitTime, np.where(df.microtransit_available, 2*microtransitWaitTime, df.totalWaitSharedTNC)) * df.time_factor",,,,,,,,,,,,coef_wait,,, +util_TNC Shared - Cost,TNC Shared - Cost,"@np.where(df.nev_available, 2*nevCost, np.where(df.microtransit_available, 2*microtransitCost, (np.maximum(TNC_shared_baseFare*2 + (df.s3_dist_skims_out + df.s3_dist_skims_inb) * TNC_shared_costPerMile + (df.s3_time_skims_out + df.s3_time_skims_inb)* TNC_shared_costPerMinute, TNC_shared_costMinimum*2)*100 + df.s3_cost_skims_out + df.s3_cost_skims_inb)))/df.cost_sensitivity",,,,,,,,,,,,coef_income,,, +#,School bus,,,,,,,,,,,,,,,, +util_School Bus - Unavailable,School Bus - Unavailable,SCHBUS_available==0,,,,,,,,,,,,,-999,, +util_School Bus - In-vehicle Time at 20 miles per hour,School Bus - In-vehicle Time at 20 miles per hour,(da_dist_skims_out + da_dist_skims_inb)*3 * time_factor,,,,,,,,,,,,,coef_ivt,, +util_School Bus - Wait Time (asserted),School Bus - Wait Time (asserted) * c_fwt,10 * time_factor,,,,,,,,,,,,,coef_wait,, +util_School Bus - Walk Time (asserted),School Bus - Walk Time (asserted),10 * time_factor,,,,,,,,,,,,,coef_acctime,, +util_School Bus - Age 1 to 5,School Bus - Age 1 to 5,@(df.age > 0) & (df.age < 6),,,,,,,,,,,,,coef_age0105_schb,, +util_School Bus - Age 6 to 12,School Bus - Age 6 to 12,@(df.age > 5) & (df.age < 13),,,,,,,,,,,,,coef_age0612_schb,, +util_School Bus - Age 13 to 15,School Bus - Age 13 to 15,@(df.age > 12) & (df.age < 16),,,,,,,,,,,,,coef_age1315_schb,, +util_School Bus - Female,School Bus - Female,@(df.female),,,,,,,,,,,,,coef_female_schb,, +#calibration,,,,,,,,,,,,,,,,, +util_calib_KNR Transit - Distance Parameter,abm2+ calibration constant,"@np.maximum((10-df.da_dist_skims_out),0)",,,,,,,,coef_calib_distance_KNR_TRANSIT,coef_calib_distance_TNC_TRANSIT,,,,,, +util_calib_Walk-Transit - Distance Parameter,abm2+ calibration constant,"@np.maximum((200+(-133*df.da_dist_skims_out)),0)",,,,,,coef_calib_distance_WALK_TRANSIT,,,,,,,,, +util_calib_PNR Transit - Distance Parameter ,abm2+ calibration constant,"@np.maximum((45+(-2.5*df.da_dist_skims_out)),0)",,,,,,,coef_calib_distance_PNR_TRANSIT,,,,,,,, +util_calib_parkingdistrict,abm2+ calibration constant,parkingConstrainedArea==1,,,,,,coef_calib_parkingconst_WLK_TRANSIT,coef_calib_parkingconst_DRV_TRANSIT,coef_calib_parkingconst_DRV_TRANSIT,coef_calib_parkingconst_DRV_TRANSIT,,,,,, +#util_calib_zeroautohh,abm2+ calibration constant,@(df.auto_ownership == 0),coef_calib_zeroautohh_DRIVEALONE,coef_calib_zeroautohh_SHARED2,coef_calib_zeroautohh_SHARED3,coef_calib_zeroautohh_WALK,coef_calib_zeroautohh_BIKE,coef_calib_zeroautohh_WALK_TRANSIT,coef_calib_zeroautohh_PNR_TRANSIT,coef_calib_zeroautohh_KNR_TRANSIT,coef_calib_zeroautohh_TNC_TRANSIT,coef_calib_zeroautohh_TAXI,coef_calib_zeroautohh_TNC_SINGLE,coef_calib_zeroautohh_TNC_SHARED,coef_calib_zeroautohh_SCH_BUS,, +#util_calib_autodeficienthh,abm2+ calibration constant,@((df.auto_ownership < df.num_adults) & (df.auto_ownership > 0)),,coef_calib_autodeficienthh_SHARED2,coef_calib_autodeficienthh_SHARED3,coef_calib_autodeficienthh_WALK,coef_calib_autodeficienthh_BIKE,coef_calib_autodeficienthh_WALK_TRANSIT,coef_calib_autodeficienthh_PNR_TRANSIT,coef_calib_autodeficienthh_KNR_TRANSIT,coef_calib_autodeficienthh_TNC_TRANSIT,coef_calib_autodeficienthh_TAXI,coef_calib_autodeficienthh_TNC_SINGLE,coef_calib_autodeficienthh_TNC_SHARED,coef_calib_autodeficienthh_SCH_BUS,, +#util_calib_autosufficienth,abm2+ calibration constant,@((df.auto_ownership >= df.num_adults) & (df.auto_ownership > 0)),,coef_calib_autosufficienthh_SHARED2,coef_calib_autosufficienthh_SHARED3,coef_calib_autosufficienthh_WALK,coef_calib_autosufficienthh_BIKE,coef_calib_autosufficienthh_WALK_TRANSIT,coef_calib_autosufficienthh_PNR_TRANSIT,coef_calib_autosufficienthh_KNR_TRANSIT,coef_calib_autosufficienthh_TNC_TRANSIT,coef_calib_autosufficienthh_TAXI,coef_calib_autosufficienthh_TNC_SINGLE,coef_calib_autosufficienthh_TNC_SHARED,coef_calib_autosufficienthh_SCH_BUS,, +util_calib_zeroautohhindiv,abm2+ calibration constant,@(df.is_indiv & (df.auto_ownership == 0)),,coef_calib_zeroautohhindivtou_SHARED2,coef_calib_zeroautohhindivtou_SHARED3,coef_calib_zeroautohhindivtou_WALK,coef_calib_zeroautohhindivtou_BIKE,coef_calib_zeroautohhindivtou_WALK_TRANSIT,coef_calib_zeroautohhindivtou_PNR_TRANSIT,coef_calib_zeroautohhindivtou_KNR_TRANSIT,coef_calib_zeroautohhindivtou_TNC_TRANSIT,coef_calib_zeroautohhindivtou_TAXI,coef_calib_zeroautohhindivtou_TNC_SINGLE,coef_calib_zeroautohhindivtou_TNC_SHARED,coef_calib_zeroautohhindivtou_SCH_BUS,coef_calib_zeroautohhindivtou_EBIKE,coef_calib_zeroautohhindivtou_ESCOOTER +util_calib_autodeficienthh,abm2+ calibration constant,@(df.is_indiv & (df.auto_ownership < df.num_adults) & (df.auto_ownership > 0)),,coef_calib_autodeficienthhind_SHARED2,coef_calib_autodeficienthhind_SHARED3,coef_calib_autodeficienthhind_WALK,coef_calib_autodeficienthhind_BIKE,coef_calib_autodeficienthhind_WALK_TRANSIT,coef_calib_autodeficienthhind_PNR_TRANSIT,coef_calib_autodeficienthhind_KNR_TRANSIT,coef_calib_autodeficienthhind_TNC_TRANSIT,coef_calib_autodeficienthhind_TAXI,coef_calib_autodeficienthhind_TNC_SINGLE,coef_calib_autodeficienthhind_TNC_SHARED,coef_calib_autodeficienthhind_SCH_BUS,coef_calib_autodeficienthhind_EBIKE,coef_calib_autodeficienthhind_ESCOOTER +util_calib_autosufficienth,abm2+ calibration constant,@(df.is_indiv & (df.auto_ownership >= df.num_adults) & (df.auto_ownership > 0)),,coef_calib_autosufficienthhin_SHARED2,coef_calib_autosufficienthhin_SHARED3,coef_calib_autosufficienthhin_WALK,coef_calib_autosufficienthhin_BIKE,coef_calib_autosufficienthhin_WALK_TRANSIT,coef_calib_autosufficienthhin_PNR_TRANSIT,coef_calib_autosufficienthhin_KNR_TRANSIT,coef_calib_autosufficienthhin_TNC_TRANSIT,coef_calib_autosufficienthhin_TAXI,coef_calib_autosufficienthhin_TNC_SINGLE,coef_calib_autosufficienthhin_TNC_SHARED,coef_calib_autosufficienthhin_SCH_BUS,coef_calib_autosufficienthhin_EBIKE,coef_calib_autosufficienthhin_ESCOOTER +util_calib_zeroautohhjoint,abm2+ calibration constant,@(df.is_joint & (df.auto_ownership == 0)),,,coef_calib_zeroautohhjointtou_SHARED3,coef_calib_zeroautohhjointtou_WALK,coef_calib_zeroautohhjointtou_BIKE,coef_calib_zeroautohhjointtou_WALK_TRANSIT,coef_calib_zeroautohhjointtou_PNR_TRANSIT,coef_calib_zeroautohhjointtou_KNR_TRANSIT,coef_calib_zeroautohhjointtou_TNC_TRANSIT,coef_calib_zeroautohhjointtou_TAXI,coef_calib_zeroautohhjointtou_TNC_SINGLE,coef_calib_zeroautohhjointtou_TNC_SHARED,,coef_calib_zeroautohhjointtou_EBIKE,coef_calib_zeroautohhjointtou_ESCOOTER +util_calib_autodeficienthh,abm2+ calibration constant,@(df.is_joint & (df.auto_ownership < df.num_adults) & (df.auto_ownership > 0)),,,coef_calib_autodeficienthhjoi_SHARED3,coef_calib_autodeficienthhjoi_WALK,coef_calib_autodeficienthhjoi_BIKE,coef_calib_autodeficienthhjoi_WALK_TRANSIT,coef_calib_autodeficienthhjoi_PNR_TRANSIT,coef_calib_autodeficienthhjoi_KNR_TRANSIT,coef_calib_autodeficienthhjoi_TNC_TRANSIT,coef_calib_autodeficienthhjoi_TAXI,coef_calib_autodeficienthhjoi_TNC_SINGLE,coef_calib_autodeficienthhjoi_TNC_SHARED,,coef_calib_autodeficienthhjoi_EBIKE,coef_calib_autodeficienthhjoi_ESCOOTER +util_calib_autosufficienth,abm2+ calibration constant,@(df.is_joint & (df.auto_ownership >= df.num_adults) & (df.auto_ownership > 0)),,,coef_calib_autosufficienthhjo_SHARED3,coef_calib_autosufficienthhjo_WALK,coef_calib_autosufficienthhjo_BIKE,coef_calib_autosufficienthhjo_WALK_TRANSIT,coef_calib_autosufficienthhjo_PNR_TRANSIT,coef_calib_autosufficienthhjo_KNR_TRANSIT,coef_calib_autosufficienthhjo_TNC_TRANSIT,coef_calib_autosufficienthhjo_TAXI,coef_calib_autosufficienthhjo_TNC_SINGLE,coef_calib_autosufficienthhjo_TNC_SHARED,,coef_calib_autosufficienthhjo_EBIKE,coef_calib_autosufficienthhjo_ESCOOTER +util_calib_escorttour,abm2+ calibration constant,tour_type == 'escort',,,,coef_calib_escorttour_WALK,coef_calib_escorttour_BIKE,coef_calib_escorttour_WALK_TRANSIT,coef_calib_escorttour_PNR_TRANSIT,coef_calib_escorttour_KNR_TRANSIT,coef_calib_escorttour_TNC_TRANSIT,,,,,, +#, School Escorting eligibility-odd looking where/isnan is to allow this to work with numba fastmath,,,,,,,,,,,,,,,, +util_one_or_more_school_escort,No SOV if on school escort tour,"@(np.where(np.isnan(df.get('num_escortees', 0)), 0 , df.get('num_escortees', 0)) >= 1)",-999,,,,,,,,,,,,,, +util_two_or_more_school_escort,Can't take HOV2 if taking two children and yourself,"@(np.where(np.isnan(df.get('num_escortees', 0)), 0 , df.get('num_escortees', 0)) >= 2)",,-999,,,,,,,,,,,,, +#,Micromobility (e-scooter/e-bike),,,,,,,,,,,,,,,, +util_ebike - Unavailable,ebike - Unavailable,ebike_available==0,,,,,,,,,,,,,,-999, +util_ebike_long_access,Shut off ebike if access time > threshold,"@(((df.micro_access_out > microAccessThreshold) | (df.micro_access_inb > microAccessThreshold)) & (~df.ebike_owner)) | (df.get('num_escortees', 0)>0)",,,,,,,,,,,,,,-999, +util_escooter_long_access,Shut off escooter if access time > threshold,"@((df.micro_access_out > microAccessThreshold) | (df.micro_access_inb > microAccessThreshold) | (df.get('num_escortees', 0)>0))",,,,,,,,,,,,,,,-999 +util_micromobility_long_trip,Shut off ebike if distance > threshold,ebikeMaxDistance,,,,,,,,,,,,,,-999, +util_micromobility_long_trip,Shut off escooter if distance > threshold,escooterMaxDistance,,,,,,,,,,,,,,,-999 +util_ebike_ivt,Ebike utility for in-vehicle time,@(df.ebike_time_inb + df.ebike_time_out)*df.time_factor,,,,,,,,,,,,,,coef_ivt, +util_ebike_access,Ebike utility for access/egress time,"@(np.where(df.ebike_owner, 0, microRentTime + df.micro_access_inb + df.micro_access_out))*df.time_factor",,,,,,,,,,,,,,coef_acctime, +util_ebike_cost_inb,Ebike utility for inbound cost,@((~df.ebike_owner)&((microFixedCost + microVarCost*df.ebike_time_inb)/df.cost_sensitivity)),,,,,,,,,,,,,,coef_income, +util_ebike_cost_out,Ebike utility for outbound cost,@((~df.ebike_owner)&((microFixedCost + microVarCost*df.ebike_time_out)/df.cost_sensitivity)),,,,,,,,,,,,,,coef_income, +util_escooter - Unavailable,escooter - Unavailable,escooter_available==0,,,,,,,,,,,,,,,-999 +util_escooter_ivt,escooter utility for in-vehicle time,@(df.escooter_time_inb + df.escooter_time_out)*df.time_factor,,,,,,,,,,,,,,,coef_ivt +util_escooter_access,escooter utility for in-vehicle time,@(microRentTime + df.micro_access_inb + df.micro_access_out)*df.time_factor,,,,,,,,,,,,,,,coef_acctime +util_escooter_cost_inb,escooter utility for inbound cost,@((microFixedCost + microVarCost*df.escooter_time_inb)/df.cost_sensitivity),,,,,,,,,,,,,,,coef_income +util_escooter_cost_out,escooter utility for outbound cost,@((microFixedCost + microVarCost*df.escooter_time_out)/df.cost_sensitivity),,,,,,,,,,,,,,,coef_income +#,Calibration from on-board survey,,,,,,,,,,,,,,,, +util_calib_onboard,Calibration coefficient to match implied number of tours from on-board survey,1,,,,,,coef_calib_onboard,coef_calib_onboard,coef_calib_onboard,coef_calib_onboard,,,,,, +#,Flexible fleet calibration for zero-auto households,,,,,,,,,,,,,,,, +util_calib_mt_zeroautohh,Calibration coefficient for zero-auto households traveling within microtransit service area,@(df.microtransit_available & (df.auto_ownership == 0)),,,,,,,,,,,,coef_calib_mt_zeroautohh,,, +util_calib_nev_zeroautohh,Calibration coefficient for zero-auto households traveling within NEV service area,@(df.nev_available & (df.auto_ownership == 0)),,,,,,,,,,,,coef_calib_nev_zeroautohh,,, +util_calib_ebike_owner,Calibration coefficient for ebike owners,@(df.ebike_owner),,,,,coef_calib_ebike_owner_BIKE,,,,,,,,,coef_calib_ebike_owner_EBIKE, +util_calib_shared_ebike,Calibration coefficient for shared ebike,@(~df.ebike_owner),,,,,,,,,,,,,,coef_calib_ebike_shared, diff --git a/resident/configs/tour_mode_choice.yaml b/resident/configs/tour_mode_choice.yaml new file mode 100644 index 0000000..326e9de --- /dev/null +++ b/resident/configs/tour_mode_choice.yaml @@ -0,0 +1,96 @@ +LOGIT_TYPE: NL +#LOGIT_TYPE: MNL + +NESTS: + name: root + coefficient: coef_nest_root + alternatives: + - name: AUTO + coefficient: coef_nest_AUTO + alternatives: + - DRIVEALONE + - SHARED2 + - SHARED3 + - name: NONMOTORIZED + coefficient: coef_nest_NONMOTORIZED + alternatives: + - WALK + - BIKE + - name: MICROMOBILITY + coefficient: coef_nest_MICROMOBILITY + alternatives: + - EBIKE + - ESCOOTER + - name: TRANSIT + coefficient: coef_nest_TRANSIT + alternatives: + - WALK_TRANSIT + - PNR_TRANSIT + - KNR_TRANSIT + - BIKE_TRANSIT + - name: RIDEHAIL + coefficient: coef_nest_RIDEHAIL + alternatives: + - TAXI + - TNC_SINGLE + - TNC_SHARED + - name: SCHOOL_BUS + coefficient: coef_nest_SCHOOL_BUS + alternatives: + - SCH_BUS + +SPEC: tour_mode_choice.csv +COEFFICIENTS: tour_mode_choice_coefficients.csv +COEFFICIENT_TEMPLATE: tour_mode_choice_coefficients_template.csv + +run_atwork_pnr_lot_choice: True +include_pnr_for_logsums: True + +# so far, we can use the same spec as for non-joint tours +preprocessor: + SPEC: tour_mode_choice_annotate_choosers_preprocessor + DF: choosers + TABLES: + - land_use + - tours + - vehicles + - households + - persons + +nontour_preprocessor: + SPEC: tour_mode_choice_annotate_choosers_preprocessor + DF: choosers + TABLES: + - land_use + +annotate_tours: + SPEC: annotate_tours_tour_mode_choice + DF: choosers + TABLES: + - tours + + +# to reduce memory needs filter chooser table to these fields +LOGSUM_CHOOSER_COLUMNS: + - tour_type + - hhsize + - density_index + - age + - SEX + - auto_ownership + - num_adults + - ptype + - number_of_participants + - tour_category + - value_of_time + - free_parking_at_work + - income_segment + - income + - time_factor_work + - time_factor_nonwork + - num_escortees + - ebike_owner + - transit_pass_subsidy + - transit_pass_ownership + +MODE_CHOICE_LOGSUM_COLUMN_NAME: mode_choice_logsum diff --git a/resident/configs/tour_mode_choice_annotate_choosers_preprocessor.csv b/resident/configs/tour_mode_choice_annotate_choosers_preprocessor.csv new file mode 100644 index 0000000..698c080 --- /dev/null +++ b/resident/configs/tour_mode_choice_annotate_choosers_preprocessor.csv @@ -0,0 +1,244 @@ +Description,Target,Expression, +#,,, +local,_DF_IS_TOUR,'tour_type' in df.columns, +,is_atwork_subtour,(df.tour_category == 'atwork') if _DF_IS_TOUR else False, +,_parent_tour_veh,"reindex(tours['selected_vehicle'], df['parent_tour_id']) if 'selected_vehicle' in df.columns else np.nan", +,_parent_tour_mode,"reindex(tours['tour_mode'], df['parent_tour_id']) if 'tour_mode' in df.columns else np.nan", +,sov_veh_option,"np.where(is_atwork_subtour, _parent_tour_veh, df.get('vehicle_occup_1', np.nan))", +,sr2_veh_option,"np.where(is_atwork_subtour, _parent_tour_veh, df.get('vehicle_occup_2', np.nan))", +,sr3p_veh_option,"np.where(is_atwork_subtour, _parent_tour_veh, df.get('vehicle_occup_3.5', np.nan))", +#AV factors,,, +SOV tour Vehicle is AV,sov_tour_uses_av,"np.where(pd.notna(df.get('vehicle_occup_1', np.nan)), pd.Series(sov_veh_option, dtype = 'str').str.contains('AV'), False)" +SR2 tour Vehicle is AV,sr2_tour_uses_av,"np.where(pd.notna(df.get('vehicle_occup_2', np.nan)), pd.Series(sr2_veh_option, dtype = 'str').str.contains('AV'), False)" +SR3 tour Vehicle is AV,sr3_tour_uses_av,"np.where(pd.notna(df.get('vehicle_occup_3.5', np.nan)), pd.Series(sr3p_veh_option, dtype = 'str').str.contains('AV'), False)" +SOV AV IVT Factor Adjustment,autoIVTFactor_DA,"np.where(sov_tour_uses_av, autoIVTFactorAV, 1.0)" +SR2 AV IVT Factor Adjustment,autoIVTFactor_SR2,"np.where(sr2_tour_uses_av, autoIVTFactorAV, 1.0)" +SR3+ AV IVT Factor Adjustment,autoIVTFactor_SR3,"np.where(sr3_tour_uses_av, autoIVTFactorAV, 1.0)" +SOV AV Parking Cost Factor Adjustment,autoParkingCostFactor_DA,"np.where(sov_tour_uses_av, autoParkingCostFactorAV, 1.0)" +SR2 AV Parking Cost Factor Adjustment,autoParkingCostFactor_SR2,"np.where(sr2_tour_uses_av, autoParkingCostFactorAV, 1.0)" +SR3+ AV Parking Cost Factor Adjustment,autoParkingCostFactor_SR3,"np.where(sr3_tour_uses_av, autoParkingCostFactorAV, 1.0)" +SOV AV Auto Terminal Time Factor,autoTermTimeFactor_DA,"np.where(sov_tour_uses_av, autoTerminalTimeFactorAV, 1.0)" +SR2 AV Auto Terminal Time Factor,autoTermTimeFactor_SR2,"np.where(sr2_tour_uses_av, autoTerminalTimeFactorAV, 1.0)" +SR3+ AV Auto Terminal Time Factor,autoTermTimeFactor_SR3,"np.where(sr3_tour_uses_av, autoTerminalTimeFactorAV, 1.0)" +SOV AV Auto Terminal Time Factor,autoCPMFactor_DA,"np.where(sov_tour_uses_av, autoCostPerMileFactorAV, 1.0)" +SR2 AV Auto Terminal Time Factor,autoCPMFactor_SR2,"np.where(sr2_tour_uses_av, autoCostPerMileFactorAV, 1.0)" +SR3+ AV Auto Terminal Time Factor,autoCPMFactor_SR3,"np.where(sr3_tour_uses_av, autoCostPerMileFactorAV, 1.0)" +AV Min Age to Drive Alone,minimumAgeDA,"np.where(sov_tour_uses_av, minAgeDriveAloneAV, 16)" +reimburseProportion placeholder,reimburseProportion,0, +"Tour duration, in hours",tourDuration,df.duration/2, +#,,, +,num_participants,df.number_of_participants if _DF_IS_TOUR else 1, +,is_joint,(df.tour_category=='joint') if _DF_IS_TOUR else False, +treat tours as work if tour_type not yet decided,tour_type,"df.get('tour_type', default='work')", +#,,, +,is_mandatory,(df.tour_category=='mandatory') if 'tour_category' in df.columns else False, +,is_indiv,~is_joint, +,is_escort,(tour_type == 'escort') if _DF_IS_TOUR else False, +# Auto operating costs,,, +,sov_auto_op_cost,"reindex(vehicles.groupby('vehicle_type')['auto_operating_cost'].mean(), pd.Series(sov_veh_option, df.index)) if 'vehicle_occup_1' in df.columns else np.nan", +,sov_auto_op_cost,"np.where(sov_auto_op_cost.isna() | (pd.Series(sov_veh_option, df.index) == 'non_hh_veh'), costPerMile, sov_auto_op_cost)", +,sr2_auto_op_cost,"reindex(vehicles.groupby('vehicle_type')['auto_operating_cost'].mean(), pd.Series(sr2_veh_option, df.index)) if 'vehicle_occup_2' in df.columns else np.nan", +,sr2_auto_op_cost,"np.where(sr2_auto_op_cost.isna() | (pd.Series(sr2_veh_option, df.index) == 'non_hh_veh'), costPerMile, sr2_auto_op_cost)", +,sr3p_auto_op_cost,"reindex(vehicles.groupby('vehicle_type')['auto_operating_cost'].mean(), pd.Series(sr3p_veh_option, df.index)) if 'vehicle_occup_3.5' in df.columns else np.nan", +,sr3p_auto_op_cost,"np.where(sr3p_auto_op_cost.isna() | (pd.Series(sr3p_veh_option, df.index) == 'non_hh_veh'), costPerMile, sr3p_auto_op_cost)", +# RIDEHAIL,,, +# FIXME population_density replaced PopDenPerSqMi which may not be calculated the same,, +population_density calculated in annotate landuse in acres,_origin_density_measure,"reindex(land_use.population_density, df[orig_col_name]) / 640", +population_density calculated in annotate landuse in acres,_dest_density_measure,"reindex(land_use.population_density, df[dest_col_name]) / 640", +,origin_density,"pd.cut(_origin_density_measure, bins=[-np.inf, 500, 2000, 5000, 15000, np.inf], labels=[5, 4, 3, 2, 1]).astype(int)", +,dest_density,"pd.cut(_dest_density_measure, bins=[-np.inf, 500, 2000, 5000, 15000, np.inf], labels=[5, 4, 3, 2, 1]).astype(int)", +,origin_zone_taxi_wait_time_mean,"origin_density.map({k: v for k, v in Taxi_waitTime_mean.items()})", +,origin_zone_taxi_wait_time_sd,"origin_density.map({k: v for k, v in Taxi_waitTime_sd.items()})", +,dest_zone_taxi_wait_time_mean,"dest_density.map({k: v for k, v in Taxi_waitTime_mean.items()})", +,dest_zone_taxi_wait_time_sd,"dest_density.map({k: v for k, v in Taxi_waitTime_sd.items()})", +# ,, Note that the mean and standard deviation are not the values for the distribution itself but of the underlying normal distribution it is derived from +,origTaxiWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_taxi_wait_time_mean, sigma=origin_zone_taxi_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)", +,destTaxiWaitTime,"rng.lognormal_for_df(df, mu=dest_zone_taxi_wait_time_mean, sigma=dest_zone_taxi_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)", +,origin_zone_singleTNC_wait_time_mean,"origin_density.map({k: v for k, v in TNC_single_waitTime_mean.items()})", +,origin_zone_singleTNC_wait_time_sd,"origin_density.map({k: v for k, v in TNC_single_waitTime_sd.items()})", +,dest_zone_singleTNC_wait_time_mean,"dest_density.map({k: v for k, v in TNC_single_waitTime_mean.items()})", +,dest_zone_singleTNC_wait_time_sd,"dest_density.map({k: v for k, v in TNC_single_waitTime_sd.items()})", +,origSingleTNCWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_singleTNC_wait_time_mean, sigma=origin_zone_singleTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)", +,destSingleTNCWaitTime,"rng.lognormal_for_df(df, mu=dest_zone_singleTNC_wait_time_mean, sigma=dest_zone_singleTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)", +,origin_zone_sharedTNC_wait_time_mean,"origin_density.map({k: v for k, v in TNC_shared_waitTime_mean.items()})", +,origin_zone_sharedTNC_wait_time_sd,"origin_density.map({k: v for k, v in TNC_shared_waitTime_sd.items()})", +,dest_zone_sharedTNC_wait_time_mean,"dest_density.map({k: v for k, v in TNC_shared_waitTime_mean.items()})", +,dest_zone_sharedTNC_wait_time_sd,"dest_density.map({k: v for k, v in TNC_shared_waitTime_sd.items()})", +,origSharedTNCWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_sharedTNC_wait_time_mean, sigma=origin_zone_sharedTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)", +,destSharedTNCWaitTime,"rng.lognormal_for_df(df, mu=dest_zone_sharedTNC_wait_time_mean, sigma=dest_zone_sharedTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)", +,totalWaitTaxi,origTaxiWaitTime + destTaxiWaitTime, +,totalWaitSingleTNC,origSingleTNCWaitTime + destSingleTNCWaitTime, +,totalWaitSharedTNC,origSharedTNCWaitTime + destSharedTNCWaitTime, +#parking,,, +,free_parking_available,(tour_type == 'work') & df.free_parking_at_work if _DF_IS_TOUR else False, +person has free on-site parking at workplace,freeOnsite,"(free_parking_available)*np.where(is_indiv,1,0)", +# new reimbursement amount,reimburseProportion,"df.reimburseProportion*np.where(is_indiv,1,0)", +new daily parking cost with reimbursement in cents,parkingCostDay,"reindex(land_use.PRKCST_DAY, df[dest_col_name])", +new hourly parking cost with reimbursement in cents,parkingCostHour,"reindex(land_use.PRKCST_HR, df[dest_col_name])", +new monthly parking cost with reimbursement in cents,parkingCostMonth,"reindex(land_use.PRKCST_MNTH, df[dest_col_name])", +Parking area,parkingConstrainedArea,"np.where(reindex(land_use.parking_type, df[dest_col_name]) == 1, 1, 0)", +Tour parking cost for full-time workers and university students,_parkingCostBeforeReimb,"df.ptype.isin([1,3]) * is_indiv * np.minimum(parkingCostMonth/22, parkingCostDay)", +Tour parking cost for full-time workers and university students,_parkingCostBeforeReimb,"df.ptype.isin([1,3]) * is_indiv * np.minimum(_parkingCostBeforeReimb, parkingCostHour * tourDuration)", +Tour parking cost for other person types,parkingCostBeforeReimb,"np.where((~df.ptype.isin([1,3]) * is_indiv) | (is_joint), np.minimum(parkingCostDay, parkingCostHour * tourDuration), _parkingCostBeforeReimb)", +Reimbursement applies to this tour purpose,reimbursePurpose,df.tour_type == 'work' if _DF_IS_TOUR else False, +Effective parking cost for free parkers,_parkingCost,"0 * np.where(is_indiv*reimbursePurpose*freeOnsite,1,0)", +Effective parking cost for reimbursed parkers,_parkingCost,"np.where(is_indiv*reimbursePurpose*(1-freeOnsite), np.maximum((1-reimburseProportion) * parkingCostBeforeReimb, 0),_parkingCost)", +Effective parking cost applied to tour purpose,parkingCost,"np.where(is_joint+is_indiv*(1-reimbursePurpose), parkingCostBeforeReimb,_parkingCost)", +# cost coef,,, +,_income_exponent,"np.where(tour_type == 'work', 0.6, 0.5)", +,cost_sensitivity,"np.maximum(df.income,1000).pow(_income_exponent)", +,c_cost,coef_income / cost_sensitivity, +# ivt coef,,, +,time_factor,"np.where(tour_type=='work', df.time_factor_work, df.time_factor_nonwork)", +,c_ivt,coef_ivt * time_factor, +#,,, +,vot_da,c_ivt / c_cost * 0.6, +,vot_s2,vot_da / cost_share_s2, +,vot_s3,vot_da / cost_share_s3, +,_vot_bin_da,"np.where(vot_da < vot_threshold_low, 1, np.where(vot_da < vot_threshold_med, 2, 3))", +,_vot_bin_s2,"np.where(vot_s2 < vot_threshold_low, 1, np.where(vot_s2 < vot_threshold_med, 2, 3))", +,_vot_bin_s3,"np.where(vot_s3 < vot_threshold_low, 1, np.where(vot_s3 < vot_threshold_med, 2, 3))", +# vot-indexed skims,,, +,da_dist_skims_out,"(odt_skims['SOV_L_DIST'] * (_vot_bin_da == 1)) + (odt_skims['SOV_M_DIST'] * (_vot_bin_da == 2)) + (odt_skims['SOV_H_DIST'] * (_vot_bin_da == 3))", +,da_dist_skims_inb,"(dot_skims['SOV_L_DIST'] * (_vot_bin_da == 1)) + (dot_skims['SOV_M_DIST'] * (_vot_bin_da == 2)) + (dot_skims['SOV_H_DIST'] * (_vot_bin_da == 3))", +,da_cost_skims_out,"(odt_skims['SOV_L_COST'] * (_vot_bin_da == 1)) + (odt_skims['SOV_M_COST'] * (_vot_bin_da == 2)) + (odt_skims['SOV_H_COST'] * (_vot_bin_da == 3))", +,da_cost_skims_inb,"(dot_skims['SOV_L_COST'] * (_vot_bin_da == 1)) + (dot_skims['SOV_M_COST'] * (_vot_bin_da == 2)) + (dot_skims['SOV_H_COST'] * (_vot_bin_da == 3))", +,da_time_skims_out,"(odt_skims['SOV_L_TIME'] * (_vot_bin_da == 1)) + (odt_skims['SOV_M_TIME'] * (_vot_bin_da == 2)) + (odt_skims['SOV_H_TIME'] * (_vot_bin_da == 3))", +,da_time_skims_inb,"(dot_skims['SOV_L_TIME'] * (_vot_bin_da == 1)) + (dot_skims['SOV_M_TIME'] * (_vot_bin_da == 2)) + (dot_skims['SOV_H_TIME'] * (_vot_bin_da == 3))", +,s2_dist_skims_out,(((odt_skims['SR2_L_DIST']) * (_vot_bin_s2 == 1)) + ((odt_skims['SR2_M_DIST']) * (_vot_bin_s2 == 2)) + ((odt_skims['SR2_H_DIST']) * (_vot_bin_s2 == 3))), +,s2_dist_skims_inb,(((dot_skims['SR2_L_DIST']) * (_vot_bin_s2 == 1)) + ((dot_skims['SR2_M_DIST']) * (_vot_bin_s2 == 2)) + ((dot_skims['SR2_H_DIST']) * (_vot_bin_s2 == 3))), +,s2_cost_skims_out,(((odt_skims['SR2_L_COST']) * (_vot_bin_s2 == 1)) + ((odt_skims['SR2_M_COST']) * (_vot_bin_s2 == 2)) + ((odt_skims['SR2_H_COST']) * (_vot_bin_s2 == 3))), +,s2_cost_skims_inb,(((dot_skims['SR2_L_COST']) * (_vot_bin_s2 == 1)) + ((dot_skims['SR2_M_COST']) * (_vot_bin_s2 == 2)) + ((dot_skims['SR2_H_COST']) * (_vot_bin_s2 == 3))), +,s2_time_skims_out,(((odt_skims['SR2_L_TIME']) * (_vot_bin_s2 == 1)) + ((odt_skims['SR2_M_TIME']) * (_vot_bin_s2 == 2)) + ((odt_skims['SR2_H_TIME']) * (_vot_bin_s2 == 3))), +,s2_time_skims_inb,(((dot_skims['SR2_L_TIME']) * (_vot_bin_s2 == 1)) + ((dot_skims['SR2_M_TIME']) * (_vot_bin_s2 == 2)) + ((dot_skims['SR2_H_TIME']) * (_vot_bin_s2 == 3))), +,s3_dist_skims_out,(((odt_skims['SR3_L_DIST']) * (_vot_bin_s3 == 1)) + ((odt_skims['SR3_M_DIST']) * (_vot_bin_s3 == 2)) + ((odt_skims['SR3_H_DIST']) * (_vot_bin_s3 == 3))), +,s3_dist_skims_inb,(((dot_skims['SR3_L_DIST']) * (_vot_bin_s3 == 1)) + ((dot_skims['SR3_M_DIST']) * (_vot_bin_s3 == 2)) + ((dot_skims['SR3_H_DIST']) * (_vot_bin_s3 == 3))), +,s3_cost_skims_out,(((odt_skims['SR3_L_COST']) * (_vot_bin_s3 == 1)) + ((odt_skims['SR3_M_COST']) * (_vot_bin_s3 == 2)) + ((odt_skims['SR3_H_COST']) * (_vot_bin_s3 == 3))), +,s3_cost_skims_inb,(((dot_skims['SR3_L_COST']) * (_vot_bin_s3 == 1)) + ((dot_skims['SR3_M_COST']) * (_vot_bin_s3 == 2)) + ((dot_skims['SR3_H_COST']) * (_vot_bin_s3 == 3))), +,s3_time_skims_out,(((odt_skims['SR3_L_TIME']) * (_vot_bin_s3 == 1)) + ((odt_skims['SR3_M_TIME']) * (_vot_bin_s3 == 2)) + ((odt_skims['SR3_H_TIME']) * (_vot_bin_s3 == 3))), +,s3_time_skims_inb,(((dot_skims['SR3_L_TIME']) * (_vot_bin_s3 == 1)) + ((dot_skims['SR3_M_TIME']) * (_vot_bin_s3 == 2)) + ((dot_skims['SR3_H_TIME']) * (_vot_bin_s3 == 3))), +,walk_time_skims_out,od_skims['DISTWALK'] / walkSpeed * 60, +,walk_time_skims_inb,do_skims['DISTWALK'] / walkSpeed * 60, +#,,, +Determining Tour Origin,origin,df.origin if 'origin' in df.columns else df.home_zone_id if 'home_zone_id' in df.columns else df.workplace_zone_id, +#,,, +Person age,age,df.age, +# Household income (in dollars),income,df.incomeInDollars, +#,,, +Joint tour,is_joint,"df.get('tour_category', default=False) == 'joint'", +Determining Tour Destination,destination,df.destination if 'destination' in df.columns else df.alt_dest, +Origin MGRA Dwelling Unit Density,oMGRADUDen,"reindex(land_use.duden,origin)", +Origin MGRA Employment Density,oMGRAEmpDen,"reindex(land_use.empden,origin)", +Origin MGRA Total Intersections,oMGRATotInt,"reindex(land_use.totint,origin)", +Destination MGRA Dwelling Unit Density,dMGRADUDen,"reindex(land_use.duden,destination)", +Destination MGRA Employment Density,dMGRAEmpDen,"reindex(land_use.empden,destination)", +Destination MGRA Total Intersections,dMGRATotInt,"reindex(land_use.totint,destination)", +Origin MGRA Mix,oMGRAMix,"np.where(oMGRADUDen+oMGRAEmpDen > 0, (oMGRADUDen*oMGRAEmpDen)/(oMGRADUDen+oMGRAEmpDen),0)", +Destination MGRA Mix,dMGRAMix,"np.where(dMGRADUDen+dMGRAEmpDen > 0, (dMGRADUDen*dMGRAEmpDen)/(dMGRADUDen+dMGRAEmpDen),0)", +Origin MGRA Dwelling Unit Density Normalized by Average in Bike Estimation Sample,oMGRADUDenNorm_bike,oMGRADUDen/6.77, +Origin MGRA Employment Density Normalized by Average in Bike Estimation Sample,oMGRAEmpDenNorm_bike,oMGRAEmpDen/6.47, +Origin MGRA Dwelling Unit Density Normalized by Average in Walk Estimation Sample,oMGRADUDenNorm_walk,oMGRADUDen/7.18, +Origin MGRA Total Intersections Normalized by Average in Walk Estimation Sample,oMGRATotIntNorm_walk,oMGRATotInt/56.19, +Destination MGRA Employment Density Normalized by Average in Walk Estimation Sample,dMGRAEmpDenNorm_walk,dMGRAEmpDen/13.16, +Normalized Landuse Variable Sum [Origin Employment + DU],LUVarsNormalized_bike,oMGRADUDenNorm_bike + oMGRAEmpDenNorm_bike, +Normalized Landuse Variable Sum [Origin Intersection + DU],LUVarsNormalized_walk,oMGRADUDenNorm_walk + oMGRATotIntNorm_walk, +persontype,personType,"df.ptype*np.where(is_indiv,1,0)", +Origin Terminal Time,oTermTime,"reindex(land_use.TERMTIME,origin)", +Destination Terminal Time,dTermTime,"reindex(land_use.TERMTIME,destination)", +#,,, +# FIXME need bike logsums and times,,, +Person is female,female,(df.SEX == 2), +#bike logsum inbound,bikeLSI,odt_skims['BIKE_LOGSUM'], +#bike logsum outbound (same as inbound),bikeLSO,dot_skims['BIKE_LOGSUM'], +#bike time inbound,bike_time_inb,dot_skims['BIKE_TIME'], +#bike time outbound,bike_time_out,odt_skims['BIKE_TIME'], +bike logsum inbound,bikeLSI,0, +bike logsum outbound (same as inbound),bikeLSO,0, +bike time inbound,bike_time_inb,do_skims['DISTWALK'] / bikeSpeed * 60, +bike time outbound,bike_time_out,od_skims['DISTWALK'] / bikeSpeed * 60, +bike availability,bikeAvailable,(bikeLSI > -300) & (bikeLSO > -300) & (od_skims['DISTWALK']>0), + +#,,, +"Cost factor for shared 2 tours, 1/(2^0.8)",costFactorS2,0.57, +"Cost factor for shared 3+ tours, 1/(3.5^0.8)",costFactorS3,0.37, +,sov_available,"(age>=minimumAgeDA) * np.where(is_joint,0,1) * np.where(is_atwork_subtour==0,1,np.where(_parent_tour_mode=='DRIVEALONE',1,0))", +,sr2_available,"np.where(is_joint==0,1,np.where(num_participants>=3,0,1))", +,walkAvailable,"np.where(walk_time_skims_out < max_walk_time, 1,0) * np.where(walk_time_skims_inb < max_walk_time, 1,0)", +,SCHBUS_available,(tour_type =='school') & (personType!=3), +#,,, +Determining Tour Origin,origin,df.origin if 'origin' in df.columns else df.home_zone_id if 'home_zone_id' in df.columns else df.workplace_zone_id, +Determining Tour Destination,destination,df.destination if 'destination' in df.columns else df.alt_dest, +#access egress distances,,, +,origin_local_dist,"reindex(land_use.walk_dist_local_bus, origin)", +,origin_micro_local_dist_tncout,"odt_skims['KTW_ACC']/60 * driveSpeed", +,origin_micro_local_dist_tncin,"odt_skims['WTK_ACC']/60 * driveSpeed", +,dest_local_dist,"reindex(land_use.walk_dist_local_bus, destination)", +,dest_micro_local_dist,"reindex(land_use.micro_dist_local_bus, destination)", +#access egress times,,, +,origin_local_time,origin_local_dist * 60/walkSpeed, +,dest_local_time,dest_local_dist * 60/walkSpeed, +#,,, +,walk_local_available,(odt_skims['WTW_TIV']>0) & (dot_skims['WTW_TIV']>0), +,knr_local_available,(odt_skims['KTW_TIV']>0) & (dot_skims['WTK_TIV']>0), +,tnc_local_available,(odt_skims['KTW_TIV']>0) & (dot_skims['WTK_TIV']>0), +# Micromobility times,,, +ebike time inbound,ebike_time_inb,bike_time_inb * bikeSpeed / ebikeSpeed, +ebike time outbound,ebike_time_out,bike_time_out * bikeSpeed / ebikeSpeed, +ebike available,ebike_available,(ebike_time_inb>0) & (ebike_time_out>0), +escooter time inbound,escooter_time_inb,bike_time_inb * bikeSpeed / escooterSpeed, +escooter time outbound,escooter_time_out,bike_time_out * bikeSpeed / escooterSpeed, +escooter available,escooter_available,(escooter_time_inb>0) & (escooter_time_out>0), +Micromobility access time outbound,micro_access_out,"reindex(land_use.MicroAccessTime,origin)", +Micromobility access time inbound,micro_access_inb,"reindex(land_use.MicroAccessTime,destination)", +ebike max distance availability,ebikeMaxDistance,"(od_skims[('SOV_M_DIST', 'MD')] > ebikeMaxDist)", +escooter max distance availability,escooterMaxDistance,"(od_skims[('SOV_M_DIST', 'MD')] > escooterMaxDist)", +# Microtransit and NEV,,, +microtransit available at origin,microtransit_orig,"reindex(land_use.microtransit, df[orig_col_name])", +microtransit available at destination,microtransit_dest,"reindex(land_use.microtransit, df[dest_col_name])", +microtransit operating at time of tour,microtransit_operating,True, +microtransit available,microtransit_available,(microtransit_orig > 0) & (microtransit_orig == microtransit_dest) & (s3_dist_skims_out < microtransitMaxDist) & (s3_dist_skims_inb < microtransitMaxDist) & microtransit_operating, +microtransit direct time,microtransit_direct_time,"np.maximum(s3_dist_skims_out/microtransitSpeed*60, s3_time_skims_out) + np.maximum(s3_dist_skims_inb/microtransitSpeed*60, s3_time_skims_inb)", +microtransit total time,microtransit_time,"np.maximum(microtransit_direct_time + microtransitDiversionConstant, microtransitDiversionFactor*microtransit_direct_time)", +nev available at origin,nev_orig,"reindex(land_use.nev, df[orig_col_name])", +nev available at destination,nev_dest,"reindex(land_use.nev, df[dest_col_name])", +nev operating at time of tour,nev_operating,True, +nev available,nev_available,(nev_orig > 0) & (nev_orig == nev_dest) & (s3_dist_skims_out < nevMaxDist) & (s3_dist_skims_inb < nevMaxDist) & nev_operating, +nev direct time,nev_direct_time,"np.maximum(s3_dist_skims_out/nevSpeed*60, s3_time_skims_out) + np.maximum(s3_dist_skims_inb/nevSpeed*60, s3_time_skims_inb)", +nev total time,nev_time,"np.maximum(nev_direct_time + nevDiversionConstant, nevDiversionFactor*nev_direct_time)", +# Microtransit and NEV access to transit,,, +microtransit access to local available,microtransit_local_access_available,(microtransit_orig>0) & (origin_micro_local_dist_tncout0) & (origin_micro_local_dist_tncout0) & (dest_micro_local_dist>maxWalkIfMTAccessAvailable) & (dest_micro_local_dist0) & (dest_micro_local_dist>maxWalkIfMTAccessAvailable) & (dest_micro_local_dist 0) & (ldt_skims['WTW_TIV']>0) & (dlt_skims['WTW_TIV']>0)) if _CALC_PNR else False, +outbound auto time for pnr,auto_time_pnr_out,"0 if not _CALC_PNR else np.where(is_valid_pnr, (olt_skims['SOV_L_TIME'] * (_vot_bin_da == 1)) + (olt_skims['SOV_M_TIME'] * (_vot_bin_da == 2)) + (olt_skims['SOV_H_TIME'] * (_vot_bin_da == 3)), 0)", +inbound auto time for pnr,auto_time_pnr_inb,"0 if not _CALC_PNR else np.where(is_valid_pnr, (lot_skims['SOV_L_TIME'] * (_vot_bin_da == 1)) + (lot_skims['SOV_M_TIME'] * (_vot_bin_da == 2)) + (lot_skims['SOV_H_TIME'] * (_vot_bin_da == 3)), 0)", +outbound auto dist for pnr,auto_dist_pnr_out,"0 if not _CALC_PNR else np.where(is_valid_pnr, (olt_skims['SOV_L_DIST'] * (_vot_bin_da == 1)) + (olt_skims['SOV_M_DIST'] * (_vot_bin_da == 2)) + (olt_skims['SOV_H_DIST'] * (_vot_bin_da == 3)), 0)", +inbound auto dist for pnr,auto_dist_pnr_inb,"0 if not _CALC_PNR else np.where(is_valid_pnr, (lot_skims['SOV_L_DIST'] * (_vot_bin_da == 1)) + (lot_skims['SOV_M_DIST'] * (_vot_bin_da == 2)) + (lot_skims['SOV_H_DIST'] * (_vot_bin_da == 3)), 0)", +in vehicle transit time pnr,transit_time_pnr,"0 if not _CALC_PNR else np.where(is_valid_pnr, (ldt_skims['WTW_TIV'] + dlt_skims['WTW_TIV']), 0)", +transfers outbound pnr,transfers_pnr,"0 if not _CALC_PNR else np.where(is_valid_pnr, (ldt_skims['WTW_XFR'] + dlt_skims['WTW_XFR']), 0)", +transfer walk times pnr,xfer_walk_pnr,"0 if not _CALC_PNR else np.where(is_valid_pnr, (ldt_skims['WTW_AUX'] + dlt_skims['WTW_AUX']), 0)", +transfer wait times pnr,xfer_wait_pnr,"0 if not _CALC_PNR else np.where(is_valid_pnr, (ldt_skims['WTW_XWT'] + dlt_skims['WTW_XWT']), 0)", +first wait times pnr,iwait_pnr,"0 if not _CALC_PNR else np.where(is_valid_pnr, (ldt_skims['WTW_FWT'] + dlt_skims['WTW_FWT']), 0)", +walking access and egress times at dest end pnr,walk_at_dest_end_pnr,"2 * dest_local_time", +# FIXME need transit fares,,, +#fare pnr,fare_pnr,"0 if not _CALC_PNR else np.where(is_valid_pnr, (ldt_skims['WTW_FARE'] + dlt_skims['WTW_FARE']), 0)", +fare pnr,fare_pnr,0, diff --git a/resident/configs/tour_mode_choice_coefficients.csv b/resident/configs/tour_mode_choice_coefficients.csv new file mode 100644 index 0000000..ebab6a4 --- /dev/null +++ b/resident/configs/tour_mode_choice_coefficients.csv @@ -0,0 +1,754 @@ +coefficient_name,value,constrain +coef_zero,0.0,T +coef_one,1.0,T +coef_nest_root,1.0,T +coef_nest_AUTO,0.5,T +coef_nest_NONMOTORIZED,0.5,T +coef_nest_MICROMOBILITY,0.5,T +coef_nest_TRANSIT,0.5,T +coef_nest_RIDEHAIL,0.5,T +coef_nest_SCHOOL_BUS,0.5,T +coef_ivt_work,-0.016,F +coef_ivt_univ,-0.016,F +coef_ivt_school,-0.01,F +coef_ivt_maint,-0.017,F +coef_ivt_disc,-0.015,F +coef_ivt_atwork,-0.032,F +coef_rel_out_work,-0.224,F +coef_rel_out_univ,-0.192,F +coef_rel_out_school,-0.12,F +coef_rel_out_maint,-0.204,F +coef_rel_out_disc,-0.18,F +coef_rel_out_atwork,-0.384,F +coef_rel_inb_work,-0.192,F +coef_rel_inb_univ,-0.192,F +coef_rel_inb_school,-0.12,F +coef_rel_inb_maint,-0.204,F +coef_rel_inb_disc,-0.18,F +coef_rel_inb_atwork,-0.384,F +coef_income_work,-0.625,F +coef_income_univ,-0.262,F +coef_income_school,-0.262,F +coef_income_maint,-0.262,F +coef_income_disc,-0.262,F +coef_income_atwork,-0.262,F +coef_walktime_work,-0.0424,F +coef_walktime_univ,-0.0424,F +coef_walktime_school,-0.068,F +coef_walktime_maint,-0.03995,F +coef_walktime_disc,-0.045,F +coef_walktime_atwork,-0.0848,F +coef_bikels_work,0.134333061,F +coef_bikels_univ,0.134333061,F +coef_bikels_school,0.214932897,F +coef_bikels_maint,0.22,F +coef_bikels_disc,0.22,F +coef_bikels_atwork,0.23,F +coef_wait_work,-0.024,F +coef_wait_univ,-0.024,F +coef_wait_school,-0.015,F +coef_wait_maint,-0.0255,F +coef_wait_disc,-0.0225,F +coef_wait_atwork,-0.048,F +coef_xwalk_work,-0.04,F +coef_xwalk_univ,-0.04,F +coef_xwalk_school,-0.025,F +coef_xwalk_maint,-0.0425,F +coef_xwalk_disc,-0.0375,F +coef_xwalk_atwork,-0.08,F +coef_xwait_work,-0.032,F +coef_xwait_univ,-0.032,F +coef_xwait_school,-0.02,F +coef_xwait_maint,-0.034,F +coef_xwait_disc,-0.03,F +coef_xwait_atwork,-0.064,F +coef_xfer_work,-0.024,F +coef_xfer_univ,-0.024,F +coef_xfer_school,-0.015,F +coef_xfer_maint,-0.0255,F +coef_xfer_disc,-0.0225,F +coef_xfer_atwork,-0.048,F +coef_xferdrive_work,-0.032,F +coef_xferdrive_univ,-0.032,F +coef_xferdrive_school,-0.02,F +coef_xferdrive_maint,-0.034,F +coef_xferdrive_disc,-0.03,F +coef_xferdrive_atwork,-0.064,F +coef_acctime_work,-0.032,F +coef_acctime_univ,-0.032,F +coef_acctime_school,-0.02,F +coef_acctime_maint,-0.034,F +coef_acctime_disc,-0.03,F +coef_acctime_atwork,-0.064,F +coef_oMix_nmot_work,0.210144,F +coef_oMix_wTran_work,0.0,F +coef_oIntDen_nmot_work,0.002998,F +coef_oIntDen_wTran_work,0.0,F +coef_dEmpDen_nmot_work,0.020707,F +coef_dEmpDen_wTran_work,0.0,F +coef_dEmpDen_dTran_work,0.0,F +coef_age1624_sr2_work,-0.21388,F +coef_age1624_sr3p_work,-1.79023,F +coef_age1624_nmot_work,0.303216,F +coef_age1624_tran_work,0.794718,F +coef_age4155_sr2_work,-0.30638,F +coef_age4155_sr3p_work,-0.41025,F +coef_age4155_nmot_work,-0.17752,F +coef_age4155_tran_work,-0.42301,F +coef_age5664_sr2_work,-1.02962,F +coef_age5664_sr3p_work,-0.85641,F +coef_age5664_nmot_work,-0.64534,F +coef_age5664_tran_work,-0.44991,F +coef_age65pl_sr2_work,-0.67111,F +coef_age65pl_sr3p_work,-1.43462,F +coef_age65pl_nmot_work,-1.45334,F +coef_age65pl_tran_work,-1.1231,F +coef_female_sr2_work,0.594728,F +coef_female_sr3p_work,0.848064,F +coef_female_tran_work,0.157786,F +coef_female_nmot_work,0.0,F +coef_ageund16_bike_work,0.0,F +coef_age1624_bike_work,0.0,F +coef_age4155_bike_work,-0.73111,F +coef_age5664_bike_work,-0.64352,F +coef_age65pl_bike_work,-1.54867,F +coef_female_bike_work,-1.19364,F +coef_inc100plus_bike_work,0.641381,F +coef_LUnorm_bike_work,0.083266,F +coef_oMlCoast_bike_work,-1.42018,F +coef_oMlCoast2p_bike_work,1.335076,F +coef_oMlCoast5p_bike_work,0.079563,F +coef_ageund16_walk_work,2.188395,F +coef_age1624_walk_work,1.430593,F +coef_age4155_walk_work,-0.43207,F +coef_age5664_walk_work,-0.51999,F +coef_age65pl_walk_work,-0.83162,F +coef_female_walk_work,0.0,F +coef_inc60100_walk_work,-0.20839,F +coef_LUnorm_walk_work,0.009309,F +coef_dEmpNorm_walk_work,0.099811,F +coef_hhsize2_sr2_work,1.069642353,F +coef_hhsize2_sr3p_work,-0.467357298,F +coef_hhsize3_sr2_work,1.58018418,F +coef_hhsize3_sr3p_work,0.654631352,F +coef_hhsize4p_sr2_work,1.688389262,F +coef_hhsize4p_sr3p_work,1.49870326,F +coef_oMix_nmot_univ,0.122315,F +coef_oMix_wTran_univ,0.0,F +coef_oIntDen_nmot_univ,0.009072,F +coef_oIntDen_wTran_univ,0.0,F +coef_dEmpDen_nmot_univ,0.081786,F +coef_dEmpDen_wTran_univ,0.0,F +coef_dEmpDen_dTran_univ,0.0,F +coef_age1624_sr2_univ,0.0,F +coef_age1624_sr3p_univ,0.0,F +coef_age1624_nmot_univ,0.0,F +coef_age1624_tran_univ,0.461169,F +coef_age4155_sr2_univ,0.0,F +coef_age4155_sr3p_univ,0.0,F +coef_age4155_nmot_univ,0.0,F +coef_age4155_tran_univ,0.0,F +coef_age5664_sr2_univ,0.0,F +coef_age5664_sr3p_univ,0.0,F 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+coef_hhsize2_sr2_univ,1.871179646,F +coef_hhsize2_sr3p_univ,1.871179646,F +coef_hhsize3_sr2_univ,1.871179646,F +coef_hhsize3_sr3p_univ,1.871179646,F +coef_hhsize4p_sr2_univ,2.426268851,F +coef_hhsize4p_sr3p_univ,2.426268851,F +coef_oMix_nmot_school,0.0,F +coef_oMix_wTran_school,0.0,F +coef_oIntDen_nmot_school,0.002951,F +coef_oIntDen_wTran_school,0.0,F +coef_dEmpDen_nmot_school,0.0,F +coef_dEmpDen_wTran_school,0.0,F +coef_dEmpDen_dTran_school,0.0,F +coef_age0105_schb_school,0.0,F +coef_age0105_nmot_school,-1.16217,F +coef_age0105_tran_school,-6.49996,F +coef_age0612_schb_school,1.448589,F +coef_age0612_nmot_school,-0.57675,F +coef_age0612_tran_school,-4.5987,F +coef_age1315_schb_school,1.296494,F +coef_age1315_nmot_school,0.687165,F +coef_age1315_tran_school,-1.18344,F +coef_female_sr2_school,0.370191,F +coef_female_sr3p_school,0.326161,F +coef_female_tran_school,0.609312,F +coef_female_nmot_school,0.0,F +coef_female_schb_school,0.0,F +coef_ageund16_bike_school,0.0,F 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+coef_age5664_sr3p_atwork,0.0,F +coef_age5664_nmot_atwork,0.0,F +coef_age5664_tran_atwork,-1.263,F +coef_age65pl_sr2_atwork,0.0,F +coef_age65pl_sr3p_atwork,0.0,F +coef_age65pl_nmot_atwork,0.0,F +coef_age65pl_tran_atwork,0.0,F +coef_sr_da_atwork,-0.824,F +coef_sr_sr_atwork,2.435,F +coef_female_sr2_atwork,0.0,F +coef_female_sr3p_atwork,0.0,F +coef_female_tran_atwork,0.0,F +coef_female_nmot_atwork,0.0,F +coef_oMix_nmot_atwork,0.214,F +coef_oMix_wTran_atwork,0.0,F +coef_oIntDen_nmot_atwork,0.0,F +coef_oIntDen_wTran_atwork,0.0,F +coef_dEmpDen_nmot_atwork,0.0,F +coef_dEmpDen_wTran_atwork,0.0,F +coef_dEmpDen_dTran_atwork,0.0,F +coef_hhsize2_sr2_atwork,0.0,F +coef_hhsize2_sr3p_atwork,0.0,F +coef_hhsize3_sr2_atwork,0.0,F +coef_hhsize3_sr3p_atwork,0.0,F +coef_hhsize4p_sr2_atwork,0.0,F +coef_hhsize4p_sr3p_atwork,0.0,F +coef_calib_civtebikeownership_BIKE_school,1.0,F +coef_calib_civtebikeownership_BIKE_atwork,1.0,F +coef_calib_civtebikeownership_BIKE_maint,1.0,F +coef_calib_escorttour_WALK_maint,-1.2579,F +coef_calib_escorttour_PNR_TRANSIT_maint,-5.8388,F +coef_calib_civtebikeownership_BIKE_disc,1.0,F +coef_calib_civtebikeownership_BIKE_univ,1.0,F +coef_calib_escorttour_WALK_TRANSIT_maint,-5.8388,F +coef_calib_escorttour_BIKE_maint,-1.2579,F +coef_calib_escorttour_TNC_TRANSIT_maint,-5.8388,F +coef_calib_escorttour_KNR_TRANSIT_maint,-5.8388,F +coef_calib_civtebikeownership_BIKE_work,1.0,F +coef_calib_parkingconst_WLK_TRANSIT_work,0.64,F +coef_calib_parkingconst_WLK_TRANSIT_univ,0.64,F +coef_calib_parkingconst_WLK_TRANSIT_school,0.4,F +coef_calib_parkingconst_WLK_TRANSIT_maint,0.765,F +coef_calib_parkingconst_WLK_TRANSIT_disc,0.9,F +coef_calib_parkingconst_WLK_TRANSIT_atwork,0.96,F +coef_calib_parkingconst_DRV_TRANSIT_work,1.28,F +coef_calib_parkingconst_DRV_TRANSIT_univ,1.28,F +coef_calib_parkingconst_DRV_TRANSIT_school,0.8,F +coef_calib_parkingconst_DRV_TRANSIT_maint,1.36,F +coef_calib_parkingconst_DRV_TRANSIT_disc,1.2,F +coef_calib_parkingconst_DRV_TRANSIT_atwork,1.92,F +coef_calib_distance_WALK_TRANSIT_work,-0.016,F +coef_calib_distance_WALK_TRANSIT_univ,-0.016,F +coef_calib_distance_WALK_TRANSIT_school,-0.01,F +coef_calib_distance_WALK_TRANSIT_maint,-0.017,F +coef_calib_distance_WALK_TRANSIT_disc,-0.15,F +coef_calib_distance_WALK_TRANSIT_atwork,-0.032,F +coef_calib_distance_PNR_TRANSIT_work,-0.016,F +coef_calib_distance_PNR_TRANSIT_univ,-0.016,F +coef_calib_distance_PNR_TRANSIT_school,-0.01,F +coef_calib_distance_PNR_TRANSIT_maint,-0.017,F +coef_calib_distance_PNR_TRANSIT_disc,-0.015,F +coef_calib_distance_PNR_TRANSIT_atwork,-0.032,F +coef_calib_distance_KNR_TRANSIT_work,-0.08,F +coef_calib_distance_KNR_TRANSIT_univ,-0.08,F +coef_calib_distance_KNR_TRANSIT_school,-0.05,F +coef_calib_distance_KNR_TRANSIT_maint,-0.085,F +coef_calib_distance_KNR_TRANSIT_disc,-0.075,F +coef_calib_distance_KNR_TRANSIT_atwork,-0.16,F +coef_calib_distance_TNC_TRANSIT_work,-0.016,F +coef_calib_distance_TNC_TRANSIT_univ,-0.016,F +coef_calib_distance_TNC_TRANSIT_school,-0.01,F +coef_calib_distance_TNC_TRANSIT_maint,-0.017,F +coef_calib_distance_TNC_TRANSIT_disc,-0.015,F +coef_calib_distance_TNC_TRANSIT_atwork,-0.032,F +#coef_calib_zeroautohhindivtou_DRIVEALONE_work,-0.8236,F +coef_calib_zeroautohhindivtou_SHARED2_work,-1.0611602231451105,F +coef_calib_zeroautohhindivtou_SHARED3_work,-4.1746,F +coef_calib_zeroautohhindivtou_WALK_work,2.557215851820022,F +coef_calib_zeroautohhindivtou_BIKE_work,-8.079656586782757,F +coef_calib_zeroautohhindivtou_WALK_TRANSIT_work,-1.000,F +coef_calib_zeroautohhindivtou_PNR_TRANSIT_work,-999.0,F +coef_calib_zeroautohhindivtou_KNR_TRANSIT_work,-4.075069451,F +coef_calib_zeroautohhindivtou_TNC_TRANSIT_work,-929.0,F +coef_calib_zeroautohhindivtou_TAXI_work,-1.9627,F +coef_calib_zeroautohhindivtou_TNC_SINGLE_work,1.8216385166062703,F +coef_calib_zeroautohhindivtou_TNC_SHARED_work,-3.9627,F 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+coef_calib_zeroautohhjointtou_KNR_TRANSIT_disc,-67.84999945,F +coef_calib_zeroautohhjointtou_TNC_TRANSIT_disc,-999.0,F +coef_calib_zeroautohhjointtou_TAXI_disc,-999.0,F +coef_calib_zeroautohhjointtou_TNC_SINGLE_disc,-999.0,F +coef_calib_zeroautohhjointtou_TNC_SHARED_disc,-999.0,F +coef_calib_zeroautohhjointtou_EBIKE_disc,-60.164697525020216,F +coef_calib_zeroautohhjointtou_ESCOOTER_disc,-57.8720071731557,F +#coef_calib_autodeficienthhjoi_SHARED2_disc,10.5330624,F +coef_calib_autodeficienthhjoi_SHARED3_disc,-48.16957674661429,F +coef_calib_autodeficienthhjoi_WALK_disc,-48.63043930165117,F +coef_calib_autodeficienthhjoi_BIKE_disc,-887.4773565867829,F +coef_calib_autodeficienthhjoi_WALK_TRANSIT_disc,-51.55258834772342,F +coef_calib_autodeficienthhjoi_PNR_TRANSIT_disc,-53.1684399,F +coef_calib_autodeficienthhjoi_KNR_TRANSIT_disc,-53.12678499,F +coef_calib_autodeficienthhjoi_TNC_TRANSIT_disc,-999.0,F +coef_calib_autodeficienthhjoi_TAXI_disc,-52.71598351,F +coef_calib_autodeficienthhjoi_TNC_SINGLE_disc,-54.71598351,F +coef_calib_autodeficienthhjoi_TNC_SHARED_disc,-54.71598351,F +coef_calib_autodeficienthhjoi_EBIKE_disc,-53.59405712502022,F +coef_calib_autodeficienthhjoi_ESCOOTER_disc,-54.8720071731557,F +#coef_calib_autosufficienthhjo_SHARED2_disc,4.253195896,F +coef_calib_autosufficienthhjo_SHARED3_disc,-2.175409902383395,F +coef_calib_autosufficienthhjo_WALK_disc,-1.753924105381346,F +coef_calib_autosufficienthhjo_BIKE_disc,-17.70975822678276,F +coef_calib_autosufficienthhjo_WALK_TRANSIT_disc,-18.918341,F +coef_calib_autosufficienthhjo_PNR_TRANSIT_disc,-15.46037795,F +coef_calib_autosufficienthhjo_KNR_TRANSIT_disc,-22.31188559,F +coef_calib_autosufficienthhjo_TNC_TRANSIT_disc,-19.31188559,F +coef_calib_autosufficienthhjo_TAXI_disc,-18.0336306,F +coef_calib_autosufficienthhjo_TNC_SINGLE_disc,-20.0336306,F +coef_calib_autosufficienthhjo_TNC_SHARED_disc,-20.0336306,F +coef_calib_autosufficienthhjo_EBIKE_disc,-9.51289261499182,F +coef_calib_autosufficienthhjo_ESCOOTER_disc,-18.872007173155698,F +coef_calib_zeroautohhindivtou_ESCOOTER_work,-6.992007173155711,F +coef_calib_zeroautohhindivtou_EBIKE_work,-7.2846975250202135,F +coef_calib_zeroautohhindivtou_ESCOOTER_univ,-6.992007173155711,F +coef_calib_zeroautohhindivtou_EBIKE_univ,-7.2846975250202135,F +coef_calib_zeroautohhindivtou_ESCOOTER_school,-4.2720071731557105,F +coef_calib_zeroautohhindivtou_EBIKE_school,-0.5646975250202154,F +coef_calib_zeroautohhindivtou_ESCOOTER_atwork,-31.912007173155704,F +coef_calib_zeroautohhindivtou_EBIKE_atwork,-88.20469752502021,F +coef_calib_autodeficienthhind_ESCOOTER_work,-8.99200717315571,F +coef_calib_autodeficienthhind_EBIKE_work,-7.2846975250202135,F +coef_calib_autodeficienthhind_ESCOOTER_univ,-4.992007173155711,F +coef_calib_autodeficienthhind_EBIKE_univ,-5.284697525020215,F +coef_calib_autodeficienthhind_ESCOOTER_school,-6.2720071731557105,F +coef_calib_autodeficienthhind_EBIKE_school,-4.564697525020215,F +coef_calib_autodeficienthhind_ESCOOTER_atwork,-10.912007173155711,F +coef_calib_autodeficienthhind_EBIKE_atwork,-11.204697525020217,F +coef_calib_autosufficienthhin_ESCOOTER_work,-8.99200717315571,F +coef_calib_autosufficienthhin_EBIKE_work,-5.606539508020214,F +coef_calib_autosufficienthhin_ESCOOTER_univ,-4.992007173155711,F +coef_calib_autosufficienthhin_EBIKE_univ,-5.284697525020215,F +coef_calib_autosufficienthhin_ESCOOTER_school,-6.2720071731557105,F +coef_calib_autosufficienthhin_EBIKE_school,-2.9282323636347396,F +coef_calib_autosufficienthhin_ESCOOTER_atwork,-10.912007173155711,F +coef_calib_autosufficienthhin_EBIKE_atwork,-5.877228265020214,F +coef_calib_onboard,-0.293475781,F +coef_calib_mt_zeroautohh,4.825,F +coef_calib_nev_zeroautohh,4.825,F +coef_calib_ebike_owner_BIKE,-1.407,F +coef_calib_ebike_owner_EBIKE,0.942,F +coef_calib_ebike_shared,-4.139014765889209,F \ No newline at end of file diff --git a/resident/configs/tour_mode_choice_coefficients_template.csv b/resident/configs/tour_mode_choice_coefficients_template.csv new file mode 100644 index 0000000..e56c5e5 --- /dev/null +++ b/resident/configs/tour_mode_choice_coefficients_template.csv @@ -0,0 +1,191 @@ +coefficient_name,work,univ,school,shopping,escort,othmaint,social,eatout,othdiscr,atwork +#same for all segments,,,,,,,,,, +coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one +coef_nest_root,coef_nest_root,coef_nest_root,coef_nest_root,coef_nest_root,coef_nest_root,coef_nest_root,coef_nest_root,coef_nest_root,coef_nest_root,coef_nest_root +coef_nest_AUTO,coef_nest_AUTO,coef_nest_AUTO,coef_nest_AUTO,coef_nest_AUTO,coef_nest_AUTO,coef_nest_AUTO,coef_nest_AUTO,coef_nest_AUTO,coef_nest_AUTO,coef_nest_AUTO +coef_nest_NONMOTORIZED,coef_nest_NONMOTORIZED,coef_nest_NONMOTORIZED,coef_nest_NONMOTORIZED,coef_nest_NONMOTORIZED,coef_nest_NONMOTORIZED,coef_nest_NONMOTORIZED,coef_nest_NONMOTORIZED,coef_nest_NONMOTORIZED,coef_nest_NONMOTORIZED,coef_nest_NONMOTORIZED +coef_nest_MICROMOBILITY,coef_nest_MICROMOBILITY,coef_nest_MICROMOBILITY,coef_nest_MICROMOBILITY,coef_nest_MICROMOBILITY,coef_nest_MICROMOBILITY,coef_nest_MICROMOBILITY,coef_nest_MICROMOBILITY,coef_nest_MICROMOBILITY,coef_nest_MICROMOBILITY,coef_nest_MICROMOBILITY +coef_nest_TRANSIT,coef_nest_TRANSIT,coef_nest_TRANSIT,coef_nest_TRANSIT,coef_nest_TRANSIT,coef_nest_TRANSIT,coef_nest_TRANSIT,coef_nest_TRANSIT,coef_nest_TRANSIT,coef_nest_TRANSIT,coef_nest_TRANSIT +coef_nest_RIDEHAIL,coef_nest_RIDEHAIL,coef_nest_RIDEHAIL,coef_nest_RIDEHAIL,coef_nest_RIDEHAIL,coef_nest_RIDEHAIL,coef_nest_RIDEHAIL,coef_nest_RIDEHAIL,coef_nest_RIDEHAIL,coef_nest_RIDEHAIL,coef_nest_RIDEHAIL +coef_nest_SCHOOL_BUS,coef_nest_SCHOOL_BUS,coef_nest_SCHOOL_BUS,coef_nest_SCHOOL_BUS,coef_nest_SCHOOL_BUS,coef_nest_SCHOOL_BUS,coef_nest_SCHOOL_BUS,coef_nest_SCHOOL_BUS,coef_nest_SCHOOL_BUS,coef_nest_SCHOOL_BUS,coef_nest_SCHOOL_BUS +coef_ivt,coef_ivt_work,coef_ivt_univ,coef_ivt_school,coef_ivt_maint,coef_ivt_maint,coef_ivt_maint,coef_ivt_disc,coef_ivt_disc,coef_ivt_disc,coef_ivt_atwork +coef_rel_out,coef_rel_out_work,coef_rel_out_univ,coef_rel_out_school,coef_rel_out_maint,coef_rel_out_maint,coef_rel_out_maint,coef_rel_out_disc,coef_rel_out_disc,coef_rel_out_disc,coef_rel_out_atwork +coef_rel_inb,coef_rel_inb_work,coef_rel_inb_univ,coef_rel_inb_school,coef_rel_inb_maint,coef_rel_inb_maint,coef_rel_inb_maint,coef_rel_inb_disc,coef_rel_inb_disc,coef_rel_inb_disc,coef_rel_inb_atwork +coef_income,coef_income_work,coef_income_univ,coef_income_school,coef_income_maint,coef_income_maint,coef_income_maint,coef_income_disc,coef_income_disc,coef_income_disc,coef_income_atwork +coef_walktime,coef_walktime_work,coef_walktime_univ,coef_walktime_school,coef_walktime_maint,coef_walktime_maint,coef_walktime_maint,coef_walktime_disc,coef_walktime_disc,coef_walktime_disc,coef_walktime_atwork +coef_bikeLogsum,coef_bikels_work,coef_bikels_univ,coef_bikels_school,coef_bikels_maint,coef_bikels_maint,coef_bikels_maint,coef_bikels_disc,coef_bikels_disc,coef_bikels_disc,coef_bikels_atwork +#,,,,,,,,,, +coef_wait,coef_wait_work,coef_wait_univ,coef_wait_school,coef_wait_maint,coef_wait_maint,coef_wait_maint,coef_wait_disc,coef_wait_disc,coef_wait_disc,coef_wait_atwork +coef_xwait,coef_xwait_work,coef_xwait_univ,coef_xwait_school,coef_xwait_maint,coef_xwait_maint,coef_xwait_maint,coef_xwait_disc,coef_xwait_disc,coef_xwait_disc,coef_xwait_atwork +coef_acctime,coef_acctime_work,coef_acctime_univ,coef_acctime_school,coef_acctime_maint,coef_acctime_maint,coef_acctime_maint,coef_acctime_disc,coef_acctime_disc,coef_acctime_disc,coef_acctime_atwork +coef_xfer,coef_xfer_work,coef_xfer_univ,coef_xfer_school,coef_xfer_maint,coef_xfer_maint,coef_xfer_maint,coef_xfer_disc,coef_xfer_disc,coef_xfer_disc,coef_xfer_atwork +coef_xferdrive,coef_xferdrive_work,coef_xferdrive_univ,coef_xferdrive_school,coef_xferdrive_maint,coef_xferdrive_maint,coef_xferdrive_maint,coef_xferdrive_disc,coef_xferdrive_disc,coef_xferdrive_disc,coef_xferdrive_atwork +coef_xwalk,coef_xwalk_work,coef_xwalk_univ,coef_xwalk_school,coef_xwalk_maint,coef_xwalk_maint,coef_xwalk_maint,coef_xwalk_disc,coef_xwalk_disc,coef_xwalk_disc,coef_xwalk_atwork +#,,,,,,,,,, +coef_oMix_nmot,coef_oMix_nmot_work,coef_oMix_nmot_univ,coef_oMix_nmot_school,coef_oMix_nmot_maint,coef_oMix_nmot_maint,coef_oMix_nmot_maint,coef_oMix_nmot_disc,coef_oMix_nmot_disc,coef_oMix_nmot_disc,coef_oMix_nmot_atwork +coef_oMix_wTran,coef_oMix_wTran_work,coef_oMix_wTran_univ,coef_oMix_wTran_school,coef_oMix_wTran_maint,coef_oMix_wTran_maint,coef_oMix_wTran_maint,coef_oMix_wTran_disc,coef_oMix_wTran_disc,coef_oMix_wTran_disc,coef_oMix_wTran_atwork +coef_oIntDen_nmot,coef_oIntDen_nmot_work,coef_oIntDen_nmot_univ,coef_oIntDen_nmot_school,coef_oIntDen_nmot_maint,coef_oIntDen_nmot_maint,coef_oIntDen_nmot_maint,coef_oIntDen_nmot_disc,coef_oIntDen_nmot_disc,coef_oIntDen_nmot_disc,coef_oIntDen_nmot_atwork +coef_oIntDen_wTran,coef_oIntDen_wTran_work,coef_oIntDen_wTran_univ,coef_oIntDen_wTran_school,coef_oIntDen_wTran_maint,coef_oIntDen_wTran_maint,coef_oIntDen_wTran_maint,coef_oIntDen_wTran_disc,coef_oIntDen_wTran_disc,coef_oIntDen_wTran_disc,coef_oIntDen_wTran_atwork +coef_dEmpDen_nmot,coef_dEmpDen_nmot_work,coef_dEmpDen_nmot_univ,coef_dEmpDen_nmot_school,coef_dEmpDen_nmot_maint,coef_dEmpDen_nmot_maint,coef_dEmpDen_nmot_maint,coef_dEmpDen_nmot_disc,coef_dEmpDen_nmot_disc,coef_dEmpDen_nmot_disc,coef_dEmpDen_nmot_atwork +coef_dEmpDen_wTran,coef_dEmpDen_wTran_work,coef_dEmpDen_wTran_univ,coef_dEmpDen_wTran_school,coef_dEmpDen_wTran_maint,coef_dEmpDen_wTran_maint,coef_dEmpDen_wTran_maint,coef_dEmpDen_wTran_disc,coef_dEmpDen_wTran_disc,coef_dEmpDen_wTran_disc,coef_dEmpDen_wTran_atwork +coef_dEmpDen_dTran,coef_dEmpDen_dTran_work,coef_dEmpDen_dTran_univ,coef_dEmpDen_dTran_school,coef_dEmpDen_dTran_maint,coef_dEmpDen_dTran_maint,coef_dEmpDen_dTran_maint,coef_dEmpDen_dTran_disc,coef_dEmpDen_dTran_disc,coef_dEmpDen_dTran_disc,coef_dEmpDen_dTran_atwork +coef_age1624_sr2,coef_age1624_sr2_work,coef_age1624_sr2_univ,coef_zero,coef_age1624_sr2_maint,coef_age1624_sr2_maint,coef_age1624_sr2_maint,coef_age1624_sr2_disc,coef_age1624_sr2_disc,coef_age1624_sr2_disc,coef_age1624_sr2_atwork +coef_age1624_sr3p,coef_age1624_sr3p_work,coef_age1624_sr3p_univ,coef_zero,coef_age1624_sr3p_maint,coef_age1624_sr3p_maint,coef_age1624_sr3p_maint,coef_age1624_sr3p_disc,coef_age1624_sr3p_disc,coef_age1624_sr3p_disc,coef_age1624_sr3p_atwork +coef_age1624_nmot,coef_age1624_nmot_work,coef_age1624_nmot_univ,coef_zero,coef_age1624_nmot_maint,coef_age1624_nmot_maint,coef_age1624_nmot_maint,coef_age1624_nmot_disc,coef_age1624_nmot_disc,coef_age1624_nmot_disc,coef_age1624_nmot_atwork +coef_age1624_tran,coef_age1624_tran_work,coef_age1624_tran_univ,coef_zero,coef_age1624_tran_maint,coef_age1624_tran_maint,coef_age1624_tran_maint,coef_age1624_tran_disc,coef_age1624_tran_disc,coef_age1624_tran_disc,coef_age1624_tran_atwork +coef_age4155_sr2,coef_age4155_sr2_work,coef_age4155_sr2_univ,coef_zero,coef_age4155_sr2_maint,coef_age4155_sr2_maint,coef_age4155_sr2_maint,coef_age4155_sr2_disc,coef_age4155_sr2_disc,coef_age4155_sr2_disc,coef_age4155_sr2_atwork +coef_age4155_sr3p,coef_age4155_sr3p_work,coef_age4155_sr3p_univ,coef_zero,coef_age4155_sr3p_maint,coef_age4155_sr3p_maint,coef_age4155_sr3p_maint,coef_age4155_sr3p_disc,coef_age4155_sr3p_disc,coef_age4155_sr3p_disc,coef_age4155_sr3p_atwork +coef_age4155_nmot,coef_age4155_nmot_work,coef_age4155_nmot_univ,coef_zero,coef_age4155_nmot_maint,coef_age4155_nmot_maint,coef_age4155_nmot_maint,coef_age4155_nmot_disc,coef_age4155_nmot_disc,coef_age4155_nmot_disc,coef_age4155_nmot_atwork +coef_age4155_tran,coef_age4155_tran_work,coef_age4155_tran_univ,coef_zero,coef_age4155_tran_maint,coef_age4155_tran_maint,coef_age4155_tran_maint,coef_age4155_tran_disc,coef_age4155_tran_disc,coef_age4155_tran_disc,coef_age4155_tran_atwork +coef_age5664_sr2,coef_age5664_sr2_work,coef_age5664_sr2_univ,coef_zero,coef_age5664_sr2_maint,coef_age5664_sr2_maint,coef_age5664_sr2_maint,coef_age5664_sr2_disc,coef_age5664_sr2_disc,coef_age5664_sr2_disc,coef_age5664_sr2_atwork +coef_age5664_sr3p,coef_age5664_sr3p_work,coef_age5664_sr3p_univ,coef_zero,coef_age5664_sr3p_maint,coef_age5664_sr3p_maint,coef_age5664_sr3p_maint,coef_age5664_sr3p_disc,coef_age5664_sr3p_disc,coef_age5664_sr3p_disc,coef_age5664_sr3p_atwork +coef_age5664_nmot,coef_age5664_nmot_work,coef_age5664_nmot_univ,coef_zero,coef_age5664_nmot_maint,coef_age5664_nmot_maint,coef_age5664_nmot_maint,coef_age5664_nmot_disc,coef_age5664_nmot_disc,coef_age5664_nmot_disc,coef_age5664_nmot_atwork +coef_age5664_tran,coef_age5664_tran_work,coef_age5664_tran_univ,coef_zero,coef_age5664_tran_maint,coef_age5664_tran_maint,coef_age5664_tran_maint,coef_age5664_tran_disc,coef_age5664_tran_disc,coef_age5664_tran_disc,coef_age5664_tran_atwork +coef_age65pl_sr2,coef_age65pl_sr2_work,coef_age65pl_sr2_univ,coef_zero,coef_age65pl_sr2_maint,coef_age65pl_sr2_maint,coef_age65pl_sr2_maint,coef_age65pl_sr2_disc,coef_age65pl_sr2_disc,coef_age65pl_sr2_disc,coef_age65pl_sr2_atwork +coef_age65pl_sr3p,coef_age65pl_sr3p_work,coef_age65pl_sr3p_univ,coef_zero,coef_age65pl_sr3p_maint,coef_age65pl_sr3p_maint,coef_age65pl_sr3p_maint,coef_age65pl_sr3p_disc,coef_age65pl_sr3p_disc,coef_age65pl_sr3p_disc,coef_age65pl_sr3p_atwork +coef_age65pl_nmot,coef_age65pl_nmot_work,coef_age65pl_nmot_univ,coef_zero,coef_age65pl_nmot_maint,coef_age65pl_nmot_maint,coef_age65pl_nmot_maint,coef_age65pl_nmot_disc,coef_age65pl_nmot_disc,coef_age65pl_nmot_disc,coef_age65pl_nmot_atwork +coef_age65pl_tran,coef_age65pl_tran_work,coef_age65pl_tran_univ,coef_zero,coef_age65pl_tran_maint,coef_age65pl_tran_maint,coef_age65pl_tran_maint,coef_age65pl_tran_disc,coef_age65pl_tran_disc,coef_age65pl_tran_disc,coef_age65pl_tran_atwork +coef_female_sr2,coef_female_sr2_work,coef_female_sr2_univ,coef_female_sr2_school,coef_female_sr2_maint,coef_female_sr2_maint,coef_female_sr2_maint,coef_female_sr2_disc,coef_female_sr2_disc,coef_female_sr2_disc,coef_female_sr2_atwork +coef_female_sr3p,coef_female_sr3p_work,coef_female_sr3p_univ,coef_female_sr3p_school,coef_female_sr3p_maint,coef_female_sr3p_maint,coef_female_sr3p_maint,coef_female_sr3p_disc,coef_female_sr3p_disc,coef_female_sr3p_disc,coef_female_sr3p_atwork +coef_female_tran,coef_female_tran_work,coef_female_tran_univ,coef_female_tran_school,coef_female_tran_maint,coef_female_tran_maint,coef_female_tran_maint,coef_female_tran_disc,coef_female_tran_disc,coef_female_tran_disc,coef_female_tran_atwork +coef_female_nmot,coef_female_nmot_work,coef_female_nmot_univ,coef_female_nmot_school,coef_female_nmot_maint,coef_female_nmot_maint,coef_female_nmot_maint,coef_female_nmot_disc,coef_female_nmot_disc,coef_female_nmot_disc,coef_female_nmot_atwork +coef_hhsize2_sr2,coef_hhsize2_sr2_work,coef_hhsize2_sr2_univ,coef_hhsize2_sr2_school,coef_hhsize2_sr2_maint,coef_hhsize2_sr2_maint,coef_hhsize2_sr2_maint,coef_hhsize2_sr2_disc,coef_hhsize2_sr2_disc,coef_hhsize2_sr2_disc,coef_hhsize2_sr2_atwork +coef_hhsize2_sr3p,coef_hhsize2_sr3p_work,coef_hhsize2_sr3p_univ,coef_hhsize2_sr3p_school,coef_hhsize2_sr3p_maint,coef_hhsize2_sr3p_maint,coef_hhsize2_sr3p_maint,coef_hhsize2_sr3p_disc,coef_hhsize2_sr3p_disc,coef_hhsize2_sr3p_disc,coef_hhsize2_sr3p_atwork +coef_hhsize3_sr2,coef_hhsize3_sr2_work,coef_hhsize3_sr2_univ,coef_hhsize3_sr2_school,coef_hhsize3_sr2_maint,coef_hhsize3_sr2_maint,coef_hhsize3_sr2_maint,coef_hhsize3_sr2_disc,coef_hhsize3_sr2_disc,coef_hhsize3_sr2_disc,coef_hhsize3_sr2_atwork +coef_hhsize3_sr3p,coef_hhsize3_sr3p_work,coef_hhsize3_sr3p_univ,coef_hhsize3_sr3p_school,coef_hhsize3_sr3p_maint,coef_hhsize3_sr3p_maint,coef_hhsize3_sr3p_maint,coef_hhsize3_sr3p_disc,coef_hhsize3_sr3p_disc,coef_hhsize3_sr3p_disc,coef_hhsize3_sr3p_atwork +coef_hhsize4p_sr2,coef_hhsize4p_sr2_work,coef_hhsize4p_sr2_univ,coef_hhsize4p_sr2_school,coef_hhsize4p_sr2_maint,coef_hhsize4p_sr2_maint,coef_hhsize4p_sr2_maint,coef_hhsize4p_sr2_disc,coef_hhsize4p_sr2_disc,coef_hhsize4p_sr2_disc,coef_hhsize4p_sr2_atwork +coef_hhsize4p_sr3p,coef_hhsize4p_sr3p_work,coef_hhsize4p_sr3p_univ,coef_hhsize4p_sr3p_school,coef_hhsize4p_sr3p_maint,coef_hhsize4p_sr3p_maint,coef_hhsize4p_sr3p_maint,coef_hhsize4p_sr3p_disc,coef_hhsize4p_sr3p_disc,coef_hhsize4p_sr3p_disc,coef_hhsize4p_sr3p_atwork +coef_ageund16_bike,coef_ageund16_bike_work,coef_ageund16_bike_univ,coef_ageund16_bike_school,coef_ageund16_bike_maint,coef_ageund16_bike_maint,coef_ageund16_bike_maint,coef_ageund16_bike_disc,coef_ageund16_bike_disc,coef_ageund16_bike_disc,coef_zero +coef_age1624_bike,coef_age1624_bike_work,coef_age1624_bike_univ,coef_age1624_bike_school,coef_age1624_bike_maint,coef_age1624_bike_maint,coef_age1624_bike_maint,coef_age1624_bike_disc,coef_age1624_bike_disc,coef_age1624_bike_disc,coef_zero +coef_age4155_bike,coef_age4155_bike_work,coef_age4155_bike_univ,coef_age4155_bike_school,coef_age4155_bike_maint,coef_age4155_bike_maint,coef_age4155_bike_maint,coef_age4155_bike_disc,coef_age4155_bike_disc,coef_age4155_bike_disc,coef_zero +coef_age5664_bike,coef_age5664_bike_work,coef_age5664_bike_univ,coef_age5664_bike_school,coef_age5664_bike_maint,coef_age5664_bike_maint,coef_age5664_bike_maint,coef_age5664_bike_disc,coef_age5664_bike_disc,coef_age5664_bike_disc,coef_zero +coef_age65pl_bike,coef_age65pl_bike_work,coef_age65pl_bike_univ,coef_age65pl_bike_school,coef_age65pl_bike_maint,coef_age65pl_bike_maint,coef_age65pl_bike_maint,coef_age65pl_bike_disc,coef_age65pl_bike_disc,coef_age65pl_bike_disc,coef_zero +coef_female_bike,coef_female_bike_work,coef_female_bike_univ,coef_female_bike_school,coef_female_bike_maint,coef_female_bike_maint,coef_female_bike_maint,coef_female_bike_disc,coef_female_bike_disc,coef_female_bike_disc,coef_zero +coef_inc100plus_bike,coef_inc100plus_bike_work,coef_inc100plus_bike_univ,coef_inc100plus_bike_school,coef_inc100plus_bike_maint,coef_inc100plus_bike_maint,coef_inc100plus_bike_maint,coef_inc100plus_bike_disc,coef_inc100plus_bike_disc,coef_inc100plus_bike_disc,coef_zero +coef_LUnorm_bike,coef_LUnorm_bike_work,coef_LUnorm_bike_univ,coef_LUnorm_bike_school,coef_LUnorm_bike_maint,coef_LUnorm_bike_maint,coef_LUnorm_bike_maint,coef_LUnorm_bike_disc,coef_LUnorm_bike_disc,coef_LUnorm_bike_disc,coef_zero +coef_oMlCoast_bike,coef_oMlCoast_bike_work,coef_oMlCoast_bike_univ,coef_oMlCoast_bike_school,coef_oMlCoast_bike_maint,coef_oMlCoast_bike_maint,coef_oMlCoast_bike_maint,coef_oMlCoast_bike_disc,coef_oMlCoast_bike_disc,coef_oMlCoast_bike_disc,coef_zero +coef_oMlCoast2p_bike,coef_oMlCoast2p_bike_work,coef_oMlCoast2p_bike_univ,coef_oMlCoast2p_bike_school,coef_oMlCoast2p_bike_maint,coef_oMlCoast2p_bike_maint,coef_oMlCoast2p_bike_maint,coef_oMlCoast2p_bike_disc,coef_oMlCoast2p_bike_disc,coef_oMlCoast2p_bike_disc,coef_zero +coef_oMlCoast5p_bike,coef_oMlCoast5p_bike_work,coef_oMlCoast5p_bike_univ,coef_oMlCoast5p_bike_school,coef_oMlCoast5p_bike_maint,coef_oMlCoast5p_bike_maint,coef_oMlCoast5p_bike_maint,coef_oMlCoast5p_bike_disc,coef_oMlCoast5p_bike_disc,coef_oMlCoast5p_bike_disc,coef_zero +coef_ageund16_walk,coef_ageund16_walk_work,coef_ageund16_walk_univ,coef_ageund16_walk_school,coef_ageund16_walk_maint,coef_ageund16_walk_maint,coef_ageund16_walk_maint,coef_ageund16_walk_disc,coef_ageund16_walk_disc,coef_ageund16_walk_disc,coef_zero +coef_age1624_walk,coef_age1624_walk_work,coef_age1624_walk_univ,coef_age1624_walk_school,coef_age1624_walk_maint,coef_age1624_walk_maint,coef_age1624_walk_maint,coef_age1624_walk_disc,coef_age1624_walk_disc,coef_age1624_walk_disc,coef_zero +coef_age4155_walk,coef_age4155_walk_work,coef_age4155_walk_univ,coef_age4155_walk_school,coef_age4155_walk_maint,coef_age4155_walk_maint,coef_age4155_walk_maint,coef_age4155_walk_disc,coef_age4155_walk_disc,coef_age4155_walk_disc,coef_zero +coef_age5664_walk,coef_age5664_walk_work,coef_age5664_walk_univ,coef_age5664_walk_school,coef_age5664_walk_maint,coef_age5664_walk_maint,coef_age5664_walk_maint,coef_age5664_walk_disc,coef_age5664_walk_disc,coef_age5664_walk_disc,coef_zero +coef_age65pl_walk,coef_age65pl_walk_work,coef_age65pl_walk_univ,coef_age65pl_walk_school,coef_age65pl_walk_maint,coef_age65pl_walk_maint,coef_age65pl_walk_maint,coef_age65pl_walk_disc,coef_age65pl_walk_disc,coef_age65pl_walk_disc,coef_zero +coef_female_walk,coef_female_walk_work,coef_female_walk_univ,coef_female_walk_school,coef_female_walk_maint,coef_female_walk_maint,coef_female_walk_maint,coef_female_walk_disc,coef_female_walk_disc,coef_female_walk_disc,coef_zero +coef_inc60100_walk,coef_inc60100_walk_work,coef_inc60100_walk_univ,coef_inc60100_walk_school,coef_inc60100_walk_maint,coef_inc60100_walk_maint,coef_inc60100_walk_maint,coef_inc60100_walk_disc,coef_inc60100_walk_disc,coef_inc60100_walk_disc,coef_zero +coef_LUnorm_walk,coef_LUnorm_walk_work,coef_LUnorm_walk_univ,coef_LUnorm_walk_school,coef_LUnorm_walk_maint,coef_LUnorm_walk_maint,coef_LUnorm_walk_maint,coef_LUnorm_walk_disc,coef_LUnorm_walk_disc,coef_LUnorm_walk_disc,coef_zero +coef_dEmpNorm_walk,coef_dEmpNorm_walk_work,coef_dEmpNorm_walk_univ,coef_dEmpNorm_walk_school,coef_dEmpNorm_walk_maint,coef_dEmpNorm_walk_maint,coef_dEmpNorm_walk_maint,coef_dEmpNorm_walk_disc,coef_dEmpNorm_walk_disc,coef_dEmpNorm_walk_disc,coef_zero +coef_female_schb,coef_zero,coef_zero,coef_female_schb_school,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero +coef_age0105_schb,coef_zero,coef_zero,coef_female_schb_school,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero +coef_age0105_nmot,coef_zero,coef_zero,coef_age0105_schb_school,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero +coef_age0105_tran,coef_zero,coef_zero,coef_age0105_nmot_school,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero +coef_age0612_schb,coef_zero,coef_zero,coef_age0105_tran_school,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero +coef_age0612_nmot,coef_zero,coef_zero,coef_age0612_schb_school,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero +coef_age0612_tran,coef_zero,coef_zero,coef_age0612_nmot_school,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero +coef_age1315_schb,coef_zero,coef_zero,coef_age0612_tran_school,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero +coef_age1315_nmot,coef_zero,coef_zero,coef_age1315_schb_school,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero +coef_age1315_tran,coef_zero,coef_zero,coef_age1315_nmot_school,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero +coef_beachParkBikeConstant,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero,coef_beachParkBikeConstant,coef_beachParkBikeConstant,coef_beachParkBikeConstant,coef_zero +#,,,,,,,,,, +coef_calib_parkingconst_WLK_TRANSIT,coef_calib_parkingconst_WLK_TRANSIT_work,coef_calib_parkingconst_WLK_TRANSIT_univ,coef_calib_parkingconst_WLK_TRANSIT_school,coef_calib_parkingconst_WLK_TRANSIT_maint,coef_calib_parkingconst_WLK_TRANSIT_maint,coef_calib_parkingconst_WLK_TRANSIT_maint,coef_calib_parkingconst_WLK_TRANSIT_disc,coef_calib_parkingconst_WLK_TRANSIT_disc,coef_calib_parkingconst_WLK_TRANSIT_disc,coef_calib_parkingconst_WLK_TRANSIT_atwork +coef_calib_parkingconst_DRV_TRANSIT,coef_calib_parkingconst_DRV_TRANSIT_work,coef_calib_parkingconst_DRV_TRANSIT_univ,coef_calib_parkingconst_DRV_TRANSIT_school,coef_calib_parkingconst_DRV_TRANSIT_maint,coef_calib_parkingconst_DRV_TRANSIT_maint,coef_calib_parkingconst_DRV_TRANSIT_maint,coef_calib_parkingconst_DRV_TRANSIT_disc,coef_calib_parkingconst_DRV_TRANSIT_disc,coef_calib_parkingconst_DRV_TRANSIT_disc,coef_calib_parkingconst_DRV_TRANSIT_atwork +coef_calib_autodeficienthhind_SCH_BUS,coef_zero,coef_zero,coef_calib_autodeficienthhind_SCH_BUS_school,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero +coef_calib_autodeficienthhind_SHARED2,coef_calib_autodeficienthhind_SHARED2_work,coef_calib_autodeficienthhind_SHARED2_univ,coef_calib_autodeficienthhind_SHARED2_school,coef_calib_autodeficienthhind_SHARED2_maint,coef_calib_autodeficienthhind_SHARED2_maint,coef_calib_autodeficienthhind_SHARED2_maint,coef_calib_autodeficienthhind_SHARED2_disc,coef_calib_autodeficienthhind_SHARED2_disc,coef_calib_autodeficienthhind_SHARED2_disc,coef_calib_autodeficienthhind_SHARED2_atwork +coef_calib_autodeficienthhind_SHARED3,coef_calib_autodeficienthhind_SHARED3_work,coef_calib_autodeficienthhind_SHARED3_univ,coef_calib_autodeficienthhind_SHARED3_school,coef_calib_autodeficienthhind_SHARED3_maint,coef_calib_autodeficienthhind_SHARED3_maint,coef_calib_autodeficienthhind_SHARED3_maint,coef_calib_autodeficienthhind_SHARED3_disc,coef_calib_autodeficienthhind_SHARED3_disc,coef_calib_autodeficienthhind_SHARED3_disc,coef_calib_autodeficienthhind_SHARED3_atwork +coef_calib_autodeficienthhind_WALK,coef_calib_autodeficienthhind_WALK_work,coef_calib_autodeficienthhind_WALK_univ,coef_calib_autodeficienthhind_WALK_school,coef_calib_autodeficienthhind_WALK_maint,coef_calib_autodeficienthhind_WALK_maint,coef_calib_autodeficienthhind_WALK_maint,coef_calib_autodeficienthhind_WALK_disc,coef_calib_autodeficienthhind_WALK_disc,coef_calib_autodeficienthhind_WALK_disc,coef_calib_autodeficienthhind_WALK_atwork +coef_calib_autodeficienthhind_BIKE,coef_calib_autodeficienthhind_BIKE_work,coef_calib_autodeficienthhind_BIKE_univ,coef_calib_autodeficienthhind_BIKE_school,coef_calib_autodeficienthhind_BIKE_maint,coef_calib_autodeficienthhind_BIKE_maint,coef_calib_autodeficienthhind_BIKE_maint,coef_calib_autodeficienthhind_BIKE_disc,coef_calib_autodeficienthhind_BIKE_disc,coef_calib_autodeficienthhind_BIKE_disc,coef_calib_autodeficienthhind_BIKE_atwork +coef_calib_autodeficienthhind_WALK_TRANSIT,coef_calib_autodeficienthhind_WALK_TRANSIT_work,coef_calib_autodeficienthhind_WALK_TRANSIT_univ,coef_calib_autodeficienthhind_WALK_TRANSIT_school,coef_calib_autodeficienthhind_WALK_TRANSIT_maint,coef_calib_autodeficienthhind_WALK_TRANSIT_maint,coef_calib_autodeficienthhind_WALK_TRANSIT_maint,coef_calib_autodeficienthhind_WALK_TRANSIT_disc,coef_calib_autodeficienthhind_WALK_TRANSIT_disc,coef_calib_autodeficienthhind_WALK_TRANSIT_disc,coef_calib_autodeficienthhind_WALK_TRANSIT_atwork +coef_calib_autodeficienthhind_PNR_TRANSIT,coef_calib_autodeficienthhind_PNR_TRANSIT_work,coef_calib_autodeficienthhind_PNR_TRANSIT_univ,coef_calib_autodeficienthhind_PNR_TRANSIT_school,coef_calib_autodeficienthhind_PNR_TRANSIT_maint,coef_calib_autodeficienthhind_PNR_TRANSIT_maint,coef_calib_autodeficienthhind_PNR_TRANSIT_maint,coef_calib_autodeficienthhind_PNR_TRANSIT_disc,coef_calib_autodeficienthhind_PNR_TRANSIT_disc,coef_calib_autodeficienthhind_PNR_TRANSIT_disc,coef_calib_autodeficienthhind_PNR_TRANSIT_atwork +coef_calib_autodeficienthhind_KNR_TRANSIT,coef_calib_autodeficienthhind_KNR_TRANSIT_work,coef_calib_autodeficienthhind_KNR_TRANSIT_univ,coef_calib_autodeficienthhind_KNR_TRANSIT_school,coef_calib_autodeficienthhind_KNR_TRANSIT_maint,coef_calib_autodeficienthhind_KNR_TRANSIT_maint,coef_calib_autodeficienthhind_KNR_TRANSIT_maint,coef_calib_autodeficienthhind_KNR_TRANSIT_disc,coef_calib_autodeficienthhind_KNR_TRANSIT_disc,coef_calib_autodeficienthhind_KNR_TRANSIT_disc,coef_calib_autodeficienthhind_KNR_TRANSIT_atwork +coef_calib_autodeficienthhind_TNC_TRANSIT,coef_calib_autodeficienthhind_TNC_TRANSIT_work,coef_calib_autodeficienthhind_TNC_TRANSIT_univ,coef_calib_autodeficienthhind_TNC_TRANSIT_school,coef_calib_autodeficienthhind_TNC_TRANSIT_maint,coef_calib_autodeficienthhind_TNC_TRANSIT_maint,coef_calib_autodeficienthhind_TNC_TRANSIT_maint,coef_calib_autodeficienthhind_TNC_TRANSIT_disc,coef_calib_autodeficienthhind_TNC_TRANSIT_disc,coef_calib_autodeficienthhind_TNC_TRANSIT_disc,coef_calib_autodeficienthhind_TNC_TRANSIT_atwork +coef_calib_autodeficienthhind_TAXI,coef_calib_autodeficienthhind_TAXI_work,coef_calib_autodeficienthhind_TAXI_univ,coef_calib_autodeficienthhind_TAXI_school,coef_calib_autodeficienthhind_TAXI_maint,coef_calib_autodeficienthhind_TAXI_maint,coef_calib_autodeficienthhind_TAXI_maint,coef_calib_autodeficienthhind_TAXI_disc,coef_calib_autodeficienthhind_TAXI_disc,coef_calib_autodeficienthhind_TAXI_disc,coef_calib_autodeficienthhind_TAXI_atwork +coef_calib_autodeficienthhind_TNC_SINGLE,coef_calib_autodeficienthhind_TNC_SINGLE_work,coef_calib_autodeficienthhind_TNC_SINGLE_univ,coef_calib_autodeficienthhind_TNC_SINGLE_school,coef_calib_autodeficienthhind_TNC_SINGLE_maint,coef_calib_autodeficienthhind_TNC_SINGLE_maint,coef_calib_autodeficienthhind_TNC_SINGLE_maint,coef_calib_autodeficienthhind_TNC_SINGLE_disc,coef_calib_autodeficienthhind_TNC_SINGLE_disc,coef_calib_autodeficienthhind_TNC_SINGLE_disc,coef_calib_autodeficienthhind_TNC_SINGLE_atwork +coef_calib_autodeficienthhind_TNC_SHARED,coef_calib_autodeficienthhind_TNC_SHARED_work,coef_calib_autodeficienthhind_TNC_SHARED_univ,coef_calib_autodeficienthhind_TNC_SHARED_school,coef_calib_autodeficienthhind_TNC_SHARED_maint,coef_calib_autodeficienthhind_TNC_SHARED_maint,coef_calib_autodeficienthhind_TNC_SHARED_maint,coef_calib_autodeficienthhind_TNC_SHARED_disc,coef_calib_autodeficienthhind_TNC_SHARED_disc,coef_calib_autodeficienthhind_TNC_SHARED_disc,coef_calib_autodeficienthhind_TNC_SHARED_atwork +coef_calib_autodeficienthhind_EBIKE,coef_calib_autodeficienthhind_EBIKE_work,coef_calib_autodeficienthhind_EBIKE_univ,coef_calib_autodeficienthhind_EBIKE_school,coef_calib_autodeficienthhind_EBIKE_maint,coef_calib_autodeficienthhind_EBIKE_maint,coef_calib_autodeficienthhind_EBIKE_maint,coef_calib_autodeficienthhind_EBIKE_disc,coef_calib_autodeficienthhind_EBIKE_disc,coef_calib_autodeficienthhind_EBIKE_disc,coef_calib_autodeficienthhind_EBIKE_atwork +coef_calib_autodeficienthhind_ESCOOTER,coef_calib_autodeficienthhind_ESCOOTER_work,coef_calib_autodeficienthhind_ESCOOTER_univ,coef_calib_autodeficienthhind_ESCOOTER_school,coef_calib_autodeficienthhind_ESCOOTER_maint,coef_calib_autodeficienthhind_ESCOOTER_maint,coef_calib_autodeficienthhind_ESCOOTER_maint,coef_calib_autodeficienthhind_ESCOOTER_disc,coef_calib_autodeficienthhind_ESCOOTER_disc,coef_calib_autodeficienthhind_ESCOOTER_disc,coef_calib_autodeficienthhind_ESCOOTER_atwork +#coef_calib_autodeficienthhjoi_SHARED2,coef_zero,coef_zero,coef_zero,coef_calib_autodeficienthhjoi_SHARED2_maint,coef_calib_autodeficienthhjoi_SHARED2_maint,coef_calib_autodeficienthhjoi_SHARED2_maint,coef_calib_autodeficienthhjoi_SHARED2_disc,coef_calib_autodeficienthhjoi_SHARED2_disc,coef_calib_autodeficienthhjoi_SHARED2_disc,coef_zero +coef_calib_autodeficienthhjoi_SHARED3,coef_zero,coef_zero,coef_zero,coef_calib_autodeficienthhjoi_SHARED3_maint,coef_calib_autodeficienthhjoi_SHARED3_maint,coef_calib_autodeficienthhjoi_SHARED3_maint,coef_calib_autodeficienthhjoi_SHARED3_disc,coef_calib_autodeficienthhjoi_SHARED3_disc,coef_calib_autodeficienthhjoi_SHARED3_disc,coef_zero +coef_calib_autodeficienthhjoi_WALK,coef_zero,coef_zero,coef_zero,coef_calib_autodeficienthhjoi_WALK_maint,coef_calib_autodeficienthhjoi_WALK_maint,coef_calib_autodeficienthhjoi_WALK_maint,coef_calib_autodeficienthhjoi_WALK_disc,coef_calib_autodeficienthhjoi_WALK_disc,coef_calib_autodeficienthhjoi_WALK_disc,coef_zero +coef_calib_autodeficienthhjoi_BIKE,coef_zero,coef_zero,coef_zero,coef_calib_autodeficienthhjoi_BIKE_maint,coef_calib_autodeficienthhjoi_BIKE_maint,coef_calib_autodeficienthhjoi_BIKE_maint,coef_calib_autodeficienthhjoi_BIKE_disc,coef_calib_autodeficienthhjoi_BIKE_disc,coef_calib_autodeficienthhjoi_BIKE_disc,coef_zero +coef_calib_autodeficienthhjoi_WALK_TRANSIT,coef_zero,coef_zero,coef_zero,coef_calib_autodeficienthhjoi_WALK_TRANSIT_maint,coef_calib_autodeficienthhjoi_WALK_TRANSIT_maint,coef_calib_autodeficienthhjoi_WALK_TRANSIT_maint,coef_calib_autodeficienthhjoi_WALK_TRANSIT_disc,coef_calib_autodeficienthhjoi_WALK_TRANSIT_disc,coef_calib_autodeficienthhjoi_WALK_TRANSIT_disc,coef_zero +coef_calib_autodeficienthhjoi_PNR_TRANSIT,coef_zero,coef_zero,coef_zero,coef_calib_autodeficienthhjoi_PNR_TRANSIT_maint,coef_calib_autodeficienthhjoi_PNR_TRANSIT_maint,coef_calib_autodeficienthhjoi_PNR_TRANSIT_maint,coef_calib_autodeficienthhjoi_PNR_TRANSIT_disc,coef_calib_autodeficienthhjoi_PNR_TRANSIT_disc,coef_calib_autodeficienthhjoi_PNR_TRANSIT_disc,coef_zero +coef_calib_autodeficienthhjoi_KNR_TRANSIT,coef_zero,coef_zero,coef_zero,coef_calib_autodeficienthhjoi_KNR_TRANSIT_maint,coef_calib_autodeficienthhjoi_KNR_TRANSIT_maint,coef_calib_autodeficienthhjoi_KNR_TRANSIT_maint,coef_calib_autodeficienthhjoi_KNR_TRANSIT_disc,coef_calib_autodeficienthhjoi_KNR_TRANSIT_disc,coef_calib_autodeficienthhjoi_KNR_TRANSIT_disc,coef_zero +coef_calib_autodeficienthhjoi_TNC_TRANSIT,coef_zero,coef_zero,coef_zero,coef_calib_autodeficienthhjoi_TNC_TRANSIT_maint,coef_calib_autodeficienthhjoi_TNC_TRANSIT_maint,coef_calib_autodeficienthhjoi_TNC_TRANSIT_maint,coef_calib_autodeficienthhjoi_TNC_TRANSIT_disc,coef_calib_autodeficienthhjoi_TNC_TRANSIT_disc,coef_calib_autodeficienthhjoi_TNC_TRANSIT_disc,coef_zero +coef_calib_autodeficienthhjoi_TAXI,coef_zero,coef_zero,coef_zero,coef_calib_autodeficienthhjoi_TAXI_maint,coef_calib_autodeficienthhjoi_TAXI_maint,coef_calib_autodeficienthhjoi_TAXI_maint,coef_calib_autodeficienthhjoi_TAXI_disc,coef_calib_autodeficienthhjoi_TAXI_disc,coef_calib_autodeficienthhjoi_TAXI_disc,coef_zero +coef_calib_autodeficienthhjoi_TNC_SINGLE,coef_zero,coef_zero,coef_zero,coef_calib_autodeficienthhjoi_TNC_SINGLE_maint,coef_calib_autodeficienthhjoi_TNC_SINGLE_maint,coef_calib_autodeficienthhjoi_TNC_SINGLE_maint,coef_calib_autodeficienthhjoi_TNC_SINGLE_disc,coef_calib_autodeficienthhjoi_TNC_SINGLE_disc,coef_calib_autodeficienthhjoi_TNC_SINGLE_disc,coef_zero +coef_calib_autodeficienthhjoi_TNC_SHARED,coef_zero,coef_zero,coef_zero,coef_calib_autodeficienthhjoi_TNC_SHARED_maint,coef_calib_autodeficienthhjoi_TNC_SHARED_maint,coef_calib_autodeficienthhjoi_TNC_SHARED_maint,coef_calib_autodeficienthhjoi_TNC_SHARED_disc,coef_calib_autodeficienthhjoi_TNC_SHARED_disc,coef_calib_autodeficienthhjoi_TNC_SHARED_disc,coef_zero +coef_calib_autodeficienthhjoi_EBIKE,coef_zero,coef_zero,coef_zero,coef_calib_autodeficienthhjoi_EBIKE_maint,coef_calib_autodeficienthhjoi_EBIKE_maint,coef_calib_autodeficienthhjoi_EBIKE_maint,coef_calib_autodeficienthhjoi_EBIKE_disc,coef_calib_autodeficienthhjoi_EBIKE_disc,coef_calib_autodeficienthhjoi_EBIKE_disc,coef_zero 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+coef_calib_autosufficienthhin_SHARED3,coef_calib_autosufficienthhin_SHARED3_work,coef_calib_autosufficienthhin_SHARED3_univ,coef_calib_autosufficienthhin_SHARED3_school,coef_calib_autosufficienthhin_SHARED3_maint,coef_calib_autosufficienthhin_SHARED3_maint,coef_calib_autosufficienthhin_SHARED3_maint,coef_calib_autosufficienthhin_SHARED3_disc,coef_calib_autosufficienthhin_SHARED3_disc,coef_calib_autosufficienthhin_SHARED3_disc,coef_calib_autosufficienthhin_SHARED3_atwork +coef_calib_autosufficienthhin_WALK,coef_calib_autosufficienthhin_WALK_work,coef_calib_autosufficienthhin_WALK_univ,coef_calib_autosufficienthhin_WALK_school,coef_calib_autosufficienthhin_WALK_maint,coef_calib_autosufficienthhin_WALK_maint,coef_calib_autosufficienthhin_WALK_maint,coef_calib_autosufficienthhin_WALK_disc,coef_calib_autosufficienthhin_WALK_disc,coef_calib_autosufficienthhin_WALK_disc,coef_calib_autosufficienthhin_WALK_atwork +coef_calib_autosufficienthhin_BIKE,coef_calib_autosufficienthhin_BIKE_work,coef_calib_autosufficienthhin_BIKE_univ,coef_calib_autosufficienthhin_BIKE_school,coef_calib_autosufficienthhin_BIKE_maint,coef_calib_autosufficienthhin_BIKE_maint,coef_calib_autosufficienthhin_BIKE_maint,coef_calib_autosufficienthhin_BIKE_disc,coef_calib_autosufficienthhin_BIKE_disc,coef_calib_autosufficienthhin_BIKE_disc,coef_calib_autosufficienthhin_BIKE_atwork +coef_calib_autosufficienthhin_WALK_TRANSIT,coef_calib_autosufficienthhin_WALK_TRANSIT_work,coef_calib_autosufficienthhin_WALK_TRANSIT_univ,coef_calib_autosufficienthhin_WALK_TRANSIT_school,coef_calib_autosufficienthhin_WALK_TRANSIT_maint,coef_calib_autosufficienthhin_WALK_TRANSIT_maint,coef_calib_autosufficienthhin_WALK_TRANSIT_maint,coef_calib_autosufficienthhin_WALK_TRANSIT_disc,coef_calib_autosufficienthhin_WALK_TRANSIT_disc,coef_calib_autosufficienthhin_WALK_TRANSIT_disc,coef_calib_autosufficienthhin_WALK_TRANSIT_atwork +coef_calib_autosufficienthhin_PNR_TRANSIT,coef_calib_autosufficienthhin_PNR_TRANSIT_work,coef_calib_autosufficienthhin_PNR_TRANSIT_univ,coef_calib_autosufficienthhin_PNR_TRANSIT_school,coef_calib_autosufficienthhin_PNR_TRANSIT_maint,coef_calib_autosufficienthhin_PNR_TRANSIT_maint,coef_calib_autosufficienthhin_PNR_TRANSIT_maint,coef_calib_autosufficienthhin_PNR_TRANSIT_disc,coef_calib_autosufficienthhin_PNR_TRANSIT_disc,coef_calib_autosufficienthhin_PNR_TRANSIT_disc,coef_calib_autosufficienthhin_PNR_TRANSIT_atwork +coef_calib_autosufficienthhin_KNR_TRANSIT,coef_calib_autosufficienthhin_KNR_TRANSIT_work,coef_calib_autosufficienthhin_KNR_TRANSIT_univ,coef_calib_autosufficienthhin_KNR_TRANSIT_school,coef_calib_autosufficienthhin_KNR_TRANSIT_maint,coef_calib_autosufficienthhin_KNR_TRANSIT_maint,coef_calib_autosufficienthhin_KNR_TRANSIT_maint,coef_calib_autosufficienthhin_KNR_TRANSIT_disc,coef_calib_autosufficienthhin_KNR_TRANSIT_disc,coef_calib_autosufficienthhin_KNR_TRANSIT_disc,coef_calib_autosufficienthhin_KNR_TRANSIT_atwork +coef_calib_autosufficienthhin_TNC_TRANSIT,coef_calib_autosufficienthhin_TNC_TRANSIT_work,coef_calib_autosufficienthhin_TNC_TRANSIT_univ,coef_calib_autosufficienthhin_TNC_TRANSIT_school,coef_calib_autosufficienthhin_TNC_TRANSIT_maint,coef_calib_autosufficienthhin_TNC_TRANSIT_maint,coef_calib_autosufficienthhin_TNC_TRANSIT_maint,coef_calib_autosufficienthhin_TNC_TRANSIT_disc,coef_calib_autosufficienthhin_TNC_TRANSIT_disc,coef_calib_autosufficienthhin_TNC_TRANSIT_disc,coef_calib_autosufficienthhin_TNC_TRANSIT_atwork +coef_calib_autosufficienthhin_TAXI,coef_calib_autosufficienthhin_TAXI_work,coef_calib_autosufficienthhin_TAXI_univ,coef_calib_autosufficienthhin_TAXI_school,coef_calib_autosufficienthhin_TAXI_maint,coef_calib_autosufficienthhin_TAXI_maint,coef_calib_autosufficienthhin_TAXI_maint,coef_calib_autosufficienthhin_TAXI_disc,coef_calib_autosufficienthhin_TAXI_disc,coef_calib_autosufficienthhin_TAXI_disc,coef_calib_autosufficienthhin_TAXI_atwork +coef_calib_autosufficienthhin_TNC_SINGLE,coef_calib_autosufficienthhin_TNC_SINGLE_work,coef_calib_autosufficienthhin_TNC_SINGLE_univ,coef_calib_autosufficienthhin_TNC_SINGLE_school,coef_calib_autosufficienthhin_TNC_SINGLE_maint,coef_calib_autosufficienthhin_TNC_SINGLE_maint,coef_calib_autosufficienthhin_TNC_SINGLE_maint,coef_calib_autosufficienthhin_TNC_SINGLE_disc,coef_calib_autosufficienthhin_TNC_SINGLE_disc,coef_calib_autosufficienthhin_TNC_SINGLE_disc,coef_calib_autosufficienthhin_TNC_SINGLE_atwork +coef_calib_autosufficienthhin_TNC_SHARED,coef_calib_autosufficienthhin_TNC_SHARED_work,coef_calib_autosufficienthhin_TNC_SHARED_univ,coef_calib_autosufficienthhin_TNC_SHARED_school,coef_calib_autosufficienthhin_TNC_SHARED_maint,coef_calib_autosufficienthhin_TNC_SHARED_maint,coef_calib_autosufficienthhin_TNC_SHARED_maint,coef_calib_autosufficienthhin_TNC_SHARED_disc,coef_calib_autosufficienthhin_TNC_SHARED_disc,coef_calib_autosufficienthhin_TNC_SHARED_disc,coef_calib_autosufficienthhin_TNC_SHARED_atwork +coef_calib_autosufficienthhin_EBIKE,coef_calib_autosufficienthhin_EBIKE_work,coef_calib_autosufficienthhin_EBIKE_univ,coef_calib_autosufficienthhin_EBIKE_school,coef_calib_autosufficienthhin_EBIKE_maint,coef_calib_autosufficienthhin_EBIKE_maint,coef_calib_autosufficienthhin_EBIKE_maint,coef_calib_autosufficienthhin_EBIKE_disc,coef_calib_autosufficienthhin_EBIKE_disc,coef_calib_autosufficienthhin_EBIKE_disc,coef_calib_autosufficienthhin_EBIKE_atwork +coef_calib_autosufficienthhin_ESCOOTER,coef_calib_autosufficienthhin_ESCOOTER_work,coef_calib_autosufficienthhin_ESCOOTER_univ,coef_calib_autosufficienthhin_ESCOOTER_school,coef_calib_autosufficienthhin_ESCOOTER_maint,coef_calib_autosufficienthhin_ESCOOTER_maint,coef_calib_autosufficienthhin_ESCOOTER_maint,coef_calib_autosufficienthhin_ESCOOTER_disc,coef_calib_autosufficienthhin_ESCOOTER_disc,coef_calib_autosufficienthhin_ESCOOTER_disc,coef_calib_autosufficienthhin_ESCOOTER_atwork +#coef_calib_autosufficienthhjo_SHARED2,coef_zero,coef_zero,coef_zero,coef_calib_autosufficienthhjo_SHARED2_maint,coef_calib_autosufficienthhjo_SHARED2_maint,coef_calib_autosufficienthhjo_SHARED2_maint,coef_calib_autosufficienthhjo_SHARED2_disc,coef_calib_autosufficienthhjo_SHARED2_disc,coef_calib_autosufficienthhjo_SHARED2_disc,coef_zero 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+coef_calib_distance_WALK_TRANSIT,coef_calib_distance_WALK_TRANSIT_work,coef_calib_distance_WALK_TRANSIT_univ,coef_calib_distance_WALK_TRANSIT_school,coef_calib_distance_WALK_TRANSIT_maint,coef_calib_distance_WALK_TRANSIT_maint,coef_calib_distance_WALK_TRANSIT_maint,coef_calib_distance_WALK_TRANSIT_disc,coef_calib_distance_WALK_TRANSIT_disc,coef_calib_distance_WALK_TRANSIT_disc,coef_calib_distance_WALK_TRANSIT_atwork +coef_calib_distance_PNR_TRANSIT,coef_calib_distance_PNR_TRANSIT_work,coef_calib_distance_PNR_TRANSIT_univ,coef_calib_distance_PNR_TRANSIT_school,coef_calib_distance_PNR_TRANSIT_maint,coef_calib_distance_PNR_TRANSIT_maint,coef_calib_distance_PNR_TRANSIT_maint,coef_calib_distance_PNR_TRANSIT_disc,coef_calib_distance_PNR_TRANSIT_disc,coef_calib_distance_PNR_TRANSIT_disc,coef_calib_distance_PNR_TRANSIT_atwork 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+coef_calib_zeroautohhjointtou_TNC_TRANSIT,coef_zero,coef_zero,coef_zero,coef_calib_zeroautohhjointtou_TNC_TRANSIT_maint,coef_calib_zeroautohhjointtou_TNC_TRANSIT_maint,coef_calib_zeroautohhjointtou_TNC_TRANSIT_maint,coef_calib_zeroautohhjointtou_TNC_TRANSIT_disc,coef_calib_zeroautohhjointtou_TNC_TRANSIT_disc,coef_calib_zeroautohhjointtou_TNC_TRANSIT_disc,coef_zero +coef_calib_zeroautohhjointtou_TAXI,coef_zero,coef_zero,coef_zero,coef_calib_zeroautohhjointtou_TAXI_maint,coef_calib_zeroautohhjointtou_TAXI_maint,coef_calib_zeroautohhjointtou_TAXI_maint,coef_calib_zeroautohhjointtou_TAXI_disc,coef_calib_zeroautohhjointtou_TAXI_disc,coef_calib_zeroautohhjointtou_TAXI_disc,coef_zero +coef_calib_zeroautohhjointtou_TNC_SINGLE,coef_zero,coef_zero,coef_zero,coef_calib_zeroautohhjointtou_TNC_SINGLE_maint,coef_calib_zeroautohhjointtou_TNC_SINGLE_maint,coef_calib_zeroautohhjointtou_TNC_SINGLE_maint,coef_calib_zeroautohhjointtou_TNC_SINGLE_disc,coef_calib_zeroautohhjointtou_TNC_SINGLE_disc,coef_calib_zeroautohhjointtou_TNC_SINGLE_disc,coef_zero +coef_calib_zeroautohhjointtou_TNC_SHARED,coef_zero,coef_zero,coef_zero,coef_calib_zeroautohhjointtou_TNC_SHARED_maint,coef_calib_zeroautohhjointtou_TNC_SHARED_maint,coef_calib_zeroautohhjointtou_TNC_SHARED_maint,coef_calib_zeroautohhjointtou_TNC_SHARED_disc,coef_calib_zeroautohhjointtou_TNC_SHARED_disc,coef_calib_zeroautohhjointtou_TNC_SHARED_disc,coef_zero +coef_calib_zeroautohhjointtou_EBIKE,coef_zero,coef_zero,coef_zero,coef_calib_zeroautohhjointtou_EBIKE_maint,coef_calib_zeroautohhjointtou_EBIKE_maint,coef_calib_zeroautohhjointtou_EBIKE_maint,coef_calib_zeroautohhjointtou_EBIKE_disc,coef_calib_zeroautohhjointtou_EBIKE_disc,coef_calib_zeroautohhjointtou_EBIKE_disc,coef_zero +coef_calib_zeroautohhjointtou_ESCOOTER,coef_zero,coef_zero,coef_zero,coef_calib_zeroautohhjointtou_ESCOOTER_maint,coef_calib_zeroautohhjointtou_ESCOOTER_maint,coef_calib_zeroautohhjointtou_ESCOOTER_maint,coef_calib_zeroautohhjointtou_ESCOOTER_disc,coef_calib_zeroautohhjointtou_ESCOOTER_disc,coef_calib_zeroautohhjointtou_ESCOOTER_disc,coef_zero +coef_calib_onboard,coef_calib_onboard,coef_calib_onboard,coef_calib_onboard,coef_calib_onboard,coef_calib_onboard,coef_calib_onboard,coef_calib_onboard,coef_calib_onboard,coef_calib_onboard,coef_calib_onboard +coef_calib_mt_zeroautohh,coef_calib_mt_zeroautohh,coef_calib_mt_zeroautohh,coef_calib_mt_zeroautohh,coef_calib_mt_zeroautohh,coef_calib_mt_zeroautohh,coef_calib_mt_zeroautohh,coef_calib_mt_zeroautohh,coef_calib_mt_zeroautohh,coef_calib_mt_zeroautohh,coef_calib_mt_zeroautohh +coef_calib_nev_zeroautohh,coef_calib_nev_zeroautohh,coef_calib_nev_zeroautohh,coef_calib_nev_zeroautohh,coef_calib_nev_zeroautohh,coef_calib_nev_zeroautohh,coef_calib_nev_zeroautohh,coef_calib_nev_zeroautohh,coef_calib_nev_zeroautohh,coef_calib_nev_zeroautohh,coef_calib_nev_zeroautohh +coef_calib_ebike_owner_BIKE,coef_calib_ebike_owner_BIKE,coef_calib_ebike_owner_BIKE,coef_calib_ebike_owner_BIKE,coef_calib_ebike_owner_BIKE,coef_calib_ebike_owner_BIKE,coef_calib_ebike_owner_BIKE,coef_calib_ebike_owner_BIKE,coef_calib_ebike_owner_BIKE,coef_calib_ebike_owner_BIKE,coef_calib_ebike_owner_BIKE +coef_calib_ebike_owner_EBIKE,coef_calib_ebike_owner_EBIKE,coef_calib_ebike_owner_EBIKE,coef_calib_ebike_owner_EBIKE,coef_calib_ebike_owner_EBIKE,coef_calib_ebike_owner_EBIKE,coef_calib_ebike_owner_EBIKE,coef_calib_ebike_owner_EBIKE,coef_calib_ebike_owner_EBIKE,coef_calib_ebike_owner_EBIKE,coef_calib_ebike_owner_EBIKE +coef_calib_ebike_shared,coef_calib_ebike_shared,coef_calib_ebike_shared,coef_calib_ebike_shared,coef_calib_ebike_shared,coef_calib_ebike_shared,coef_calib_ebike_shared,coef_calib_ebike_shared,coef_calib_ebike_shared,coef_calib_ebike_shared,coef_calib_ebike_shared \ No newline at end of file diff --git a/resident/configs/tour_scheduling_atwork.csv b/resident/configs/tour_scheduling_atwork.csv new file mode 100644 index 0000000..c1e9adb --- /dev/null +++ b/resident/configs/tour_scheduling_atwork.csv @@ -0,0 +1,50 @@ +Label,Description,Expression,Coefficient +util_Female_Departure_after_1230_pm_Linear,Female - Departure after 12:30 pm - Linear,"@np.where(((df.female) & (df.start>19)), (np.where((df.start<= 19), np.minimum(19 - df.start, 48), 0) + np.where((df.start > 19), np.minimum(df.start - 19, 48), 0)), 0)",coef_Female_Departure_after_1230_pm_Linear +util_Parttime_worker_Departure_after_1230_pm__Linear,Part-time worker - Departure after 12:30 pm - Linear,"@np.where(((df.ptype == 2) & (df.start>19)), (np.where((df.start<= 19), np.minimum(19 - df.start, 48), 0) + np.where((df.start > 19), np.minimum(df.start - 19, 48), 0)), 0)",coef_Parttime_worker_Departure_after_1230_pm__Linear +util_Parttime_worker_Duration_greater_than_0p5_hours_depart_and_arrive_in_the_same_period,Part-time worker - Duration greater than 0.5 hours (depart and arrive in the same period),"@np.where(((df.ptype == 2) & (df.duration>1)), (np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0)), 0)",coef_Parttime_worker_Duration_greater_than_0p5_hours_depart_and_arrive_in_the_same_period +util_Low_income_lt25000_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,Low income (<25000) - Duration less than 0.5 hours (depart and arrive in the same period),"@np.where(((df.is_income_less25K) & (df.duration<1)), (np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0)), 0)",coef_Low_income_lt25000_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period +util_Med_income_25k_to_60k_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,Med income (25k to 60k) - Duration less than 0.5 hours (depart and arrive in the same period),"@np.where(((df.is_income_25K_to_60K) & (df.duration<1)), (np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0)), 0)",coef_Med_income_25k_to_60k_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period +util_Med_income_25k_to_60k_Duration_greater_than_0p5_hours,Med income (25k to 60k) - Duration greater than 0.5 hours,"@np.where(((df.is_income_25K_to_60K) & (df.duration>1)), (np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0)), 0)",coef_Med_income_25k_to_60k_Duration_greater_than_0p5_hours +util_Medhigh_income_60k_to_120k_Duration_greater_than_0p5_hours,Med-high income (60k to 120k) - Duration greater than 0.5 hours,"@np.where(((df.is_income_60K_to_120K) & (df.duration>1)), (np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0)), 0)",coef_Medhigh_income_60k_to_120k_Duration_greater_than_0p5_hours +#util_Blue_collar_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,Blue collar - Duration less than 0.5 hours (depart and arrive in the same period),"@np.where(((df.work_segment == 5) & (df.duration<1)), (np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0)), 0)",coef_Blue_collar_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period +#util_Blue_collar_Duration_greater_than_0p5_hours,Blue collar - Duration greater than 0.5 hours,"@np.where(((df.work_segment == 5) & (df.duration>1)), (np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0)), 0)",coef_Blue_collar_Duration_greater_than_0p5_hours +#util_Health_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,Health - Duration less than 0.5 hours (depart and arrive in the same period),"@np.where(((df.work_segment == 3) & (df.duration<1)), (np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0)), 0)",coef_Health_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period +util_Distance_to_destination_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,Distance to destination - Duration less than 0.5 hours (depart and arrive in the same period),"@np.where((df.duration<1), ((np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0))) * df.od_distance, 0)",coef_Distance_to_destination_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period +util_Distance_to_destination_Duration_greater_than_0p5_hours,Distance to destination - Duration greater than 0.5 hours,"@np.where((df.duration>1), ((np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0))) * df.od_distance, 0)",coef_Distance_to_destination_Duration_greater_than_0p5_hours +util_Subtour_purpose_Business_Departure_before_1200_pm__Linear,Subtour purpose: Business - Departure before 12:00 pm - Linear,"@np.where(((df.tour_type == 'business') & (df.start<19)), (np.where((df.start<= 19), np.minimum(19 - df.start, 48), 0) + np.where((df.start > 19), np.minimum(df.start - 19, 48), 0)), 0)",coef_Subtour_purpose_Business_Departure_before_1200_pm__Linear +util_Subtour_purpose_Business_Departure_after_1230_pm_Linear,Subtour purpose: Business - Departure after 12:30 pm - Linear,"@np.where(((df.tour_type == 'business') & (df.start>19)), (np.where((df.start<= 19), np.minimum(19 - df.start, 48), 0) + np.where((df.start > 19), np.minimum(df.start - 19, 48), 0)), 0)",coef_Subtour_purpose_Business_Departure_after_1230_pm_Linear +util_Subtour_purpose_Business_Duration_greater_than_0p5_hours,Subtour purpose: Business - Duration greater than 0.5 hours,"@np.where(((df.tour_type == 'business') & (df.duration>1)), (np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0)), 0)",coef_Subtour_purpose_Business_Duration_greater_than_0p5_hours +util_Subtour_purpose_Eatout_Departure_before_1200_pm__Linear,Subtour purpose: Eat-out - Departure before 12:00 pm - Linear,"@np.where(((df.tour_type == 'eat') & (df.start<19)), (np.where((df.start<= 19), np.minimum(19 - df.start, 48), 0) + np.where((df.start > 19), np.minimum(df.start - 19, 48), 0)), 0)",coef_Subtour_purpose_Eatout_Departure_before_1200_pm__Linear +util_Subtour_purpose_Eatout_Departure_after_1230_pm_Linear,Subtour purpose: Eat-out - Departure after 12:30 pm - Linear,"@np.where(((df.tour_type == 'eat') & (df.start>19)), (np.where((df.start<= 19), np.minimum(19 - df.start, 48), 0) + np.where((df.start > 19), np.minimum(df.start - 19, 48), 0)), 0)",coef_Subtour_purpose_Eatout_Departure_after_1230_pm_Linear +util_Subtour_purpose_Eatout_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,Subtour purpose: Eat-out - Duration less than 0.5 hours (depart and arrive in the same period),"@np.where(((df.tour_type == 'eat') & (df.duration<1)), (np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0)), 0)",coef_Subtour_purpose_Eatout_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period +util__Departure_constants,# Departure constants,,coef__Departure_constants +util_Shift_for_every_30_minutes_before_1030_am_Linear,Shift for every 30 minutes before 10:30 am - Linear,"@np.where((df.start<16), (np.where((df.start< 16), np.minimum(16 - df.start, 9), 0) + np.where((df.start> 21), np.minimum(df.start - 21, 11), 0)), 0)",coef_Shift_for_every_30_minutes_before_1030_am_Linear +util_Before_1100_AM,Before 11:00 AM,@(df.start<17),coef_start_Before_1100_AM +util_1100_AM_1130_AM_start,11:00 AM - 11:30 AM,@(df.start==17),coef_start_1100_AM_1130_AM +util_1130_AM_1200_PM_start,11:30 AM - 12:00 PM,@(df.start==18),coef_start_1130_AM_1200_PM +util_1200_AM_1230_PM_start,12:00 AM - 12:30 PM,@(df.start==19),coef_start_1200_AM_1230_PM +util_1230_PM_0100_PM_start,12:30 PM - 01:00 PM,@(df.start==20),coef_start_1230_PM_0100_PM +util_After_0100_PM,After 01:00 PM,@(df.start>20),coef_start_After_0100_PM +util_Shift_for_every_30_minutes_after_130_pm_Square_Root,Shift for every 30 minutes after 1:30 pm - Square Root,"@np.where((df.start>21), ((np.where((df.start < 16), np.minimum(16 - df.start, 9), 0) + np.where((df.start > 21), np.minimum(df.start - 21, 11), 0))** 0.5), 0)",coef_Shift_for_every_30_minutes_after_130_pm_Square_Root +util__Arrival_constants,# Arrival constants,,coef__Arrival_constants +util_Shift_for_every_30_minutes_before_1130_am_Linear,Shift for every 30 minutes before 11:30 am - Linear,"@np.where((df.end<18), (np.where((df.end < 14), np.minimum(14 - df.end, 9), 0) + np.where((df.end > 24), np.minimum(df.end - 24, 10), 0)), 0)",coef_Shift_for_every_30_minutes_before_1130_am_Linear +util_Before_1200_PM,Before 12:00 PM,@(df.end<19),coef_end_Before_1200_PM +util_1200_AM_1230_PM_end,12:00 AM - 12:30 PM,@(df.end==19),coef_end_1200_AM_1230_PM +util_1230_PM_0100_PM_end,12:30 PM - 01:00 PM,@(df.end==20),coef_end_1230_PM_0100_PM +util_0100_PM_0130_PM_end,01:00 PM - 01:30 PM,@(df.end==21),coef_end_0100_PM_0130_PM +util_0130_PM_0200_PM_end,01:30 PM - 02:00 PM,@(df.end==22),coef_end_0130_PM_0200_PM +util_0200_PM_0230_PM_end,02:00 PM - 02:30 PM,@(df.end==23),coef_end_0200_PM_0230_PM +util_After_0230_PM,After 02:30 PM,@(df.end>23),coef_end_After_0230_PM +util_Shift_for_every_30_minutes_after_300_pm_Linear,Shift for every 30 minutes after 3:00 pm - Linear,"@np.where((df.end>24), (np.where((df.end < 14), np.minimum(14 - df.end, 9), 0) + np.where((df.end > 24), np.minimum(df.end - 24, 10), 0)), 0)",coef_Shift_for_every_30_minutes_after_300_pm_Linear +util__Duration_constants,# Duration constants,,coef__Duration_constants +util_0_hrs,0 hrs,@(df.duration==0),coef_0_hrs +util_0p5_hrs,0.5 hrs,@(df.duration==1),coef_0p5_hrs +util_1_hrs,1 hrs,@(df.duration==2),coef_1_hrs +util_1p5hrs,1.5hrs,@(df.duration==3),coef_1p5hrs +util_2_hrs,2 hrs,@(df.duration==4),coef_2_hrs +util_Longer_than_2_hrs,Longer than 2 hrs,@(df.duration>4),coef_Longer_than_2_hrs +util_Shift_for_every_30_minutes_more_than_2p5_hrs_Square_Root,Shift for every 30 minutes more than 2.5 hrs - Square Root,"@np.where((df.duration>5), ((np.where((df.duration < 0), np.minimum(0 - df.duration, 47), 0) + np.where((df.duration > 5), np.minimum(df.duration - 5, 13), 0)) ** 0.5), 0)",coef_Shift_for_every_30_minutes_more_than_2p5_hrs_Square_Root +util_Calibration_Constant_Departure_eq_18,Calibration Constant - Departure = 18,@(df.start==18),coef_Calibration_Constant_Departure_eq_18 +util_Calibration_Constant_Departure_eq_19,Calibration Constant - Departure = 19,@(df.start==19),coef_Calibration_Constant_Departure_eq_19 +util_Calibration_Constant_Arrival_eq_20,Calibration Constant - Arrival = 20,@(df.end==20),coef_Calibration_Constant_Arrival_eq_20 +util_Calibration_Constant_Arrival_eq_21,Calibration Constant - Arrival = 21,@(df.end==21),coef_Calibration_Constant_Arrival_eq_21 diff --git a/resident/configs/tour_scheduling_atwork.yaml b/resident/configs/tour_scheduling_atwork.yaml new file mode 100644 index 0000000..f04f93b --- /dev/null +++ b/resident/configs/tour_scheduling_atwork.yaml @@ -0,0 +1,16 @@ + +SPEC: tour_scheduling_atwork.csv +COEFFICIENTS: tour_scheduling_atwork_coeffs.csv + +preprocessor: + SPEC: tour_scheduling_atwork_preprocessor + DF: df +# TABLES: +# - land_use +# - tours + +SIMULATE_CHOOSER_COLUMNS: + - od_distance + +CONSTANTS: + time_cap: 30 \ No newline at end of file diff --git a/resident/configs/tour_scheduling_atwork_coefficients.csv b/resident/configs/tour_scheduling_atwork_coefficients.csv new file mode 100644 index 0000000..dd09d22 --- /dev/null +++ b/resident/configs/tour_scheduling_atwork_coefficients.csv @@ -0,0 +1,50 @@ +coefficient_name,value,constrain +coef_early_start_at_5,-7.765548476,F +coef_am_peak_start_at_6,-6.156717827,F +coef_am_peak_start_at_7,-4.061708142,F +coef_am_peak_start_at_8,-2.330535201,F +coef_am_peak_start_at_9,-1.881593386,F +coef_midday_start_at_10_11_12,0,T +coef_midday_start_at_13_14_15,-0.77502158,F +coef_pm_peak_start_at_16_17_18,-0.227528489,F +coef_evening_start_at_19_20_21,-1.015090023,F +coef_late_start_at_22_23,-0.737570054,F +coef_early_end_at_5_6,-2.928312295,F +coef_am_peak_end,-2.928312295,F +coef_midday_end_at_10_11_12,-2.297264374,F +coef_midday_end_at_13_14,0,T +coef_pm_peak_end_at_15,-0.578344457,F +coef_pm_peak_end_at_16,-1.09408722,F +coef_pm_peak_end_at_17,-1.1658466,F +coef_pm_peak_end_at_18,-1.496131081,F +coef_evening_end_at_19_20_21,-2.31998226,F +coef_late_end_at_22_23,-2.31998226,F +coef_duration_of_0_hours,-0.906681512,F +coef_duration_of_1_hour,0,T +coef_duration_of_2_to_3_hours,-1.362175802,F +coef_duration_of_4_to_5_hours,-0.819617616,F +coef_duration_of_6_to_7_hours,1.088111072,F +coef_duration_of_8_to_10_hours,1.734038505,F +coef_duration_of_11_to_13_hours,0.3,F +coef_duration_of_14_to_18_hours,0,T +coef_start_shift_for_outbound_auto_travel_time_off_peak,0.00065,F +coef_start_shift_for_inbound_auto_travel_time_off_peak,0.00065,F +coef_duration_shift_for_outbound_auto_travel_time_off_peak,0.00981,F +coef_duration_shift_for_inbound_auto_travel_time_off_peak,0.00981,F +coef_start_shift_for_business_related_,-0.1113,F +coef_duration_shift_for_business_related_,0.2646,F +coef_start_shift_for_first_sub_tour_of_same_work_tour,-0.5433,F +coef_duration_shift_for_first_sub_tour_of_same_work_tour,-0.3992,F +coef_start_shift_for_subsequent_sub_tour_of_same_work_tour,-0.1844,F +coef_duration_shift_for_subsequent_sub_tour_of_same_work_tour,-0.1844,F +coef_start_shift_for_number_of_mandatory_tours,-0.0193,F +coef_duration_shift_for_number_of_mandatory_tours,-0.7702,F +coef_start_shift_for_number_of_joint_tours,-0.0206,F +coef_duration_shift_for_number_of_joint_tours,-0.2497,F +coef_start_shift_for_number_of_individual_nonmandatory_tours,-0.0128,F +coef_duration_shift_for_number_of_individual_nonmandatory_tours,-0.0422,F +coef_dummy_for_business_related_purpose_and_duration_from_0_to_1,-1.543,F +coef_dummy_for_eating_out_purpose_and_duration_of_1_hour,0.3999,F +coef_dummy_for_eating_out_purpose_and_departure_at_11,1.511,F +coef_dummy_for_eating_out_purpose_and_departure_at_12,2.721,F +coef_dummy_for_eating_out_purpose_and_departure_at_13,2.122,F diff --git a/resident/configs/tour_scheduling_atwork_coeffs.csv b/resident/configs/tour_scheduling_atwork_coeffs.csv new file mode 100644 index 0000000..0e7ab1a --- /dev/null +++ b/resident/configs/tour_scheduling_atwork_coeffs.csv @@ -0,0 +1,47 @@ +coefficient_name,value,constrain +coef_Female_Departure_after_1230_pm_Linear,0.05574558,F +coef_Parttime_worker_Departure_after_1230_pm__Linear,0.129291333,F +coef_Parttime_worker_Duration_greater_than_0p5_hours_depart_and_arrive_in_the_same_period,0.162008704,F +coef_Low_income_lt25000_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,0.885322446,F +coef_Med_income_25k_to_60k_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,0.526935246,F +coef_Med_income_25k_to_60k_Duration_greater_than_0p5_hours,-0.081917021,F +coef_Medhigh_income_60k_to_120k_Duration_greater_than_0p5_hours,-0.068358924,F +coef_Blue_collar_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,1.191378628,F +coef_Blue_collar_Duration_greater_than_0p5_hours,0.123072852,F +coef_Health_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,0.791205377,F +coef_Distance_to_destination_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,-0.292363361,F +coef_Distance_to_destination_Duration_greater_than_0p5_hours,0.006885922,F +coef_Subtour_purpose_Business_Departure_before_1200_pm__Linear,0.268963895,F +coef_Subtour_purpose_Business_Departure_after_1230_pm_Linear,0.17631122,F +coef_Subtour_purpose_Business_Duration_greater_than_0p5_hours,0.362189199,F +coef_Subtour_purpose_Eatout_Departure_before_1200_pm__Linear,-0.250770206,F +coef_Subtour_purpose_Eatout_Departure_after_1230_pm_Linear,-0.169861029,F +coef_Subtour_purpose_Eatout_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,-0.678939929,F +coef_Shift_for_every_30_minutes_before_1030_am_Linear,-0.731880037,F +coef_start_Before_1100_AM,-2.176744062,F +coef_start_1100_AM_1130_AM,-1.190017952,F +coef_start_1130_AM_1200_PM,-0.198229872,F +coef_start_1200_AM_1230_PM,0,T +coef_start_1230_PM_0100_PM,-0.084950396,F +coef_start_After_0100_PM,-0.205562723,F +coef_Shift_for_every_30_minutes_after_130_pm_Square_Root,0.539088697,F +coef_Shift_for_every_30_minutes_before_1130_am_Linear,0.414546555,F +coef_end_Before_1200_PM,0.279351638,F +coef_end_1200_AM_1230_PM,-0.045281832,F +coef_end_1230_PM_0100_PM,0.214070736,F +coef_end_0100_PM_0130_PM,0,T +coef_end_0130_PM_0200_PM,-0.69742748,F +coef_end_0200_PM_0230_PM,-1.284283533,F +coef_end_After_0230_PM,-2.119733896,F +coef_Shift_for_every_30_minutes_after_300_pm_Linear,-0.508006414,F +coef_0_hrs,-0.969734874,F +coef_0p5_hrs,0,T +coef_1_hrs,0.177457256,F +coef_1p5hrs,-0.171124657,F +coef_2_hrs,-0.4678094,F +coef_Longer_than_2_hrs,-0.523935526,F +coef_Shift_for_every_30_minutes_more_than_2p5_hrs_Square_Root,-0.424301372,F +coef_Calibration_Constant_Departure_eq_18,-0.045958531,F +coef_Calibration_Constant_Departure_eq_19,-0.099009925,F +coef_Calibration_Constant_Arrival_eq_20,-0.0698094,F +coef_Calibration_Constant_Arrival_eq_21,-0.064355276,F diff --git a/resident/configs/tour_scheduling_atwork_preprocessor.csv b/resident/configs/tour_scheduling_atwork_preprocessor.csv new file mode 100644 index 0000000..df2aa14 --- /dev/null +++ b/resident/configs/tour_scheduling_atwork_preprocessor.csv @@ -0,0 +1,3 @@ +Description,Target,Expression +#,, +local scalar distance skim,od_distance,"od_skims[('SOV_M_DIST', 'MD')]" diff --git a/resident/configs/tour_scheduling_joint.csv b/resident/configs/tour_scheduling_joint.csv new file mode 100644 index 0000000..c914769 --- /dev/null +++ b/resident/configs/tour_scheduling_joint.csv @@ -0,0 +1,241 @@ +Label,Description,Expression,Coefficient +#SHOPPING,#SHOPPING,,SHOPPING +util_shop_Joint_Shopping_tours_dummy_Departure_before_10_00_AM_Linear,SHOPPING - Joint Shopping tours dummy: Departure before 10:00 AM - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start<15)), ((15-df.start)*(df.start<=15) + (df.start-15)*(df.start>15)), 0)",coef_shop_Joint_Shopping_tours_dummy_Departure_before_10_00_AM_Linear +util_shop_Joint_Shopping_tours_dummy_Departure_after_10_30_AM_Linear,SHOPPING - Joint Shopping tours dummy: Departure after 10:30 AM - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start>15)), ((15-df.start)*(df.start<=15) + (df.start-15)*(df.start>15)), 0)",coef_shop_Joint_Shopping_tours_dummy_Departure_after_10_30_AM_Linear +util_shop_Joint_Tours_Party_Size_gt_2_Duration_lt_1p5_hrs,SHOPPING - Joint Tours Party Size > 2: Duration < 1.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration<3) & (df.number_of_participants > 2)), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)), 0)",coef_shop_Joint_Tours_Party_Size_gt_2_Duration_lt_1p5_hrs +util_shop_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr,SHOPPING - Joint Tours Party Size > 2: Duration > 1.5 hr,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration>3) & (df.number_of_participants > 2)), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)), 0)",coef_shop_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr +util_shop_Joint_Tour_with_only_adults_Duration_lt_1p5_hrs,SHOPPING - Joint Tour with only adults: Duration < 1.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration<3) & (df.composition=='adults')), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)), 0)",coef_shop_Joint_Tour_with_only_adults_Duration_lt_1p5_hrs +util_shop_Kids_in_Joint_Tour_Duration_lt_1p5_hrs,SHOPPING - Kids in Joint Tour: Duration < 1.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration<3) & ((df.composition=='children')|(df.composition=='mixed'))), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)), 0)",coef_shop_Kids_in_Joint_Tour_Duration_lt_1p5_hrs +util_shop_Kids_in_Joint_Tour_Duration_gt_1p5_hr,SHOPPING - Kids in Joint Tour: Duration > 1.5 hr,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration>3) & ((df.composition=='children')|(df.composition=='mixed'))), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)), 0)",coef_shop_Kids_in_Joint_Tour_Duration_gt_1p5_hr +util_shop_Low_Income_lteq25_000_Duration_gt_1p5_hr,"SHOPPING - Low Income (<=$25,000): Duration > 1.5 hr","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.is_income_less25K) & (df.duration>3)), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)), 0)",coef_shop_Low_Income_lteq25_000_Duration_gt_1p5_hr +util_shop_Medium_Income_25_001_to_60_000_Duration_lt_1p5_hrs,"SHOPPING - Medium Income ($25,001 to $60,000): Duration < 1.5 hrs","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.is_income_25K_to_60K) & (df.duration<3)), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)), 0)",coef_shop_Medium_Income_25_001_to_60_000_Duration_lt_1p5_hrs +util_shop_MediumHigh_Income_60_001_to_120_00_Duration_gt_1p5_hr,"SHOPPING - Medium-High Income ($60,001 to $120,00): Duration > 1.5 hr","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.is_income_60K_to_120K) & (df.duration>3)), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)), 0)",coef_shop_MediumHigh_Income_60_001_to_120_00_Duration_gt_1p5_hr +util_shop_Distance_Duration_lt_1p5_hrs,SHOPPING - Distance: Duration < 1.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration<3)), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)) * (df.origin_to_destination_distance), 0)",coef_shop_Distance_Duration_lt_1p5_hrs +util_shop_Distance_Duration_gt_1p5_hr,SHOPPING - Distance: Duration > 1.5 hr,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration>3)), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)) * (df.origin_to_destination_distance), 0)",coef_shop_Distance_Duration_gt_1p5_hr +util_shop_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Linear,SHOPPING - Departure Constant: Shift for every 30 minutes before 08:30 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start<12)), ((12-df.start)*(df.start<=12) + (df.start-12)*(df.start>12)), 0)",coef_shop_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Linear +util_shop_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Square_root,SHOPPING - Departure Constant: Shift for every 30 minutes before 08:30 am - Square root,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start<12)), ((12-df.start)*(df.start<=12) + (df.start-12)*(df.start>12))**0.5, 0)",coef_shop_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Square_root +util_shop_Departure_Constant_Before_09_00_AM,SHOPPING - Departure Constant: Before 09:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start<13)),coef_shop_Departure_Constant_Before_09_00_AM +util_shop_Departure_Constant_09_00_AM_09_30_AM,SHOPPING - Departure Constant: 09:00 AM - 09:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start==13)),coef_shop_Departure_Constant_09_00_AM_09_30_AM +util_shop_Departure_Constant_09_30_AM_10_00_AM,SHOPPING - Departure Constant: 09:30 AM - 10:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start==14)),coef_shop_Departure_Constant_09_30_AM_10_00_AM +util_shop_Departure_Constant_10_00_AM_10_30_AM,SHOPPING - Departure Constant: 10:00 AM - 10:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start==15)),coef_shop_Departure_Constant_10_00_AM_10_30_AM +util_shop_Departure_Constant_10_30_AM_11_00_AM,SHOPPING - Departure Constant: 10:30 AM - 11:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start==16)),coef_shop_Departure_Constant_10_30_AM_11_00_AM +util_shop_Departure_Constant_After_11_00_AM,SHOPPING - Departure Constant: After 11:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start>16)),coef_shop_Departure_Constant_After_11_00_AM +util_shop_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Linear,SHOPPING - Departure Constant: Shift for every 30 minutes after 11:30 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start>17)), ((17-df.start)*(df.start<=17) + (df.start-17)*(df.start>17)), 0)",coef_shop_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Linear +util_shop_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Squared,SHOPPING - Departure Constant: Shift for every 30 minutes after 11:30 am - Squared,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start>17)), (((17-df.start)*(df.start<=17) + (df.start-17)*(df.start>17)) ** 2), 0)",coef_shop_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Squared +util_shop_Arrival_Constant_Shift_for_every_30_minutes_before_12_00_pm_Linear,SHOPPING - Arrival Constant: Shift for every 30 minutes before 12:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end<19)), ((19-df.end)*(df.end<=19) + (df.end-19)*(df.end>19)), 0)",coef_shop_Arrival_Constant_Shift_for_every_30_minutes_before_12_00_pm_Linear +util_shop_Arrival_Constant_Before_12_30_PM,SHOPPING - Arrival Constant: Before 12:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end<20)),coef_shop_Arrival_Constant_Before_12_30_PM +util_shop_Arrival_Constant_12_30_PM_03_00_PM,SHOPPING - Arrival Constant: 12:30 PM - 03:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & ( df.end>=20) & (df.end<=24)),coef_shop_Arrival_Constant_12_30_PM_03_00_PM +util_shop_Arrival_Constant_03_00_PM_03_30_PM,SHOPPING - Arrival Constant: 03:00 PM - 03:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end==25)),coef_shop_Arrival_Constant_03_00_PM_03_30_PM +util_shop_Arrival_Constant_03_30_PM_04_00_PM,SHOPPING - Arrival Constant: 03:30 PM - 04:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end==26)),coef_shop_Arrival_Constant_03_30_PM_04_00_PM +util_shop_Arrival_Constant_04_00_PM_04_30_PM,SHOPPING - Arrival Constant: 04:00 PM - 04:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end==27)),coef_shop_Arrival_Constant_04_00_PM_04_30_PM +util_shop_Arrival_Constant_04_30_PM_05_00_PM,SHOPPING - Arrival Constant: 04:30 PM - 05:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end==28)),coef_shop_Arrival_Constant_04_30_PM_05_00_PM +util_shop_Arrival_Constant_05_00_PM_05_30_PM,SHOPPING - Arrival Constant: 05:00 PM - 05:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end==29)),coef_shop_Arrival_Constant_05_00_PM_05_30_PM +util_shop_Arrival_Constant_05_30_PM_07_00_PM,SHOPPING - Arrival Constant: 05:30 PM - 07:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end>=30) & (df.end<=32)),coef_shop_Arrival_Constant_05_30_PM_07_00_PM +util_shop_Arrival_Constant_07_00_PM_09_30_PM,SHOPPING - Arrival Constant: 07:00 PM - 09:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end>=33) & (df.end<=37)),coef_shop_Arrival_Constant_07_00_PM_09_30_PM +util_shop_Arrival_Constant_After_09_30_PM,SHOPPING - Arrival Constant: After 09:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end>37)),coef_shop_Arrival_Constant_After_09_30_PM +util_shop_Arrival_Constant_Shift_for_every_30_minutes_after_10_00_pm_Linear,SHOPPING - Arrival Constant: Shift for every 30 minutes after 10:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end>38)), ((38-df.end)*(df.end<=38) + (df.end-38)*(df.end>38)), 0)",coef_shop_Arrival_Constant_Shift_for_every_30_minutes_after_10_00_pm_Linear +util_shop_Duration_Constant_0_hrs,SHOPPING - Duration Constant: 0 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration==0)),coef_shop_Duration_Constant_0_hrs +util_shop_Duration_Constant_0p5_hrs,SHOPPING - Duration Constant: 0.5 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration==1)),coef_shop_Duration_Constant_0p5_hrs +util_shop_Duration_Constant_1_hrs,SHOPPING - Duration Constant: 1 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration==2)),coef_shop_Duration_Constant_1_hrs +util_shop_Duration_Constant_1p5hrs,SHOPPING - Duration Constant: 1.5hrs,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration==3)),coef_shop_Duration_Constant_1p5hrs +util_shop_Duration_Constant_2_hrs,SHOPPING - Duration Constant: 2 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration==4)),coef_shop_Duration_Constant_2_hrs +util_shop_Duration_Constant_Longer_than_2_hrs,SHOPPING - Duration Constant: Longer than 2 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration>4)),coef_shop_Duration_Constant_Longer_than_2_hrs +util_shop_Duration_Constant_Duration_gt_2p5_hrs_Linear,SHOPPING - Duration Constant: Duration > 2.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration>5)), ((5-df.duration)*(df.duration<=5) + (df.duration-5)*(df.duration>5)), 0)",coef_shop_Duration_Constant_Duration_gt_2p5_hrs_Linear +util_shop_Duration_Constant_Duration_gt_2p5_hrs_Square_root,SHOPPING - Duration Constant: Duration > 2.5 hrs - Square root,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration>5)), (((5-df.duration)*(df.duration<=5) + (df.duration-5)*(df.duration>5)) ** 0.5), 0)",coef_shop_Duration_Constant_Duration_gt_2p5_hrs_Square_root +util_shop_Calibration_Constant_Duration_eq_1,SHOPPING - Calibration Constant - Duration = 1,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration==0)),coef_shop_Calibration_Constant_Duration_eq_1 +util_shop_Calibration_Constant_Duration_eq_2,SHOPPING - Calibration Constant - Duration = 2,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration==1)),coef_shop_Calibration_Constant_Duration_eq_2 +util_shop_Calibration_Constant_Duration_eq_3,SHOPPING - Calibration Constant - Duration = 3,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration==2)),coef_shop_Calibration_Constant_Duration_eq_3 +#MAINTENANCE,#MAINTENANCE,,MAINTENANCE +util_maint_Joint_Maintenance_tours_dummy_Departure_before_10_00_AM_Linear,MAINTENANCE - Joint Maintenance tours dummy: Departure before reference,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start<13)), ((13-df.start)*(df.start<=13) + (df.start-13)*(df.start>13)), 0)",coef_maint_Joint_Maintenance_tours_dummy_Departure_before_10_00_AM_Linear +util_maint_Joint_Maintenance_tours_dummy_Departure_after_10_30_AM_Linear,MAINTENANCE - Joint Maintenance tours dummy: Departure after reference,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start>13)), ((13-df.start)*(df.start<=13) + (df.start-13)*(df.start>13)), 0)",coef_maint_Joint_Maintenance_tours_dummy_Departure_after_10_30_AM_Linear +util_maint_Joint_Tours_Party_Size_gt_2_Duration_lt_1p5_hrs,MAINTENANCE - Joint Tours Party Size > 2: Duration < reference,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration<4) & (df.number_of_participants > 2)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_maint_Joint_Tours_Party_Size_gt_2_Duration_lt_1p5_hrs +util_maint_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr,MAINTENANCE - Joint Tours Party Size > 2: Duration > reference,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration>4) & (df.number_of_participants > 2)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_maint_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr +util_maint_Joint_Tour_with_only_adults_Duration_lt_1p5_hrs,MAINTENANCE - Joint Tour with only adults: Duration < reference,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration<4) & (df.composition=='adults')), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_maint_Joint_Tour_with_only_adults_Duration_lt_1p5_hrs +util_maint_Kids_in_Joint_Tour_Duration_lt_1p5_hrs,MAINTENANCE - Kids in Joint Tour: Duration < reference,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration<4) & ((df.composition=='children')|(df.composition=='mixed'))), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_maint_Kids_in_Joint_Tour_Duration_lt_1p5_hrs +util_maint_Kids_in_Joint_Tour_Duration_gt_1p5_hr,MAINTENANCE - Kids in Joint Tour: Duration > reference,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration>4) & ((df.composition=='children')|(df.composition=='mixed'))), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_maint_Kids_in_Joint_Tour_Duration_gt_1p5_hr +util_maint_Low_Income_lteq25_000_Duration_gt_1p5_hr,"MAINTENANCE - Low Income (<=$25,000): Duration > reference","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.is_income_less25K) & (df.duration>4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_maint_Low_Income_lteq25_000_Duration_gt_1p5_hr +util_maint_Medium_Income_25_001_to_60_000_Duration_lt_1p5_hrs,"MAINTENANCE - Medium Income ($25,001 to $60,000): Duration < reference","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.is_income_25K_to_60K) & (df.duration<4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_maint_Medium_Income_25_001_to_60_000_Duration_lt_1p5_hrs +util_maint_Medium_Income_25_001_to_60_000_Duration_gt_1p5_hr,"MAINTENANCE - Medium Income ($25,001 to $60,000): Duration > reference","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.is_income_25K_to_60K) & (df.duration>4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_maint_Medium_Income_25_001_to_60_000_Duration_gt_1p5_hr +util_maint_MediumHigh_Income_60_001_to_120_00_Duration_gt_1p5_hr,"MAINTENANCE - Medium-High Income ($60,001 to $120,00): Duration > reference","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.is_income_60K_to_120K) & (df.duration>4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_maint_MediumHigh_Income_60_001_to_120_00_Duration_gt_1p5_hr +util_maint_Distance_Duration_lt_1p5_hrs,MAINTENANCE - Distance: Duration < reference,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration<4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)) *(df.origin_to_destination_distance), 0)",coef_maint_Distance_Duration_lt_1p5_hrs +util_maint_Distance_Duration_gt_1p5_hr,MAINTENANCE - Distance: Duration > reference,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration>4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)) * (df.origin_to_destination_distance), 0)",coef_maint_Distance_Duration_gt_1p5_hr +util_maint_Departure_Constant_Shift_for_every_30_minutes_before_07_30_am_Linear,MAINTENANCE - Departure Constant: Shift for every 30 minutes before 07:30 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start<10)), ((10-df.start)*(df.start<=10) + (df.start-10)*(df.start>10)), 0)",coef_maint_Departure_Constant_Shift_for_every_30_minutes_before_07_30_am_Linear +util_maint_Departure_Constant_Shift_for_every_30_minutes_before_07_30_am_Square_Root,MAINTENANCE - Departure Constant: Shift for every 30 minutes before 07:30 am - Square Root,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start<10)), (((10-df.start)*(df.start<=10) + (df.start-10)*(df.start>10))** 0.5), 0)",coef_maint_Departure_Constant_Shift_for_every_30_minutes_before_07_30_am_Square_Root +util_maint_Departure_Constant_Before_08_00_AM,MAINTENANCE - Departure Constant: Before 08:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start<11)),coef_maint_Departure_Constant_Before_08_00_AM +util_maint_Departure_Constant_08_00_AM_08_30_AM,MAINTENANCE - Departure Constant: 08:00 AM - 08:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start==11)),coef_maint_Departure_Constant_08_00_AM_08_30_AM +util_maint_Departure_Constant_08_30_AM_09_00_AM,MAINTENANCE - Departure Constant: 08:30 AM - 09:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start==12)),coef_maint_Departure_Constant_08_30_AM_09_00_AM +util_maint_Departure_Constant_09_00_AM_09_30_AM,MAINTENANCE - Departure Constant: 09:00 AM - 09:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start==13)),coef_maint_Departure_Constant_09_00_AM_09_30_AM +util_maint_Departure_Constant_09_30_AM_10_00_AM,MAINTENANCE - Departure Constant: 09:30 AM - 10:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start==14)),coef_maint_Departure_Constant_09_30_AM_10_00_AM +util_maint_Departure_Constant_10_00_AM_10_30_AM,MAINTENANCE - Departure Constant: 10:00 AM - 10:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start==15)),coef_maint_Departure_Constant_10_00_AM_10_30_AM +util_maint_Departure_Constant_10_30_AM_11_00_AM,MAINTENANCE - Departure Constant: 10:30 AM - 11:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start==16)),coef_maint_Departure_Constant_10_30_AM_11_00_AM +util_maint_Departure_Constant_After_11_00_AM,MAINTENANCE - Departure Constant: After 11:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start>16)),coef_maint_Departure_Constant_After_11_00_AM +util_maint_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Linear,MAINTENANCE - Departure Constant: Shift for every 30 minutes after 11:30 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start>17)), ((17-df.start)*(df.start<=17) + (df.start-17)*(df.start>17)), 0)",coef_maint_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Linear +util_maint_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Squared,MAINTENANCE - Departure Constant: Shift for every 30 minutes after 11:30 am - Squared,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start>17)), (((17-df.start)*(df.start<=17) + (df.start-17)*(df.start>17)) ** 2), 0)",coef_maint_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Squared +util_maint_Arrival_Constant_Shift_for_every_30_minutes_before_10_00_am_Linear,MAINTENANCE - Arrival Constant: Shift for every 30 minutes before 10:00 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end<15)), ((15-df.end)*(df.end<=15) + (df.end-15)*(df.end>15)), 0)",coef_maint_Arrival_Constant_Shift_for_every_30_minutes_before_10_00_am_Linear +util_maint_Arrival_Constant_Before_10_30_AM,MAINTENANCE - Arrival Constant: Before 10:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end<16)),coef_maint_Arrival_Constant_Before_10_30_AM +util_maint_Arrival_Constant_10_30_AM_11_00_AM,MAINTENANCE - Arrival Constant: 10:30 AM - 11:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end==16)),coef_maint_Arrival_Constant_10_30_AM_11_00_AM +util_maint_Arrival_Constant_11_00_AM_11_30_AM,MAINTENANCE - Arrival Constant: 11:00 AM - 11:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end==17)),coef_maint_Arrival_Constant_11_00_AM_11_30_AM +util_maint_Arrival_Constant_11_30_AM_01_30_PM,MAINTENANCE - Arrival Constant: 11:30 AM - 01:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end>=18) & (df.end<=21)),coef_maint_Arrival_Constant_11_30_AM_01_30_PM +util_maint_Arrival_Constant_01_30_PM_02_30_PM,MAINTENANCE - Arrival Constant: 01:30 PM - 02:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end>=22) & (df.end<=23)),coef_maint_Arrival_Constant_01_30_PM_02_30_PM +util_maint_Arrival_Constant_02_30_PM_04_00_PM,MAINTENANCE - Arrival Constant: 02:30 PM - 04:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end>=24) & (df.end<=26)),coef_maint_Arrival_Constant_02_30_PM_04_00_PM +util_maint_Arrival_Constant_04_00_PM_04_30_PM,MAINTENANCE - Arrival Constant: 04:00 PM - 04:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end==27)),coef_maint_Arrival_Constant_04_00_PM_04_30_PM +util_maint_Arrival_Constant_After_04_30_PM,MAINTENANCE - Arrival Constant: After 04:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end>27)),coef_maint_Arrival_Constant_After_04_30_PM +util_maint_Arrival_Constant_Shift_for_every_30_minutes_after_5_00_pm_Linear,MAINTENANCE - Arrival Constant: Shift for every 30 minutes after 5:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end>28)), ((28-df.end)*(df.end<=28) + (df.end-28)*(df.end>28)), 0)",coef_maint_Arrival_Constant_Shift_for_every_30_minutes_after_5_00_pm_Linear +util_maint_Duration_Constant_0_hrs,MAINTENANCE - Duration Constant: 0 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration==0)),coef_maint_Duration_Constant_0_hrs +util_maint_Duration_Constant_0p5_hrs,MAINTENANCE - Duration Constant: 0.5 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration==1)),coef_maint_Duration_Constant_0p5_hrs +util_maint_Duration_Constant_Longer_than_0p5_hrs,MAINTENANCE - Duration Constant: Longer than 0.5 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration>1)),coef_maint_Duration_Constant_Longer_than_0p5_hrs +util_maint_Duration_Constant_Duration_gt_1_hrs_Linear,MAINTENANCE - Duration Constant: Duration > 1 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration>2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_maint_Duration_Constant_Duration_gt_1_hrs_Linear +util_maint_Duration_Constant_Duration_gt_1_hrs_Square_Root,MAINTENANCE - Duration Constant: Duration > 1 hrs - Square Root,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration>2)), (((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2))** 0.5), 0)",coef_maint_Duration_Constant_Duration_gt_1_hrs_Square_Root +util_maint_Calibration_Constant_Duration_eq_1,MAINTENANCE - Calibration Constant - Duration = 1,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration==0)),coef_maint_Calibration_Constant_Duration_eq_1 +util_maint_Calibration_Constant_Duration_eq_2,MAINTENANCE - Calibration Constant - Duration = 2,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration==1)),coef_maint_Calibration_Constant_Duration_eq_2 +util_maint_Calibration_Constant_Duration_eq_3,MAINTENANCE - Calibration Constant - Duration = 3,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration==2)),coef_maint_Calibration_Constant_Duration_eq_3 +util_maint_Calibration_Constant_Duration_eq_4,MAINTENANCE - Calibration Constant - Duration = 4,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration==3)),coef_maint_Calibration_Constant_Duration_eq_4 +util_maint_Calibration_Constant_Duration_eq_5,MAINTENANCE - Calibration Constant - Duration = 5,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration==4)),coef_maint_Calibration_Constant_Duration_eq_5 +#EATOUT,#EAT-OUT,,EATOUT +util_eatout_Distance_to_destination_Duration_lt_1_hrs,EAT-OUT - Distance to destination - Duration < 1 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration<2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)) * (df.origin_to_destination_distance), 0)",coef_eatout_Distance_to_destination_Duration_lt_1_hrs +util_eatout_Distance_to_destination_Duration_gt_1_hrs,EAT-OUT - Distance to destination - Duration > 1 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration>2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)) *(df.origin_to_destination_distance), 0)",coef_eatout_Distance_to_destination_Duration_gt_1_hrs +util_eatout_Low_income_lt25000_Duration_lt_1_hrs,EAT-OUT - Low income (<25000) - Duration < 1 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.is_income_less25K) & (df.duration<2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_eatout_Low_income_lt25000_Duration_lt_1_hrs +util_eatout_Medium_25k_to_60k_Duration_lt_1_hrs,EAT-OUT - Medium (25k to 60k) - Duration < 1 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.is_income_25K_to_60K) & (df.duration<2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_eatout_Medium_25k_to_60k_Duration_lt_1_hrs +util_eatout_Zero_auto_HH_Duration_gt_1_hrs,EAT-OUT - Zero auto HH - Duration > 1 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.auto_ownership == 0) & (df.duration>2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_eatout_Zero_auto_HH_Duration_gt_1_hrs +util_eatout_Kids_in_Joint_tour_Duration_lt_1_hrs,EAT-OUT - Kids in Joint tour - Duration < 1 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration<2) & ((df.composition=='children')|(df.composition=='mixed'))), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_eatout_Kids_in_Joint_tour_Duration_lt_1_hrs +util_eatout_Joint_Tours_Party_Size_greater_than_2_Duration_lt_1_hrs,EAT-OUT - Joint Tours Party Size greater than 2 - Duration < 1 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration<2) & (df.number_of_participants > 2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_eatout_Joint_Tours_Party_Size_greater_than_2_Duration_lt_1_hrs +util_eatout_Departure_Constant_11_00_AM_12_00_PM,EAT-OUT - Departure Constant: 11:00 AM - 12:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start>=17) & (df.start<=18)),coef_eatout_Departure_Constant_11_00_AM_12_00_PM +util_eatout_Departure_Constant_12_00_PM_12_30_PM,EAT-OUT - Departure Constant: 12:00 PM - 12:30 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start==19),coef_eatout_Departure_Constant_12_00_PM_12_30_PM +util_eatout_Departure_Constant_12_30_PM_to_01_00_PM,EAT-OUT - Departure Constant: 12:30 PM to 01:00 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start==20),coef_eatout_Departure_Constant_12_30_PM_to_01_00_PM +util_eatout_Departure_Constant_Shift_for_every_30_minutes_before_05_00_pm_Linear,EAT-OUT - Departure Constant: Shift for every 30 minutes before 05:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start<29)), ((29-df.start)*(df.start<=29) + (df.start-29)*(df.start>29)), 0)",coef_eatout_Departure_Constant_Shift_for_every_30_minutes_before_05_00_pm_Linear +util_eatout_Departure_Constant_Before_05_30_PM,EAT-OUT - Departure Constant: Before 05:30 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start<30),coef_eatout_Departure_Constant_Before_05_30_PM +util_eatout_Departure_Constant_05_30_PM_06_00_PM,EAT-OUT - Departure Constant: 05:30 PM - 06:00 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start==30),coef_eatout_Departure_Constant_05_30_PM_06_00_PM +util_eatout_Departure_Constant_06_00_PM_06_30_PM,EAT-OUT - Departure Constant: 06:00 PM - 06:30 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start==31),coef_eatout_Departure_Constant_06_00_PM_06_30_PM +util_eatout_Departure_Constant_06_30_PM_07_00_PM,EAT-OUT - Departure Constant: 06:30 PM - 07:00 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start==32),coef_eatout_Departure_Constant_06_30_PM_07_00_PM +util_eatout_Departure_Constant_07_00_PM_07_30_PM,EAT-OUT - Departure Constant: 07:00 PM - 07:30 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start==33),coef_eatout_Departure_Constant_07_00_PM_07_30_PM +util_eatout_Departure_Constant_After_07_30_PM,EAT-OUT - Departure Constant: After 07:30 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start>33),coef_eatout_Departure_Constant_After_07_30_PM +util_eatout_Departure_Constant_Shift_for_every_30_minutes_after_08_00_pm_Linear,EAT-OUT - Departure Constant: Shift for every 30 minutes after 08:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start>34)), ((34-df.start)*(df.start<=34) + (df.start-34)*(df.start>34)), 0)",coef_eatout_Departure_Constant_Shift_for_every_30_minutes_after_08_00_pm_Linear +util_eatout_Arrival_Constant_12_30_PM_to_01_00_PM,EAT-OUT - Arrival Constant: 12:30 PM to 01:00 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.end==20),coef_eatout_Arrival_Constant_12_30_PM_to_01_00_PM +util_eatout_Arrival_Constant_01_00_PM_to_01_30_PM,EAT-OUT - Arrival Constant: 01:00 PM to 01:30 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.end==21),coef_eatout_Arrival_Constant_01_00_PM_to_01_30_PM +util_eatout_Arrival_Constant_01_30_PM_to_02_00_PM,EAT-OUT - Arrival Constant: 01:30 PM to 02:00 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.end==22),coef_eatout_Arrival_Constant_01_30_PM_to_02_00_PM +util_eatout_Arrival_Constant_02_00_PM_to_02_30_PM,EAT-OUT - Arrival Constant: 02:00 PM to 02:30 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.end==23),coef_eatout_Arrival_Constant_02_00_PM_to_02_30_PM +util_eatout_Arrival_Constant_Shift_for_every_30_minutes_before_06_30_pm_Linear,EAT-OUT - Arrival Constant: Shift for every 30 minutes before 06:30 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.end<32)), ((32-df.end)*(df.end<=32) + (df.end-32)*(df.end>32)), 0)",coef_eatout_Arrival_Constant_Shift_for_every_30_minutes_before_06_30_pm_Linear +util_eatout_Arrival_Constant_Before_7_00_PM,EAT-OUT - Arrival Constant: Before 7:00 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.end<33),coef_eatout_Arrival_Constant_Before_7_00_PM +util_eatout_Arrival_Constant_7_00_PM_to_7_30_PM,EAT-OUT - Arrival Constant: 7:00 PM to 7:30 PM,@(df.tour_category == 'joint') & (df.tour_type== 'eatout') & (df.end==33),coef_eatout_Arrival_Constant_7_00_PM_to_7_30_PM +util_eatout_Arrival_Constant_7_30_PM_to_8_00_PM,EAT-OUT - Arrival Constant: 7:30 PM to 8:00 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.end==34),coef_eatout_Arrival_Constant_7_30_PM_to_8_00_PM +util_eatout_Arrival_Constant_8_00_PM_to_8_30_PM,EAT-OUT - Arrival Constant: 8:00 PM to 8:30 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.end==35),coef_eatout_Arrival_Constant_8_00_PM_to_8_30_PM +util_eatout_Arrival_Constant_8_30_PM_to_9_00_PM,EAT-OUT - Arrival Constant: 8:30 PM to 9:00 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.end==36),coef_eatout_Arrival_Constant_8_30_PM_to_9_00_PM +util_eatout_Arrival_Constant_After_09_00_PM,EAT-OUT - Arrival Constant: After 09:00 PM,@(df.tour_category == 'joint') & (df.tour_type== 'eatout') & (df.end>36),coef_eatout_Arrival_Constant_After_09_00_PM +util_eatout_Arrival_Constant_Shift_for_every_30_minutes_after_09_30_pm_Linear,EAT-OUT - Arrival Constant: Shift for every 30 minutes after 09:30 pm - Linear,"@np.where(((df.tour_type== 'eatout') & (df.end>37) & (df.tour_category == 'joint')), ((37-df.end)*(df.end<=37) + (df.end-37)*(df.end>37)), 0)",coef_eatout_Arrival_Constant_Shift_for_every_30_minutes_after_09_30_pm_Linear +util_eatout_Duration_Constant_Shift_for_every_30_minutes_more_than_3_hrs_Linear,EAT-OUT - Duration Constant: Shift for every 30 minutes more than 3 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration>6)), ((6-df.duration)*(df.duration<=6) + (df.duration-6)*(df.duration>6)), 0)",coef_eatout_Duration_Constant_Shift_for_every_30_minutes_more_than_3_hrs_Linear +util_eatout_Duration_Constant_0_hours,EAT-OUT - Duration Constant: 0 hours,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration==0)),coef_eatout_Duration_Constant_0_hours +util_eatout_Duration_Constant_0p5_hous,EAT-OUT - Duration Constant: 0.5 hous,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration==1)),coef_eatout_Duration_Constant_0p5_hous +util_eatout_Duration_Constant_1_hour,EAT-OUT - Duration Constant: 1 hour,@((df.tour_category == 'joint') & (df.tour_type== 'eatout') & (df.duration==2)),coef_eatout_Duration_Constant_1_hour +util_eatout_Duration_Constant_1p5_hours,EAT-OUT - Duration Constant: 1.5 hours,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration==3)),coef_eatout_Duration_Constant_1p5_hours +util_eatout_Duration_Constant_2_hours_or_more,EAT-OUT - Duration Constant: 2 hours or more,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration>4),coef_eatout_Duration_Constant_2_hours_or_more +util_eatout_Duration_Constant_Shift_for_every_30_minutes_more_than_2p5_hrs_Linear,EAT-OUT - Duration Constant: Shift for every 30 minutes more than 2.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration>5)), ((5-df.duration)*(df.duration<=5) + (df.duration-5)*(df.duration>5)), 0)",coef_eatout_Duration_Constant_Shift_for_every_30_minutes_more_than_2p5_hrs_Linear +util_eatout_Calibration_Constant_Duration_eq_1,EAT-OUT - Calibration Constant - Duration = 1,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration==0)),coef_eatout_Calibration_Constant_Duration_eq_1 +util_eatout_Calibration_Constant_Duration_eq_2,EAT-OUT - Calibration Constant - Duration = 2,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration==1)),coef_eatout_Calibration_Constant_Duration_eq_2 +util_eatout_Calibration_Constant_Duration_eq_3,EAT-OUT - Calibration Constant - Duration = 3,@((df.tour_category == 'joint') & (df.tour_type== 'eatout') & (df.duration==2)),coef_eatout_Calibration_Constant_Duration_eq_3 +util_eatout_Calibration_Constant_Duration_eq_4,EAT-OUT - Calibration Constant - Duration = 4,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration==3)),coef_eatout_Calibration_Constant_Duration_eq_4 +util_eatout_Calibration_Constant_Departure_eq_1,EAT-OUT - Calibration Constant - Departure = 1,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start == 1)),coef_eatout_Calibration_Constant_Departure_eq_1 +util_eatout_Calibration_Constant_Departure_eq_2,EAT-OUT - Calibration Constant - Departure = 2,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start == 2)),coef_eatout_Calibration_Constant_Departure_eq_2 +util_eatout_Calibration_Constant_Departure_eq_3,EAT-OUT - Calibration Constant - Departure = 3,@((df.tour_category == 'joint') & (df.tour_type== 'eatout') & (df.start == 3)),coef_eatout_Calibration_Constant_Departure_eq_3 +util_eatout_Calibration_Constant_Departure_eq_17,EAT-OUT - Calibration Constant - Departure = 17,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start ==17)),coef_eatout_Calibration_Constant_Departure_eq_17 +util_eatout_Calibration_Constant_Departure_eq_18,EAT-OUT - Calibration Constant - Departure = 18,@((df.tour_category == 'joint') & (df.tour_type== 'eatout') & (df.start ==18)),coef_eatout_Calibration_Constant_Departure_eq_18 +util_eatout_Calibration_Constant_Departure_eq_19,EAT-OUT - Calibration Constant - Departure = 19,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start ==19)),coef_eatout_Calibration_Constant_Departure_eq_19 +util_eatout_Calibration_Constant_Departure_eq_20,EAT-OUT - Calibration Constant - Departure = 20,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start ==20)),coef_eatout_Calibration_Constant_Departure_eq_20 +util_eatout_Calibration_Constant_Departure_eq_21,EAT-OUT - Calibration Constant - Departure = 21,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start ==21)),coef_eatout_Calibration_Constant_Departure_eq_21 +#SOCIAL,#SOCIAL,,SOCIAL +util_social_Retiree_Nonworking_senior_only_HH_Duration_lt_1p5_hrs_Linear,SOCIAL - Retiree/ Non-working senior only HH: Duration < reference,"@np.where(((df.tour_category == 'joint') & (df.num_retired_adults == df.hhsize) & (df.tour_type == 'social') & (df.duration<4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_social_Retiree_Nonworking_senior_only_HH_Duration_lt_1p5_hrs_Linear +util_social_Zero_auto_households_Duration_lt_1p5_hrs_Linear,SOCIAL - Zero auto households: Duration < reference,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.auto_ownership == 0) & (df.duration<4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_social_Zero_auto_households_Duration_lt_1p5_hrs_Linear +util_social_Zero_auto_households_Duration_gt_1p5_hrs_Linear,SOCIAL - Zero auto households: Duration > reference,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.auto_ownership == 0) & (df.duration>4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_social_Zero_auto_households_Duration_gt_1p5_hrs_Linear +util_social_Number_of_auto_more_that_number_of_adults_Duration_lt_1p5_hrs_Linear,SOCIAL - Number of auto more that number of adults: Duration < reference - Linear,"@np.where(((df.tour_category == 'joint')&(df.tour_type == 'social') & (df.auto_ownership > 0) &(df.auto_ownership > df.num_adults) & (df.duration<4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_social_Number_of_auto_more_that_number_of_adults_Duration_lt_1p5_hrs_Linear +util_social_Number_of_auto_more_that_number_of_adults_Duration_gt_1p5_hrs_Linear,SOCIAL - Number of auto more that number of adults: Duration > reference - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.auto_ownership > 0) & (df.auto_ownership > df.num_adults) & (df.duration>4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_social_Number_of_auto_more_that_number_of_adults_Duration_gt_1p5_hrs_Linear +util_social_Kids_in_Joint_Tour_Duration_lt_1p5_hrs,SOCIAL - Kids in Joint Tour: Duration < reference,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration<4) & ((df.composition=='children')|(df.composition=='mixed'))), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_social_Kids_in_Joint_Tour_Duration_lt_1p5_hrs +util_social_Kids_in_Joint_Tour_Duration_gt_1p5_hr,SOCIAL - Kids in Joint Tour: Duration > reference,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration>4) & ((df.composition=='children')|(df.composition=='mixed'))), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_social_Kids_in_Joint_Tour_Duration_gt_1p5_hr +util_social_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr,SOCIAL - Joint Tours Party Size > 2: Duration > reference,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration>4) & (df.number_of_participants > 2)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_social_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr +util_social_Auto_Distance_Duration_lt_1_hrs_Linear,SOCIAL - Auto Distance: Duration < reference - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration<4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)) * (df.origin_to_destination_distance), 0) ",coef_social_Auto_Distance_Duration_lt_1_hrs_Linear +util_social_Auto_Distance_Duration_gt_1_hrs_Linear,SOCIAL - Auto Distance: Duration > reference - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration>4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)) * (df.origin_to_destination_distance), 0)",coef_social_Auto_Distance_Duration_gt_1_hrs_Linear +util_social_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Linear,SOCIAL - Departure Constant: Shift for every 30 minutes before 08:30 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start<12)), ((12-df.start)*(df.start<=12) + (df.start-12)*(df.start>12)), 0)",coef_social_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Linear +util_social_Departure_Constant_Before_09_00_AM,SOCIAL - Departure Constant: Before 09:00 AM,@(df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start<13),coef_social_Departure_Constant_Before_09_00_AM +util_social_Departure_Constant_09_00_AM_to_09_30_AM,SOCIAL - Departure Constant: 09:00 AM to 09:30 AM,@(df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start==13),coef_social_Departure_Constant_09_00_AM_to_09_30_AM +util_social_Departure_Constant_Shift_for_every_30_minutes_before_05_00_pm_Linear,SOCIAL - Departure Constant: Shift for every 30 minutes before 05:00 pm - Linear,"@np.where((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start<29), ((29-df.start)*(df.start<=29) + (df.start-29)*(df.start>29)), 0)",coef_social_Departure_Constant_Shift_for_every_30_minutes_before_05_00_pm_Linear +util_social_Departure_Constant_Before_05_30_PM,SOCIAL - Departure Constant: Before 05:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start<30)),coef_social_Departure_Constant_Before_05_30_PM +util_social_Departure_Constant_05_30_PM_06_00_PM,SOCIAL - Departure Constant: 05:30 PM - 06:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start==30)),coef_social_Departure_Constant_05_30_PM_06_00_PM +util_social_Departure_Constant_06_00_PM_06_30_PM,SOCIAL - Departure Constant: 06:00 PM - 06:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start==31)),coef_social_Departure_Constant_06_00_PM_06_30_PM +util_social_Departure_Constant_06_30_PM_07_00_PM,SOCIAL - Departure Constant: 06:30 PM - 07:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start==32)),coef_social_Departure_Constant_06_30_PM_07_00_PM +util_social_Departure_Constant_07_00_PM_07_30_PM,SOCIAL - Departure Constant: 07:00 PM - 07:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start==33)),coef_social_Departure_Constant_07_00_PM_07_30_PM +util_social_Departure_Constant_After_07_30_PM,SOCIAL - Departure Constant: After 07:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start>33)),coef_social_Departure_Constant_After_07_30_PM +util_social_Departure_Constant_Shift_for_every_30_minutes_after_08_00_pm_Linear,SOCIAL - Departure Constant: Shift for every 30 minutes after 08:00 pm - Linear,"@np.where((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start>34), ((34-df.start)*(df.start<=34) + (df.start-34)*(df.start>34)), 0)",coef_social_Departure_Constant_Shift_for_every_30_minutes_after_08_00_pm_Linear +util_social_Arrival_Constant_03_00_PM_to_03_30_PM,SOCIAL - Arrival Constant: 03:00 PM to 03:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end==25)),coef_social_Arrival_Constant_03_00_PM_to_03_30_PM +util_social_Arrival_Constant_03_30_PM_to_04_00_PM,SOCIAL - Arrival Constant: 03:30 PM to 04:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end==26)),coef_social_Arrival_Constant_03_30_PM_to_04_00_PM +util_social_Arrival_Constant_04_00_PM_to_04_30_PM,SOCIAL - Arrival Constant: 04:00 PM to 04:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end==27)),coef_social_Arrival_Constant_04_00_PM_to_04_30_PM +util_social_Arrival_Constant_05_00_PM_to_06_00_PM,SOCIAL - Arrival Constant: 05:00 PM to 06:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end>=29) & (df.end<=30)),coef_social_Arrival_Constant_05_00_PM_to_06_00_PM +util_social_Arrival_Constant_Shift_for_every_30_minutes_before_08_00_pm_Linear,SOCIAL - Arrival Constant: Shift for every 30 minutes before 08:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end<35)), ((35-df.end)*(df.end<=35) + (df.end-35)*(df.end>35)), 0)",coef_social_Arrival_Constant_Shift_for_every_30_minutes_before_08_00_pm_Linear +util_social_Arrival_Constant_Before_8_30_PM,SOCIAL - Arrival Constant: Before 8:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end<36)),coef_social_Arrival_Constant_Before_8_30_PM +util_social_Arrival_Constant_8_30_PM_to_9_00_PM,SOCIAL - Arrival Constant: 8:30 PM to 9:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end==36)),coef_social_Arrival_Constant_8_30_PM_to_9_00_PM +util_social_Arrival_Constant_9_00_PM_to_9_30_PM,SOCIAL - Arrival Constant: 9:00 PM to 9:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end==37)),coef_social_Arrival_Constant_9_00_PM_to_9_30_PM +util_social_Arrival_Constant_9_30_PM_to10_00_PM,SOCIAL - Arrival Constant: 9:30 PM to10:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end==38)),coef_social_Arrival_Constant_9_30_PM_to10_00_PM +util_social_Arrival_Constant_10_00_PM_to_10_30_PM,SOCIAL - Arrival Constant: 10:00 PM to 10:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end==39)),coef_social_Arrival_Constant_10_00_PM_to_10_30_PM +util_social_Arrival_Constant_After_10_30_PM,SOCIAL - Arrival Constant: After 10:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end>39)),coef_social_Arrival_Constant_After_10_30_PM +util_social_Arrival_Constant_Shift_for_every_30_minutes_after_11_00_pm_Linear,SOCIAL - Arrival Constant: Shift for every 30 minutes after 11:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end>40)), ((40-df.end)*(df.end<=40) + (df.end-40)*(df.end>40)), 0)",coef_social_Arrival_Constant_Shift_for_every_30_minutes_after_11_00_pm_Linear +util_social_Duration_Constant_Shift_for_every_30_minutes_less_than_1p5_hrs_Linear,SOCIAL - Duration Constant: Shift for every 30 minutes less than 1.5 hrs - Linear,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration<3)) * ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)),coef_social_Duration_Constant_Shift_for_every_30_minutes_less_than_1p5_hrs_Linear +util_social_Duration_Constant_Less_than_2_hours,SOCIAL - Duration Constant: Less than 2 hours,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration<4)),coef_social_Duration_Constant_Less_than_2_hours +util_social_Duration_Constant_2_hours,SOCIAL - Duration Constant: 2 hours,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration==4)),coef_social_Duration_Constant_2_hours +util_social_Duration_Constant_2p5_hours,SOCIAL - Duration Constant: 2.5 hours,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration==5)),coef_social_Duration_Constant_2p5_hours +util_social_Duration_Constant_3_hours_or_more,SOCIAL - Duration Constant: 3 hours or more,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration>5)),coef_social_Duration_Constant_3_hours_or_more +util_social_Duration_Constant_Shift_for_every_30_minutes_more_than_3p5_hrs_Linear,SOCIAL - Duration Constant: Shift for every 30 minutes more than 3.5 hrs - Linear,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration>6)) * ((6-df.duration)*(df.duration<=6) + (df.duration-6)*(df.duration>6)),coef_social_Duration_Constant_Shift_for_every_30_minutes_more_than_3p5_hrs_Linear +util_social_Calibration_Constant_Duration_eq_1,SOCIAL - Calibration Constant - Duration = 1,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration ==0)),coef_social_Calibration_Constant_Duration_eq_1 +util_social_Calibration_Constant_Duration_eq_2,SOCIAL - Calibration Constant - Duration = 2,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration == 1)),coef_social_Calibration_Constant_Duration_eq_2 +util_social_Calibration_Constant_Duration_eq_3,SOCIAL - Calibration Constant - Duration = 3,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration ==2)),coef_social_Calibration_Constant_Duration_eq_3 +util_social_Calibration_Constant_Duration_eq_4,SOCIAL - Calibration Constant - Duration = 4,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration ==3)),coef_social_Calibration_Constant_Duration_eq_4 +util_social_Calibration_Constant_Duration_eq_5,SOCIAL - Calibration Constant - Duration = 5,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration ==4)),coef_social_Calibration_Constant_Duration_eq_5 +util_social_Calibration_Constant_Duration_eq_6,SOCIAL - Calibration Constant - Duration = 6,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration ==5)),coef_social_Calibration_Constant_Duration_eq_6 +util_social_Calibration_Constant_Duration_eq_7,SOCIAL - Calibration Constant - Duration = 7,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration ==6)),coef_social_Calibration_Constant_Duration_eq_7 +util_social_Calibration_Constant_Duration_eq_8,SOCIAL - Calibration Constant - Duration = 8,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration ==7)),coef_social_Calibration_Constant_Duration_eq_8 +util_social_Calibration_Constant_Duration_eq_9,SOCIAL - Calibration Constant - Duration = 9,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration ==8)),coef_social_Calibration_Constant_Duration_eq_9 +#DISCRETIONARY,#DISCRETIONARY,,DISCRETIONARY +util_disc_Retiree_Nonworking_senior_only_HH_Duration_lt_1p5_hrs_Linear,DISCRETIONARY - Retiree/ Non-working senior only HH: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.num_retired_adults == df.hhsize) & (df.tour_type == 'othdiscr') & (df.duration<3)), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)), 0)",coef_disc_Retiree_Nonworking_senior_only_HH_Duration_lt_1p5_hrs_Linear +util_disc_Zero_auto_households_Duration_lt_1p5_hrs_Linear,DISCRETIONARY - Zero auto households: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.auto_ownership == 0) & (df.tour_type == 'othdiscr') & (df.duration<3)), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)), 0)",coef_disc_Zero_auto_households_Duration_lt_1p5_hrs_Linear +util_disc_Zero_auto_households_Duration_gt_1p5_hrs_Linear,DISCRETIONARY - Zero auto households: Duration > 1.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.auto_ownership == 0) & (df.tour_type == 'othdiscr') & (df.duration>3)), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)), 0)",coef_disc_Zero_auto_households_Duration_gt_1p5_hrs_Linear +util_disc_Number_of_auto_more_that_number_of_adults_Duration_lt_1p5_hrs_Linear,DISCRETIONARY - Number of auto more that number of adults: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.auto_ownership > 0) & (df.tour_type == 'othdiscr') & (df.auto_ownership > df.num_adults) & (df.duration<3)), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)),0)",coef_disc_Number_of_auto_more_that_number_of_adults_Duration_lt_1p5_hrs_Linear +util_disc_Number_of_auto_more_that_number_of_adults_Duration_gt_1p5_hrs_Linear,DISCRETIONARY - Number of auto more that number of adults: Duration > 1.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.auto_ownership > 0) &(df.tour_type == 'othdiscr')&(df.auto_ownership > df.num_adults) & (df.duration>3)), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)), 0)",coef_disc_Number_of_auto_more_that_number_of_adults_Duration_gt_1p5_hrs_Linear +util_disc_Kids_in_Joint_Tour_Duration_lt_1p5_hrs,DISCRETIONARY - Kids in Joint Tour: Duration < 1.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration<3) & ((df.ptype == 6) | (df.ptype == 7) | (df.ptype == 8))), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)), 0)",coef_disc_Kids_in_Joint_Tour_Duration_lt_1p5_hrs +util_disc_Kids_in_Joint_Tour_Duration_gt_1p5_hr,DISCRETIONARY - Kids in Joint Tour: Duration > 1.5 hr,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration>3) & ((df.ptype == 6) | (df.ptype == 7) | (df.ptype == 8))), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)), 0)",coef_disc_Kids_in_Joint_Tour_Duration_gt_1p5_hr +util_disc_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr,DISCRETIONARY - Joint Tours Party Size > 2: Duration > 1.5 hr,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration>3) & (df.number_of_participants > 2)), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)), 0)",coef_disc_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr +util_disc_Auto_Distance_Duration_lt_1_hrs_Linear,DISCRETIONARY - Auto Distance: Duration < 1 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration<3)), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)) * (df.origin_to_destination_distance), 0)",coef_disc_Auto_Distance_Duration_lt_1_hrs_Linear +util_disc_Auto_Distance_Duration_gt_1_hrs_Linear,DISCRETIONARY - Auto Distance: Duration > 1 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration>3)), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)) * (df.origin_to_destination_distance), 0)",coef_disc_Auto_Distance_Duration_gt_1_hrs_Linear +util_disc_Departure_Constant_Shift_for_every_30_minutes_before_07_30_pm_Linear,DISCRETIONARY - Departure Constant: Shift for every 30 minutes before 07:30 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start<9)), ((9-df.start)*(df.start<=9) + (df.start-9)*(df.start>9)), 0)",coef_disc_Departure_Constant_Shift_for_every_30_minutes_before_07_30_pm_Linear +util_disc_Departure_Constant_Before_7_30_AM_,DISCRETIONARY - Departure Constant: Before 7:30 AM ,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start<10)),coef_disc_Departure_Constant_Before_7_30_AM_ +util_disc_Departure_Constant_7_30_AM_to_8_00_AM,DISCRETIONARY - Departure Constant: 7:30 AM to 8:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==10)),coef_disc_Departure_Constant_7_30_AM_to_8_00_AM +util_disc_Departure_Constant_8_00_AM_to_8_30_AM,DISCRETIONARY - Departure Constant: 8:00 AM to 8:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==11)),coef_disc_Departure_Constant_8_00_AM_to_8_30_AM +util_disc_Departure_Constant_8_30_AM_to_9_00_AM,DISCRETIONARY - Departure Constant: 8:30 AM to 9:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==12)),coef_disc_Departure_Constant_8_30_AM_to_9_00_AM +util_disc_Departure_Constant_9_00_AM_to_9_30_AM,DISCRETIONARY - Departure Constant: 9:00 AM to 9:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==13)),coef_disc_Departure_Constant_9_00_AM_to_9_30_AM +util_disc_Departure_Constant_9_30_AM_to_10_00_AM,DISCRETIONARY - Departure Constant: 9:30 AM to 10:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==14)),coef_disc_Departure_Constant_9_30_AM_to_10_00_AM +util_disc_Departure_Constant_10_00_AM_to_10_30_AM,DISCRETIONARY - Departure Constant: 10:00 AM to 10:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==15)),coef_disc_Departure_Constant_10_00_AM_to_10_30_AM +util_disc_Departure_Constant_Shift_for_every_30_minutes_before_04_30_pm_Linear,DISCRETIONARY - Departure Constant: Shift for every 30 minutes before 04:30 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start<28)), ((28-df.start)*(df.start<=28) + (df.start-28)*(df.start>28)), 0)",coef_disc_Departure_Constant_Shift_for_every_30_minutes_before_04_30_pm_Linear +util_disc_Departure_Constant_Before_05_00_PM,DISCRETIONARY - Departure Constant: Before 05:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start<29)),coef_disc_Departure_Constant_Before_05_00_PM +util_disc_Departure_Constant_05_00_PM_05_30_PM,DISCRETIONARY - Departure Constant: 05:00 PM - 05:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==29)),coef_disc_Departure_Constant_05_00_PM_05_30_PM +util_disc_Departure_Constant_05_30_PM_06_00_PM,DISCRETIONARY - Departure Constant: 05:30 PM - 06:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==30)),coef_disc_Departure_Constant_05_30_PM_06_00_PM +util_disc_Departure_Constant_06_00_PM_06_30_PM,DISCRETIONARY - Departure Constant: 06:00 PM - 06:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==31)),coef_disc_Departure_Constant_06_00_PM_06_30_PM +util_disc_Departure_Constant_06_30_PM_07_00_PM,DISCRETIONARY - Departure Constant: 06:30 PM - 07:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==32)),coef_disc_Departure_Constant_06_30_PM_07_00_PM +util_disc_Departure_Constant_After_07_00_PM,DISCRETIONARY - Departure Constant: After 07:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start>32)),coef_disc_Departure_Constant_After_07_00_PM +util_disc_Departure_Constant_Shift_for_every_30_minutes_after_07_30_pm_Linear,DISCRETIONARY - Departure Constant: Shift for every 30 minutes after 07:30 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start>33)), ((33-df.start)*(df.start<=33) + (df.start-33)*(df.start>33)), 0)",coef_disc_Departure_Constant_Shift_for_every_30_minutes_after_07_30_pm_Linear +util_disc_Arrival_Constant_Shift_for_every_30_minutes_before_06_00_pm_Linear,DISCRETIONARY - Arrival Constant: Shift for every 30 minutes before 06:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.end<31)), ((31-df.end)*(df.end<=31) + (df.end-31)*(df.end>31)), 0)",coef_disc_Arrival_Constant_Shift_for_every_30_minutes_before_06_00_pm_Linear +util_disc_Arrival_Constant_Before_6_30_PM,DISCRETIONARY - Arrival Constant: Before 6:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.end<32)),coef_disc_Arrival_Constant_Before_6_30_PM +util_disc_Arrival_Constant_6_30_PM_to_7_00_PM,DISCRETIONARY - Arrival Constant: 6:30 PM to 7:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.end==32)),coef_disc_Arrival_Constant_6_30_PM_to_7_00_PM +util_disc_Arrival_Constant_7_00_PM_to_7_30_PM,DISCRETIONARY - Arrival Constant: 7:00 PM to 7:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.end==33)),coef_disc_Arrival_Constant_7_00_PM_to_7_30_PM +util_disc_Arrival_Constant_7_30_PM_to_8_00_PM,DISCRETIONARY - Arrival Constant: 7:30 PM to 8:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.end==34)),coef_disc_Arrival_Constant_7_30_PM_to_8_00_PM +util_disc_Arrival_Constant_8_00_PM_to_8_30_PM,DISCRETIONARY - Arrival Constant: 8:00 PM to 8:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.end==35)),coef_disc_Arrival_Constant_8_00_PM_to_8_30_PM +util_disc_Arrival_Constant_8_30_PM_to_9_00_PM,DISCRETIONARY - Arrival Constant: 8:30 PM to 9:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.end==36)),coef_disc_Arrival_Constant_8_30_PM_to_9_00_PM +util_disc_Arrival_Constant_After_9_00_PM,DISCRETIONARY - Arrival Constant: After 9:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.end>36)),coef_disc_Arrival_Constant_After_9_00_PM +util_disc_Arrival_Constant_Shift_for_every_30_minutes_after_09_30_pm_Linear,DISCRETIONARY - Arrival Constant: Shift for every 30 minutes after 09:30 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.end>37)), ((37-df.end)*(df.end<=37) + (df.end-37)*(df.end>37)), 0)",coef_disc_Arrival_Constant_Shift_for_every_30_minutes_after_09_30_pm_Linear +util_disc_Duration_Constant_0_hours,DISCRETIONARY - Duration Constant: 0 hours,@(df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration==0),coef_disc_Duration_Constant_0_hours +util_disc_Duration_Constant_0p5_hous,DISCRETIONARY - Duration Constant: 0.5 hous,@(df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration==1),coef_disc_Duration_Constant_0p5_hous +util_disc_Duration_Constant_1_hour,DISCRETIONARY - Duration Constant: 1 hour,@(df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration==2),coef_disc_Duration_Constant_1_hour +util_disc_Duration_Constant_1p5_hours,DISCRETIONARY - Duration Constant: 1.5 hours,@(df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration==3),coef_disc_Duration_Constant_1p5_hours +util_disc_Duration_Constant_2_hours,DISCRETIONARY - Duration Constant: 2 hours,@(df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration==4),coef_disc_Duration_Constant_2_hours +util_disc_Duration_Constant_2p5_hours_or_more,DISCRETIONARY - Duration Constant: 2.5 hours or more,@(df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration>4),coef_disc_Duration_Constant_2p5_hours_or_more +util_disc_Duration_Constant_Shift_for_every_30_minutes_more_than_3_hrs_Linear,DISCRETIONARY - Duration Constant: Shift for every 30 minutes more than 3 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration>5)), ((5-df.duration)*(df.duration<=5) + (df.duration-5)*(df.duration>5)), 0)",coef_disc_Duration_Constant_Shift_for_every_30_minutes_more_than_3_hrs_Linear +util_disc_Calibration_Constant_Duration_eq_4,DISCRETIONARY -Calibration Constant - Duration = 4,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration==3)),coef_disc_Calibration_Constant_Duration_eq_4 +util_disc_Calibration_Constant_Duration_eq_5,DISCRETIONARY -Calibration Constant - Duration = 5,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration==4)),coef_disc_Calibration_Constant_Duration_eq_5 +util_disc_Calibration_Constant_Departure_eq_29,DISCRETIONARY -Calibration Constant - Departure = 29,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==29)),coef_disc_Calibration_Constant_Departure_eq_29 +util_disc_Calibration_Constant_Departure_eq_30,DISCRETIONARY -Calibration Constant - Departure = 30,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==30)),coef_disc_Calibration_Constant_Departure_eq_30 +util_disc_Calibration_Constant_Departure_eq_31,DISCRETIONARY -Calibration Constant - Departure = 31,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==31)),coef_disc_Calibration_Constant_Departure_eq_31 +util_disc_Calibration_Constant_Departure_eq_32,DISCRETIONARY -Calibration Constant - Departure = 32,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==32)),coef_disc_Calibration_Constant_Departure_eq_32 diff --git a/resident/configs/tour_scheduling_joint_coefficients.csv b/resident/configs/tour_scheduling_joint_coefficients.csv new file mode 100644 index 0000000..f1ea1d9 --- /dev/null +++ b/resident/configs/tour_scheduling_joint_coefficients.csv @@ -0,0 +1,307 @@ +coefficient_name,value,constrain +coef_escort_Mode_Choice_Logsum,1.1731730340000002,F +coef_escort_Distance_to_destination_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,-0.335017673,F +coef_escort_Distance_to_destination_Duration_greater_than_0p5_hours,0.005298165,F +coef_escort_Fulltime_worker_Departure_after_8_00_am_Linear,-0.037980109,F +coef_escort_Fulltime_worker_Departure_after_3_00_am_Linear,0.163254125,F +coef_escort_Fulltime_worker_Duration_lt_0p5_hrs,-0.275077482,F +coef_escort_Fulltime_worker_Duration_gt_0p5_hrs,0.051530545,F +coef_escort_University_student_Duration_lt_0p5_hrs,-0.4268027179999999,F +coef_escort_Nondriving_age_student_Duration_gt_0p5_hrs,0.240582361,F +coef_escort_Driving_age_student_Duration_lt_0p5_hrs,-0.5541461910000001,F +coef_escort_Driving_age_student_Duration_gt_0p5_hrs,0.299387708,F +coef_escort_Preschool_kid_Duration_gt_0p5_hrs,0.195482563,F +coef_escort_Medhigh_income_60k_to_120k_Duration_gt_0p5_hrs,-0.029281467,F +coef_escort_Households_with_no_kids_Dummy_1_0_Departure_before_7_30_AM,0.589083327,F +coef_escort_Households_with_no_kids_Dummy_1_0_Departure_after_8_00_AM,0.086690827,F +coef_escort_Households_with_no_kids_Dummy_1_0_Departure_before_2_30_PM,0.4775826479999999,F +coef_escort_Households_with_no_kids_Dummy_1_0_Departure_after_3_00_PM,-0.2040655019999999,F +coef_escort_Households_with_no_kids_Dummy_1_0_Arrival_before_8_00_AM,-0.360039254,F +coef_escort_Households_with_no_kids_Dummy_1_0_Arrival_after_8_30_AM,0.0916141069999999,F +coef_escort_Households_with_no_kids_Dummy_1_0_Arrival_before_3_00_PM,0.432854268,F +coef_escort_Households_with_no_kids_Dummy_1_0_Arrival_after_3_30_PM,0.131037275,F +coef_escort_PreSchool_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Departure_after_8_00_AM,0.109700265,F +coef_escort_PreSchool_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Departure_after_3_00_PM,-0.224568648,F +coef_escort_PreSchool_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Arrival_before_8_00_AM,-0.357416434,F +coef_escort_PreSchool_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Arrival_before_3_00_PM,0.629285298,F +coef_escort_Driving_age_School_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Departure_after_8_00_AM,0.0390051479999999,F +coef_escort_Driving_age_School_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Arrival_after_8_30_AM,-0.06556611,F +coef_escort_Driving_age_School_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Arrival_after_3_30_PM,0.1176809769999999,F +coef_escort_Number_of_autos_greater_than_number_of_adults_Duration_gt_0p5_hrs,-0.057322708,F +coef_escort_Number_of_Individual_Tours_excluding_escorting_Duration_gt_0p5_hrs,-0.062899692,F +coef_escort_Number_of_joint_tours_Duration_gt_0p5_hrs,-0.048533895,F +coef_escort_Departure_Constant_Shift_for_every_30_minutes_before_06_30_am_Linear,-1.4692400019999998,F +coef_escort_Departure_Constant_Before_07_00_AM,-2.070292862,F +coef_escort_Departure_Constant_07_00_AM_07_30_AM,-0.642734296,F +coef_escort_Departure_Constant_07_30_AM_08_00_AM,0.0,T +coef_escort_Departure_Constant_08_00_AM_08_30_AM,-0.2146176669999999,F +coef_escort_Departure_Constant_08_30_AM_09_00_AM,-0.147266606,F +coef_escort_Departure_Constant_After_09_00_AM,-1.356686422,F +coef_escort_Departure_Constant_01_30_PM_02_00_PM,0.368092381,F +coef_escort_Departure_Constant_02_00_PM_02_30_PM,1.166803383,F +coef_escort_Departure_Constant_02_30_PM_03_00_PM,1.28466083,F +coef_escort_Departure_Constant_03_00_PM_03_30_PM,0.581891245,F +coef_escort_Departure_Constant_After_03_30_PM,0.834510243,F +coef_escort_Departure_Constant_Shift_for_every_30_minutes_after_9_30_am_Linear,0.175257649,F +coef_escort_Departure_Constant_Shift_for_every_30_minutes_after_4_00_pm_Linear,-0.019161202,F +coef_escort_Arrival_Constant_Shift_for_every_30_minutes_before_6_30_am_Linear,0.44978138,F +coef_escort_Arrival_Constant_Before_07_00_AM,0.549584585,F +coef_escort_Arrival_Constant_07_00_AM_07_30_AM,0.488181278,F +coef_escort_Arrival_Constant_07_30_AM_08_00_AM,0.236447651,F +coef_escort_Arrival_Constant_08_00_AM_08_30_AM,0.0,T +coef_escort_Arrival_Constant_08_30_AM_09_00_AM,-0.6837568009999999,F +coef_escort_Arrival_Constant_After_09_00_AM,-1.428888485,F +coef_escort_Arrival_Constant_02_30_PM_03_00_PM,1.311480662,F +coef_escort_Arrival_Constant_03_00_PM_03_30_PM,1.316883154,F +coef_escort_Arrival_Constant_03_30_PM_04_00_PM,1.3968383919999998,F +coef_escort_Arrival_Constant_04_00_PM_04_30_PM,1.03146139,F +coef_escort_Arrival_Constant_After_04_30_PM,0.907344583,F +coef_escort_Arrival_Constant_Shift_for_every_30_minutes_after_9_30_am_Linear,-0.148408887,F +coef_escort_Arrival_Constant_Shift_for_every_30_minutes_after_5_00_pm_Linear,-0.389082896,F +coef_escort_Duration_Constant_0_hrs,-0.173757322,F +coef_escort_Duration_Constant_0p5_hrs,0.0,T +coef_escort_Duration_Constant_1_hrs,-0.4312877429999999,F +coef_escort_Duration_Constant_1p5hrs,-0.7004739590000001,F +coef_escort_Duration_Constant_2_hrs,-1.071871358,F +coef_escort_Duration_Constant_Longer_than_2_hrs,-1.691098421,F +coef_escort_Calibration_Constant_Duration_eq_1,-0.047200214,F +coef_escort_Calibration_Constant_Duration_eq_2,0.035611332,F +coef_escort_Calibration_Constant_Departure_eq_9,0.106814756,F +coef_escort_Calibration_Constant_Departure_eq_10,0.2153868639999999,F +coef_escort_Calibration_Constant_Departure_eq_23,-0.255087318,F +coef_escort_Calibration_Constant_Departure_eq_24,-0.296870428,F +coef_shop_Joint_Shopping_tours_dummy_Departure_before_10_00_AM_Linear,0.0,F +coef_shop_Joint_Shopping_tours_dummy_Departure_after_10_30_AM_Linear,0.0,F +coef_shop_Joint_Tours_Party_Size_gt_2_Duration_lt_1p5_hrs,0.0,F +coef_shop_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr,0.0,F +coef_shop_Joint_Tour_with_only_adults_Duration_lt_1p5_hrs,0.0,F +coef_shop_Kids_in_Joint_Tour_Duration_lt_1p5_hrs,0.0,F +coef_shop_Kids_in_Joint_Tour_Duration_gt_1p5_hr,0.0,F +coef_shop_Low_Income_lteq25_000_Duration_gt_1p5_hr,0.0,F +coef_shop_Medium_Income_25_001_to_60_000_Duration_lt_1p5_hrs,0.0,F +coef_shop_MediumHigh_Income_60_001_to_120_00_Duration_gt_1p5_hr,0.0,F +coef_shop_Distance_Duration_lt_1p5_hrs,-0.028607893883491196,F +coef_shop_Distance_Duration_gt_1p5_hr,0.017461281006706703,F +coef_shop_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Linear,-1.0512104309818944,F +coef_shop_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Square_root,0.0,F +coef_shop_Departure_Constant_Before_09_00_AM,0.0,F +coef_shop_Departure_Constant_09_00_AM_09_30_AM,0.0,F +coef_shop_Departure_Constant_09_30_AM_10_00_AM,0.0,F +coef_shop_Departure_Constant_10_00_AM_10_30_AM,0.0,T +coef_shop_Departure_Constant_10_30_AM_11_00_AM,0.0,F +coef_shop_Departure_Constant_After_11_00_AM,0.0,F +coef_shop_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Linear,0.0,F +coef_shop_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Squared,-0.00408426422243028,F +coef_shop_Arrival_Constant_Shift_for_every_30_minutes_before_12_00_pm_Linear,-0.17368025954187133,F +coef_shop_Arrival_Constant_Before_12_30_PM,3.1418509688037584,F +coef_shop_Arrival_Constant_12_30_PM_03_00_PM,2.9684267424496387,F +coef_shop_Arrival_Constant_03_00_PM_03_30_PM,3.0135292397053517,F +coef_shop_Arrival_Constant_03_30_PM_04_00_PM,2.540812806437939,F +coef_shop_Arrival_Constant_04_00_PM_04_30_PM,2.634709629448098,T +coef_shop_Arrival_Constant_04_30_PM_05_00_PM,2.8095914129852653,F +coef_shop_Arrival_Constant_05_00_PM_05_30_PM,2.9949590441118166,F +coef_shop_Arrival_Constant_05_30_PM_07_00_PM,2.703929925055346,F +coef_shop_Arrival_Constant_07_00_PM_09_30_PM,0.0,F +coef_shop_Arrival_Constant_After_09_30_PM,1.4252235053772202,F +coef_shop_Arrival_Constant_Shift_for_every_30_minutes_after_10_00_pm_Linear,-0.7628329902801455,F +coef_shop_Duration_Constant_0_hrs,-1.4315217235346374,F +coef_shop_Duration_Constant_0p5_hrs,0.0,F +coef_shop_Duration_Constant_1_hrs,0.0,T +coef_shop_Duration_Constant_1p5hrs,0.5674112263052391,F +coef_shop_Duration_Constant_2_hrs,0.33353948408736633,F +coef_shop_Duration_Constant_Longer_than_2_hrs,0.0,F +coef_shop_Duration_Constant_Duration_gt_2p5_hrs_Linear,-0.5010185517563869,F +coef_shop_Duration_Constant_Duration_gt_2p5_hrs_Square_root,0.0,F +coef_shop_Calibration_Constant_Duration_eq_1,0.0,F +coef_shop_Calibration_Constant_Duration_eq_2,0.0,F +coef_shop_Calibration_Constant_Duration_eq_3,0.0,F +coef_maint_Joint_Maintenance_tours_dummy_Departure_before_10_00_AM_Linear,-0.7266678344083689,F +coef_maint_Joint_Maintenance_tours_dummy_Departure_after_10_30_AM_Linear,0.4158223149950908,F +coef_maint_Joint_Tours_Party_Size_gt_2_Duration_lt_1p5_hrs,0.0,F +coef_maint_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr,0.0,F +coef_maint_Joint_Tour_with_only_adults_Duration_lt_1p5_hrs,0.0,F +coef_maint_Kids_in_Joint_Tour_Duration_lt_1p5_hrs,0.0,F +coef_maint_Kids_in_Joint_Tour_Duration_gt_1p5_hr,0.0,F +coef_maint_Low_Income_lteq25_000_Duration_gt_1p5_hr,0.0,F +coef_maint_Medium_Income_25_001_to_60_000_Duration_lt_1p5_hrs,0.0,F +coef_maint_Medium_Income_25_001_to_60_000_Duration_gt_1p5_hr,0.0,T +coef_maint_MediumHigh_Income_60_001_to_120_00_Duration_gt_1p5_hr,0.0,F +coef_maint_Distance_Duration_lt_1p5_hrs,-0.10895512058986324,F +coef_maint_Distance_Duration_gt_1p5_hr,0.0076226279567043346,F +coef_maint_Departure_Constant_Shift_for_every_30_minutes_before_07_30_am_Linear,0.0,F +coef_maint_Departure_Constant_Shift_for_every_30_minutes_before_07_30_am_Square_Root,0.0,F +coef_maint_Departure_Constant_Before_08_00_AM,0.0,F +coef_maint_Departure_Constant_08_00_AM_08_30_AM,0.0,F +coef_maint_Departure_Constant_08_30_AM_09_00_AM,0.0,F +coef_maint_Departure_Constant_09_00_AM_09_30_AM,0.0,F +coef_maint_Departure_Constant_09_30_AM_10_00_AM,0.0,F +coef_maint_Departure_Constant_10_00_AM_10_30_AM,0.0,T +coef_maint_Departure_Constant_10_30_AM_11_00_AM,0.0,F +coef_maint_Departure_Constant_After_11_00_AM,0.0,F +coef_maint_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Linear,0.0,F +coef_maint_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Squared,0.0,F +coef_maint_Arrival_Constant_Shift_for_every_30_minutes_before_10_00_am_Linear,0.0,F +coef_maint_Arrival_Constant_Before_10_30_AM,4.343768880893401,F +coef_maint_Arrival_Constant_10_30_AM_11_00_AM,4.083350521602396,T +coef_maint_Arrival_Constant_11_00_AM_11_30_AM,3.8938125537705606,F +coef_maint_Arrival_Constant_11_30_AM_01_30_PM,2.2487727466277105,F +coef_maint_Arrival_Constant_01_30_PM_02_30_PM,1.2142794905263539,F +coef_maint_Arrival_Constant_02_30_PM_04_00_PM,0.0,F +coef_maint_Arrival_Constant_04_00_PM_04_30_PM,-0.6951930245707939,F +coef_maint_Arrival_Constant_After_04_30_PM,-0.8895314765132972,F +coef_maint_Arrival_Constant_Shift_for_every_30_minutes_after_5_00_pm_Linear,-0.6960130813905834,F +coef_maint_Duration_Constant_0_hrs,0.0,F +coef_maint_Duration_Constant_0p5_hrs,0.0,T +coef_maint_Duration_Constant_Longer_than_0p5_hrs,0.0,F +coef_maint_Duration_Constant_Duration_gt_1_hrs_Linear,0.15548429306392084,F +coef_maint_Duration_Constant_Duration_gt_1_hrs_Square_Root,0.0,F +coef_maint_Calibration_Constant_Duration_eq_1,0.0,F +coef_maint_Calibration_Constant_Duration_eq_2,0.0,F +coef_maint_Calibration_Constant_Duration_eq_3,0.0,F +coef_maint_Calibration_Constant_Duration_eq_4,0.0,F +coef_maint_Calibration_Constant_Duration_eq_5,0.0,F +coef_eatout_Distance_to_destination_Duration_lt_1_hrs,-0.19108787133440508,F +coef_eatout_Distance_to_destination_Duration_gt_1_hrs,0.023006152654857007,F +coef_eatout_Low_income_lt25000_Duration_lt_1_hrs,0.0,F +coef_eatout_Medium_25k_to_60k_Duration_lt_1_hrs,-1.180918837464798,F +coef_eatout_Zero_auto_HH_Duration_gt_1_hrs,0.0,F +coef_eatout_Kids_in_Joint_tour_Duration_lt_1_hrs,1.3374472139338802,F +coef_eatout_Joint_Tours_Party_Size_greater_than_2_Duration_lt_1_hrs,-1.0580259127605358,F +coef_eatout_Departure_Constant_11_00_AM_12_00_PM,0.9434750816323397,F +coef_eatout_Departure_Constant_12_00_PM_12_30_PM,0.6554959058136046,F +coef_eatout_Departure_Constant_12_30_PM_to_01_00_PM,0.0,F +coef_eatout_Departure_Constant_Shift_for_every_30_minutes_before_05_00_pm_Linear,-0.9675269394009861,F +coef_eatout_Departure_Constant_Before_05_30_PM,-1.302314187436131,F +coef_eatout_Departure_Constant_05_30_PM_06_00_PM,0.0,F +coef_eatout_Departure_Constant_06_00_PM_06_30_PM,0.0,T +coef_eatout_Departure_Constant_06_30_PM_07_00_PM,1.6531878364152586,F +coef_eatout_Departure_Constant_07_00_PM_07_30_PM,2.8956060466853746,F +coef_eatout_Departure_Constant_After_07_30_PM,2.8296134095518095,F +coef_eatout_Departure_Constant_Shift_for_every_30_minutes_after_08_00_pm_Linear,0.48366538096929135,F +coef_eatout_Arrival_Constant_12_30_PM_to_01_00_PM,0.0,F +coef_eatout_Arrival_Constant_01_00_PM_to_01_30_PM,1.0896358400995683,F +coef_eatout_Arrival_Constant_01_30_PM_to_02_00_PM,0.9881110200110167,F +coef_eatout_Arrival_Constant_02_00_PM_to_02_30_PM,0.0,F +coef_eatout_Arrival_Constant_Shift_for_every_30_minutes_before_06_30_pm_Linear,0.8009605390373155,F +coef_eatout_Arrival_Constant_Before_7_00_PM,4.585410018832126,F +coef_eatout_Arrival_Constant_7_00_PM_to_7_30_PM,3.4838111763141164,F +coef_eatout_Arrival_Constant_7_30_PM_to_8_00_PM,2.9063054928957155,F +coef_eatout_Arrival_Constant_8_00_PM_to_8_30_PM,0.0,T +coef_eatout_Arrival_Constant_8_30_PM_to_9_00_PM,0.7383205534380342,F +coef_eatout_Arrival_Constant_After_09_00_PM,0.0,F +coef_eatout_Arrival_Constant_Shift_for_every_30_minutes_after_09_30_pm_Linear,-1.322397671171091,F +coef_eatout_Duration_Constant_Shift_for_every_30_minutes_more_than_3_hrs_Linear,0.0,F +coef_eatout_Duration_Constant_0_hours,-2.938143755725757,F +coef_eatout_Duration_Constant_0p5_hous,-0.8272449464493281,F +coef_eatout_Duration_Constant_1_hour,0.0,T +coef_eatout_Duration_Constant_1p5_hours,0.5471544500112203,F +coef_eatout_Duration_Constant_2_hours_or_more,1.6524845358696236,F +coef_eatout_Duration_Constant_Shift_for_every_30_minutes_more_than_2p5_hrs_Linear,0.0,F +coef_eatout_Calibration_Constant_Duration_eq_1,0.0,F +coef_eatout_Calibration_Constant_Duration_eq_2,0.0,F +coef_eatout_Calibration_Constant_Duration_eq_3,0.0,F +coef_eatout_Calibration_Constant_Duration_eq_4,0.0,F +coef_eatout_Calibration_Constant_Departure_eq_1,0.0,F +coef_eatout_Calibration_Constant_Departure_eq_2,0.0,F +coef_eatout_Calibration_Constant_Departure_eq_3,0.0,F +coef_eatout_Calibration_Constant_Departure_eq_17,0.0,F +coef_eatout_Calibration_Constant_Departure_eq_18,0.0,F +coef_eatout_Calibration_Constant_Departure_eq_19,0.0,F +coef_eatout_Calibration_Constant_Departure_eq_20,0.0,F +coef_eatout_Calibration_Constant_Departure_eq_21,0.0,F +coef_social_Retiree_Nonworking_senior_only_HH_Duration_lt_1p5_hrs_Linear,0.0,F +coef_social_Zero_auto_households_Duration_lt_1p5_hrs_Linear,0.0,F +coef_social_Zero_auto_households_Duration_gt_1p5_hrs_Linear,0.0,F +coef_social_Number_of_auto_more_that_number_of_adults_Duration_lt_1p5_hrs_Linear,0.0,F +coef_social_Number_of_auto_more_that_number_of_adults_Duration_gt_1p5_hrs_Linear,0.0,F +coef_social_Kids_in_Joint_Tour_Duration_lt_1p5_hrs,0.0,F +coef_social_Kids_in_Joint_Tour_Duration_gt_1p5_hr,0.0,F +coef_social_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr,0.0,F +coef_social_Auto_Distance_Duration_lt_1_hrs_Linear,-0.1028458537293977,F +coef_social_Auto_Distance_Duration_gt_1_hrs_Linear,0.007031551489618816,F +coef_social_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Linear,-1.2628280327123185,F +coef_social_Departure_Constant_Before_09_00_AM,0.0,F +coef_social_Departure_Constant_09_00_AM_to_09_30_AM,0.0,F +coef_social_Departure_Constant_Shift_for_every_30_minutes_before_05_00_pm_Linear,-0.8559017615401381,F +coef_social_Departure_Constant_Before_05_30_PM,-2.6714616893962466,F +coef_social_Departure_Constant_05_30_PM_06_00_PM,-0.9852611855634258,F +coef_social_Departure_Constant_06_00_PM_06_30_PM,0.0,T +coef_social_Departure_Constant_06_30_PM_07_00_PM,0.0,F +coef_social_Departure_Constant_07_00_PM_07_30_PM,1.3469930919944328,F +coef_social_Departure_Constant_After_07_30_PM,1.9688176180344494,F +coef_social_Departure_Constant_Shift_for_every_30_minutes_after_08_00_pm_Linear,0.0,F +coef_social_Arrival_Constant_03_00_PM_to_03_30_PM,0.0,F +coef_social_Arrival_Constant_03_30_PM_to_04_00_PM,0.0,F +coef_social_Arrival_Constant_04_00_PM_to_04_30_PM,0.0,F +coef_social_Arrival_Constant_05_00_PM_to_06_00_PM,0.0,F +coef_social_Arrival_Constant_Shift_for_every_30_minutes_before_08_00_pm_Linear,0.850025482539915,F +coef_social_Arrival_Constant_Before_8_30_PM,3.8598870393892537,F +coef_social_Arrival_Constant_8_30_PM_to_9_00_PM,2.8509434322380205,F +coef_social_Arrival_Constant_9_00_PM_to_9_30_PM,2.4329664354881664,F +coef_social_Arrival_Constant_9_30_PM_to10_00_PM,0.0,T +coef_social_Arrival_Constant_10_00_PM_to_10_30_PM,0.0,F +coef_social_Arrival_Constant_After_10_30_PM,-1.5468997489955005,F +coef_social_Arrival_Constant_Shift_for_every_30_minutes_after_11_00_pm_Linear,-1.2916125250149844,F +coef_social_Duration_Constant_Shift_for_every_30_minutes_less_than_1p5_hrs_Linear,-1.0969745826065251,F +coef_social_Duration_Constant_Less_than_2_hours,0.0,F +coef_social_Duration_Constant_2_hours,0.0,F +coef_social_Duration_Constant_2p5_hours,1.2748916919490108,T +coef_social_Duration_Constant_3_hours_or_more,1.9047173259221608,F +coef_social_Duration_Constant_Shift_for_every_30_minutes_more_than_3p5_hrs_Linear,0.5841656016243181,F +coef_social_Calibration_Constant_Duration_eq_1,0.0,F +coef_social_Calibration_Constant_Duration_eq_2,0.0,F +coef_social_Calibration_Constant_Duration_eq_3,0.0,F +coef_social_Calibration_Constant_Duration_eq_4,0.0,F +coef_social_Calibration_Constant_Duration_eq_5,0.0,F +coef_social_Calibration_Constant_Duration_eq_6,0.0,F +coef_social_Calibration_Constant_Duration_eq_7,0.0,F +coef_social_Calibration_Constant_Duration_eq_8,0.0,F +coef_social_Calibration_Constant_Duration_eq_9,0.0,F +coef_disc_Retiree_Nonworking_senior_only_HH_Duration_lt_1p5_hrs_Linear,0.0,F +coef_disc_Zero_auto_households_Duration_lt_1p5_hrs_Linear,0.0,F +coef_disc_Zero_auto_households_Duration_gt_1p5_hrs_Linear,0.24546994148668883,F +coef_disc_Number_of_auto_more_that_number_of_adults_Duration_lt_1p5_hrs_Linear,0.0,F +coef_disc_Number_of_auto_more_that_number_of_adults_Duration_gt_1p5_hrs_Linear,0.0,F +coef_disc_Kids_in_Joint_Tour_Duration_lt_1p5_hrs,0.0,F +coef_disc_Kids_in_Joint_Tour_Duration_gt_1p5_hr,0.0,F +coef_disc_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr,0.0,F +coef_disc_Auto_Distance_Duration_lt_1_hrs_Linear,-0.320622923534169,F +coef_disc_Auto_Distance_Duration_gt_1_hrs_Linear,0.00675561463982907,F +coef_disc_Departure_Constant_Shift_for_every_30_minutes_before_07_30_pm_Linear,-0.5186347200161862,F +coef_disc_Departure_Constant_Before_7_30_AM_,0.0,F +coef_disc_Departure_Constant_7_30_AM_to_8_00_AM,0.0,F +coef_disc_Departure_Constant_8_00_AM_to_8_30_AM,0.635421144057047,F +coef_disc_Departure_Constant_8_30_AM_to_9_00_AM,1.71269871512322,F +coef_disc_Departure_Constant_9_00_AM_to_9_30_AM,1.4799612716000743,F +coef_disc_Departure_Constant_9_30_AM_to_10_00_AM,1.3795958669264374,F +coef_disc_Departure_Constant_10_00_AM_to_10_30_AM,1.3995816792658615,F +coef_disc_Departure_Constant_Shift_for_every_30_minutes_before_04_30_pm_Linear,-2.6360279383280787,F +coef_disc_Departure_Constant_Before_05_00_PM,-8.336424427296372,F +coef_disc_Departure_Constant_05_00_PM_05_30_PM,-5.57379327428183,F +coef_disc_Departure_Constant_05_30_PM_06_00_PM,-2.6690034899691253,F +coef_disc_Departure_Constant_06_00_PM_06_30_PM,0.0,F +coef_disc_Departure_Constant_06_30_PM_07_00_PM,2.3719797586368685,T +coef_disc_Departure_Constant_After_07_00_PM,4.1766375477943045,F +coef_disc_Departure_Constant_Shift_for_every_30_minutes_after_07_30_pm_Linear,1.9886912851879812,F +coef_disc_Arrival_Constant_Shift_for_every_30_minutes_before_06_00_pm_Linear,2.5709925185002573,F +coef_disc_Arrival_Constant_Before_6_30_PM,12.082668378061866,F +coef_disc_Arrival_Constant_6_30_PM_to_7_00_PM,9.784761393655252,F +coef_disc_Arrival_Constant_7_00_PM_to_7_30_PM,7.3488908585128465,F +coef_disc_Arrival_Constant_7_30_PM_to_8_00_PM,5.142548978840155,F +coef_disc_Arrival_Constant_8_00_PM_to_8_30_PM,2.624682599127591,T +coef_disc_Arrival_Constant_8_30_PM_to_9_00_PM,0.0,F +coef_disc_Arrival_Constant_After_9_00_PM,-2.912055108760538,F +coef_disc_Arrival_Constant_Shift_for_every_30_minutes_after_09_30_pm_Linear,-3.0596989905953733,F +coef_disc_Duration_Constant_0_hours,-8.83825066827061,F +coef_disc_Duration_Constant_0p5_hous,-4.72064177864728,F +coef_disc_Duration_Constant_1_hour,-1.7354740845419863,F +coef_disc_Duration_Constant_1p5_hours,0.0,F +coef_disc_Duration_Constant_2_hours,2.4362046406329068,T +coef_disc_Duration_Constant_2p5_hours_or_more,4.76036400219139,F +coef_disc_Duration_Constant_Shift_for_every_30_minutes_more_than_3_hrs_Linear,2.272588544620332,F +coef_disc_Calibration_Constant_Duration_eq_4,0.0,F +coef_disc_Calibration_Constant_Duration_eq_5,0.0,F +coef_disc_Calibration_Constant_Departure_eq_29,0.0,F +coef_disc_Calibration_Constant_Departure_eq_30,0.0,F +coef_disc_Calibration_Constant_Departure_eq_31,0.0,F +coef_disc_Calibration_Constant_Departure_eq_32,0.0,F diff --git a/resident/configs/tour_scheduling_nonmandatory_eatout.csv b/resident/configs/tour_scheduling_nonmandatory_eatout.csv new file mode 100644 index 0000000..4803d20 --- /dev/null +++ b/resident/configs/tour_scheduling_nonmandatory_eatout.csv @@ -0,0 +1,65 @@ +Label,Description,Expression,Coefficient +#EAT-OUT,#EAT-OUT,,#EAT-OUT +util_mode_choice_logsum,Mode choice logsum,mode_choice_logsum,coef_mode_choice_logsum +util_eatout_distance_to_destination_duration_less_than_ref,EAT-OUT - Distance to destination - Duration < Reference,"@np.where((df.duration<2), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)) * (df.origin_to_destination_distance), 0)",coef_eatout_distance_to_destination_duration_less_than_ref +util_eatout_distance_to_destination_duration_greater_than_ref,EAT-OUT - Distance to destination - Duration > Reference,"@np.where((df.duration>2), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)) * (df.origin_to_destination_distance), 0)",coef_eatout_distance_to_destination_duration_greater_than_ref +util_eatout_low_income_duration_less_than_ref,EAT-OUT - Low income (<25000) - Duration < Reference,"@np.where(((df.is_income_less25K) & (df.duration<2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_eatout_low_income_duration_less_than_ref +util_eatout_medium_income_duration_less_than_ref,EAT-OUT - Medium (25k to 60k) - Duration < Reference,"@np.where(((df.is_income_25K_to_60K) & (df.duration<2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_eatout_medium_income_duration_less_than_ref +util_eatout_zeroauto_HH_duration_greater_than_ref,EAT-OUT - Zero auto HH - Duration > Reference,"@np.where(((df.auto_ownership == 0) & (df.duration>2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_eatout_zeroauto_HH_duration_greater_than_ref +util_eatout_university_student_departure_after_7_pm_linear,EAT-OUT - University student - Departure after 7:00 pm - Linear,"@np.where(((df.start>31) & (df.ptype == 3)), ((31-df.start)*(df.start<=31) + (df.start-31)*(df.start>31)), 0)",coef_eatout_university_student_departure_after_7_pm_linear +util_eatout_female_duration_less_than_ref,EAT-OUT - Female - Duration < Reference,"@np.where(((df.duration<2) & (df.female)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_eatout_female_duration_less_than_ref +util_eatout_female_duration_greater_than_ref,EAT-OUT - Female - Duration > Reference,"@np.where(((df.duration>2) & (df.female)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_eatout_female_duration_greater_than_ref +util_eatout_time_pressure_departure_before_6_30_pm,EAT-OUT - Time Pressure - Departure before 6:30 pm,"@np.where(((df.start<31)), ((31-df.start)*(df.start<=31) + (df.start-31)*(df.start>31)) * (np.log10 (30 *(tt.max_time_block_available(df.person_id)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_eatout_time_pressure_departure_before_6_30_pm +util_eatout_time_pressure_duration_less_than_ref,EAT-OUT - Time Pressure - Duration < Reference,"@np.where(((df.duration<2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)) * (np.log10 (30 * (tt.max_time_block_available(df.person_id)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_eatout_time_pressure_duration_less_than_ref +util_eatout_departure_constant_7_30_am_to_9_am,EAT-OUT - Departure Constant: 07:30 AM - 09:00 AM,@((df.start>=10) & (df.start<=12)),coef_eatout_departure_constant_7_30_am_to_9_am +util_eatout_departure_constant_10_30_am_to_11_am,EAT-OUT - Departure Constant: 10:30 AM - 11:00 AM,@((df.start==16)),coef_eatout_departure_constant_10_30_am_to_11_am +util_eatout_departure_constant_11_am_to_11_30_am,EAT-OUT - Departure Constant: 11:00 AM - 11:30 AM,@((df.start==17)),coef_eatout_departure_constant_11_am_to_11_30_am +util_eatout_departure_constant_11_30_am_to_12_pm,EAT-OUT - Departure Constant: 11:30 AM - 12:00 PM,@((df.start==18)),coef_eatout_departure_constant_11_30_am_to_12_pm +util_eatout_departure_constant_12_pm_to_12_30_pm,EAT-OUT - Departure Constant: 12:00 PM - 12:30 PM,@((df.start==19)),coef_eatout_departure_constant_12_pm_to_12_30_pm +util_eatout_departure_constant_12_30_pm_to_1_pm,EAT-OUT - Departure Constant: 12:30 PM - 01:00 PM,@((df.start==20)),coef_eatout_departure_constant_12_30_pm_to_1_pm +util_eatout_departure_constant_1_pm_to_1_30_pm,EAT-OUT - Departure Constant: 01:00 PM - 01:30 PM,@((df.start==21)),coef_eatout_departure_constant_1_pm_to_1_30_pm +util_eatout_departure_constant_shift_for_every_30_minutes_before_5_30_pm_linear,EAT-OUT - Departure Constant: Shift for every 30 minutes before 05:30 pm - Linear,"@np.where(((df.start<30)), ((30-df.start)*(df.start<=30) + (df.start-30)*(df.start>30)), 0)",coef_eatout_departure_constant_shift_for_every_30_minutes_before_5_30_pm_linear +util_eatout_departure_constant_before_6_pm,EAT-OUT - Departure Constant: Before 06:00 PM,@((df.start<31)),coef_eatout_departure_constant_before_6_pm +util_eatout_departure_constant_6_pm_to_6_30_pm,EAT-OUT - Departure Constant: 06:00 PM - 06:30 PM,@((df.start==31)),coef_eatout_departure_constant_6_pm_to_6_30_pm +util_eatout_departure_constant_6_30_pm_to_7_pm,EAT-OUT - Departure Constant: 06:30 PM - 07:00 PM,@((df.start==32)),coef_eatout_departure_constant_6_30_pm_to_7_pm +util_eatout_departure_constant_7_pm_to_7_30_pm,EAT-OUT - Departure Constant: 07:00 PM - 07:30 PM,@((df.start==33)),coef_eatout_departure_constant_7_pm_to_7_30_pm +util_eatout_departure_constant_after_7_30_pm,EAT-OUT - Departure Constant: After 07:30 PM,@((df.start>33)),coef_eatout_departure_constant_after_7_30_pm +util_eatout_departure_constant_shift_for_every_30_minutes_after_8_pm_linear,EAT-OUT - Departure Constant: Shift for every 30 minutes after 08:00 pm - Linear,"@np.where(((df.start>34)), ((34-df.start)*(df.start<=34) + (df.start-34)*(df.start>34)), 0)",coef_eatout_departure_constant_shift_for_every_30_minutes_after_8_pm_linear +util_eatout_arrival_constant_9_30_am_to_11_am,EAT-OUT - Arrival Constant: 9:30 AM to 11:00 AM,@((df.end>=14) & (df.end<=16)),coef_eatout_arrival_constant_9_30_am_to_11_am +util_eatout_arrival_constant_12_30_pm_to_1_pm,EAT-OUT - Arrival Constant: 12:30 PM to 01:00 PM,@((df.end==20)),coef_eatout_arrival_constant_12_30_pm_to_1_pm +util_eatout_arrival_constant_1_pm_to_1_30_pm,EAT-OUT - Arrival Constant: 01:00 PM to 01:30 PM,@((df.end==21)),coef_eatout_arrival_constant_1_pm_to_1_30_pm +util_eatout_arrival_constant_1_30_pm_to_2_pm,EAT-OUT - Arrival Constant: 01:30 PM to 02:00 PM,@((df.end==22)),coef_eatout_arrival_constant_1_30_pm_to_2_pm +util_eatout_arrival_constant_2_pm_to_2_30_pm,EAT-OUT - Arrival Constant: 02:00 PM to 02:30 PM,@((df.end==23)),coef_eatout_arrival_constant_2_pm_to_2_30_pm +util_eatout_arrival_constant_2_30_pm_to_3_pm,EAT-OUT - Arrival Constant: 02:30 PM to 03:00 PM,@((df.end==24)),coef_eatout_arrival_constant_2_30_pm_to_3_pm +util_eatout_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear,EAT-OUT - Arrival Constant: Shift for every 30 minutes before 06:00 pm - Linear,@((df.end<31)),coef_eatout_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear +util_eatout_arrival_constant_before_6_30_pm,EAT-OUT - Arrival Constant: Before 6:30 PM,"@np.where(((df.end<32)), ((32-df.end)*(df.end<=32) + (df.end-32)*(df.end>32)), 0)",coef_eatout_arrival_constant_before_6_30_pm +util_eatout_arrival_constant_6_30_pm_to_7_pm,EAT-OUT - Arrival Constant: 6:30 PM to 7:00 PM,@((df.end==32)),coef_eatout_arrival_constant_6_30_pm_to_7_pm +util_eatout_arrival_constant_7_pm_to_7_30_pm,EAT-OUT - Arrival Constant: 7:00 PM to 7:30 PM,@((df.end==33)),coef_eatout_arrival_constant_7_pm_to_7_30_pm +util_eatout_arrival_constant_7_30_pm_to_8_pm,EAT-OUT - Arrival Constant: 7:30 PM to 8:00 PM,@((df.end==34)),coef_eatout_arrival_constant_7_30_pm_to_8_pm +util_eatout_arrival_constant_8_pm_to_8_30_pm,EAT-OUT - Arrival Constant: 8:00 PM to 8:30 PM,@((df.end==35)),coef_eatout_arrival_constant_8_pm_to_8_30_pm +util_eatout_arrival_constant_8_30_pm_to_9_pm,EAT-OUT - Arrival Constant: 8:30 PM to 9:00 PM,@((df.end==36)),coef_eatout_arrival_constant_8_30_pm_to_9_pm +util_eatout_arrival_constant_after_9_pm,EAT-OUT - Arrival Constant: After 9:00 PM,@((df.end>36)),coef_eatout_arrival_constant_after_9_pm +util_eatout_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear,EAT-OUT - Arrival Constant: Shift for every 30 minutes after 09:30 pm - Linear,"@np.where(((df.end>37)), ((37-df.end)*(df.end<=37) + (df.end-37)*(df.end>37)), 0)",coef_eatout_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear +util_eatout_duration_constant_0_hour,EAT-OUT - Duration Constant: 0 hour,@((df.duration==0)),coef_eatout_duration_constant_0_hour +util_eatout_duration_constant_30_minutes,EAT-OUT - Duration Constant: 0.5 hour,@((df.duration==1)),coef_eatout_duration_constant_30_minutes +util_eatout_duration_constant_1_hour,EAT-OUT - Duration Constant: 1 hour,@((df.duration==2)),coef_eatout_duration_constant_1_hour +util_eatout_duration_constant_1_hour_30_minutes,EAT-OUT - Duration Constant: 1.5 hours,@((df.duration==3)),coef_eatout_duration_constant_1_hour_30_minutes +util_eatout_duration_constant_2_hours,EAT-OUT - Duration Constant: 2 hours,@((df.duration==4)),coef_eatout_duration_constant_2_hours +util_eatout_duration_constant_2_hour_30_minutes_or_more,EAT-OUT - Duration Constant: 2.5 hours or more,@((df.duration>4)),coef_eatout_duration_constant_2_hour_30_minutes_or_more +util_eatout_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear,EAT-OUT - Duration Constant: Shift for every 30 minutes more than 3 hrs - Linear,"@np.where((df.duration>5), ((5-df.duration)*(df.duration<=5) + (df.duration-5)*(df.duration>5)), 0)",coef_eatout_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear +util_eatout_calibration_constant_duration_1,EAT-OUT - Calibration Constant - Duration = 1,@((df.duration==0)),coef_eatout_calibration_constant_duration_1 +util_eatout_calibration_constant_duration_2,EAT-OUT - Calibration Constant - Duration = 2,@((df.duration==1)),coef_eatout_calibration_constant_duration_2 +util_eatout_calibration_constant_duration_3,EAT-OUT - Calibration Constant - Duration = 3,@((df.duration==2)),coef_eatout_calibration_constant_duration_3 +util_eatout_calibration_constant_duration_4,EAT-OUT - Calibration Constant - Duration = 4,@((df.duration==3)),coef_eatout_calibration_constant_duration_4 +util_eatout_calibration_constant_departure_1,EAT-OUT - Calibration Constant - Departure = 1,@((df.start == 1)),coef_eatout_calibration_constant_departure_1 +util_eatout_calibration_constant_departure_2,EAT-OUT - Calibration Constant - Departure = 2,@((df.start == 2)),coef_eatout_calibration_constant_departure_2 +util_eatout_calibration_constant_departure_3,EAT-OUT - Calibration Constant - Departure = 3,@((df.start == 3)),coef_eatout_calibration_constant_departure_3 +util_eatout_calibration_constant_departure_17,EAT-OUT - Calibration Constant - Departure = 17,@((df.start ==17)),coef_eatout_calibration_constant_departure_17 +util_eatout_calibration_constant_departure_18,EAT-OUT - Calibration Constant - Departure = 18,@((df.start ==18)),coef_eatout_calibration_constant_departure_18 +util_eatout_calibration_constant_departure_19,EAT-OUT - Calibration Constant - Departure = 19,@((df.start ==19)),coef_eatout_calibration_constant_departure_19 +util_eatout_calibration_constant_departure_20,EAT-OUT - Calibration Constant - Departure = 20,@((df.start ==20)),coef_eatout_calibration_constant_departure_20 +util_eatout_calibration_constant_departure_21,EAT-OUT - Calibration Constant - Departure = 21,@((df.start ==21)),coef_eatout_calibration_constant_departure_21 +# Including terms for school escorting,,, +util_outbound_school_escort_tour_start,Outbound school escort tours must match the start time of the escort tour,is_outbound_school_escort_tour & (start != school_escort_tour_start),coef_unavailable +util_outbound_school_escort_tour_next_start,Outbound school escort tours must end before next escort tour start,is_outbound_school_escort_tour & (end > school_escort_tour_next_start) & (school_escort_tour_next_start > 0),coef_unavailable +util_inbound_school_escort_tour_end,Inbound school escort tours must match the end time of the escort tour,is_inbound_school_escort_tour & (end != school_escort_tour_end),coef_unavailable \ No newline at end of file diff --git a/resident/configs/tour_scheduling_nonmandatory_eatout_coefficients.csv b/resident/configs/tour_scheduling_nonmandatory_eatout_coefficients.csv new file mode 100644 index 0000000..e8232c0 --- /dev/null +++ b/resident/configs/tour_scheduling_nonmandatory_eatout_coefficients.csv @@ -0,0 +1,61 @@ +coefficient_name,value,constrain +coef_eatout_distance_to_destination_duration_less_than_ref,-0.22213088025234518,F +coef_eatout_distance_to_destination_duration_greater_than_ref,0.006882886760254519,F +coef_eatout_low_income_duration_less_than_ref,0.0,F +coef_eatout_medium_income_duration_less_than_ref,0.0,F +coef_eatout_zeroauto_HH_duration_greater_than_ref,0.0,F +coef_eatout_university_student_departure_after_7_pm_linear,0.0,F +coef_eatout_female_duration_less_than_ref,0.0,F +coef_eatout_female_duration_greater_than_ref,0.07587262787357908,F +coef_eatout_time_pressure_departure_before_6_30_pm,0.12152468184322475,F +coef_eatout_time_pressure_duration_less_than_ref,-1.1004669059917198,F +coef_eatout_departure_constant_7_30_am_to_9_am,0.5692134031282525,F +coef_eatout_departure_constant_10_30_am_to_11_am,0.8995439384100027,F +coef_eatout_departure_constant_11_am_to_11_30_am,1.0520049600035701,F +coef_eatout_departure_constant_11_30_am_to_12_pm,1.0552687311323523,F +coef_eatout_departure_constant_12_pm_to_12_30_pm,0.0,F +coef_eatout_departure_constant_12_30_pm_to_1_pm,0.5514341640403775,F +coef_eatout_departure_constant_1_pm_to_1_30_pm,0.0,F +coef_eatout_departure_constant_shift_for_every_30_minutes_before_5_30_pm_linear,-0.7733197656863885,F +coef_eatout_departure_constant_before_6_pm,1.2101753740729,F +coef_eatout_departure_constant_6_pm_to_6_30_pm,0.0,F +coef_eatout_departure_constant_6_30_pm_to_7_pm,2.01511052873696,T +coef_eatout_departure_constant_7_pm_to_7_30_pm,2.154439497883138,F +coef_eatout_departure_constant_after_7_30_pm,2.3557017509401206,F +coef_eatout_departure_constant_shift_for_every_30_minutes_after_8_pm_linear,0.0,F +coef_eatout_arrival_constant_9_30_am_to_11_am,0.5477594779688595,F +coef_eatout_arrival_constant_12_30_pm_to_1_pm,0.0,F +coef_eatout_arrival_constant_1_pm_to_1_30_pm,0.0,F +coef_eatout_arrival_constant_1_30_pm_to_2_pm,0.6150279185861535,F +coef_eatout_arrival_constant_2_pm_to_2_30_pm,0.0,F +coef_eatout_arrival_constant_2_30_pm_to_3_pm,0.0,F +coef_eatout_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear,1.1113521693562283,F +coef_eatout_arrival_constant_before_6_30_pm,0.31077849460990015,F +coef_eatout_arrival_constant_6_30_pm_to_7_pm,1.411751007495926,F +coef_eatout_arrival_constant_7_pm_to_7_30_pm,0.580944990788283,F +coef_eatout_arrival_constant_7_30_pm_to_8_pm,0.8068518512811886,F +coef_eatout_arrival_constant_8_pm_to_8_30_pm,0.0,T +coef_eatout_arrival_constant_8_30_pm_to_9_pm,0.0,F +coef_eatout_arrival_constant_after_9_pm,-0.45642114111135573,F +coef_eatout_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear,-0.5535494562837153,F +coef_eatout_duration_constant_0_hour,7.7136294531232235,F +coef_eatout_duration_constant_30_minutes,5.243585849897661,F +coef_eatout_duration_constant_1_hour,0.0,F +coef_eatout_duration_constant_1_hour_30_minutes,1.2046725893713652,T +coef_eatout_duration_constant_2_hours,1.3298595485891556,F +coef_eatout_duration_constant_2_hour_30_minutes_or_more,1.4613341980210828,F +coef_eatout_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear,0.0,F +coef_eatout_calibration_constant_duration_1,0.0,F +coef_eatout_calibration_constant_duration_2,0.0,F +coef_eatout_calibration_constant_duration_3,0.0,F +coef_eatout_calibration_constant_duration_4,0.0,F +coef_eatout_calibration_constant_departure_1,0.0,F +coef_eatout_calibration_constant_departure_2,0.0,F +coef_eatout_calibration_constant_departure_3,0.0,F +coef_eatout_calibration_constant_departure_17,0.0,F +coef_eatout_calibration_constant_departure_18,0.0,F +coef_eatout_calibration_constant_departure_19,0.0,F +coef_eatout_calibration_constant_departure_20,0.0,F +coef_eatout_calibration_constant_departure_21,0.0,F +coef_mode_choice_logsum,0.2,F +coef_unavailable,-999.0,F \ No newline at end of file diff --git a/resident/configs/tour_scheduling_nonmandatory_escort.csv b/resident/configs/tour_scheduling_nonmandatory_escort.csv new file mode 100644 index 0000000..c89079f --- /dev/null +++ b/resident/configs/tour_scheduling_nonmandatory_escort.csv @@ -0,0 +1,80 @@ +Label,Description,Expression,Coefficient +# ESCORT,,, +util_escort_mode_choice_logsum,ESCORT - Mode Choice Logsum,"@np.where(df.tour_type == 'escort', df.mode_choice_logsum, 0) ",coef_escort_mode_choice_logsum +"#Note: In CTRAMP expressions, duration alternative is from 1 to 48 but in ActivitySim, it is from 0 to 47 since the duration alternative ID was calculated as (end - start). Therefore, duration in ActivitySim expression = CTRAMP duration expresssion - 1 ",,,# +util_escort_distance_to_destination_duration_less_than_30_minutes,ESCORT - Distance to destination - Duration less than 0.5 hour (depart and arrive in the same period),"@np.where(((df.duration<1)), ((df.origin_to_destination_distance) * ((1-df.duration)*(df.duration<=1) + (df.duration-1)*(df.duration>1))), 0)",coef_escort_distance_to_destination_duration_less_than_30_minutes +util_escort_distance_to_destination_duration_greater_than_30_minutes,ESCORT - Distance to destination - Duration greater than 0.5 hour,"@np.where(((df.duration>1)), ((df.origin_to_destination_distance) * ((1-df.duration)*(df.duration<=1) + (df.duration-1)*(df.duration>1))), 0)",coef_escort_distance_to_destination_duration_greater_than_30_minutes +util_escort_fulltime_worker_departure_after_8_am_linear,ESCORT - Full-time worker - Departure after Reference - Linear,"@np.where(((df.ptype == 1) & (df.start>9)), ((9-df.start)*(df.start<=9) + (df.start-9)*(df.start>9)),0)",coef_escort_fulltime_worker_departure_after_8_am_linear +"#Note: In CTRAMP expression file, the description below says departure is after 3 am but from the expression it seems that it would be 3 pm instead of 3 am",,, +util_escort_fulltime_worker_departure_after_3_pm_linear,ESCORT - Full-time worker - Departure after 3:00 pm - Linear,"@np.where(((df.ptype == 1) & (df.start>24)), ((24-df.start)*(df.start<=24) + (df.start-24)*(df.start>24)), 0)",coef_escort_fulltime_worker_departure_after_3_pm_linear +util_escort_fulltime_worker_duration_less_than_30_minutes,ESCORT - Full-time worker - Duration < 0.5 hr,"@np.where(((df.ptype == 1) & (df.duration<1)), ((1-df.duration)*(df.duration<=1) + (df.duration-1)*(df.duration>1)), 0)",coef_escort_fulltime_worker_duration_less_than_30_minutes +util_escort_fulltime_worker_duration_greater_than_30_minutes,ESCORT - Full-time worker - Duration > 0.5 hr,"@np.where(((df.ptype == 1) & (df.duration>1)), ((1-df.duration)*(df.duration<=1) + (df.duration-1)*(df.duration>1)), 0)",coef_escort_fulltime_worker_duration_greater_than_30_minutes +util_escort_university_student_duration_less_than_30_minutes,ESCORT - University student - Duration < 0.5 hr,"@np.where(((df.ptype == 3) & (df.duration<1)), ((1-df.duration)*(df.duration<=1) + (df.duration-1)*(df.duration>1)), 0)",coef_escort_university_student_duration_less_than_30_minutes +util_escort_non_driving_age_student_duration_greater_than_30_minutes,ESCORT - Non-driving age student - Duration > 0.5 hr,"@np.where((((df.ptype == 7)|(df.ptype == 8)) & (df.duration>1)), ((1-df.duration)*(df.duration<=1) + (df.duration-1)*(df.duration>1)), 0)",coef_escort_non_driving_age_student_duration_greater_than_30_minutes +util_escort_driving_age_student_duration_less_than_30_minutes,ESCORT - Driving age student - Duration < 0.5 hr,"@np.where(((df.ptype == 6) & (df.duration<1)), ((1-df.duration)*(df.duration<=1) + (df.duration-1)*(df.duration>1)), 0)",coef_escort_driving_age_student_duration_less_than_30_minutes +util_escort_driving_age_student_duration_greater_than_30_minutes,ESCORT - Driving age student - Duration > 0.5 hr,"@np.where(((df.ptype == 6) & (df.duration>1)), ((1-df.duration)*(df.duration<=1) + (df.duration-1)*(df.duration>1)), 0)",coef_escort_driving_age_student_duration_greater_than_30_minutes +"#Note: In CTRAMP expression file, description says the expression below is for duration > 0.5 hr but the expression says duration < 0.5 hr",,, +util_escort_pre_school_kid_duration_greater_than_30_minutes,ESCORT - Pre-school kid - Duration > 0.5 hr,"@np.where(((df.ptype == 8) & (df.duration<1)), ((1-df.duration)*(df.duration<=1) + (df.duration-1)*(df.duration>1)), 0)",coef_escort_pre_school_kid_duration_greater_than_30_minutes +util_escort_med_high_income_duration_greater_than_30_minutes,ESCORT - Med-high income (60k to 120k) - Duration > 0.5 hr,"@np.where(((df.is_income_60K_to_120K) & (df.duration>1)), ((1-df.duration)*(df.duration<=1) + (df.duration-1)*(df.duration>1)), 0)",coef_escort_med_high_income_duration_greater_than_30_minutes +util_escort_households_with_no_kids_departure_before_7_30_am,"ESCORT - Households with no kids (Dummy- 1,0) - Departure before 7:30 AM","@np.where(((df.num_children == 0) & (df.start<10)), ((10-df.start)*(df.start<=10) + (df.start-10)*(df.start>10)), 0)",coef_escort_households_with_no_kids_departure_before_7_30_am +util_escort_households_with_no_kids_departure_after_8_00_am,"ESCORT - Households with no kids (Dummy- 1,0) - Departure after 8:00 AM","@np.where(((df.num_children == 0) & (df.start>10)), ((10-df.start)*(df.start<=10) + (df.start-10)*(df.start>10)), 0)",coef_escort_households_with_no_kids_departure_after_8_00_am +util_escort_households_with_no_kids_departure_before_2_30_pm,"ESCORT - Households with no kids (Dummy- 1,0) - Departure before 2:30 PM","@np.where(((df.num_children == 0) & (df.start<24)), ((24-df.start)*(df.start<=24) + (df.start-24)*(df.start>24)), 0)",coef_escort_households_with_no_kids_departure_before_2_30_pm +util_escort_households_with_no_kids_departure_after_3_00_pm,"ESCORT - Households with no kids (Dummy- 1,0) - Departure after 3:00 PM","@np.where(((df.num_children == 0) & (df.start>24)), ((24-df.start)*(df.start<=24) + (df.start-24)*(df.start>24)), 0)",coef_escort_households_with_no_kids_departure_after_3_00_pm +util_escort_households_with_no_kids_arrival_before_8_am,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival before 8:00 AM","@np.where(((df.num_children == 0) & (df.end<11)), ((11-df.end)*(df.end<=11) + (df.end-11)*(df.end>11)), 0)",coef_escort_households_with_no_kids_arrival_before_8_am +util_escort_households_with_no_kids_arrival_after_8_30_am,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival after 8:30 AM","@np.where(((df.num_children == 0) & (df.end>11)), ((11-df.end)*(df.end<=11) + (df.end-11)*(df.end>11)), 0)",coef_escort_households_with_no_kids_arrival_after_8_30_am +util_escort_households_with_no_kids_arrival_before_3_pm,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival before 3:00 PM","@np.where(((df.num_children == 0) & (df.end<25)), ((25-df.end)*(df.end<=25) + (df.end-25)*(df.end>25)), 0)",coef_escort_households_with_no_kids_arrival_before_3_pm +util_escort_households_with_no_kids_arrival_after_3_30_pm,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival after 3:30 PM","@np.where(((df.num_children == 0) & (df.end>25)), ((25-df.end)*(df.end<=25) + (df.end-25)*(df.end>25)), 0)",coef_escort_households_with_no_kids_arrival_after_3_30_pm +util_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_8_am,"ESCORT - Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Departure after 8:00 AM","@np.where(((df.has_pre_school_child_with_mandatory > 0) & (df.start>10)), ((10-df.start)*(df.start<=10) + (df.start-10)*(df.start>10)), 0)",coef_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_8_am +util_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_3_pm,"ESCORT - Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Departure after 3:00 PM","@np.where(((df.has_pre_school_child_with_mandatory > 0) & (df.start>24)), ((24-df.start)*(df.start<=24) + (df.start-24)*(df.start>24)), 0)",coef_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_3_pm +util_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_8_am,"ESCORT -Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival before 8:00 AM","@np.where(((df.has_pre_school_child_with_mandatory > 0) & (df.end<11)), ((11-df.end)*(df.end<=11) + (df.end-11)*(df.end>11)), 0)",coef_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_8_am +util_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_3_pm,"ESCORT - Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival before 3:00 PM","@np.where(((df.has_pre_school_child_with_mandatory > 0) & (df.end<25)), ((25-df.end)*(df.end<=25) + (df.end-25)*(df.end>25)), 0)",coef_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_3_pm +util_escort_driving_age_school_child_in_hh_with_mandatory_tour_departure_after_8_am,"ESCORT - Driving age School Child in HH with Mandatory tour (Dummy- 1,0) - Departure after 8:00 AM","@np.where(((df.has_driving_age_child_with_mandatory > 0) & (df.start>10)), ((10-df.start)*(df.start<=10) + (df.start-10)*(df.start>10)), 0)",coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_departure_after_8_am +util_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_8_30_am,"ESCORT - Driving age School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival after 8:30 AM","@np.where(((df.has_driving_age_child_with_mandatory > 0) & (df.end>11)), ((11-df.end)*(df.end<=11) + (df.end-11)*(df.end>11)), 0)",coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_8_30_am +util_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_3_30_pm,"ESCORT - Driving age School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival after 3:30 PM","@np.where(((df.has_driving_age_child_with_mandatory > 0) & (df.end>25)), ((25-df.end)*(df.end<=25) + (df.end-25)*(df.end>25)), 0)",coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_3_30_pm +util_escort_number_of_autos_greater_than_number_of_adults_duration_greater_than_30_minutes,ESCORT - Number of autos greater than number of adults - Duration > 0.5 hr,"@np.where(((df.auto_ownership > 0) & (df.auto_ownership > df.num_adults) & (df.duration>1)), ((1-df.duration)*(df.duration<=1) + (df.duration-1)*(df.duration>1)), 0)",coef_escort_number_of_autos_greater_than_number_of_adults_duration_greater_than_30_minutes +util_escort_number_of_individual_tours_duration_greater_than_30_minutes,ESCORT -Number of Individual Tours (excluding escorting) - Duration > 0.5 hr,"@np.where(((df.num_non_escort_tours > 0) & (df.duration>1)), ((1-df.duration)*(df.duration<=1) + (df.duration-1)*(df.duration>1)) * (df.num_non_escort_tours), 0)",coef_escort_number_of_individual_tours_duration_greater_than_30_minutes +util_escort_number_of_joint_tours_duration_greater_than_30_minutes,ESCORT - Number of joint tours - Duration > 0.5 hr,"@np.where(((df.duration>1)), ((1-df.duration)*(df.duration<=1) + (df.duration-1)*(df.duration>1)) *(df.num_joint_tours), 0)",coef_escort_number_of_joint_tours_duration_greater_than_30_minutes +util_escort_departure_constant_shift_for_every_30_minutes_before_6_30_am_linear,ESCORT - Departure Constant: Shift for every 30 minutes before 06:30 am - Linear,"@np.where(((df.start<8)), ((8-df.start)*(df.start<=8) + (df.start-8)*(df.start>8)), 0)",coef_escort_departure_constant_shift_for_every_30_minutes_before_6_30_am_linear +util_escort_departure_constant_before_7_am,ESCORT - Departure Constant: Before 07:00 AM,@((df.start<9)),coef_escort_departure_constant_before_7_am +util_escort_departure_constant_7_am_to_7_30_am,ESCORT - Departure Constant: 07:00 AM - 07:30 AM,@((df.start==9)),coef_escort_departure_constant_7_am_to_7_30_am +util_escort_departure_constant_7_30_am_to_8_am,ESCORT - Departure Constant: 07:30 AM - 08:00 AM,@((df.start==10)),coef_escort_departure_constant_7_30_am_to_8_am +util_escort_departure_constant_8_am_to_8_30_am,ESCORT - Departure Constant: 08:00 AM - 08:30 AM,@((df.start==11)),coef_escort_departure_constant_8_am_to_8_30_am +util_escort_departure_constant_8_30_am_to_9_am,ESCORT - Departure Constant: 08:30 AM - 09:00 AM,@((df.start==12)),coef_escort_departure_constant_8_30_am_to_9_am +util_escort_departure_constant_after_9_am,ESCORT - Departure Constant: After 09:00 AM,@((df.start>12)),coef_escort_departure_constant_after_9_am +util_escort_departure_constant_1_30_pm_to_2_pm,ESCORT - Departure Constant: 01:30 PM - 02:00 PM,@((df.start==22)),coef_escort_departure_constant_1_30_pm_to_2_pm +util_escort_departure_constant_2_pm_to_2_30_pm,ESCORT - Departure Constant: 02:00 PM - 02:30 PM,@((df.start==23)),coef_escort_departure_constant_2_pm_to_2_30_pm +util_escort_departure_constant_2_30_pm_to_3_pm,ESCORT - Departure Constant: 02:30 PM - 03:00 PM,@((df.start==24)),coef_escort_departure_constant_2_30_pm_to_3_pm +util_escort_departure_constant_3_pm_to_3_30_pm,ESCORT - Departure Constant: 03:00 PM - 03:30 PM,@((df.start==25)),coef_escort_departure_constant_3_pm_to_3_30_pm +util_escort_departure_constant_after_3_30_pm,ESCORT - Departure Constant: After 03:30 PM,@((df.start>25)),coef_escort_departure_constant_after_3_30_pm +util_escort_departure_constant_shift_for_every_30_minutes_after_9_30_am_linear,ESCORT - Departure Constant: Shift for every 30 minutes after 9:30 am - Linear,"@np.where(((df.start>13)), ((13-df.start)*(df.start<=13) + (df.start-13)*(df.start>13)), 0)",coef_escort_departure_constant_shift_for_every_30_minutes_after_9_30_am_linear +util_escort_departure_constant_shift_for_every_30_minutes_after_4_pm_linear,ESCORT - Departure Constant: Shift for every 30 minutes after 4:00 pm - Linear,"@np.where(((df.start>26)), ((26-df.start)*(df.start<=26) + (df.start-26)*(df.start>26)), 0)",coef_escort_departure_constant_shift_for_every_30_minutes_after_4_pm_linear +util_escort_arrival_constant_shift_for_every_30_minutes_before_6_30_am_linear,ESCORT - Arrival Constant: Shift for every 30 minutes before 6:30 am - Linear,"@np.where(((df.end<8)), ((8-df.end)*(df.end<=8) + (df.end-8)*(df.end>8)), 0)",coef_escort_arrival_constant_shift_for_every_30_minutes_before_6_30_am_linear +util_escort_arrival_constant_before_7_am,ESCORT - Arrival Constant: Before 07:00 AM,@((df.end<9)),coef_escort_arrival_constant_before_7_am +util_escort_arrival_constant_7_am_to_7_30_am,ESCORT - Arrival Constant: 07:00 AM - 07:30 AM,@((df.end==9)),coef_escort_arrival_constant_7_am_to_7_30_am +util_escort_arrival_constant_7_30_am_to_8_am,ESCORT - Arrival Constant: 07:30 AM - 08:00 AM,@((df.end==10)),coef_escort_arrival_constant_7_30_am_to_8_am +util_escort_arrival_constant_8_am_to_8_30_am,ESCORT - Arrival Constant: 08:00 AM - 08:30 AM,@((df.end==11)),coef_escort_arrival_constant_8_am_to_8_30_am +util_escort_arrival_constant_8_30_am_to_9_am,ESCORT - Arrival Constant: 08:30 AM - 09:00 AM,@((df.end==12)),coef_escort_arrival_constant_8_30_am_to_9_am +util_escort_arrival_constant_after_9_am,ESCORT - Arrival Constant: After 09:00 AM,@((df.end>12)),coef_escort_arrival_constant_after_9_am +util_escort_arrival_constant_2_30_pm_to_3_pm,ESCORT - Arrival Constant: 02:30 PM - 03:00 PM,@((df.end==24)),coef_escort_arrival_constant_2_30_pm_to_3_pm +util_escort_arrival_constant_3_pm_to_3_30_pm,ESCORT - Arrival Constant: 03:00 PM - 03:30 PM,@((df.end==25)),coef_escort_arrival_constant_3_pm_to_3_30_pm +util_escort_arrival_constant_3_30_pm_to_4_pm,ESCORT - Arrival Constant: 03:30 PM - 04:00 PM,@((df.end==26)),coef_escort_arrival_constant_3_30_pm_to_4_pm +util_escort_arrival_constant_4_pm_to_4_30_pm,ESCORT - Arrival Constant: 04:00 PM - 04:30 PM,@((df.end==27)),coef_escort_arrival_constant_4_pm_to_4_30_pm +util_escort_arrival_constant_after_4_30_pm,ESCORT - Arrival Constant: After 04:30 PM,@((df.end>27)),coef_escort_arrival_constant_after_4_30_pm +util_escort_arrival_constant_shift_for_every_30_minutes_after_9_30_am_linear,ESCORT - Arrival Constant: Shift for every 30 minutes after 9:30 am - Linear,"@np.where(((df.end>13)), ((13-df.end)*(df.end<=13) + (df.end-13)*(df.end>13)), 0)",coef_escort_arrival_constant_shift_for_every_30_minutes_after_9_30_am_linear +util_escort_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear,ESCORT - Arrival Constant: Shift for every 30 minutes after 5:00 pm - Linear,"@np.where(((df.end>28)), ((28-df.end)*(df.end<=28) + (df.end-28)*(df.end>28)), 0)",coef_escort_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear +util_escort_duration_constant_0_hour,ESCORT - Duration Constant: 0 hr,@((df.duration==0)),coef_escort_duration_constant_0_hour +util_escort_duration_constant_30_minutes,ESCORT - Duration Constant: 0.5 hr,@((df.duration==1)),coef_escort_duration_constant_30_minutes +util_escort_duration_constant_1_hour,ESCORT - Duration Constant: 1 hr,@((df.duration==2)),coef_escort_duration_constant_1_hour +util_escort_duration_constant_1_hour_30_minutes,ESCORT - Duration Constant: 1.5hrs,@((df.duration==3)),coef_escort_duration_constant_1_hour_30_minutes +util_escort_duration_constant_2_hours,ESCORT - Duration Constant: 2 hrs,@((df.duration==4)),coef_escort_duration_constant_2_hours +util_escort_duration_constant_longer_than_2_hours,ESCORT - Duration Constant: Longer than 2 hrs,@((df.duration>4)),coef_escort_duration_constant_longer_than_2_hours +util_escort_calibration_constant_duration_1,ESCORT - Calibration Constant - Duration = 1,@((df.duration==0)),coef_escort_calibration_constant_duration_1 +util_escort_calibration_constant_duration_2,ESCORT - Calibration Constant - Duration = 2,@((df.duration==1)),coef_escort_calibration_constant_duration_2 +util_escort_calibration_constant_departure_9,ESCORT - Calibration Constant - Departure = 9,@((df.start==9)),coef_escort_calibration_constant_departure_9 +util_escort_calibration_constant_departure_10,ESCORT - Calibration Constant - Departure = 10,@((df.start==10)),coef_escort_calibration_constant_departure_10 +util_escort_calibration_constant_departure_23,ESCORT - Calibration Constant - Departure = 23,@((df.start==23)),coef_escort_calibration_constant_departure_23 +util_escort_calibration_constant_departure_24,ESCORT - Calibration Constant - Departure = 24,@((df.start==24)),coef_escort_calibration_constant_departure_24 +# Including terms for school escorting,,, +util_outbound_school_escort_tour_start,Outbound school escort tours must match the start time of the escort tour,is_outbound_school_escort_tour & (start != school_escort_tour_start),coef_unlikely +util_outbound_school_escort_tour_next_start,Outbound school escort tours must end before next escort tour start,is_outbound_school_escort_tour & (end > school_escort_tour_next_start) & (school_escort_tour_next_start > 0),coef_unlikely +util_inbound_school_escort_tour_end,Inbound school escort tours must match the end time of the escort tour,is_inbound_school_escort_tour & (end != school_escort_tour_end),coef_unlikely \ No newline at end of file diff --git a/resident/configs/tour_scheduling_nonmandatory_escort_coefficients.csv b/resident/configs/tour_scheduling_nonmandatory_escort_coefficients.csv new file mode 100644 index 0000000..d901984 --- /dev/null +++ b/resident/configs/tour_scheduling_nonmandatory_escort_coefficients.csv @@ -0,0 +1,74 @@ +coefficient_name,value,constrain +coef_escort_mode_choice_logsum,0.2,F +coef_escort_distance_to_destination_duration_less_than_30_minutes,-0.4733878369607008,F +coef_escort_distance_to_destination_duration_greater_than_30_minutes,0.008568718107435931,F +coef_escort_fulltime_worker_departure_after_8_am_linear,0.0,F +coef_escort_fulltime_worker_departure_after_3_pm_linear,0.10265282779895883,F +coef_escort_fulltime_worker_duration_less_than_30_minutes,0.48717921412555765,F +coef_escort_fulltime_worker_duration_greater_than_30_minutes,0.06360020624220894,F +coef_escort_university_student_duration_less_than_30_minutes,0.0,F +coef_escort_non_driving_age_student_duration_greater_than_30_minutes,0.13619138148103171,F +coef_escort_driving_age_student_duration_less_than_30_minutes,0.0,F +coef_escort_driving_age_student_duration_greater_than_30_minutes,0.2898061598287889,F +coef_escort_pre_school_kid_duration_greater_than_30_minutes,0.0,F +coef_escort_med_high_income_duration_greater_than_30_minutes,0.0,F +coef_escort_households_with_no_kids_departure_before_7_30_am,0.0,F +coef_escort_households_with_no_kids_departure_after_8_00_am,0.0,F +coef_escort_households_with_no_kids_departure_before_2_30_pm,0.0,F +coef_escort_households_with_no_kids_departure_after_3_00_pm,0.0,F +coef_escort_households_with_no_kids_arrival_before_8_am,0.0,F +coef_escort_households_with_no_kids_arrival_after_8_30_am,0.0,F +coef_escort_households_with_no_kids_arrival_before_3_pm,0.0,F +coef_escort_households_with_no_kids_arrival_after_3_30_pm,0.0,F +coef_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_8_am,0.2193957525105046,F +coef_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_3_pm,-0.33725705669741124,F +coef_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_8_am,-1.843839469217804,F +coef_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_3_pm,0.2844617723897163,F +coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_departure_after_8_am,0.0,F +coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_8_30_am,0.0,F +coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_3_30_pm,0.0,F +coef_escort_number_of_autos_greater_than_number_of_adults_duration_greater_than_30_minutes,0.0,F +coef_escort_number_of_individual_tours_duration_greater_than_30_minutes,-0.0620079827984566,F +coef_escort_number_of_joint_tours_duration_greater_than_30_minutes,0.0,F +coef_escort_departure_constant_shift_for_every_30_minutes_before_6_30_am_linear,-1.236018962947682,F +coef_escort_departure_constant_before_7_am,0.7319683480497287,F +coef_escort_departure_constant_7_am_to_7_30_am,0.0,F +coef_escort_departure_constant_7_30_am_to_8_am,4.122740011813239,T +coef_escort_departure_constant_8_am_to_8_30_am,4.835810362294344,F +coef_escort_departure_constant_8_30_am_to_9_am,5.122388952675412,F +coef_escort_departure_constant_after_9_am,4.283724261944397,F +coef_escort_departure_constant_1_30_pm_to_2_pm,0.0,F +coef_escort_departure_constant_2_pm_to_2_30_pm,0.7940143785524454,F +coef_escort_departure_constant_2_30_pm_to_3_pm,1.0810066025980387,F +coef_escort_departure_constant_3_pm_to_3_30_pm,1.326721246058783,F +coef_escort_departure_constant_after_3_30_pm,1.1856245248532122,F +coef_escort_departure_constant_shift_for_every_30_minutes_after_9_30_am_linear,0.19604399657492097,F +coef_escort_departure_constant_shift_for_every_30_minutes_after_4_pm_linear,0.0,F +coef_escort_arrival_constant_shift_for_every_30_minutes_before_6_30_am_linear,0.0,F +coef_escort_arrival_constant_before_7_am,4.009035240130145,F +coef_escort_arrival_constant_7_am_to_7_30_am,4.18385718813814,F +coef_escort_arrival_constant_7_30_am_to_8_am,2.9375790166530416,F +coef_escort_arrival_constant_8_am_to_8_30_am,0.0,T +coef_escort_arrival_constant_8_30_am_to_9_am,0.0,F +coef_escort_arrival_constant_after_9_am,-0.8516924916440327,F +coef_escort_arrival_constant_2_30_pm_to_3_pm,0.8821519927765968,F +coef_escort_arrival_constant_3_pm_to_3_30_pm,1.3755379509692238,F +coef_escort_arrival_constant_3_30_pm_to_4_pm,1.5074800730702913,F +coef_escort_arrival_constant_4_pm_to_4_30_pm,1.6943356568396335,F +coef_escort_arrival_constant_after_4_30_pm,1.2683991826495213,F +coef_escort_arrival_constant_shift_for_every_30_minutes_after_9_30_am_linear,-0.2792532861601614,F +coef_escort_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear,-0.17999830126346059,F +coef_escort_duration_constant_0_hour,-0.2939194972058852,F +coef_escort_duration_constant_30_minutes,0.0,T +coef_escort_duration_constant_1_hour,0.0,F +coef_escort_duration_constant_1_hour_30_minutes,-0.717021512011088,F +coef_escort_duration_constant_2_hours,-1.1701511666645092,F +coef_escort_duration_constant_longer_than_2_hours,-1.1702599996232288,F +coef_escort_calibration_constant_duration_1,0.0,F +coef_escort_calibration_constant_duration_2,0.0,F +coef_escort_calibration_constant_departure_9,0.0,F +coef_escort_calibration_constant_departure_10,0.0,F +coef_escort_calibration_constant_departure_23,0.0,F +coef_escort_calibration_constant_departure_24,0.0,F +coef_unavailable,-999.0,T +coef_unlikely,-50.0,T \ No newline at end of file diff --git a/resident/configs/tour_scheduling_nonmandatory_othdiscr.csv b/resident/configs/tour_scheduling_nonmandatory_othdiscr.csv new file mode 100644 index 0000000..bc20b39 --- /dev/null +++ b/resident/configs/tour_scheduling_nonmandatory_othdiscr.csv @@ -0,0 +1,59 @@ +Label,Description,Expression,Coefficient +#DISCRETIONARY,#DISCRETIONARY,,#DISCRETIONARY +util_mode_choice_logsum,Mode choice logsum,mode_choice_logsum,coef_mode_choice_logsum +util_discretionary_person_less_than_18_years_old_duration_less_than_reference_linear,DISCRETIONARY - Person< 18 years old: Duration < Reference - Linear,"@np.where(((df.duration<4) & (df.age<18)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_discretionary_person_less_than_18_years_old_duration_less_than_reference_linear +util_discretionary_person_less_than_18_years_old_duration_greater_than_reference_linear,DISCRETIONARY - Person< 18 years old: Duration > Reference - Linear,"@np.where(((df.duration>4) & (df.age<18)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_discretionary_person_less_than_18_years_old_duration_greater_than_reference_linear +util_discretionary_non_working_senior_retiree_duration_less_than_reference_linear,DISCRETIONARY - Non-working senior/ retiree: Duration < Reference - Linear,"@np.where(((df.duration<4) & (df.ptype == 5)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_discretionary_non_working_senior_retiree_duration_less_than_reference_linear +util_discretionary_retiree_non_working_senior_only_HH_duration_reference_linear,DISCRETIONARY - Retiree/ Non-working senior only HH: Duration < Reference - Linear,"@np.where(((df.retired_adults_only_hh) & (df.duration<4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_discretionary_retiree_non_working_senior_only_HH_duration_reference_linear +util_discretionary_zero_auto_households_duration_less_than_reference_linear,DISCRETIONARY - Zero auto households: Duration < Reference - Linear,"@np.where(((df.auto_ownership == 0) & (df.duration<4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_discretionary_zero_auto_households_duration_less_than_reference_linear +util_discretionary_zero_auto_households_duration_greater_than_reference_linear,DISCRETIONARY - Zero auto households: Duration > Reference - Linear,"@np.where(((df.auto_ownership == 0) & (df.duration>4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_discretionary_zero_auto_households_duration_greater_than_reference_linear +util_discretionary_number_of_auto_more_than_number_of_adults_duration_less_than_reference_linear,DISCRETIONARY - Number of auto more that number of adults: Duration < Reference - Linear,"@np.where(((df.auto_ownership > 0) & (df.auto_ownership > df.num_adults) & (df.duration<4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)),0)",coef_discretionary_number_of_auto_more_than_number_of_adults_duration_less_than_reference_linear +util_discretionary_number_of_auto_more_than_number_of_adults_duration_greater_than_reference_linear,DISCRETIONARY - Number of auto more that number of adults: Duration > Reference - Linear,"@np.where(((df.auto_ownership > 0) & (df.auto_ownership > df.num_adults) & (df.duration>4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_discretionary_number_of_auto_more_than_number_of_adults_duration_greater_than_reference_linear +util_discretionary_auto_distance_duration_less_than_reference_linear,DISCRETIONARY - Auto Distance: Duration < Reference,"@np.where(((df.duration<4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)) * (df.origin_to_destination_distance), 0)",coef_discretionary_auto_distance_duration_less_than_reference_linear +util_discretionary_auto_distance_duration_greater_than_reference_linear,DISCRETIONARY - Auto Distance: Duration > Reference,"@np.where(((df.duration>4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)) * (df.origin_to_destination_distance), 0)",coef_discretionary_auto_distance_duration_greater_than_reference_linear +util_discretionary_time_pressure_duration_less_reference,DISCRETIONARY - Time Pressure - Duration < Reference,"@np.where(((df.duration<4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)) * (np.log10 (30 * (tt.max_time_block_available(df.person_id)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_discretionary_time_pressure_duration_less_reference +util_discretionary_time_pressure_duration_greater_reference,DISCRETIONARY - Time Pressure - Duration > Reference,"@np.where(((df.duration>4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)) * (np.log10 (30 * (tt.max_time_block_available(df.person_id)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_discretionary_time_pressure_duration_greater_reference +util_discretionary_number_of_additional_individual_social_and_dicretionary_tours_duration_less_reference,DISCRETIONARY - Number of additional individual social and dicretionary tours - Duration < Reference,"@np.where((df.duration<4), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4))*(df.num_add_soc_discr_tours),0)",coef_discretionary_number_of_additional_individual_social_and_dicretionary_tours_duration_less_reference +util_discretionary_departure_constant_shift_for_every_30_minutes_before_7_30_pm_linear,DISCRETIONARY - Departure Constant: Shift for every 30 minutes before 07:30 pm - Linear,"@np.where(((df.start<9)), ((9-df.start)*(df.start<=9) + (df.start-9)*(df.start>9)), 0)",coef_discretionary_departure_constant_shift_for_every_30_minutes_before_7_30_pm_linear +util_discretionary_departure_constant_before_7_30_am,DISCRETIONARY - Departure Constant: Before 7:30 AM ,@((df.start<10)),coef_discretionary_departure_constant_before_7_30_am +util_discretionary_departure_constant_7_30_am_to_8_am,DISCRETIONARY - Departure Constant: 7:30 AM to 8:00 AM,@((df.start==10)),coef_discretionary_departure_constant_7_30_am_to_8_am +util_discretionary_departure_constant_8_am_to_8_30_am,DISCRETIONARY - Departure Constant: 8:00 AM to 8:30 AM,@((df.start==11)),coef_discretionary_departure_constant_8_am_to_8_30_am +util_discretionary_departure_constant_8_30_am_to_9_am,DISCRETIONARY - Departure Constant: 8:30 AM to 9:00 AM,@((df.start==12)),coef_discretionary_departure_constant_8_30_am_to_9_am +util_discretionary_departure_constant_9_am_to_9_30_am,DISCRETIONARY - Departure Constant: 9:00 AM to 9:30 AM,@((df.start==13)),coef_discretionary_departure_constant_9_am_to_9_30_am +util_discretionary_departure_constant_9_30_am_to_10_am,DISCRETIONARY - Departure Constant: 9:30 AM to 10:00 AM,@((df.start==14)),coef_discretionary_departure_constant_9_30_am_to_10_am +util_discretionary_departure_constant_10_am_to_10_30_am,DISCRETIONARY - Departure Constant: 10:00 AM to 10:30 AM,@((df.start==15)),coef_discretionary_departure_constant_10_am_to_10_30_am +util_discretionary_departure_constant_shift_for_every_30_minutes_before_4_30_pm_linear,DISCRETIONARY - Departure Constant: Shift for every 30 minutes before 04:30 pm - Linear,"@np.where(((df.start<28)), ((28-df.start)*(df.start<=28) + (df.start-28)*(df.start>28)), 0)",coef_discretionary_departure_constant_shift_for_every_30_minutes_before_4_30_pm_linear +util_discretionary_departure_constant_before_5_pm,DISCRETIONARY - Departure Constant: Before 05:00 PM,@((df.start<29)),coef_discretionary_departure_constant_before_5_pm +util_discretionary_departure_constant_5_pm_to_5_30_pm,DISCRETIONARY - Departure Constant: 05:00 PM - 05:30 PM,@((df.start==29)),coef_discretionary_departure_constant_5_pm_to_5_30_pm +util_discretionary_departure_constant_5_30_pm_to_6_pm,DISCRETIONARY - Departure Constant: 05:30 PM - 06:00 PM,@((df.start==30)),coef_discretionary_departure_constant_5_30_pm_to_6_pm +util_discretionary_departure_constant_6_pm_to_6_30_pm,DISCRETIONARY - Departure Constant: 06:00 PM - 06:30 PM,@((df.start==31)),coef_discretionary_departure_constant_6_pm_to_6_30_pm +util_discretionary_departure_constant_6_30_pm_to_7_pm,DISCRETIONARY - Departure Constant: 06:30 PM - 07:00 PM,@((df.start==32)),coef_discretionary_departure_constant_6_30_pm_to_7_pm +util_discretionary_departure_constant_after_7_pm,DISCRETIONARY - Departure Constant: After 07:00 PM,@((df.start>32)),coef_discretionary_departure_constant_after_7_pm +util_discretionary_departure_constant_shift_for_every_30_minutes_after_7_30_pm_linear,DISCRETIONARY - Departure Constant: Shift for every 30 minutes after 07:30 pm - Linear,"@np.where(((df.start>33)), ((33-df.start)*(df.start<=33) + (df.start-33)*(df.start>33)), 0)",coef_discretionary_departure_constant_shift_for_every_30_minutes_after_7_30_pm_linear +util_discretionary_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear,DISCRETIONARY - Arrival Constant: Shift for every 30 minutes before 06:00 pm - Linear,"@np.where(((df.end<31)), ((31-df.end)*(df.end<=31) + (df.end-31)*(df.end>31)), 0)",coef_discretionary_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear +util_discretionary_arrival_constant_before_6_30_pm,DISCRETIONARY - Arrival Constant: Before 6:30 PM,@((df.end<32)),coef_discretionary_arrival_constant_before_6_30_pm +util_discretionary_arrival_constant_6_30_pm_to_7_pm,DISCRETIONARY - Arrival Constant: 6:30 PM to 7:00 PM,@((df.end==32)),coef_discretionary_arrival_constant_6_30_pm_to_7_pm +util_discretionary_arrival_constant_7_pm_to_7_30_pm,DISCRETIONARY - Arrival Constant: 7:00 PM to 7:30 PM,@((df.end==33)),coef_discretionary_arrival_constant_7_pm_to_7_30_pm +util_discretionary_arrival_constant_7_30_pm_to_8_pm,DISCRETIONARY - Arrival Constant: 7:30 PM to 8:00 PM,@((df.end==34)),coef_discretionary_arrival_constant_7_30_pm_to_8_pm +util_discretionary_arrival_constant_8_pm_to_8_30_pm,DISCRETIONARY - Arrival Constant: 8:00 PM to 8:30 PM,@((df.end==35)),coef_discretionary_arrival_constant_8_pm_to_8_30_pm +util_discretionary_arrival_constant_8_30_pm_to_9_pm,DISCRETIONARY - Arrival Constant: 8:30 PM to 9:00 PM,@((df.end==36)),coef_discretionary_arrival_constant_8_30_pm_to_9_pm +util_discretionary_arrival_constant_after_9_pm,DISCRETIONARY - Arrival Constant: After 9:00 PM,@((df.end>36)),coef_discretionary_arrival_constant_after_9_pm +util_discretionary_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear,DISCRETIONARY - Arrival Constant: Shift for every 30 minutes after 09:30 pm - Linear,"@np.where(((df.end>37)), ((37-df.end)*(df.end<=37) + (df.end-37)*(df.end>37)), 0)",coef_discretionary_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear +util_discretionary_duration_constant_0_hour,DISCRETIONARY - Duration Constant: 0 hour,@((df.duration==0)),coef_discretionary_duration_constant_0_hour +util_discretionary_duration_constant_30_minutes,DISCRETIONARY -Duration Constant: 0.5 hour,@((df.duration==1)),coef_discretionary_duration_constant_30_minutes +util_discretionary_duration_constant_1_hour,DISCRETIONARY -Duration Constant: 1 hour,@((df.duration==2)),coef_discretionary_duration_constant_1_hour +util_discretionary_duration_constant_1_hr_30_minutes,DISCRETIONARY -Duration Constant: 1.5 hours,@((df.duration==3)),coef_discretionary_duration_constant_1_hr_30_minutes +util_discretionary_duration_constant_2_hours,DISCRETIONARY -Duration Constant: 2 hours,@((df.duration==4)),coef_discretionary_duration_constant_2_hours +util_discretionary_duration_constant_2_hr_30_minutes,DISCRETIONARY -Duration Constant: 2.5 hours,@((df.duration==5)),coef_discretionary_duration_constant_2_hr_30_minutes +util_discretionary_duration_constant_3_hours_or_more,DISCRETIONARY -Duration Constant: 3 hours or more,@((df.duration>5)),coef_discretionary_duration_constant_3_hours_or_more +util_discretionary_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear,DISCRETIONARY -Duration Constant: Shift for every 30 minutes more than 3 hrs - Linear,"@np.where(((df.duration>6)), ((6-df.duration)*(df.duration<=6) + (df.duration-6)*(df.duration>6)), 0)",coef_discretionary_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear +util_discretionary_calibration_constant_duration_4,DISCRETIONARY -Calibration Constant - Duration = 4,@((df.duration==3)),coef_discretionary_calibration_constant_duration_4 +util_discretionary_calibration_constant_duration_5,DISCRETIONARY -Calibration Constant - Duration = 5,@((df.duration==4)),coef_discretionary_calibration_constant_duration_5 +util_discretionary_calibration_constant_departure_29,DISCRETIONARY -Calibration Constant - Departure = 29,@((df.start==29)),coef_discretionary_calibration_constant_departure_29 +util_discretionary_calibration_constant_departure_30,DISCRETIONARY -Calibration Constant - Departure = 30,@((df.start==30)),coef_discretionary_calibration_constant_departure_30 +util_discretionary_calibration_constant_departure_31,DISCRETIONARY -Calibration Constant - Departure = 31,@((df.start==31)),coef_discretionary_calibration_constant_departure_31 +util_discretionary_calibration_constant_departure_32,DISCRETIONARY -Calibration Constant - Departure = 32,@((df.start==32)),coef_discretionary_calibration_constant_departure_32 +# Including terms for school escorting,,, +util_outbound_school_escort_tour_start,Outbound school escort tours must match the start time of the escort tour,is_outbound_school_escort_tour & (start != school_escort_tour_start),coef_unavailable +util_outbound_school_escort_tour_next_start,Outbound school escort tours must end before next escort tour start,is_outbound_school_escort_tour & (end > school_escort_tour_next_start) & (school_escort_tour_next_start > 0),coef_unavailable +util_inbound_school_escort_tour_end,Inbound school escort tours must match the end time of the escort tour,is_inbound_school_escort_tour & (end != school_escort_tour_end),coef_unavailable \ No newline at end of file diff --git a/resident/configs/tour_scheduling_nonmandatory_othdiscr_coefficients.csv b/resident/configs/tour_scheduling_nonmandatory_othdiscr_coefficients.csv new file mode 100644 index 0000000..87e3924 --- /dev/null +++ b/resident/configs/tour_scheduling_nonmandatory_othdiscr_coefficients.csv @@ -0,0 +1,55 @@ +coefficient_name,value,constrain +coef_discretionary_person_less_than_18_years_old_duration_less_than_reference_linear,0.098366285148487,F +coef_discretionary_person_less_than_18_years_old_duration_greater_than_reference_linear,0.0,F +coef_discretionary_non_working_senior_retiree_duration_less_than_reference_linear,-0.1705916260525488,F +coef_discretionary_retiree_non_working_senior_only_HH_duration_reference_linear,0.0,F +coef_discretionary_zero_auto_households_duration_less_than_reference_linear,0.0,F +coef_discretionary_zero_auto_households_duration_greater_than_reference_linear,0.05222497126898865,F +coef_discretionary_number_of_auto_more_than_number_of_adults_duration_less_than_reference_linear,0.0,F +coef_discretionary_number_of_auto_more_than_number_of_adults_duration_greater_than_reference_linear,0.0,F +coef_discretionary_auto_distance_duration_less_than_reference_linear,-0.03606825659562718,F +coef_discretionary_auto_distance_duration_greater_than_reference_linear,0.004596600975929468,F +coef_discretionary_time_pressure_duration_less_reference,0.6728960123740134,F +coef_discretionary_time_pressure_duration_greater_reference,0.25402774161065456,F +coef_discretionary_number_of_additional_individual_social_and_dicretionary_tours_duration_less_reference,0.08104208628580123,F +coef_discretionary_departure_constant_shift_for_every_30_minutes_before_7_30_pm_linear,-0.6104747153010481,F +coef_discretionary_departure_constant_before_7_30_am,-0.3390521412898814,F +coef_discretionary_departure_constant_7_30_am_to_8_am,0.0,F +coef_discretionary_departure_constant_8_am_to_8_30_am,0.0,F +coef_discretionary_departure_constant_8_30_am_to_9_am,0.0,F +coef_discretionary_departure_constant_9_am_to_9_30_am,0.33705457332633537,F +coef_discretionary_departure_constant_9_30_am_to_10_am,0.14050652594559154,F +coef_discretionary_departure_constant_10_am_to_10_30_am,0.0,F +coef_discretionary_departure_constant_shift_for_every_30_minutes_before_4_30_pm_linear,2.055051892448251,F +coef_discretionary_departure_constant_before_5_pm,10.078028457555423,F +coef_discretionary_departure_constant_5_pm_to_5_30_pm,8.809104255087856,F +coef_discretionary_departure_constant_5_30_pm_to_6_pm,7.066441289657018,F +coef_discretionary_departure_constant_6_pm_to_6_30_pm,4.909660731901318,F +coef_discretionary_departure_constant_6_30_pm_to_7_pm,2.825818687356497,T +coef_discretionary_departure_constant_after_7_pm,0.0,F +coef_discretionary_departure_constant_shift_for_every_30_minutes_after_7_30_pm_linear,-2.407069142986441,F +coef_discretionary_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear,-1.9913325130310646,F +coef_discretionary_arrival_constant_before_6_30_pm,-8.318569502929208,F +coef_discretionary_arrival_constant_6_30_pm_to_7_pm,-6.434917972534465,F +coef_discretionary_arrival_constant_7_pm_to_7_30_pm,-4.138799434182262,F +coef_discretionary_arrival_constant_7_30_pm_to_8_pm,-2.1058225272583,F +coef_discretionary_arrival_constant_8_pm_to_8_30_pm,0.0,T +coef_discretionary_arrival_constant_8_30_pm_to_9_pm,2.1493158879532435,F +coef_discretionary_arrival_constant_after_9_pm,3.8558666359863616,F +coef_discretionary_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear,1.6858826820414816,F +coef_discretionary_duration_constant_0_hour,0.0,F +coef_discretionary_duration_constant_30_minutes,0.0,F +coef_discretionary_duration_constant_1_hour,0.0,F +coef_discretionary_duration_constant_1_hr_30_minutes,0.0,F +coef_discretionary_duration_constant_2_hours,0.0,F +coef_discretionary_duration_constant_2_hr_30_minutes,-2.9894794551717427,T +coef_discretionary_duration_constant_3_hours_or_more,-6.039866672397804,F +coef_discretionary_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear,-2.9211224892215153,F +coef_discretionary_calibration_constant_duration_4,0.0,F +coef_discretionary_calibration_constant_duration_5,0.0,F +coef_discretionary_calibration_constant_departure_29,0.0,F +coef_discretionary_calibration_constant_departure_30,0.0,F +coef_discretionary_calibration_constant_departure_31,0.0,F +coef_discretionary_calibration_constant_departure_32,0.0,F +coef_mode_choice_logsum,0.06338187336512106,F +coef_unavailable,-999.0,F \ No newline at end of file diff --git a/resident/configs/tour_scheduling_nonmandatory_othmaint.csv b/resident/configs/tour_scheduling_nonmandatory_othmaint.csv new file mode 100644 index 0000000..d9bda3c --- /dev/null +++ b/resident/configs/tour_scheduling_nonmandatory_othmaint.csv @@ -0,0 +1,59 @@ +Label,Description,Expression,Coefficient +#MAINTENANCE,#MAINTENANCE,,#MAINTENANCE +util_mode_choice_logsum,Mode choice logsum,mode_choice_logsum,coef_mode_choice_logsum +util_maintenance_driving_age_student_duration_greater_than_reference,MAINTENANCE - Driving age student: Duration > Reference,"@np.where(((df.duration>2) & (df.ptype == 6)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_maintenance_driving_age_student_duration_greater_than_reference +util_maintenance_full_time_worker_duration_greater_than_reference,MAINTENANCE - Full-time worker: Duration > Reference,"@np.where(((df.duration>2) & (df.ptype == 1)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_maintenance_full_time_worker_duration_greater_than_reference +util_maintenance_non_driving_student_duration_greater_than_reference,MAINTENANCE - Non-driving Student: Duration > Reference,"@np.where (((df.duration>2) & ((df.ptype == 7)|(df.ptype == 8))), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_maintenance_non_driving_student_duration_greater_than_reference +util_maintenance_pre_school_child_duration_less_than_reference,MAINTENANCE - Pre-school Child: Duration < Reference,"@np.where(((df.duration<2) & (df.ptype == 8)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_maintenance_pre_school_child_duration_less_than_reference +util_maintenance_part_time_worker_duration_less_than_reference,MAINTENANCE - Part Time Worker: Duration < Reference,"@np.where(((df.duration<2) & (df.ptype == 2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_maintenance_part_time_worker_duration_less_than_reference +util_maintenance_part_time_worker_duration_greater_than_reference,MAINTENANCE - Part Time Worker: Duration > Reference,"@np.where(((df.duration>2) & (df.ptype == 2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_maintenance_part_time_worker_duration_greater_than_reference +util_maintenance_retired_duration_less_than_reference,MAINTENANCE - Retired: Duration < Reference,"@np.where(((df.duration<2) & (df.ptype == 5)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_maintenance_retired_duration_less_than_reference +util_maintenance_retired_duration_greater_than_reference,MAINTENANCE - Retired: Duration > 1.5 hr,"@np.where(((df.duration>2) & (df.ptype == 5)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_maintenance_retired_duration_greater_than_reference +util_maintenance_university_student_duration_greater_than_reference,MAINTENANCE - University Student: Duration > Reference,"@np.where(((df.duration>2) & (df.ptype == 3)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_maintenance_university_student_duration_greater_than_reference +util_maintenance_female_duration_less_than_reference,MAINTENANCE - Female: Duration < Reference,"@np.where(((df.duration<2) & (df.female)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_maintenance_female_duration_less_than_reference +util_maintenance_female_duration_greater_than_reference,MAINTENANCE - Female: Duration > Reference,"@np.where(((df.duration>2) & (df.female)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_maintenance_female_duration_greater_than_reference +util_maintenance_low_income_duration_greater_than_reference,"MAINTENANCE - Low Income (<=$25,000): Duration > Reference","@np.where(((df.is_income_less25K) & (df.duration>2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_maintenance_low_income_duration_greater_than_reference +util_maintenance_medium_income_duration_less_than_reference,"MAINTENANCE - Medium Income ($25,001 to $60,000): Duration < Reference","@np.where(((df.is_income_25K_to_60K) & (df.duration<2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_maintenance_medium_income_duration_less_than_reference +util_maintenance_medium_income_duration_greater_than_reference,"MAINTENANCE - Medium Income ($25,001 to $60,000): Duration > Reference","@np.where(((df.is_income_25K_to_60K) & (df.duration>2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_maintenance_medium_income_duration_greater_than_reference +util_maintenance_medium_high_income_duration_greater_than_reference,"MAINTENANCE - Medium-High Income ($60,001 to $120,00): Duration > Reference","@np.where(((df.is_income_60K_to_120K) & (df.duration>2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_maintenance_medium_high_income_duration_greater_than_reference +util_maintenance_distance_duration_less_than_reference,MAINTENANCE - Distance: Duration < Reference,"@np.where(((df.duration<2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)) *(df.origin_to_destination_distance), 0)",coef_maintenance_distance_duration_less_than_reference +util_maintenance_distance_duration_greater_than_reference,MAINTENANCE - Distance: Duration > Reference,"@np.where(((df.duration>2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)) * (df.origin_to_destination_distance), 0)",coef_maintenance_distance_duration_greater_than_reference +util_maintenance_time_pressure_duration_greater_than_reference,Time Pressure - Duration > Reference,"@np.where(((df.duration>2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)) * (np.log10 (30 * (tt.max_time_block_available(df.person_id)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_maintenance_time_pressure_duration_greater_than_reference +util_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_less_than_reference,MAINTENANCE - Number of additional individual shop and maint. tours - Duration < Reference,"@np.where((df.duration<2), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)) * (df.num_add_shop_maint_tours), 0)",coef_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_less_than_reference +util_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_reference,MAINTENANCE - Number of additional individual shop and maint. tours - Duration > Reference,"@np.where((df.duration>2), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)) * (df.num_add_shop_maint_tours), 0)",coef_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_reference +util_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_linear,MAINTENANCE - Departure Constant: Shift for every 30 minutes before 07:30 am - Linear,"@np.where((df.start<10), ((10-df.start)*(df.start<=10) + (df.start-10)*(df.start>10)), 0)",coef_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_linear +util_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_square_root,MAINTENANCE - Departure Constant: Shift for every 30 minutes before 07:30 am - Square Root,"@np.where((df.start<10), (((10-df.start)*(df.start<=10) + (df.start-10)*(df.start>10))** 0.5), 0)",coef_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_square_root +util_maintenance_departure_constant_before_8_am,MAINTENANCE - Departure Constant: Before 08:00 AM,@((df.start<11)),coef_maintenance_departure_constant_before_8_am +util_maintenance_departure_constant_8_am_to_8_30_am,MAINTENANCE - Departure Constant: 08:00 AM - 08:30 AM,@((df.start==11)),coef_maintenance_departure_constant_8_am_to_8_30_am +util_maintenance_departure_constant_8_30_am_to_9_00_am,MAINTENANCE - Departure Constant: 08:30 AM - 09:00 AM,@((df.start==12)),coef_maintenance_departure_constant_8_30_am_to_9_00_am +util_maintenance_departure_constant_9_am_to_9_30_am,MAINTENANCE - Departure Constant: 09:00 AM - 09:30 AM,@((df.start==13)),coef_maintenance_departure_constant_9_am_to_9_30_am +util_maintenance_departure_constant_9_30_am_to_10_am,MAINTENANCE - Departure Constant: 09:30 AM - 10:00 AM,@((df.start==14)),coef_maintenance_departure_constant_9_30_am_to_10_am +util_maintenance_departure_constant_10_am_to_10_30_am,MAINTENANCE - Departure Constant: 10:00 AM - 10:30 AM,@((df.start==15)),coef_maintenance_departure_constant_10_am_to_10_30_am +util_maintenance_departure_constant_10_30_am_to_11_am,MAINTENANCE - Departure Constant: 10:30 AM - 11:00 AM,@((df.start==16)),coef_maintenance_departure_constant_10_30_am_to_11_am +util_maintenance_departure_constant_after_11_am,MAINTENANCE - Departure Constant: After 11:00 AM,@((df.start>16)),coef_maintenance_departure_constant_after_11_am +util_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear,MAINTENANCE - Departure Constant: Shift for every 30 minutes after 11:30 am - Linear,"@np.where((df.start>17), ((17-df.start)*(df.start<=17) + (df.start-17)*(df.start>17)), 0)",coef_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear +util_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared,MAINTENANCE - Departure Constant: Shift for every 30 minutes after 11:30 am - Squared,"@np.where((df.start>17), (((17-df.start)*(df.start<=17) + (df.start-17)*(df.start>17)) ** 2), 0)",coef_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared +util_maintenance_arrival_constant_shift_for_every_30_minutes_before_10_am_linear,MAINTENANCE - Arrival Constant: Shift for every 30 minutes before 10:00 am - Linear,"@np.where(((df.end<15)), ((15-df.end)*(df.end<=15) + (df.end-15)*(df.end>15)), 0)",coef_maintenance_arrival_constant_shift_for_every_30_minutes_before_10_am_linear +util_maintenance_arrival_constant_before_10_30_am,MAINTENANCE - Arrival Constant: Before 10:30 AM,@((df.end<16)),coef_maintenance_arrival_constant_before_10_30_am +util_maintenance_arrival_constant_10_30_am_to_11_am,MAINTENANCE - Arrival Constant: 10:30 AM - 11:00 AM,@((df.end==16)),coef_maintenance_arrival_constant_10_30_am_to_11_am +util_maintenance_arrival_constant_11_am_to_11_30_am,MAINTENANCE - Arrival Constant: 11:00 AM - 11:30 AM,@((df.end==17)),coef_maintenance_arrival_constant_11_am_to_11_30_am +util_maintenance_arrival_constant_11_30_am_to_1_30_pm,MAINTENANCE - Arrival Constant: 11:30 AM - 01:30 PM,@((df.end>=18) & (df.end<=21)),coef_maintenance_arrival_constant_11_30_am_to_1_30_pm +util_maintenance_arrival_constant_1_30_pm_to_2_30_pm,MAINTENANCE - Arrival Constant: 01:30 PM - 02:30 PM,@((df.end>=22) & (df.end<=23)),coef_maintenance_arrival_constant_1_30_pm_to_2_30_pm +util_maintenance_arrival_constant_2_30_pm_to_4_pm,MAINTENANCE - Arrival Constant: 02:30 PM - 04:00 PM,@((df.end>=24) & (df.end<=26)),coef_maintenance_arrival_constant_2_30_pm_to_4_pm +util_maintenance_arrival_constant_4_pm_to_4_30_pm,MAINTENANCE - Arrival Constant: 04:00 PM - 04:30 PM,@((df.end==27)),coef_maintenance_arrival_constant_4_pm_to_4_30_pm +util_maintenance_arrival_constant_after_4_30_pm,MAINTENANCE - Arrival Constant: After 04:30 PM,@((df.end>27)),coef_maintenance_arrival_constant_after_4_30_pm +util_maintenance_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear,MAINTENANCE - Arrival Constant: Shift for every 30 minutes after 5:00 pm - Linear,"@np.where(((df.end>28)), ((28-df.end)*(df.end<=28) + (df.end-28)*(df.end>28)), 0)",coef_maintenance_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear +util_maintenance_duration_constant_0_hr,MAINTENANCE - Duration Constant: 0 hr,@((df.duration==0)),coef_maintenance_duration_constant_0_hr +util_maintenance_duration_constant_30_minutes,MAINTENANCE - Duration Constant: 0.5 hr,@((df.duration==1)),coef_maintenance_duration_constant_30_minutes +util_maintenance_duration_constant_longer_than_30_minutes,MAINTENANCE - Duration Constant: Longer than 0.5 hr,@((df.duration>1)),coef_maintenance_duration_constant_longer_than_30_minutes +util_maintenance_duration_constant_duration_greater_than_1_hr_linear,MAINTENANCE - Duration Constant: Duration > 1 hr - Linear,"@np.where(((df.duration>2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_maintenance_duration_constant_duration_greater_than_1_hr_linear +util_maintenance_duration_constant_duration_greater_than_1_hr_square_root,MAINTENANCE - Duration Constant: Duration > 1 hr - Square Root,"@np.where(((df.duration>2)), (((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2))** 0.5), 0)",coef_maintenance_duration_constant_duration_greater_than_1_hr_square_root +util_maintenance_calibration_constant_duration_1,MAINTENANCE - Calibration Constant - Duration = 1,@((df.duration==0)),coef_maintenance_calibration_constant_duration_1 +util_maintenance_calibration_constant_duration_2,MAINTENANCE - Calibration Constant - Duration = 2,@((df.duration==1)),coef_maintenance_calibration_constant_duration_2 +util_maintenance_calibration_constant_duration_3,MAINTENANCE - Calibration Constant - Duration = 3,@((df.duration==2)),coef_maintenance_calibration_constant_duration_3 +util_maintenance_calibration_constant_duration_4,MAINTENANCE - Calibration Constant - Duration = 4,@((df.duration==3)),coef_maintenance_calibration_constant_duration_4 +util_maintenance_calibration_constant_duration_5,MAINTENANCE - Calibration Constant - Duration = 5,@((df.duration==4)),coef_maintenance_calibration_constant_duration_5 +# Including terms for school escorting,,, +util_outbound_school_escort_tour_start,Outbound school escort tours must match the start time of the escort tour,is_outbound_school_escort_tour & (start != school_escort_tour_start),coef_unavailable +util_outbound_school_escort_tour_next_start,Outbound school escort tours must end before next escort tour start,is_outbound_school_escort_tour & (end > school_escort_tour_next_start) & (school_escort_tour_next_start > 0),coef_unavailable +util_inbound_school_escort_tour_end,Inbound school escort tours must match the end time of the escort tour,is_inbound_school_escort_tour & (end != school_escort_tour_end),coef_unavailable \ No newline at end of file diff --git a/resident/configs/tour_scheduling_nonmandatory_othmaint_coefficients.csv b/resident/configs/tour_scheduling_nonmandatory_othmaint_coefficients.csv new file mode 100644 index 0000000..e5d214b --- /dev/null +++ b/resident/configs/tour_scheduling_nonmandatory_othmaint_coefficients.csv @@ -0,0 +1,55 @@ +coefficient_name,value,constrain +coef_maintenance_driving_age_student_duration_greater_than_reference,0.0,F +coef_maintenance_full_time_worker_duration_greater_than_reference,0.13520704870454775,F +coef_maintenance_non_driving_student_duration_greater_than_reference,0.0,F +coef_maintenance_pre_school_child_duration_less_than_reference,-2.157370739161894,F +coef_maintenance_part_time_worker_duration_less_than_reference,0.0,F +coef_maintenance_part_time_worker_duration_greater_than_reference,0.0,F +coef_maintenance_retired_duration_less_than_reference,0.0,F +coef_maintenance_retired_duration_greater_than_reference,-0.03534027470461993,F +coef_maintenance_university_student_duration_greater_than_reference,0.0,F +coef_maintenance_female_duration_less_than_reference,-0.33642495066387595,F +coef_maintenance_female_duration_greater_than_reference,0.0,F +coef_maintenance_low_income_duration_greater_than_reference,0.040103190219137164,F +coef_maintenance_medium_income_duration_less_than_reference,0.0,F +coef_maintenance_medium_income_duration_greater_than_reference,0.0,T +coef_maintenance_medium_high_income_duration_greater_than_reference,0.0,F +coef_maintenance_distance_duration_less_than_reference,-0.28516479340386963,F +coef_maintenance_distance_duration_greater_than_reference,0.004293750630955166,F +coef_maintenance_time_pressure_duration_greater_than_reference,0.2152799372032978,F +coef_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_less_than_reference,0.0,F +coef_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_reference,-0.03864547690327943,F +coef_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_linear,-0.8990260296725098,F +coef_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_square_root,0.9516096316489431,F +coef_maintenance_departure_constant_before_8_am,-0.5598527748241708,F +coef_maintenance_departure_constant_8_am_to_8_30_am,-0.14921181662286023,F +coef_maintenance_departure_constant_8_30_am_to_9_00_am,0.0,F +coef_maintenance_departure_constant_9_am_to_9_30_am,0.0,F +coef_maintenance_departure_constant_9_30_am_to_10_am,0.0,F +coef_maintenance_departure_constant_10_am_to_10_30_am,0.0,T +coef_maintenance_departure_constant_10_30_am_to_11_am,-0.17187147382272855,F +coef_maintenance_departure_constant_after_11_am,-0.3600477839542276,F +coef_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear,0.02639222500693473,F +coef_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared,-0.0017054145463435359,F +coef_maintenance_arrival_constant_shift_for_every_30_minutes_before_10_am_linear,-0.23175571400118172,F +coef_maintenance_arrival_constant_before_10_30_am,0.0,F +coef_maintenance_arrival_constant_10_30_am_to_11_am,0.0,T +coef_maintenance_arrival_constant_11_am_to_11_30_am,0.0,F +coef_maintenance_arrival_constant_11_30_am_to_1_30_pm,0.0,F +coef_maintenance_arrival_constant_1_30_pm_to_2_30_pm,0.0,F +coef_maintenance_arrival_constant_2_30_pm_to_4_pm,0.0,F +coef_maintenance_arrival_constant_4_pm_to_4_30_pm,0.0,F +coef_maintenance_arrival_constant_after_4_30_pm,0.0,F +coef_maintenance_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear,-0.31053576324304255,F +coef_maintenance_duration_constant_0_hr,5.99100088789488,F +coef_maintenance_duration_constant_30_minutes,6.268122806796934,T +coef_maintenance_duration_constant_longer_than_30_minutes,4.8989465058844175,F +coef_maintenance_duration_constant_duration_greater_than_1_hr_linear,-0.7339031911682701,F +coef_maintenance_duration_constant_duration_greater_than_1_hr_square_root,-0.13942646219715638,F +coef_maintenance_calibration_constant_duration_1,0.0,F +coef_maintenance_calibration_constant_duration_2,0.0,F +coef_maintenance_calibration_constant_duration_3,0.0,F +coef_maintenance_calibration_constant_duration_4,0.25854243359403917,F +coef_maintenance_calibration_constant_duration_5,0.22211610318873198,F +coef_mode_choice_logsum,0.23911188143948614,F +coef_unavailable,-999.0,F \ No newline at end of file diff --git a/resident/configs/tour_scheduling_nonmandatory_shopping.csv b/resident/configs/tour_scheduling_nonmandatory_shopping.csv new file mode 100644 index 0000000..fa7fbf1 --- /dev/null +++ b/resident/configs/tour_scheduling_nonmandatory_shopping.csv @@ -0,0 +1,59 @@ +Label,Description,Expression,Coefficient +#SHOPPING,,,#SHOPPING +util_mode_choice_logsum,Mode choice logsum,mode_choice_logsum,coef_mode_choice_logsum +util_shoppping_driving_age_student_duration_greater_than_reference,SHOPPING - Driving age student: Duration > Reference,"@np.where(((df.duration>2) & (df.ptype == 6)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_shoppping_driving_age_student_duration_greater_than_reference +util_shoppping_full_time_worker_duration_greater_than_reference,SHOPPING - Full-time worker: Duration > Reference,"@np.where(((df.duration>2) & (df.ptype == 1)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_shoppping_full_time_worker_duration_greater_than_reference +util_shoppping_non_driving_student_duration_greater_than_reference,SHOPPING - Non-driving Student: Duration > Reference,"@np.where(((df.duration>2) & ((df.ptype == 7)|(df.ptype == 8))), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_shoppping_non_driving_student_duration_greater_than_reference +util_shoppping_pre_school_child_duration_less_than_reference,SHOPPING - Pre-school Child: Duration < Reference,"@np.where(((df.duration<2) & (df.ptype == 8)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_shoppping_pre_school_child_duration_less_than_reference +util_shoppping_part_time_worker_duration_less_than_reference,SHOPPING - Part Time Worker: Duration < Reference,"@np.where(((df.duration<2) & (df.ptype == 2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_shoppping_part_time_worker_duration_less_than_reference +util_shopping_part_time_worker_duration_greater_than_reference,SHOPPING - Part Time Worker: Duration > Reference,"@np.where(((df.duration>2) & (df.ptype == 2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_shopping_part_time_worker_duration_greater_than_reference +util_shopping_retired_duration_less_than_reference,SHOPPING - Retired: Duration < Reference,"@np.where(((df.duration<2) & (df.ptype == 5)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_shopping_retired_duration_less_than_reference +util_shopping_retired_duration_greater_than_reference,SHOPPING - Retired: Duration > Reference,"@np.where(((df.duration>2) & (df.ptype == 5)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_shopping_retired_duration_greater_than_reference +util_shopping_university_student_duration_greater_than_reference,SHOPPING - University Student: Duration > Reference,"@np.where(((df.duration>2) & (df.ptype == 3)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_shopping_university_student_duration_greater_than_reference +util_shopping_female_duration_less_than_reference,SHOPPING - Female: Duration < Reference,"@np.where(((df.duration<2) & (df.female)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_shopping_female_duration_less_than_reference +util_shopping_female_duration_greater_than_reference,SHOPPING - Female: Duration > Reference,"@np.where(((df.duration>2) & (df.female)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_shopping_female_duration_greater_than_reference +util_shopping_low_income_duration_greater_than_reference,"SHOPPING - Low Income (<=$25,000): Duration > Reference","@np.where(((df.is_income_less25K) & (df.duration>2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_shopping_low_income_duration_greater_than_reference +util_shopping_medium_income_duration_less_than_reference,"SHOPPING - Medium Income ($25,001 to $60,000): Duration < Reference","@np.where(((df.is_income_25K_to_60K) & (df.duration<2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_shopping_medium_income_duration_less_than_reference +util_shopping_medium_high_income_duration_greater_than_reference,"SHOPPING - Medium-High Income ($60,001 to $120,00): Duration > Reference","@np.where(((df.is_income_60K_to_120K) & (df.duration>2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_shopping_medium_high_income_duration_greater_than_reference +util_shopping_distance_duration_less_than_reference,SHOPPING - Distance: Duration < Reference,"@np.where(((df.duration<2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)) * (df.origin_to_destination_distance), 0)",coef_shopping_distance_duration_less_than_reference +util_shopping_distance_duration_greater_than_reference,SHOPPING - Distance: Duration > Reference,"@np.where(((df.duration>2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)) * (df.origin_to_destination_distance), 0)",coef_shopping_distance_duration_greater_than_reference +util_shopping_time_pressure_duration_greater_than_reference,SHOPPING - Time Pressure - Duration > Reference,"@np.where(((df.duration>2)), np.minimum(df.duration-2,26) *(np.log10 (30 *(tt.max_time_block_available(df.person_id)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_shopping_time_pressure_duration_greater_than_reference +util_shopping_number_of_additional_individual_shop_and_maint_tours_duration_less_than_reference,SHOPPING - Number of additional individual shop and maint. tours - Duration < Reference,"@np.where(((df.duration<2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)) *(df.num_add_shop_maint_tours), 0)",coef_shopping_number_of_additional_individual_shop_and_maint_tours_duration_less_than_reference +util_shopping_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_reference,SHOPPING - Number of additional individual shop and maint. tours - Duration > Reference,"@np.where(((df.tour_type == 'shopping') &(df.duration>2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)) *(df.num_add_shop_maint_tours), 0)",coef_shopping_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_reference +util_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear,SHOPPING - Departure Constant: Shift for every 30 minutes before 08:30 am - Linear,"@np.where(((df.start<12)), ((12-df.start)*(df.start<=12) + (df.start-12)*(df.start>12)), 0)",coef_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear +util_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_square_root,SHOPPING - Departure Constant: Shift for every 30 minutes before 08:30 am - Square root,"@np.where(((df.start<12)), ((12-df.start)*(df.start<=12) + (df.start-12)*(df.start>12))**0.5, 0)",coef_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_square_root +util_shopping_departure_constant_before_9_am,SHOPPING - Departure Constant: Before 09:00 AM,@((df.start<13)),coef_shopping_departure_constant_before_9_am +util_shopping_departure_constant_9_am_to_9_30_am,SHOPPING - Departure Constant: 09:00 AM - 09:30 AM,@((df.start==13)),coef_shopping_departure_constant_9_am_to_9_30_am +util_shopping_departure_constant_9_30_am_to_10_am,SHOPPING - Departure Constant: 09:30 AM - 10:00 AM,@((df.start==14)),coef_shopping_departure_constant_9_30_am_to_10_am +util_shopping_departure_constant_10_am_to_10_30_am,SHOPPING - Departure Constant: 10:00 AM - 10:30 AM,@((df.start==15)),coef_shopping_departure_constant_10_am_to_10_30_am +util_shopping_departure_constant_10_30_am_to_11_00_am,SHOPPING - Departure Constant: 10:30 AM - 11:00 AM,@((df.start==16)),coef_shopping_departure_constant_10_30_am_to_11_00_am +util_shopping_departure_constant_after_11_am,SHOPPING - Departure Constant: After 11:00 AM,@((df.start>16)),coef_shopping_departure_constant_after_11_am +util_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear,SHOPPING - Departure Constant: Shift for every 30 minutes after 11:30 am - Linear,"@np.where(((df.start>17)), ((17-df.start)*(df.start<=17) + (df.start-17)*(df.start>17)), 0)",coef_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear +util_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared,SHOPPING - Departure Constant: Shift for every 30 minutes after 11:30 am - Squared,"@np.where(((df.start>17)), (((17-df.start)*(df.start<=17) + (df.start-17)*(df.start>17)) ** 2), 0)",coef_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared +util_shopping_arrival_constant_shift_for_every_30_minutes_before_12_pm_linear,SHOPPING - Arrival Constant: Shift for every 30 minutes before 12:00 pm - Linear,"@np.where(((df.end<19)), ((19-df.end)*(df.end<=19) + (df.end-19)*(df.end>19)), 0)",coef_shopping_arrival_constant_shift_for_every_30_minutes_before_12_pm_linear +util_shopping_arrival_constant_before_12_30_pm,SHOPPING - Arrival Constant: Before 12:30 PM,@((df.end<20)),coef_shopping_arrival_constant_before_12_30_pm +util_shopping_arrival_constant_12_30_pm_to_3_pm,SHOPPING - Arrival Constant: 12:30 PM - 03:00 PM,@(( df.end>=20) & (df.end<=24)),coef_shopping_arrival_constant_12_30_pm_to_3_pm +util_shopping_arrival_constant_3_pm_to_3_30_pm,SHOPPING - Arrival Constant: 03:00 PM - 03:30 PM,@((df.end==25)),coef_shopping_arrival_constant_3_pm_to_3_30_pm +util_shopping_arrival_constant_3_30_pm_to_4_pm,SHOPPING - Arrival Constant: 03:30 PM - 04:00 PM,@((df.end==26)),coef_shopping_arrival_constant_3_30_pm_to_4_pm +util_shopping_arrival_constant_4_pm_to_4_30_pm,SHOPPING - Arrival Constant: 04:00 PM - 04:30 PM,@((df.end==27)),coef_shopping_arrival_constant_4_pm_to_4_30_pm +util_shopping_arrival_constant_4_30_pm_to_5_pm,SHOPPING - Arrival Constant: 04:30 PM - 05:00 PM,@((df.end==28)),coef_shopping_arrival_constant_4_30_pm_to_5_pm +util_shopping_arrival_constant_5_pm_to_5_30_pm,SHOPPING - Arrival Constant: 05:00 PM - 05:30 PM,@((df.end==29)),coef_shopping_arrival_constant_5_pm_to_5_30_pm +util_shopping_arrival_constant_5_30_pm_to_7_pm,SHOPPING - Arrival Constant: 05:30 PM - 07:00 PM,@((df.end>=30) & (df.end<=32)),coef_shopping_arrival_constant_5_30_pm_to_7_pm +util_shopping_arrival_constant_7_pm_to_9_30_pm,SHOPPING - Arrival Constant: 07:00 PM - 09:30 PM,@((df.end>=33) & (df.end<=37)),coef_shopping_arrival_constant_7_pm_to_9_30_pm +util_shopping_arrival_constant_after_9_30_pm,SHOPPING - Arrival Constant: After 09:30 PM,@((df.end>37)),coef_shopping_arrival_constant_after_9_30_pm +util_shopping_arrival_constant_shift_for_every_30_minutes_after_10_pm_linear,SHOPPING - Arrival Constant: Shift for every 30 minutes after 10:00 pm - Linear,"@np.where(((df.end>38)), ((38-df.end)*(df.end<=38) + (df.end-38)*(df.end>38)), 0)",coef_shopping_arrival_constant_shift_for_every_30_minutes_after_10_pm_linear +util_shopping_duration_constant_0_hr,SHOPPING - Duration Constant: 0 hr,@((df.duration==0)),coef_shopping_duration_constant_0_hr +util_shopping_duration_constant_30_minutes,SHOPPING - Duration Constant: 0.5 hr,@((df.duration==1)),coef_shopping_duration_constant_30_minutes +util_shopping_duration_constant_1_hr,SHOPPING - Duration Constant: 1 hr,@((df.duration==2)),coef_shopping_duration_constant_1_hr +util_shopping_duration_constant_1_hour_30_minutes,SHOPPING - Duration Constant: 1.5hrs,@(df.duration==3),coef_shopping_duration_constant_1_hour_30_minutes +util_shopping_duration_constant_2_hrs,SHOPPING - Duration Constant: 2 hrs,@((df.duration==4)),coef_shopping_duration_constant_2_hrs +util_shopping_duration_constant_longer_than_2_hrs,SHOPPING - Duration Constant: Longer than 2 hrs,@((df.duration>4)),coef_shopping_duration_constant_longer_than_2_hrs +util_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_linear,SHOPPING - Duration Constant: Duration > 2.5 hrs - Linear,"@np.where(((df.duration>5)), ((5-df.duration)*(df.duration<=5) + (df.duration-5)*(df.duration>5)), 0)",coef_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_linear +util_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_square_root,SHOPPING - Duration Constant: Duration > 2.5 hrs - Square root,"@np.where(((df.duration>5)), (((5-df.duration)*(df.duration<=5) + (df.duration-5)*(df.duration>5)) ** 0.5), 0)",coef_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_square_root +util_shopping_calibration_constant_duration_1,SHOPPING - Calibration Constant - Duration = 1,@((df.duration==0)),coef_shopping_calibration_constant_duration_1 +util_shopping_calibration_constant_duration_2,SHOPPING - Calibration Constant - Duration = 2,@((df.duration==1)),coef_shopping_calibration_constant_duration_2 +util_shopping_calibration_constant_duration_3,SHOPPING - Calibration Constant - Duration = 3,@((df.duration==2)),coef_shopping_calibration_constant_duration_3 +# Including terms for school escorting,,, +util_outbound_school_escort_tour_start,Outbound school escort tours must match the start time of the escort tour,is_outbound_school_escort_tour & (start != school_escort_tour_start),coef_unavailable +util_outbound_school_escort_tour_next_start,Outbound school escort tours must end before next escort tour start,is_outbound_school_escort_tour & (end > school_escort_tour_next_start) & (school_escort_tour_next_start > 0),coef_unavailable +util_inbound_school_escort_tour_end,Inbound school escort tours must match the end time of the escort tour,is_inbound_school_escort_tour & (end != school_escort_tour_end),coef_unavailable \ No newline at end of file diff --git a/resident/configs/tour_scheduling_nonmandatory_shopping_coefficients.csv b/resident/configs/tour_scheduling_nonmandatory_shopping_coefficients.csv new file mode 100644 index 0000000..d566754 --- /dev/null +++ b/resident/configs/tour_scheduling_nonmandatory_shopping_coefficients.csv @@ -0,0 +1,55 @@ +coefficient_name,value,constrain +coef_shoppping_driving_age_student_duration_greater_than_reference,0.0,F +coef_shoppping_full_time_worker_duration_greater_than_reference,0.051734822464608594,F +coef_shoppping_non_driving_student_duration_greater_than_reference,0.0,F +coef_shoppping_pre_school_child_duration_less_than_reference,0.0,F +coef_shoppping_part_time_worker_duration_less_than_reference,0.0,F +coef_shopping_part_time_worker_duration_greater_than_reference,0.0,F +coef_shopping_retired_duration_less_than_reference,-0.2659967828872843,F +coef_shopping_retired_duration_greater_than_reference,0.0,F +coef_shopping_university_student_duration_greater_than_reference,0.0,F +coef_shopping_female_duration_less_than_reference,-0.7103244823264783,F +coef_shopping_female_duration_greater_than_reference,0.061234392582043815,F +coef_shopping_low_income_duration_greater_than_reference,0.07853541383214849,F +coef_shopping_medium_income_duration_less_than_reference,0.0,F +coef_shopping_medium_high_income_duration_greater_than_reference,0.0,F +coef_shopping_distance_duration_less_than_reference,-0.26768192696442833,F +coef_shopping_distance_duration_greater_than_reference,0.011027213392240262,F +coef_shopping_time_pressure_duration_greater_than_reference,0.18521165094993614,F +coef_shopping_number_of_additional_individual_shop_and_maint_tours_duration_less_than_reference,0.0,F +coef_shopping_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_reference,-0.07338200940622887,F +coef_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear,-1.273440003246542,F +coef_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_square_root,0.5863247462472069,F +coef_shopping_departure_constant_before_9_am,-0.8347867320157243,F +coef_shopping_departure_constant_9_am_to_9_30_am,0.0,F +coef_shopping_departure_constant_9_30_am_to_10_am,0.4960610257653812,F +coef_shopping_departure_constant_10_am_to_10_30_am,0.0,T +coef_shopping_departure_constant_10_30_am_to_11_00_am,1.4667804167469976,F +coef_shopping_departure_constant_after_11_am,2.1721583581726733,F +coef_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear,0.43973246404978805,F +coef_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared,-0.002212882762524496,F +coef_shopping_arrival_constant_shift_for_every_30_minutes_before_12_pm_linear,0.4117758420908247,F +coef_shopping_arrival_constant_before_12_30_pm,2.184513901207351,F +coef_shopping_arrival_constant_12_30_pm_to_3_pm,0.0,F +coef_shopping_arrival_constant_3_pm_to_3_30_pm,-0.9695940929413058,F +coef_shopping_arrival_constant_3_30_pm_to_4_pm,-1.3735993941519913,F +coef_shopping_arrival_constant_4_pm_to_4_30_pm,-1.8739500715809843,T +coef_shopping_arrival_constant_4_30_pm_to_5_pm,-2.5156987192481095,F +coef_shopping_arrival_constant_5_pm_to_5_30_pm,-2.953009790810334,F +coef_shopping_arrival_constant_5_30_pm_to_7_pm,-4.394261858507323,F +coef_shopping_arrival_constant_7_pm_to_9_30_pm,-6.569745153088775,F +coef_shopping_arrival_constant_after_9_30_pm,-9.11110014040306,F +coef_shopping_arrival_constant_shift_for_every_30_minutes_after_10_pm_linear,-0.8231833778531431,F +coef_shopping_duration_constant_0_hr,0.0,F +coef_shopping_duration_constant_30_minutes,1.1937104784952166,F +coef_shopping_duration_constant_1_hr,0.0,T +coef_shopping_duration_constant_1_hour_30_minutes,0.0,F +coef_shopping_duration_constant_2_hrs,-0.4668647914524515,F +coef_shopping_duration_constant_longer_than_2_hrs,-1.107254910068048,F +coef_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_linear,-0.42751861595763324,F +coef_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_square_root,0.0,F +coef_shopping_calibration_constant_duration_1,0.0,F +coef_shopping_calibration_constant_duration_2,0.0,F +coef_shopping_calibration_constant_duration_3,0.0,F +coef_mode_choice_logsum,0.2,F +coef_unavailable,-999.0,F \ No newline at end of file diff --git a/resident/configs/tour_scheduling_nonmandatory_social.csv b/resident/configs/tour_scheduling_nonmandatory_social.csv new file mode 100644 index 0000000..ceb875d --- /dev/null +++ b/resident/configs/tour_scheduling_nonmandatory_social.csv @@ -0,0 +1,59 @@ +Label,Description,Expression,Coefficient +#SOCIAL,#SOCIAL,,#SOCIAL +util_mode_choice_logsum,Mode choice logsum,mode_choice_logsum,coef_mode_choice_logsum +util_social_person_less_than_18_years_old_duration_less_than_reference_linear,SOCIAL - Person< 18 years old: Duration < Reference - Linear,"@np.where(((df.duration<6) & (df.age<18)), ((6-df.duration)*(df.duration<=6) + (df.duration-6)*(df.duration>6)), 0)",coef_social_person_less_than_18_years_old_duration_less_than_reference_linear +util_social_person_less_than_18_years_old_duration_greater_than_reference_linear,SOCIAL - Person< 18 years old: Duration > Reference - Linear,"@np.where(((df.duration>6) & (df.age<18)), ((6-df.duration)*(df.duration<=6) + (df.duration-6)*(df.duration>6)), 0)",coef_social_person_less_than_18_years_old_duration_greater_than_reference_linear +util_social_non_working_senior_or_retiree_duration_less_than_reference_linear,SOCIAL - Non-working senior/ retiree: Duration < Reference - Linear,"@np.where(((df.duration<6) & (df.ptype == 5)), ((6-df.duration)*(df.duration<=6) + (df.duration-6)*(df.duration>6)), 0)",coef_social_non_working_senior_or_retiree_duration_less_than_reference_linear +util_social_retiree_or_non_working_senior_only_HH_duration_less_than_reference_linear,SOCIAL - Retiree/ Non-working senior only HH: Duration < Reference - Linear,"@np.where(((df.retired_adults_only_hh) & (df.duration<6)), ((6-df.duration)*(df.duration<=6) + (df.duration-6)*(df.duration>6)), 0)",coef_social_retiree_or_non_working_senior_only_HH_duration_less_than_reference_linear +util_social_zero_auto_households_duration_less_than_reference_linear,SOCIAL - Zero auto households: Duration < Reference - Linear,"@np.where(((df.auto_ownership == 0) & (df.duration<6)), ((6-df.duration)*(df.duration<=6) + (df.duration-6)*(df.duration>6)), 0)",coef_social_zero_auto_households_duration_less_than_reference_linear +util_social_zero_auto_households_duration_greater_than_reference_linear,SOCIAL - Zero auto households: Duration > Reference - Linear,"@np.where(((df.auto_ownership == 0) & (df.duration>6)), ((6-df.duration)*(df.duration<=6) + (df.duration-6)*(df.duration>6)), 0)",coef_social_zero_auto_households_duration_greater_than_reference_linear +util_social_number_of_auto_more_than_number_of_adults_duration_less_than_reference_linear,SOCIAL - Number of auto more than number of adults: Duration < Reference - Linear,"@np.where(((df.auto_ownership > 0) &(df.auto_ownership > df.num_adults) & (df.duration<6)), ((6-df.duration)*(df.duration<=6) + (df.duration-6)*(df.duration>6)), 0)",coef_social_number_of_auto_more_than_number_of_adults_duration_less_than_reference_linear +util_social_number_of_auto_more_than_number_of_adults_duration_greater_than_reference_linear,SOCIAL - Number of auto more than number of adults: Duration > Reference - Linear,"@np.where(((df.auto_ownership > 0) & (df.auto_ownership > df.num_adults) & (df.duration>6)), ((6-df.duration)*(df.duration<=6) + (df.duration-6)*(df.duration>6)), 0)",coef_social_number_of_auto_more_than_number_of_adults_duration_greater_than_reference_linear +"# In CTRAMP, although the description below says duration is less than 1 hr, expression is for less than 1.5 hr",,, +util_social_auto_distance_duration_less_than_reference_linear,SOCIAL - Auto Distance: Duration < Reference - Linear,"@np.where(((df.duration<6)), ((6-df.duration)*(df.duration<=6) + (df.duration-6)*(df.duration>6)) * (df.origin_to_destination_distance), 0)",coef_social_auto_distance_duration_less_than_reference_linear +util_social_auto_distance_duration_greater_than_reference_linear,SOCIAL - Auto Distance: Duration > Reference - Linear,"@np.where(((df.duration>6)), ((6-df.duration)*(df.duration<=6) + (df.duration-6)*(df.duration>6)) * (df.origin_to_destination_distance), 0)",coef_social_auto_distance_duration_greater_than_reference_linear +util_social_time_pressure_duration_less_than_reference,SOCIAL - Time Pressure - Duration < Reference,"@np.where(((df.duration<6)), ((6-df.duration)*(df.duration<=6) + (df.duration-6)*(df.duration>6))* (np.log10 (30 * (tt.max_time_block_available(df.person_id)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_social_time_pressure_duration_less_than_reference +util_social_time_pressure_duration_greater_than_reference,SOCIAL - Time Pressure - Duration > Reference,"@np.where(((df.duration>6)), ((6-df.duration)*(df.duration<=6) + (df.duration-6)*(df.duration>6)) * (np.log10 (30 * (tt.max_time_block_available(df.person_id)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_social_time_pressure_duration_greater_than_reference +util_social_number_of_additional_individual_social_and_dicretionary_tours_duration_less_than_reference,SOCIAL - Number of additional individual social and dicretionary tours - Duration < 1 hr,"@np.where(((df.duration<6)), ((6-df.duration)*(df.duration<=6) + (df.duration-6)*(df.duration>6)) * (df.num_add_soc_discr_tours), 0)",coef_social_number_of_additional_individual_social_and_dicretionary_tours_duration_less_than_reference +util_social_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear,SOCIAL - Departure Constant: Shift for every 30 minutes before 08:30 am - Linear,@((df.start<12)) * ((12-df.start)*(df.start<=12) + (df.start-12)*(df.start>12)),coef_social_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear +util_social_departure_constant_before_9_am,SOCIAL - Departure Constant: Before 09:00 AM,@(df.start<13),coef_social_departure_constant_before_9_am +util_social_departure_constant_9_am_to_9_30_am,SOCIAL - Departure Constant: 09:00 AM to 09:30 AM,@(df.start==13),coef_social_departure_constant_9_am_to_9_30_am +util_social_departure_constant_shift_for_every_30_minutes_before_5_pm_linear,SOCIAL - Departure Constant: Shift for every 30 minutes before 05:00 pm - Linear,"@np.where((df.start<29), ((29-df.start)*(df.start<=29) + (df.start-29)*(df.start>29)), 0)",coef_social_departure_constant_shift_for_every_30_minutes_before_5_pm_linear +util_social_departure_constant_before_5_30_pm,SOCIAL - Departure Constant: Before 05:30 PM,@((df.start<30)),coef_social_departure_constant_before_5_30_pm +util_social_departure_constant_5_30_pm_to_6_pm,SOCIAL - Departure Constant: 05:30 PM - 06:00 PM,@((df.start==30)),coef_social_departure_constant_5_30_pm_to_6_pm +util_social_departure_constant_6_pm_to_6_30_pm,SOCIAL - Departure Constant: 06:00 PM - 06:30 PM,@((df.start==31)),coef_social_departure_constant_6_pm_to_6_30_pm +util_social_departure_constant_6_30_pm_to_7_pm,SOCIAL - Departure Constant: 06:30 PM - 07:00 PM,@((df.start==32)),coef_social_departure_constant_6_30_pm_to_7_pm +util_social_departure_constant_7_pm_to_7_30_pm,SOCIAL - Departure Constant: 07:00 PM - 07:30 PM,@((df.start==33)),coef_social_departure_constant_7_pm_to_7_30_pm +util_social_departure_constant_after_7_30_pm,SOCIAL - Departure Constant: After 07:30 PM,@((df.start>33)),coef_social_departure_constant_after_7_30_pm +util_social_departure_constant_shift_for_every_30_minutes_after_8_pm_linear,SOCIAL - Departure Constant: Shift for every 30 minutes after 08:00 pm - Linear,"@np.where((df.start>34), ((34-df.start)*(df.start<=34) + (df.start-34)*(df.start>34)), 0)",coef_social_departure_constant_shift_for_every_30_minutes_after_8_pm_linear +util_social_arrival_constant_3_pm_to_3_30_pm,SOCIAL - Arrival Constant: 03:00 PM to 03:30 PM,@((df.end==25)),coef_social_arrival_constant_3_pm_to_3_30_pm +util_social_arrival_constant_3_30_pm_to_4_pm,SOCIAL - Arrival Constant: 03:30 PM to 04:00 PM,@((df.end==26)),coef_social_arrival_constant_3_30_pm_to_4_pm +util_social_arrival_constant_4_pm_to_4_30_pm,SOCIAL - Arrival Constant: 04:00 PM to 04:30 PM,@((df.end==27)),coef_social_arrival_constant_4_pm_to_4_30_pm +util_social_arrival_constant_5_pm_to_6_pm,SOCIAL - Arrival Constant: 05:00 PM to 06:00 PM,@((df.end>=29) & (df.end<=30)),coef_social_arrival_constant_5_pm_to_6_pm +util_social_arrival_constant_shift_for_every_30_minutes_before_8_pm_linear,SOCIAL - Arrival Constant: Shift for every 30 minutes before 08:00 pm - Linear,"@np.where(((df.end<35)), ((35-df.end)*(df.end<=35) + (df.end-35)*(df.end>35)), 0)",coef_social_arrival_constant_shift_for_every_30_minutes_before_8_pm_linear +util_social_arrival_constant_before_8_30_pm,SOCIAL - Arrival Constant: Before 8:30 PM,@((df.end<36)),coef_social_arrival_constant_before_8_30_pm +util_social_arrival_constant_8_30_pm_to_9_pm,SOCIAL - Arrival Constant: 8:30 PM to 9:00 PM,@((df.end==36)),coef_social_arrival_constant_8_30_pm_to_9_pm +util_social_arrival_constant_9_pm_to_9_30_pm,SOCIAL - Arrival Constant: 9:00 PM to 9:30 PM,@((df.end==37)),coef_social_arrival_constant_9_pm_to_9_30_pm +util_social_arrival_constant_9_30_pm_to_10_pm,SOCIAL - Arrival Constant: 9:30 PM to10:00 PM,@((df.end==38)),coef_social_arrival_constant_9_30_pm_to_10_pm +util_social_arrival_constant_10_pm_to_10_30_pm,SOCIAL - Arrival Constant: 10:00 PM to 10:30 PM,@((df.end==39)),coef_social_arrival_constant_10_pm_to_10_30_pm +util_social_arrival_constant_after_10_30_pm,SOCIAL - Arrival Constant: After 10:30 PM,@((df.end>39)),coef_social_arrival_constant_after_10_30_pm +util_social_arrival_constant_shift_for_every_30_minutes_after_11_pm_linear,SOCIAL - Arrival Constant: Shift for every 30 minutes after 11:00 pm - Linear,"@np.where(((df.end>40)), ((40-df.end)*(df.end<=40) + (df.end-40)*(df.end>40)), 0)",coef_social_arrival_constant_shift_for_every_30_minutes_after_11_pm_linear +util_social_duration_constant_shift_for_every_30_minutes_less_than_2_hr_30_minutes_linear,SOCIAL - Duration Constant: Shift for every 30 minutes less than 2.5 hrs - Linear,"@np.where(((df.duration<5)), ((5-df.duration)*(df.duration<=5) + (df.duration-5)*(df.duration>5)), 0)",coef_social_duration_constant_shift_for_every_30_minutes_less_than_2_hr_30_minutes_linear +util_social_duration_constant_less_than_3_hours,SOCIAL - Duration Constant: Less than 3 hrs,@((df.duration<6)),coef_social_duration_constant_less_than_3_hours +util_social_duration_constant_3_hours,SOCIAL - Duration Constant: 3 hours,@((df.duration==6)),coef_social_duration_constant_3_hours +util_social_duration_constant_3_hrs_30_minutes,SOCIAL - Duration Constant: 3.5 hours,@((df.duration==7)),coef_social_duration_constant_3_hrs_30_minutes +util_social_duration_constant_4_hours_or_more,SOCIAL - Duration Constant: 4 hours or more,@((df.duration>7)),coef_social_duration_constant_4_hours_or_more +util_social_duration_constant_shift_for_every_30_minutes_more_than_4_hr_30_minutes_linear,SOCIAL - Duration Constant: Shift for every 30 minutes more than 4.5 hrs - Linear,"@np.where(((df.duration>8)), ((8-df.duration)*(df.duration<=8) + (df.duration-8)*(df.duration>8)), 0)",coef_social_duration_constant_shift_for_every_30_minutes_more_than_4_hr_30_minutes_linear +util_social_calibration_constant_duration_1,SOCIAL - Calibration Constant - Duration = 1,@((df.duration ==0)),coef_social_calibration_constant_duration_1 +util_social_calibration_constant_duration_2,SOCIAL - Calibration Constant - Duration = 2,@((df.duration == 1)),coef_social_calibration_constant_duration_2 +util_social_calibration_constant_duration_3,SOCIAL - Calibration Constant - Duration = 3,@((df.duration ==2)),coef_social_calibration_constant_duration_3 +util_social_calibration_constant_duration_4,SOCIAL - Calibration Constant - Duration = 4,@((df.duration ==3)),coef_social_calibration_constant_duration_4 +util_social_calibration_constant_duration_5,SOCIAL - Calibration Constant - Duration = 5,@((df.duration ==4)),coef_social_calibration_constant_duration_5 +util_social_calibration_constant_duration_6,SOCIAL - Calibration Constant - Duration = 6,@((df.duration ==5)),coef_social_calibration_constant_duration_6 +util_social_calibration_constant_duration_7,SOCIAL - Calibration Constant - Duration = 7,@((df.duration ==6)),coef_social_calibration_constant_duration_7 +util_social_calibration_constant_duration_8,SOCIAL - Calibration Constant - Duration = 8,@((df.duration ==7)),coef_social_calibration_constant_duration_8 +util_social_calibration_constant_duration_9,SOCIAL - Calibration Constant - Duration = 9,@((df.duration ==8)),coef_social_calibration_constant_duration_9 +# Including terms for school escorting,,, +util_outbound_school_escort_tour_start,Outbound school escort tours must match the start time of the escort tour,is_outbound_school_escort_tour & (start != school_escort_tour_start),coef_unavailable +util_outbound_school_escort_tour_next_start,Outbound school escort tours must end before next escort tour start,is_outbound_school_escort_tour & (end > school_escort_tour_next_start) & (school_escort_tour_next_start > 0),coef_unavailable +util_inbound_school_escort_tour_end,Inbound school escort tours must match the end time of the escort tour,is_inbound_school_escort_tour & (end != school_escort_tour_end),coef_unavailable \ No newline at end of file diff --git a/resident/configs/tour_scheduling_nonmandatory_social_coefficients.csv b/resident/configs/tour_scheduling_nonmandatory_social_coefficients.csv new file mode 100644 index 0000000..1cc972d --- /dev/null +++ b/resident/configs/tour_scheduling_nonmandatory_social_coefficients.csv @@ -0,0 +1,54 @@ +coefficient_name,value,constrain +coef_social_person_less_than_18_years_old_duration_less_than_reference_linear,0.0,F +coef_social_person_less_than_18_years_old_duration_greater_than_reference_linear,0.1848544664093724,F +coef_social_non_working_senior_or_retiree_duration_less_than_reference_linear,0.0,F +coef_social_retiree_or_non_working_senior_only_HH_duration_less_than_reference_linear,0.0,F +coef_social_zero_auto_households_duration_less_than_reference_linear,0.0,F +coef_social_zero_auto_households_duration_greater_than_reference_linear,0.0,F +coef_social_number_of_auto_more_than_number_of_adults_duration_less_than_reference_linear,0.0,F +coef_social_number_of_auto_more_than_number_of_adults_duration_greater_than_reference_linear,0.0,F +coef_social_auto_distance_duration_less_than_reference_linear,-0.037191211367614524,F +coef_social_auto_distance_duration_greater_than_reference_linear,0.004103999782121744,F +coef_social_time_pressure_duration_less_than_reference,0.0,F +coef_social_time_pressure_duration_greater_than_reference,0.2472747423164598,F +coef_social_number_of_additional_individual_social_and_dicretionary_tours_duration_less_than_reference,0.0,F +coef_social_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear,-0.35038347561648353,F +coef_social_departure_constant_before_9_am,0.0,F +coef_social_departure_constant_9_am_to_9_30_am,0.48320177182957835,F +coef_social_departure_constant_shift_for_every_30_minutes_before_5_pm_linear,0.10579475979139634,F +coef_social_departure_constant_before_5_30_pm,2.9248450547864326,F +coef_social_departure_constant_5_30_pm_to_6_pm,3.396566813557204,F +coef_social_departure_constant_6_pm_to_6_30_pm,0.0,T +coef_social_departure_constant_6_30_pm_to_7_pm,3.2332806668467815,F +coef_social_departure_constant_7_pm_to_7_30_pm,2.9564723583835772,F +coef_social_departure_constant_after_7_30_pm,2.5033367748702537,F +coef_social_departure_constant_shift_for_every_30_minutes_after_8_pm_linear,-0.31575816708433413,F +coef_social_arrival_constant_3_pm_to_3_30_pm,0.6128608441643291,F +coef_social_arrival_constant_3_30_pm_to_4_pm,0.0,F +coef_social_arrival_constant_4_pm_to_4_30_pm,0.0,F +coef_social_arrival_constant_5_pm_to_6_pm,0.0,F +coef_social_arrival_constant_shift_for_every_30_minutes_before_8_pm_linear,-0.11543939695197131,F +coef_social_arrival_constant_before_8_30_pm,0.0,F +coef_social_arrival_constant_8_30_pm_to_9_pm,0.3725347628206525,F +coef_social_arrival_constant_9_pm_to_9_30_pm,0.34127159737778273,F +coef_social_arrival_constant_9_30_pm_to_10_pm,0.0,T +coef_social_arrival_constant_10_pm_to_10_30_pm,0.349324126243515,F +coef_social_arrival_constant_after_10_30_pm,0.0,F +coef_social_arrival_constant_shift_for_every_30_minutes_after_11_pm_linear,-0.22982597094534407,F +coef_social_duration_constant_shift_for_every_30_minutes_less_than_2_hr_30_minutes_linear,0.0,F +coef_social_duration_constant_less_than_3_hours,0.0,F +coef_social_duration_constant_3_hours,0.0,T +coef_social_duration_constant_3_hrs_30_minutes,-0.9879910839979239,F +coef_social_duration_constant_4_hours_or_more,-1.9671125098564861,F +coef_social_duration_constant_shift_for_every_30_minutes_more_than_4_hr_30_minutes_linear,-1.081195937845935,F +coef_social_calibration_constant_duration_1,0.0,F +coef_social_calibration_constant_duration_2,0.0,F +coef_social_calibration_constant_duration_3,0.0,F +coef_social_calibration_constant_duration_4,0.0,F +coef_social_calibration_constant_duration_5,0.0,F +coef_social_calibration_constant_duration_6,0.0,F +coef_social_calibration_constant_duration_7,0.0,F +coef_social_calibration_constant_duration_8,0.0,F +coef_social_calibration_constant_duration_9,0.0,F +coef_mode_choice_logsum,0.014255126759576003,F +coef_unavailable,-999.0,F \ No newline at end of file diff --git a/resident/configs/tour_scheduling_school.csv b/resident/configs/tour_scheduling_school.csv new file mode 100644 index 0000000..6470156 --- /dev/null +++ b/resident/configs/tour_scheduling_school.csv @@ -0,0 +1,62 @@ +Label,Description,Expression,Coefficient +# writing out varibles we may need for estimation,,, +round_trip_auto_time_to_work,round_trip_auto_time_to_work,@df.roundtrip_auto_time_to_work,zero_coef +age,age,@df.age,zero_coef +util_Mode_Choice_Logsum,SCHOOL - Mode Choice Logsum,mode_choice_logsum,coef_Mode_Choice_Logsum +util_Low_income_lt25000_Departure_before_730_am__Linear,SCHOOL - Low income (<25000) - Departure before 7:30 am - Linear,"@((df.is_income_less25K) & (df.start < df.departureRefBin_school)) *df.departureLinearShift1",coef_Low_income_lt25000_Departure_before_730_am__Linear +util_Low_income_lt25000_Departure_after_800_am_Linear,SCHOOL - Low income (<25000) - Departure after 8:00 am - Linear,"@((df.is_income_less25K) & (df.start > df.departureRefBin_school)) *df.departureLinearShift1",coef_Low_income_lt25000_Departure_after_800_am_Linear +util_Low_income_lt25000_Duration_lt_8hrs,SCHOOL - Low income (<25000) - Duration < 8hrs,"@((df.is_income_less25K) & (df.start < df.durationRefBin_school)) * df.durationShift_school",coef_Low_income_lt25000_Duration_lt_8hrs +util_Low_income_lt25000_Duration_gt_8hrs,SCHOOL - Low income (<25000) - Duration > 8hrs,"@((df.is_income_less25K) & (df.start > df.durationRefBin_school)) * df.durationShift_school",coef_Low_income_lt25000_Duration_gt_8hrs +util_Med_income_25k_to_60k_Departure_before_730_am__Linear,SCHOOL - Med income (25k to 60k) - Departure before 7:30 am - Linear,"@((df.is_income_25K_to_60K) & (df.start < df.departureRefBin_school)) *df.departureLinearShift1",coef_Med_income_25k_to_60k_Departure_before_730_am__Linear +util_Age_0_to_5_yrs_Departure_Before_730_am,SCHOOL - Age 0 to 5 yrs - Departure Before 7:30 am,"@(((df.age>=0) & (df.age<=5)) & (df.start < df.departureRefBin_school)) *df.departureLinearShift1",coef_Age_0_to_5_yrs_Departure_Before_730_am +util_Age_13_to_15_yrs_Departure_Before_730_am,SCHOOL - Age 13 to 15 yrs - Departure Before 7:30 am,"@(((df.age>=13) & (df.age<=15)) & (df.start < df.departureRefBin_school)) *df.departureLinearShift1",coef_Age_13_to_15_yrs_Departure_Before_730_am +util_Age_13_to_15_yrs_Departure_After_800_am,SCHOOL - Age 13 to 15 yrs - Departure After 8:00 am,"@(((df.age>=13) & (df.age<=15)) & (df.start > df.departureRefBin_school)) *df.departureLinearShift1",coef_Age_13_to_15_yrs_Departure_After_800_am +util_Age_16_to_17_yrs_Departure_After_800_am,SCHOOL - Age 16 to 17 yrs - Departure After 8:00 am,"@(((df.age>=16) & (df.age<=17)) & (df.start > df.departureRefBin_school)) *df.departureLinearShift1",coef_Age_16_to_17_yrs_Departure_After_800_am +util_Age_0_to_5_yrs_Duration_lt_8hrs,SCHOOL - Age 0 to 5 yrs - Duration < 8hrs,"@(((df.age>0) & (df.age<=5)) & (df.start < df.durationRefBin_school)) * df.durationShift_school",coef_Age_0_to_5_yrs_Duration_lt_8hrs +util_Age_0_to_5_yrs_Duration_gt_8hrs,SCHOOL - Age 0 to 5 yrs - Duration > 8hrs,"@(((df.age>0) & (df.age<=5)) & (df.start > df.durationRefBin_school)) * df.durationShift_school",coef_Age_0_to_5_yrs_Duration_gt_8hrs +util_Age_13_to_15_yrs_Duration_lt_8hrs,SCHOOL - Age 13 to 15 yrs - Duration < 8hrs,"@(((df.age>=13) & (df.age<=15)) & (df.start < df.durationRefBin_school)) * df.durationShift_school",coef_Age_13_to_15_yrs_Duration_lt_8hrs +util_Age_13_to_15_yrs_Duration_gt_8hrs,SCHOOL - Age 13 to 15 yrs - Duration > 8hrs,"@(((df.age>=13) & (df.age<=15)) & (df.start > df.durationRefBin_school)) * df.durationShift_school",coef_Age_13_to_15_yrs_Duration_gt_8hrs +util_Age_16_to_17_yrs_Duration_gt_8hrs,SCHOOL - Age 16 to 17 yrs - Duration > 8hrs,"@(((df.age>=16) & (df.age<=17)) & (df.start > df.durationRefBin_school)) * df.durationShift_school",coef_Age_16_to_17_yrs_Duration_gt_8hrs +util_Time_SOV_freeflow_to_destination_Departure_before_730_am__Linear,SCHOOL - Time (SOV freeflow) to destination - Departure before 7:30 am - Linear,"@(df.start < df.departureRefBin_school) *df.departureLinearShift1 * (df.roundtrip_auto_time_to_school)",coef_Time_SOV_freeflow_to_destination_Departure_before_730_am__Linear +util_Time_SOV_freeflow_to_destination_Departure_after_800_am_Linear,SCHOOL - Time (SOV freeflow) to destination - Departure after 8:00 am - Linear,"@(df.start > df.departureRefBin_school) *df.departureLinearShift1 * (df.roundtrip_auto_time_to_school)",coef_Time_SOV_freeflow_to_destination_Departure_after_800_am_Linear +util_Time_SOV_freeflow_to_destination_arrival_before_ref,SCHOOL - Time (SOV freeflow) to destination - Arrival < Reference Bin,"@(df.end < df.arrivalRefBin_school) * df.arrivalLinearShift1_school * (df.roundtrip_auto_time_to_school)",coef_Time_SOV_freeflow_to_destination_arrival_before_ref +util_Time_SOV_freeflow_to_destination_arrival_after_ref,SCHOOL - Time (SOV freeflow) to destination - Duration > 8hrs,"@(df.end > df.arrivalRefBin_school) * df.arrivalLinearShift1_school * (df.roundtrip_auto_time_to_school)",coef_Time_SOV_freeflow_to_destination_arrival_after_ref +util_All_adults_in_the_household_are_fulltime_workers_Departure_before_730_am__Linear,SCHOOL - All adults in the household are fulltime workers - Departure before 7:30 am - Linear,"@((df.is_all_adults_full_time_workers) & (df.start < df.departureRefBin_school)) *df.departureLinearShift1",coef_All_adults_in_the_household_are_fulltime_workers_Departure_before_730_am__Linear +util_All_adults_in_the_household_are_fulltime_workers_Departure_after_800_am_Linear,SCHOOL - All adults in the household are fulltime workers - Departure after 8:00 am - Linear,"@((df.is_all_adults_full_time_workers) & (df.start > df.departureRefBin_school)) *df.departureLinearShift1",coef_All_adults_in_the_household_are_fulltime_workers_Departure_after_800_am_Linear +util_All_adults_in_the_household_are_fulltime_workers_arrival_before_ref,SCHOOL - All adults in the household are fulltime workers - Duration < 8hrs,"@((df.is_all_adults_full_time_workers) & (df.end < df.arrivalRefBin_school)) * df.arrivalLinearShift1_school",coef_All_adults_in_the_household_are_fulltime_workers_arrival_before_ref +util_All_adults_in_the_household_are_fulltime_workers_arrival_after_ref,SCHOOL - All adults in the household are fulltime workers - Duration > 8hrs,"@((df.is_all_adults_full_time_workers) & (df.end > df.arrivalRefBin_school)) * df.arrivalLinearShift1_school",coef_All_adults_in_the_household_are_fulltime_workers_arrival_after_ref +util_Subsequent_tour_is_work_tour_Duration_lt_8_hours,SCHOOL - Subsequent tour is work tour: Duration < 8 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'work') & (df.start < df.durationRefBin_school))) * df.durationShift_school",coef_Subsequent_tour_is_work_tour_Duration_lt_8_hours +util_Subsequent_tour_is_work_tour_Duration_gt_8_hours,SCHOOL - Subsequent tour is work tour: Duration > 8 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'work') & (df.start > df.durationRefBin_school))) * df.durationShift_school",coef_Subsequent_tour_is_work_tour_Duration_gt_8_hours +util_Subsequent_tour_is_school_tour_Departure_after_800_am,SCHOOL - Subsequent tour is school tour: Departure after 8:00 am,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'school') & (df.start > df.departureRefBin_school))) *df.departureLinearShift1",coef_Subsequent_tour_is_school_tour_Departure_after_800_am +util_Subsequent_tour_is_school_tour_Duration_lt_8_hours,SCHOOL - Subsequent tour is school tour: Duration < 8 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'school') & (df.start < df.durationRefBin_school))) * df.durationShift_school",coef_Subsequent_tour_is_school_tour_Duration_lt_8_hours +util_Subsequent_tour_is_school_tour_Duration_gt_8_hours,SCHOOL - Subsequent tour is school tour: Duration > 8 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'school') & (df.start > df.durationRefBin_school))) * df.durationShift_school",coef_Subsequent_tour_is_school_tour_Duration_gt_8_hours +util_Second_tour_of_two_mandatory_tours_Duration_lt_4_hours,SCHOOL - Second tour of two mandatory tours: Duration < 4 hours,"@(((df.tour_count>1) & (df.tour_num > 1)) & (df.duration<7)) * (((7-df.duration)*(df.duration<=7)) + ((df.duration-7)*(df.duration>7)))",coef_Second_tour_of_two_mandatory_tours_Duration_lt_4_hours +util_Second_tour_of_two_mandatory_tours_Duration_gt_4_hours,SCHOOL - Second tour of two mandatory tours: Duration > 4 hours,"@(((df.tour_count>1) & (df.tour_num > 1)) & (df.duration>7)) * (((7-df.duration)*(df.duration<=7)) + ((df.duration-7)*(df.duration>7)))",coef_Second_tour_of_two_mandatory_tours_Duration_gt_4_hours +util_Departure_Constant_Before_0600_AM,SCHOOL - Departure Constant: Before 06:00 AM,@(df.start<7),coef_Departure_Constant_Before_0600_AM +util_Departure_Constant_0600_AM_to_0630_AM_7,SCHOOL - Departure Constant: 06:00 AM to 06:30 AM (7),@(df.start==7),coef_Departure_Constant_0600_AM_to_0630_AM_7 +util_Departure_Constant_0630_AM_to_0700_AM_8,SCHOOL - Departure Constant: 06:30 AM to 07:00 AM (8),@(df.start==8),coef_Departure_Constant_0630_AM_to_0700_AM_8 +util_Departure_Constant_0700_AM_to_0730_AM_9,SCHOOL - Departure Constant: 07:00 AM to 07:30 AM (9),@(df.start==9),coef_Departure_Constant_0700_AM_to_0730_AM_9 +util_Departure_Constant_0730_AM_to_0800_AM_10,SCHOOL - Departure Constant: 07:30 AM to 08:00 AM (10),@(df.start==10),coef_Departure_Constant_0730_AM_to_0800_AM_10 +util_Departure_Constant_After_0800_AM,SCHOOL - Departure Constant: After 08:00 AM,@(df.start > df.departureRefBin_school),coef_Departure_Constant_After_0800_AM +util_Departure_Constant_Shift_for_every_30_minutes_after_830_am_Linear,SCHOOL - Departure Constant: Shift for every 30 minutes after 8:30 am - Linear,"@((df.start>11)) * (((11-df.start)*(df.start<11)) + ((df.start-11)*(df.start>11)))",coef_Departure_Constant_Shift_for_every_30_minutes_after_830_am_Linear +util_Departure_Constant_Shift_for_every_30_minutes_after_830_am_Square_Root,SCHOOL - Departure Constant: Shift for every 30 minutes after 8:30 am - Square Root,"@((df.start>11) * (np.maximum(df.start - 11, 0) ** 0.5))",coef_Departure_Constant_Shift_for_every_30_minutes_after_830_am_Square_Root +util_Arrival_Constant_Before_0230_PM,SCHOOL - Arrival Constant: Before 02:30 PM,@(df.end<24),coef_Arrival_Constant_Before_0230_PM +util_Arrival_Constant_0230_PM_0300_PM_24_,SCHOOL - Arrival Constant: 02:30 PM - 03:00 PM (24) ,@(df.end==24),coef_Arrival_Constant_0230_PM_0300_PM_24_ +util_Arrival_Constant_0300_PM_0330_PM_25_,SCHOOL - Arrival Constant: 03:00 PM - 03:30 PM (25) ,@(df.end==25),coef_Arrival_Constant_0300_PM_0330_PM_25_ +util_Arrival_Constant_0330_PM_0400_PM_26_,SCHOOL - Arrival Constant: 03:30 PM - 04:00 PM (26) ,@(df.end==26),coef_Arrival_Constant_0330_PM_0400_PM_26_ +util_Arrival_Constant_0400_PM_0430_PM_27_,SCHOOL - Arrival Constant: 04:00 PM - 04:30 PM (27) ,@(df.end==27),coef_Arrival_Constant_0400_PM_0430_PM_27_ +util_Arrival_Constant_0430_PM_0500_PM_28_,SCHOOL - Arrival Constant: 04:30 PM - 05:00 PM (28) ,@(df.end==28),coef_Arrival_Constant_0430_PM_0500_PM_28_ +util_Arrival_Constant_0500_PM_0530_PM_29,SCHOOL - Arrival Constant: 05:00 PM - 05:30 PM (29),@(df.end==29),coef_Arrival_Constant_0500_PM_0530_PM_29 +util_Arrival_Constant_0530_PM_0600_PM_30_,SCHOOL - Arrival Constant: 05:30 PM - 06:00 PM (30) ,@(df.end==30),coef_Arrival_Constant_0530_PM_0600_PM_30_ +util_Arrival_Constant_After_0600_PM,SCHOOL - Arrival Constant: After 06:00 PM,@(df.end>30),coef_Arrival_Constant_After_0600_PM +util_Arrival_Constant_Shift_for_every_30_minutes_after_630_pm_Linear,SCHOOL - Arrival Constant: Shift for every 30 minutes after 6:30 pm - Linear,"@(df.end>31) * (((31-df.end)*(df.end<31)) + ((df.end-31)*(df.end>31)))",coef_Arrival_Constant_Shift_for_every_30_minutes_after_630_pm_Linear +util_Duration_Constant_Shift_for_every_30_minutes_less_than_6p5_hrs_Linear,SCHOOL - Duration Constant: Shift for every 30 minutes less than 6.5 hrs - Linear,"@((df.duration<13)) * (((13-df.duration)*(df.duration<13)) + ((df.duration-13)*(df.duration>13)))",coef_Duration_Constant_Shift_for_every_30_minutes_less_than_6p5_hrs_Linear +util_Duration_Constant_Shorter_than_7_hrs,SCHOOL - Duration Constant: Shorter than 7 hrs,@(df.duration<14),coef_Duration_Constant_Shorter_than_7_hrs +util_Duration_Constant_7_hours,SCHOOL - Duration Constant: 7 hours,@(df.duration==14),coef_Duration_Constant_7_hours +util_Duration_Constant_7p5_hours,SCHOOL - Duration Constant: 7.5 hours,@(df.duration==15),coef_Duration_Constant_7p5_hours +util_Duration_Constant_8_hours,SCHOOL - Duration Constant: 8 hours,@(df.duration==16),coef_Duration_Constant_8_hours +util_Duration_Constant_8p5_hours,SCHOOL - Duration Constant: 8.5 hours,@(df.duration==17),coef_Duration_Constant_8p5_hours +util_Duration_Constant_9_hours,SCHOOL - Duration Constant: 9 hours,@(df.duration==18),coef_Duration_Constant_9_hours +util_Duration_Constant_Longer_than_9_hrs,SCHOOL - Duration Constant: Longer than 9 hrs,@(df.duration>18),coef_Duration_Constant_Longer_than_9_hrs +util_Duration_Constant_Shift_for_every_30_minutes_more_than_9p5_hrs_Linear,SCHOOL - Duration Constant: Shift for every 30 minutes more than 9.5 hrs - Linear,"@(df.duration>19) * (((19-df.duration)*(df.duration<19)) + ((df.duration-19)*(df.duration>19)))",coef_Duration_Constant_Shift_for_every_30_minutes_more_than_9p5_hrs_Linear +util_Duration_Constant_Shift_for_every_30_minutes_more_than_9p5_hrs_Squared,SCHOOL - Duration Constant: Shift for every 30 minutes more than 9.5 hrs - Squared,"@(df.duration>19) * ((((19-df.duration)*(df.duration<19)) + ((df.duration-19)*(df.duration>19))) ** 2)",coef_Duration_Constant_Shift_for_every_30_minutes_more_than_9p5_hrs_Squared diff --git a/resident/configs/tour_scheduling_school_coeffs.csv b/resident/configs/tour_scheduling_school_coeffs.csv new file mode 100644 index 0000000..1c11c6a --- /dev/null +++ b/resident/configs/tour_scheduling_school_coeffs.csv @@ -0,0 +1,60 @@ +coefficient_name,value,constrain +coef_Mode_Choice_Logsum,0.0,F +coef_Low_income_lt25000_Departure_before_730_am__Linear,0.0,F +coef_Low_income_lt25000_Departure_after_800_am_Linear,0.0,F +coef_Low_income_lt25000_Duration_lt_8hrs,0.0,F +coef_Low_income_lt25000_Duration_gt_8hrs,0.0,F +coef_Med_income_25k_to_60k_Departure_before_730_am__Linear,0.0,F +coef_Age_0_to_5_yrs_Departure_Before_730_am,-0.8148835593502417,F +coef_Age_13_to_15_yrs_Departure_Before_730_am,0.27726279249507474,F +coef_Age_13_to_15_yrs_Departure_After_800_am,-0.7597642883716563,F +coef_Age_16_to_17_yrs_Departure_After_800_am,-0.8824101711801812,F +coef_Age_0_to_5_yrs_Duration_lt_8hrs,0.1741028592807906,F +coef_Age_0_to_5_yrs_Duration_gt_8hrs,0.241662039787307,F +coef_Age_13_to_15_yrs_Duration_lt_8hrs,0.0,F +coef_Age_13_to_15_yrs_Duration_gt_8hrs,0.5963387646724607,F +coef_Age_16_to_17_yrs_Duration_gt_8hrs,0.6956001833839539,F +coef_Time_SOV_freeflow_to_destination_Departure_before_730_am__Linear,0.00454314976439104,F +coef_Time_SOV_freeflow_to_destination_Departure_after_800_am_Linear,-0.008509123281468139,F +coef_Time_SOV_freeflow_to_destination_arrival_before_ref,-0.0026124484527274773,F +coef_Time_SOV_freeflow_to_destination_arrival_after_ref,0.00213627544235268,F +coef_All_adults_in_the_household_are_fulltime_workers_Departure_before_730_am__Linear,0.0,F +coef_All_adults_in_the_household_are_fulltime_workers_Departure_after_800_am_Linear,-0.14721190410201404,F +coef_All_adults_in_the_household_are_fulltime_workers_arrival_before_ref,-0.16604467456086497,F +coef_All_adults_in_the_household_are_fulltime_workers_arrival_after_ref,0.0,F +coef_Subsequent_tour_is_work_tour_Duration_lt_8_hours,0.0,F +coef_Subsequent_tour_is_work_tour_Duration_gt_8_hours,0.0,F +coef_Subsequent_tour_is_school_tour_Departure_after_800_am,0.0,F +coef_Subsequent_tour_is_school_tour_Duration_lt_8_hours,0.0,F +coef_Subsequent_tour_is_school_tour_Duration_gt_8_hours,0.0,F +coef_Second_tour_of_two_mandatory_tours_Duration_lt_4_hours,0.0,F +coef_Second_tour_of_two_mandatory_tours_Duration_gt_4_hours,0.0,F +coef_Departure_Constant_Before_0600_AM,-8.764796175382282,F +coef_Departure_Constant_0600_AM_to_0630_AM_7,-2.707793497506736,F +coef_Departure_Constant_0630_AM_to_0700_AM_8,-0.654253451781764,F +coef_Departure_Constant_0700_AM_to_0730_AM_9,0.0,F +coef_Departure_Constant_0730_AM_to_0800_AM_10,-0.48281221099875055,T +coef_Departure_Constant_After_0800_AM,1.0336602346049464,F +coef_Departure_Constant_Shift_for_every_30_minutes_after_830_am_Linear,0.0,F +coef_Departure_Constant_Shift_for_every_30_minutes_after_830_am_Square_Root,-0.4982940561474212,F +coef_Arrival_Constant_Before_0230_PM,0.0,F +coef_Arrival_Constant_0230_PM_0300_PM_24_,0.0,F +coef_Arrival_Constant_0300_PM_0330_PM_25_,0.22741669683682353,F +coef_Arrival_Constant_0330_PM_0400_PM_26_,0.0,F +coef_Arrival_Constant_0400_PM_0430_PM_27_,0.0,T +coef_Arrival_Constant_0430_PM_0500_PM_28_,-1.0249817399820995,F +coef_Arrival_Constant_0500_PM_0530_PM_29,-1.4403670450638697,F +coef_Arrival_Constant_0530_PM_0600_PM_30_,-1.8460834370368264,F +coef_Arrival_Constant_After_0600_PM,-2.3316983953964314,F +coef_Arrival_Constant_Shift_for_every_30_minutes_after_630_pm_Linear,-0.6853763563949641,F +coef_Duration_Constant_Shift_for_every_30_minutes_less_than_6p5_hrs_Linear,-0.19770146146375506,F +coef_Duration_Constant_Shorter_than_7_hrs,-3.0269316327052733,F +coef_Duration_Constant_7_hours,-1.8715765041190864,F +coef_Duration_Constant_7p5_hours,-0.3821662280789037,F +coef_Duration_Constant_8_hours,0.0,T +coef_Duration_Constant_8p5_hours,-0.2743767294042022,F +coef_Duration_Constant_9_hours,-0.8177669270743325,F +coef_Duration_Constant_Longer_than_9_hrs,-0.5422856490451977,F +coef_Duration_Constant_Shift_for_every_30_minutes_more_than_9p5_hrs_Linear,0.21538163084141398,F +coef_Duration_Constant_Shift_for_every_30_minutes_more_than_9p5_hrs_Squared,0.0,F +zero_coef,0.0,T diff --git a/resident/configs/tour_scheduling_university.csv b/resident/configs/tour_scheduling_university.csv new file mode 100644 index 0000000..9617383 --- /dev/null +++ b/resident/configs/tour_scheduling_university.csv @@ -0,0 +1,42 @@ +Label,Description,Expression,Coefficient +util_Mode_Choice_Logsum,UNIVERSITY - Mode Choice Logsum,mode_choice_logsum,coef_Mode_Choice_Logsum +util_Low_income_lt25000_Departure_before_800_am_Linear,UNIVERSITY - Low income (<25000) - Departure before 8:00 am - Linear,"@((df.is_income_less25K) & (df.start<11))*((np.minimum(11-df.start,8)*(df.start<=11)) + (np.minimum(df.start-11,22)*(df.start>11)))",coef_Low_income_lt25000_Departure_before_800_am_Linear +util_Low_income_lt25000_Duration_lt_4hrs,UNIVERSITY - Low income (<25000) - Duration < 4hrs,"@((df.is_income_less25K) & (df.duration<8))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))",coef_Low_income_lt25000_Duration_lt_4hrs +util_Medium_high_income_60k_to_120k_Departure_after_830_am_Linear,UNIVERSITY - Medium high income (60k to 120k) - Departure after 8:30 am - Linear,"@((df.is_income_60K_to_120K) & (df.start>11))*((np.minimum(11-df.start,8)*(df.start<=11)) + (np.minimum(df.start-11,22)*(df.start>11)))",coef_Medium_high_income_60k_to_120k_Departure_after_830_am_Linear +util_Medium_high_income_60k_to_120k_Duration_gt_4hrs,UNIVERSITY - Medium high income (60k to 120k) - Duration > 4hrs,"@((df.is_income_60K_to_120K) & (df.duration>8))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))",coef_Medium_high_income_60k_to_120k_Duration_gt_4hrs +util_High_income_120k_plus_Departure_after_830_am_Linear,UNIVERSITY - High income (120k+) - Departure after 8:30 am - Linear,"@((df.is_income_greater120K) & (df.start>11))*((np.minimum(11-df.start,8)*(df.start<=11)) + (np.minimum(df.start-11,22)*(df.start>11)))",coef_High_income_120k_plus_Departure_after_830_am_Linear +util_Age_41_plus_Departure_after_830_am_Linear,UNIVERSITY - Age 41+ - Departure after 8:30 am - Linear,"@((df.age >= 41) & (df.start>11))*((np.minimum(11-df.start,8)*(df.start<=11)) + (np.minimum(df.start-11,22)*(df.start>11)))",coef_Age_41_plus_Departure_after_830_am_Linear +util_Age_41_plus_Durationlt_4_hrs_Linear,UNIVERSITY - Age 41+ - Duration< 4 hrs -Linear,"@((df.age >= 41) & (df.duration<8))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))",coef_Age_41_plus_Durationlt_4_hrs_Linear +util_Distance_to_destination_Departure_before_800_am_Linear,UNIVERSITY - Distance to destination - Departure before 8:00 am - Linear,"@((df.start<11))*((np.minimum(11-df.start,8)*(df.start<=11)) + (np.minimum(df.start-11,22)*(df.start>11)))*(df.distance_to_school)",coef_Distance_to_destination_Departure_before_800_am_Linear +util_Distance_to_destination_Departure_after_830_am_Linear,UNIVERSITY - Distance to destination - Departure after 8:30 am - Linear,"@((df.start>11))*((np.minimum(11-df.start,8)*(df.start<=11)) + (np.minimum(df.start-11,22)*(df.start>11)))*(df.distance_to_school)",coef_Distance_to_destination_Departure_after_830_am_Linear +util_Distance_to_destination_Durationlt_4_hrs_Linear,UNIVERSITY - Distance to destination - Duration< 4 hrs -Linear,"@((df.duration<8))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))*(df.distance_to_school)",coef_Distance_to_destination_Durationlt_4_hrs_Linear +util_Distance_to_destination_Durationgt_4_hrs_Linear,UNIVERSITY - Distance to destination - Duration> 4 hrs- Linear,"@((df.duration>8))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))*(df.distance_to_school)",coef_Distance_to_destination_Durationgt_4_hrs_Linear +util_Distance_to_destination_Durationlt_4_hrs_Square_Root,UNIVERSITY - Distance to destination - Duration< 4 hrs - Square Root,"@((df.duration<8))*(abs(((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))) ** 0.5)*(df.distance_to_school)",coef_Distance_to_destination_Durationlt_4_hrs_Square_Root +util_Subsequent_tour_is_work_tour_Departure_after_830_am,UNIVERSITY - Subsequent tour is work tour: Departure after 8:30 am,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'work') & (df.start>11)))*((np.minimum(11-df.start,8)*(df.start<=11)) + (np.minimum(df.start-11,22)*(df.start>11)))",coef_Subsequent_tour_is_work_tour_Departure_after_830_am +util_Subsequent_tour_is_work_tour_Duration_lt_4_hours,UNIVERSITY - Subsequent tour is work tour: Duration < 4 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'work') & (df.duration<8)))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))",coef_Subsequent_tour_is_work_tour_Duration_lt_4_hours +util_Subsequent_tour_is_work_tour_Duration_gt_4_hours,UNIVERSITY - Subsequent tour is work tour: Duration > 4 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'work') & (df.duration>8)))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))",coef_Subsequent_tour_is_work_tour_Duration_gt_4_hours +util_Subsequent_tour_is_school_tour_Departure_after_830_am,UNIVERSITY - Subsequent tour is school tour: Departure after 8:30 am,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'school') & (df.start>11)))*((np.minimum(11-df.start,8)*(df.start<=11)) + (np.minimum(df.start-11,22)*(df.start>11)))",coef_Subsequent_tour_is_school_tour_Departure_after_830_am +util_Subsequent_tour_is_school_tour_Duration_lt_4_hours,UNIVERSITY - Subsequent tour is school tour: Duration < 4 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'school') & (df.duration<8)))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))",coef_Subsequent_tour_is_school_tour_Duration_lt_4_hours +util_Subsequent_tour_is_school_tour_Duration_gt_4_hours,UNIVERSITY - Subsequent tour is school tour: Duration > 4 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'school') & (df.duration>8)))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))",coef_Subsequent_tour_is_school_tour_Duration_gt_4_hours +util_Second_tour_of_two_mandatory_tours_Duration_lt_4_hours,UNIVERSITY - Second tour of two mandatory tours: Duration < 4 hours,"@(((df.tour_count>1) & (df.tour_num > 1)) & (df.duration<8))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))",coef_Second_tour_of_two_mandatory_tours_Duration_lt_4_hours +util_Second_tour_of_two_mandatory_tours_Duration_gt_4_hours,UNIVERSITY - Second tour of two mandatory tours: Duration > 4 hours,"@(((df.tour_count>1) & (df.tour_num > 1)) & (df.duration>8))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))",coef_Second_tour_of_two_mandatory_tours_Duration_gt_4_hours +util_Departure_Constant_Shift_for_every_30_minutes_before_0700_am_Linear,UNIVERSITY - Departure Constant: Shift for every 30 minutes before 07:00 am - Linear,"@((df.start<9))*((np.minimum(9-df.start,6)*(df.start<9)) + (np.minimum(df.start-13,20)*(df.start>13)))",coef_Departure_Constant_Shift_for_every_30_minutes_before_0700_am_Linear +util_Departure_Constant_Before_0730_AM,UNIVERSITY - Departure Constant: Before 07:30 AM,@(df.start<10),coef_Departure_Constant_Before_0730_AM +util_Departure_Constant_0730_AM_0800_AM,UNIVERSITY - Departure Constant: 07:30 AM - 08:00 AM,@(df.start==10),coef_Departure_Constant_0730_AM_0800_AM +util_Departure_Constant_0800_AM_0830_AM,UNIVERSITY - Departure Constant: 08:00 AM - 08:30 AM,@(df.start==11),coef_Departure_Constant_0800_AM_0830_AM +util_Departure_Constant_0830_AM_0900_AM,UNIVERSITY - Departure Constant: 08:30 AM - 09:00 AM,@(df.start==12),coef_Departure_Constant_0830_AM_0900_AM +util_Departure_Constant_After_0900_AM,UNIVERSITY - Departure Constant: After 09:00 AM,@(df.start>12),coef_Departure_Constant_After_0900_AM +util_Departure_Constant_Shift_for_every_30_minutes_after_0930_am_Square_Root,UNIVERSITY - Departure Constant: Shift for every 30 minutes after 09:30 am - Square Root,"@((df.start>13))*(((np.minimum(9-df.start,6)*(df.start<9)) + (np.minimum(df.start-13,20)*(df.start>13))) ** 0.5)",coef_Departure_Constant_Shift_for_every_30_minutes_after_0930_am_Square_Root +util_Arrival_Constant_Shift_for_every_30_minutes_before_0230_pm_Linear,UNIVERSITY - Arrival Constant: Shift for every 30 minutes before 02:30 pm - Linear,"@((df.end<24)) * ((np.minimum(24-df.end,12) * (df.end<24)) + (np.minimum(df.end-28,19) * (df.end>28)))",coef_Arrival_Constant_Shift_for_every_30_minutes_before_0230_pm_Linear +util_Arrival_Constant_Before_0300_PM,UNIVERSITY - Arrival Constant: Before 03:00 PM,@((df.end<25)),coef_Arrival_Constant_Before_0300_PM +util_Arrival_Constant_0300_PM_0330_PM,UNIVERSITY - Arrival Constant: 03:00 PM - 03:30 PM,@(df.end==25),coef_Arrival_Constant_0300_PM_0330_PM +util_Arrival_Constant_0330_PM_0400_PM,UNIVERSITY - Arrival Constant: 03:30 PM - 04:00 PM,@(df.end==26),coef_Arrival_Constant_0330_PM_0400_PM +util_Arrival_Constant_0400_PM_0430_PM,UNIVERSITY - Arrival Constant: 04:00 PM - 04:30 PM,@(df.end==27),coef_Arrival_Constant_0400_PM_0430_PM +util_Arrival_Constant_After_0430_PM,UNIVERSITY - Arrival Constant: After 04:30 PM,@(df.end>27),coef_Arrival_Constant_After_0430_PM +util_Arrival_Constant_Shift_for_every_30_minutes_after_0500_pm_Linear,UNIVERSITY - Arrival Constant: Shift for every 30 minutes after 05:00 pm - Linear,"@((df.end>28))*((np.minimum(24-df.end,12)*(df.end<24)) + (np.minimum(df.end-28,19)*(df.end>28)))",coef_Arrival_Constant_Shift_for_every_30_minutes_after_0500_pm_Linear +util_Arrival_Constant_Shift_for_every_30_minutes_after_0500_pm_Square_Root,UNIVERSITY - Arrival Constant: Shift for every 30 minutes after 05:00 pm - Square Root,"@((df.end>28)) *(((np.minimum(24-df.end,12)*(df.end<24)) + (np.minimum(df.end-28,19)*(df.end>28))) ** 0.5)",coef_Arrival_Constant_Shift_for_every_30_minutes_after_0500_pm_Square_Root +util_Duration_Constant_Shift_for_every_30_minutes_less_than_4p5_hrs_Square_Root,UNIVERSITY - Duration Constant: Shift for every 30 minutes less than 4.5 hrs - Square Root,"@((df.duration<9))*((np.minimum(9-df.duration,7)*(df.duration<9)) + (np.minimum(df.duration-11,25)*(df.duration>11)))",coef_Duration_Constant_Shift_for_every_30_minutes_less_than_4p5_hrs_Square_Root +util_Duration_Constant_4p5_hours_or_less,UNIVERSITY - Duration Constant: 4.5 hours or less,@(df.duration<10),coef_Duration_Constant_4p5_hours_or_less +util_Duration_Constant_5_hours,UNIVERSITY - Duration Constant: 5 hours,@(df.duration==10),coef_Duration_Constant_5_hours +util_Duration_Constant_5p5_hours_or_more,UNIVERSITY - Duration Constant: 5.5 hours or more,@(df.duration>10),coef_Duration_Constant_5p5_hours_or_more +util_Duration_Constant_Shift_for_every_30_minutes_more_than_5p5_hrs_Linear,UNIVERSITY - Duration Constant: Shift for every 30 minutes more than 5.5 hrs - Linear,"@((df.duration>11))*(((np.minimum(9-df.duration,7)*(df.duration<9)) + (np.minimum(df.duration-11,25)*(df.duration>11))) ** 0.5)",coef_Duration_Constant_Shift_for_every_30_minutes_more_than_5p5_hrs_Linear diff --git a/resident/configs/tour_scheduling_university_coeffs.csv b/resident/configs/tour_scheduling_university_coeffs.csv new file mode 100644 index 0000000..1472dfb --- /dev/null +++ b/resident/configs/tour_scheduling_university_coeffs.csv @@ -0,0 +1,42 @@ +coefficient_name,value,constrain +coef_Mode_Choice_Logsum,0.384091138,F +coef_Low_income_lt25000_Departure_before_800_am_Linear,0.246389489,F +coef_Low_income_lt25000_Duration_lt_4hrs,-0.262288853,F +coef_Medium_high_income_60k_to_120k_Departure_after_830_am_Linear,-0.039079271,F +coef_Medium_high_income_60k_to_120k_Duration_gt_4hrs,-0.041536976,F +coef_High_income_120k_plus_Departure_after_830_am_Linear,-0.039306518,F +coef_Age_41_plus_Departure_after_830_am_Linear,0.055344625,F +coef_Age_41_plus_Durationlt_4_hrs_Linear,-0.152498075,F +coef_Distance_to_destination_Departure_before_800_am_Linear,0.006869786,F +coef_Distance_to_destination_Departure_after_830_am_Linear,0.003686402,F +coef_Distance_to_destination_Durationlt_4_hrs_Linear,-0.04027172,F +coef_Distance_to_destination_Durationgt_4_hrs_Linear,0.003803244,F +coef_Distance_to_destination_Durationlt_4_hrs_Square_Root,0.041070113,F +coef_Subsequent_tour_is_work_tour_Departure_after_830_am,-0.29166292,F +coef_Subsequent_tour_is_work_tour_Duration_lt_4_hours,-0.482292817,F +coef_Subsequent_tour_is_work_tour_Duration_gt_4_hours,-0.364624965,F +coef_Subsequent_tour_is_school_tour_Departure_after_830_am,-0.286206955,F +coef_Subsequent_tour_is_school_tour_Duration_lt_4_hours,0.30341795,F +coef_Subsequent_tour_is_school_tour_Duration_gt_4_hours,-0.247436221,F +coef_Second_tour_of_two_mandatory_tours_Duration_lt_4_hours,-0.211059285,F +coef_Second_tour_of_two_mandatory_tours_Duration_gt_4_hours,-0.35316727,F +coef_Departure_Constant_Shift_for_every_30_minutes_before_0700_am_Linear,-0.947594485,F +coef_Departure_Constant_Before_0730_AM,-0.296228472,F +coef_Departure_Constant_0730_AM_0800_AM,-0.650538708,F +coef_Departure_Constant_0800_AM_0830_AM,0,T +coef_Departure_Constant_0830_AM_0900_AM,-0.525569176,F +coef_Departure_Constant_After_0900_AM,-0.536008149,F +coef_Departure_Constant_Shift_for_every_30_minutes_after_0930_am_Square_Root,-0.500045988,F +coef_Arrival_Constant_Shift_for_every_30_minutes_before_0230_pm_Linear,-0.209375282,F +coef_Arrival_Constant_Before_0300_PM,-0.962572172,F +coef_Arrival_Constant_0300_PM_0330_PM,-0.627901132,F +coef_Arrival_Constant_0330_PM_0400_PM,0,T +coef_Arrival_Constant_0400_PM_0430_PM,-0.190818088,F +coef_Arrival_Constant_After_0430_PM,-0.66545038,F +coef_Arrival_Constant_Shift_for_every_30_minutes_after_0500_pm_Linear,-0.209562151,F +coef_Arrival_Constant_Shift_for_every_30_minutes_after_0500_pm_Square_Root,0.503497689,F +coef_Duration_Constant_Shift_for_every_30_minutes_less_than_4p5_hrs_Square_Root,0.225706446,F +coef_Duration_Constant_4p5_hours_or_less,0.03106769,F +coef_Duration_Constant_5_hours,0,T +coef_Duration_Constant_5p5_hours_or_more,0.343447232,F +coef_Duration_Constant_Shift_for_every_30_minutes_more_than_5p5_hrs_Linear,-0.115312573,F diff --git a/resident/configs/tour_scheduling_work.csv b/resident/configs/tour_scheduling_work.csv new file mode 100644 index 0000000..ce63a8c --- /dev/null +++ b/resident/configs/tour_scheduling_work.csv @@ -0,0 +1,103 @@ +Label,Description,Expression,Coefficient +# writing out varibles we may need for estimation,,, +round_trip_auto_time_to_work,round_trip_auto_time_to_work,@df.roundtrip_auto_time_to_work,zero_coef +age,age,@df.age,zero_coef +util_Mode_Choice_Logsum,Mode Choice Logsum,mode_choice_logsum,coef_Mode_Choice_Logsum +util_Female_Departure_before_7_am,Female - Departure before 7:00 am - Linear,@((df.female) & (df.start < df.departureRefBin)) * df.departureLinearShift1,coef_Female_Departure_before_7_am +util_Female_Arrival_after_6_pm,Female - Arrival after 6:00 pm - Linear,@((df.female) & (df.end > df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Female_Arrival_after_6_pm +util_Female_with_preschool_child_Departure_before_7_am,Female with preschool child - Departure before 7:00 am - Linear,@((df.female) & (df.is_pre_drive_child_in_HH) & (df.start < df.departureRefBin)) * df.departureLinearShift1,coef_Female_with_preschool_child_Departure_before_7_am +util_Female_with_preschool_child_Departure_after_7_am,Female with preschool child - Departure after 7:30 am - Linear,@((df.female) & (df.is_pre_drive_child_in_HH) & (df.start > df.departureRefBin)) * df.departureLinearShift1,coef_Female_with_preschool_child_Departure_after_7_am +util_Female_with_preschool_child_Arrival_after_6_pm,Female with preschool child - Arrival after 6:00 pm - Linear,@((df.female) & (df.is_pre_drive_child_in_HH) & (df.end > df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Female_with_preschool_child_Arrival_after_6_pm +util_Low_income_lt_25000_Departure_before_7_am,Low income (<25000) - Departure before 7:00 am - Linear,@((df.is_income_less25K) & (df.start < df.departureRefBin)) * df.departureLinearShift1,coef_Low_income_lt_25000_Departure_before_7_am +util_Low_income_lt_25000_Departure_after_7_am,Low income (<25000) - Departure after 7:30 am - Linear,@((df.is_income_less25K) & (df.start > df.departureRefBin)) * df.departureLinearShift1,coef_Low_income_lt_25000_Departure_after_7_am +util_Low_income_lt_25000_Arrival_after_6_pm,Low income (<25000) - Arrival after 6:00 pm - Linear,@((df.is_income_less25K) & (df.start>30)) * df.arrivalLinearShift1,coef_Low_income_lt_25000_Arrival_after_6_pm +util_Med_income_25k_to_60k_Departure_before_7_am,Med income (25k to 60k) - Departure before 7:00 am - Linear,@((df.is_income_25K_to_60K) & (df.start < df.departureRefBin)) * df.departureLinearShift1,coef_Med_income_25k_to_60k_Departure_before_7_am +util_Med_income_25k_to_60k_Arrival_after_6_pm,Med income (25k to 60k) - Arrival after 6:00 pm - Linear,@((df.is_income_25K_to_60K) & (df.end > df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Med_income_25k_to_60k_Arrival_after_6_pm +util_Medhigh_income_60k_to_120k_Departure_before_7_am,Med-high income (60k to 120k) - Departure before 7:00 am - Linear,@((df.is_income_60K_to_120K) & (df.start < df.departureRefBin)) * df.departureLinearShift1,coef_Medhigh_income_60k_to_120k_Departure_before_7_am +util_Age_16_to_18_yrs_Departure_Before_7_am,Age 16 to 18 yrs - Departure Before 7:00 am,@(((df.age>=16) & (df.age<=18)) & (df.start < df.departureRefBin)) * df.departureLinearShift1,coef_Age_16_to_18_yrs_Departure_Before_7_am +util_Age_16_to_18_yrs_Departure_After_7_am,Age 16 to 18 yrs - Departure After 7:30 am,@(((df.age>=16) & (df.age<=18)) & (df.start > df.departureRefBin)) * df.departureLinearShift1,coef_Age_16_to_18_yrs_Departure_After_7_am +util_Age_19_to_24_yrs_Departure_After_7_am,Age 19 to 24 yrs - Departure After 7:30 am,@(((df.age>=19) & (df.age<=24)) & (df.start > df.departureRefBin)) * df.departureLinearShift1,coef_Age_19_to_24_yrs_Departure_After_7_am +util_Age_25_to_40_yrs_Departure_Before_7_am,Age 25 to 40 yrs - Departure Before 7:00 am,@(((df.age>=25) & (df.age<=40)) & (df.start < df.departureRefBin)) * df.departureLinearShift1,coef_Age_25_to_40_yrs_Departure_Before_7_am +util_Age_65_plus_yrs_Departure_After_7_am,Age 65+ yrs - Departure After 7:30 am,@((df.age>=65) & (df.start > df.departureRefBin)) * df.departureLinearShift1,coef_Age_65_plus_yrs_Departure_After_7_am +util_Age_19_to_24_yrs_Arrival_after_6_pm,Age 19 to 24 yrs - Arrival after 6:00 pm ,@(((df.age>=19) & (df.age<=24)) & (df.end > df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Age_19_to_24_yrs_Arrival_after_6_pm +util_Age_25_to_40_yrs_Arrival_before_5_pm,Age 25 to 40 yrs - Arrival before 5:30 pm ,@(((df.age>=25) & (df.age<=40)) & (df.end < df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Age_25_to_40_yrs_Arrival_before_5_pm +util_Age_56_to_64_yrs_Arrival_after_6_pm,Age 56 to 64 yrs - Arrival after 6:00 pm ,@(((df.age>=56) & (df.age<65)) & (df.end > df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Age_56_to_64_yrs_Arrival_after_6_pm +util_Age_65_plus_yrs_Arrival_before_5_pm,Age 65+ yrs - Arrival before 5:30 pm ,@((df.age>=65) & (df.end < df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Age_65_plus_yrs_Arrival_before_5_pm +util_Age_65_plus_yrs_Arrival_after_6_pm,Age 65+ yrs - Arrival after 6:00 pm ,@((df.age>=65) & (df.end > df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Age_65_plus_yrs_Arrival_after_6_pm +util_Zero_auto_HH_Departure_before_7_am,Zero auto HH - Departure before 7:00 am - Linear,@((df.auto_ownership == 0) & (df.start < df.departureRefBin)) * df.departureLinearShift1,coef_Zero_auto_HH_Departure_before_7_am +util_Zero_auto_HH_Arrival_after_6_pm,Zero auto HH - Arrival after 6:00 pm - Linear,@((df.auto_ownership == 0) & (df.end > df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Zero_auto_HH_Arrival_after_6_pm +util_Parttime_worker_Departure_before_7_am,Part-time worker - Departure before 7:00 am - Linear,@((df.ptype==2) & (df.start < df.departureRefBin)) * df.departureLinearShift1,coef_Parttime_worker_Departure_before_7_am +util_Parttime_worker_Departure_after_7_am,Part-time worker - Departure after 7:30 am - Linear,@((df.ptype==2) & (df.start > df.departureRefBin)) * df.departureLinearShift1,coef_Parttime_worker_Departure_after_7_am +util_Parttime_worker_Arrival_before_5_pm,Part-time worker - Arrival before 5:30 pm - Linear,@((df.ptype==2) & (df.end < df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Parttime_worker_Arrival_before_5_pm +util_Parttime_worker_Arrival_after_6_pm,Part-time worker - Arrival after 6:00 pm - Linear,@((df.ptype==2) & (df.end > df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Parttime_worker_Arrival_after_6_pm +util_University_student_Departure_after_7_am,University student - Departure after 7:30 am - Linear,@((df.ptype==3) & (df.start > df.departureRefBin)) * df.departureLinearShift1,coef_University_student_Departure_after_7_am +util_University_student_Arrival_before_5_pm,University student - Arrival before 5:30 pm - Linear,@((df.ptype==3) & (df.end < df.arrivalRefBin)) * df.arrivalLinearShift1,coef_University_student_Arrival_before_5_pm +util_University_student_Arrival_after_6_pm,University student - Arrival after 6:00 pm - Linear,@((df.ptype==3) & (df.end > df.arrivalRefBin)) * df.arrivalLinearShift1,coef_University_student_Arrival_after_6_pm +#util_Blue_collar_Departure_before_7_am,#Blue collar - Departure before 7:00 am - Linear,@((df.work_segment==5) & (df.start < df.departureRefBin)) * df.departureLinearShift1,coef_Blue_collar_Departure_before_7_am +#util_Blue_collar_Departure_after_7_am,#Blue collar - Departure after 7:30 am - Linear,@((df.work_segment==5)& (df.start > df.departureRefBin)) * df.departureLinearShift1,coef_Blue_collar_Departure_after_7_am +#util_Blue_collar_Arrival_before_5_pm,#Blue collar - Arrival before 5:30 pm - Linear,@((df.work_segment==5)& (df.end < df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Blue_collar_Arrival_before_5_pm +#util_Service_Departure_before_7_am,#Service - Departure before 7:00 am - Linear,@((df.work_segment==2) & (df.start < df.departureRefBin)) * df.departureLinearShift1,coef_Service_Departure_before_7_am +#util_Service_Departure_after_7_am,#Service - Departure after 7:30 am - Linear,@((df.work_segment==2) & (df.start > df.departureRefBin)) * df.departureLinearShift1,coef_Service_Departure_after_7_am +#util_Service_Arrival_before_5_pm,#Service - Arrival before 5:30 pm - Linear,@((df.work_segment==2) & (df.end < df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Service_Arrival_before_5_pm +#util_Health_Departure_before_7_am,#Health - Departure before 7:00 am - Linear,@((df.work_segment==3) & (df.start < df.departureRefBin)) * df.departureLinearShift1,coef_Health_Departure_before_7_am +#util_Health_Arrival_after_6_pm,#Health - Arrival after 6:00 pm - Linear,@((df.work_segment==3) & (df.end > df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Health_Arrival_after_6_pm +#util_Retail_and_food_Departure_after_7_am,#Retail and food - Departure after 7:30 am - Linear,@((df.work_segment==4) & (df.start > df.departureRefBin)) * df.departureLinearShift1,coef_Retail_and_food_Departure_after_7_am +#util_Retail_and_food_Arrival_before_5_pm,#Retail and food - Arrival before 5:30 pm - Linear,@((df.work_segment==4) & (df.end < df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Retail_and_food_Arrival_before_5_pm +#util_Retail_and_food_Arrival_after_6_pm,#Retail and food - Arrival after 6:00 pm - Linear,@((df.work_segment==4) & (df.end > df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Retail_and_food_Arrival_after_6_pm +util_Time_SOV_freeflowto_destination_Departure_before_7_am,Time (SOV freeflow) to destination - Departure before 7:00 am - Linear,@(df.start < df.departureRefBin) * df.departureLinearShift1* (df.roundtrip_auto_time_to_work),coef_Time_SOV_freeflowto_destination_Departure_before_7_am +util_Time_SOV_freeflowto_destination_Departure_after_7_am,Time (SOV freeflow) to destination - Departure after 7:30 am - Linear,@(df.start > df.departureRefBin) * df.departureLinearShift1 * (df.roundtrip_auto_time_to_work),coef_Time_SOV_freeflowto_destination_Departure_after_7_am +util_Time_SOV_freeflowto_destination_Arrival_before_5_pm,Time (SOV freeflow) to destination - Arrival before 5:30 pm - Linear,@(df.end < df.arrivalRefBin) * df.arrivalLinearShift1 * (df.roundtrip_auto_time_to_work),coef_Time_SOV_freeflowto_destination_Arrival_before_5_pm +util_Time_SOV_freeflowto_destination_Arrival_after_6_pm,Time (SOV freeflow) to destination - Arrival after 6:00 pm - Linear,@(df.end > df.arrivalRefBin) * df.arrivalLinearShift1 * (df.roundtrip_auto_time_to_work),coef_Time_SOV_freeflowto_destination_Arrival_after_6_pm +util_Presence_of_NonWorking_Adult_in_the_HH_Departure_before_7_am,Presence of Non-Working Adult in the HH - Departure before 7:00 am - Linear,@((df.is_non_worker_in_HH) & (df.start < df.departureRefBin)) * df.departureLinearShift1,coef_Presence_of_NonWorking_Adult_in_the_HH_Departure_before_7_am +util_Presence_of_NonWorking_Adult_in_the_HH_Arrival_before_5_pm,Presence of Non-Working Adult in the HH - Arrival before 5:30 pm - Linear,@((df.is_non_worker_in_HH) & (df.end < df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Presence_of_NonWorking_Adult_in_the_HH_Arrival_before_5_pm +util_Presence_of_PreDriving_Age_Children_in_the_HH_Departure_before_7_am,Presence of Pre-Driving Age Children in the HH - Departure before 7:30 am - Linear,@((df.is_pre_drive_child_in_HH) & (df.start < df.departureRefBin)) * df.departureLinearShift1,coef_Presence_of_PreDriving_Age_Children_in_the_HH_Departure_before_7_am +util_Presence_of_PreDriving_Age_Children_in_the_HH_Departure_after_8_am,Presence of Pre-Driving Age Children in the HH - Departure after 8 am - Linear,@((df.is_pre_drive_child_in_HH) & (df.start > df.departureRefBin)) * df.departureLinearShift1,coef_Presence_of_PreDriving_Age_Children_in_the_HH_Departure_after_8_am +util_Presence_of_PreDriving_Age_Children_in_the_HH_Arrival_before_5_pm,Presence of Pre-Driving Age Children in the HH - Arrival before 5:30 pm - Linear,@((df.is_pre_drive_child_in_HH) & (df.end < df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Presence_of_PreDriving_Age_Children_in_the_HH_Arrival_before_5_pm +util_Presence_of_PreDriving_Age_Children_in_the_HH_Arrival_after_6_pm,Presence of Pre-Driving Age Children in the HH - Arrival after 6:00 pm - Linear,@((df.is_pre_drive_child_in_HH)& (df.end > df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Presence_of_PreDriving_Age_Children_in_the_HH_Arrival_after_6_pm +util_First_of_2_plus_mandatory_tour_Departure_before_7_am,First of 2+ mandatory tour - Departure before 7:00 am,@(((df.tour_count>1) & (df.tour_num == 1)) & (df.start < df.departureRefBin)) * df.departureLinearShift1,coef_First_of_2_plus_mandatory_tour_Departure_before_7_am +util_First_of_2_plus_mandatory_tour_Departure_after_7_am,First of 2+ mandatory tour - Departure after 7:30 am,@(((df.tour_count>1) & (df.tour_num == 1)) & (df.start > df.departureRefBin)) * df.departureLinearShift1,coef_First_of_2_plus_mandatory_tour_Departure_after_7_am +util_First_of_2_plus_mandatory_tour_Duration_lt_dur_ref,First of 2+ mandatory tour - Duration < 9.5 hours,@(((df.tour_count>1) & (df.tour_num == 1)) & (df.end < df.durationRefBin)) * df.durationShift,coef_First_of_2_plus_mandatory_tour_Duration_lt_dur_ref +util_First_of_2_plus_mandatory_tour_Duration_gt_dur_ref,First of 2+ mandatory tour - Duration > 9.5 hours,@(((df.tour_count>1) & (df.tour_num == 1)) & (df.end > df.durationRefBin)) * df.durationShift,coef_First_of_2_plus_mandatory_tour_Duration_gt_dur_ref +util_2nd_or_later_of_2_plus_mandatory_tour_Departure_before_1_pm,2nd or later of 2+ mandatory tour - Departure before 1:30 pm,@(((df.tour_count>1) & (df.tour_num > 1)) & (df.start<22)) * (((22-df.start)*(df.start<22)) + ((df.start-22)*(df.start>22))),coef_2nd_or_later_of_2_plus_mandatory_tour_Departure_before_1_pm +util_2nd_or_later_of_2_plus_mandatory_tour_Departure_after_2_pm,2nd or later of 2+ mandatory tour - Departure after 2:00 pm,@(((df.tour_count>1) & (df.tour_num > 1)) & (df.start>22)) * (((22-df.start)*(df.start<22)) + ((df.start-22)*(df.start>22))),coef_2nd_or_later_of_2_plus_mandatory_tour_Departure_after_2_pm +util_2nd_or_later_of_2_plus_mandatory_tour_Duration_lt_dur_ref,2nd or later of 2+ mandatory tour - Duration < 9.5 hours,@(((df.tour_count>1) & (df.tour_num > 1)) & (df.end < df.durationRefBin)) * df.durationShift,coef_2nd_or_later_of_2_plus_mandatory_tour_Duration_lt_dur_ref +util_2nd_or_later_of_2_plus_mandatory_tour_Duration_gt_dur_ref,2nd or later of 2+ mandatory tour - Duration > 9.5 hours,@(((df.tour_count>1) & (df.tour_num > 1)) & (df.end > df.durationRefBin)) * df.durationShift,coef_2nd_or_later_of_2_plus_mandatory_tour_Duration_gt_dur_ref +#,#Departure Constants,,coef_Departure_Constants +util_Departure_Constant_Shift_for_every_30_minutes_before_6_am,Departure Constant: Shift for every 30 minutes before 6:00 am - Linear,"@((df.start<7) * (7-df.start))",coef_Departure_Constant_Shift_for_every_30_minutes_before_6_am +util_Departure_Constant_Before_06_AM,Departure Constant: Before 06:00 AM,start<7,coef_Departure_Constant_Before_06_AM +util_Departure_Constant_06_AM_06_AM_7,Departure Constant: 06:00 AM - 06:30 AM (7) ,start==7,coef_Departure_Constant_06_AM_06_AM_7 +util_Departure_Constant_06_AM_07_AM_8,Departure Constant: 06:30 AM - 07:00 AM (8) ,start==8,coef_Departure_Constant_06_AM_07_AM_8 +util_Departure_Constant_07_AM_07_AM_9,Departure Constant: 07:00 AM - 07:30 AM (9) ,start==9,coef_Departure_Constant_07_AM_07_AM_9 +util_Departure_Constant_07_AM_08_AM_10,Departure Constant: 07:30 AM - 08:00 AM (10) ,start==10,coef_Departure_Constant_07_AM_08_AM_10 +util_Departure_Constant_08_AM_08_AM_11,Departure Constant: 08:00 AM - 08:30 AM (11) ,start==11,coef_Departure_Constant_08_AM_08_AM_11 +util_Departure_Constant_08_AM_09_AM_12,Departure Constant: 08:30 AM - 09:00 AM (12) ,start==12,coef_Departure_Constant_08_AM_09_AM_12 +util_Departure_Constant_After_09_AM,Departure Constant: After 09:00 AM,start>12,coef_Departure_Constant_After_09_AM +util_Departure_Constant_Shift_for_every_30_minutes_after_9_am_Square_Root,Departure Constant: Shift for every 30 minutes after 9:30 am - Square Root,"@((df.start>13) * (np.maximum(df.start-13,0) ** 0.5))",coef_Departure_Constant_Shift_for_every_30_minutes_after_9_am_Square_Root +#,#Arrival Constants,,coef_Arrival_Constants +util_Arrival_Constant_Shift_for_every_30_minutes_before_3_pm,Arrival Constant: Shift for every 30 minutes before 3:00 pm - Linear,"@((df.end<25) * (25-df.end))",coef_Arrival_Constant_Shift_for_every_30_minutes_before_3_pm +util_Arrival_Constant_Before_03_PM,Arrival Constant: Before 03:30 PM,end<26,coef_Arrival_Constant_Before_03_PM +util_Arrival_Constant_03_PM_04_PM_26,Arrival Constant: 03:30 PM - 04:00 PM (26) ,end==26,coef_Arrival_Constant_03_PM_04_PM_26 +util_Arrival_Constant_04_PM_04_PM_27,Arrival Constant: 04:00 PM - 04:30 PM (27) ,end==27,coef_Arrival_Constant_04_PM_04_PM_27 +util_Arrival_Constant_04_PM_05_PM_28,Arrival Constant: 04:30 PM - 05:00 PM (28) ,end==28,coef_Arrival_Constant_04_PM_05_PM_28 +util_Arrival_Constant_05_PM_05_PM_29,Arrival Constant: 05:00 PM - 05:30 PM (29),end==29,coef_Arrival_Constant_05_PM_05_PM_29 +util_Arrival_Constant_05_PM_06_PM_30,Arrival Constant: 05:30 PM - 06:00 PM (30) ,end==30,coef_Arrival_Constant_05_PM_06_PM_30 +util_Arrival_Constant_06_PM_06_PM_31,Arrival Constant: 06:00 PM - 06:30 PM (31) ,end==31,coef_Arrival_Constant_06_PM_06_PM_31 +util_Arrival_Constant_06_PM_7_PM_32,Arrival Constant: 06:30 PM - 7:00 PM (32) ,end==32,coef_Arrival_Constant_06_PM_7_PM_32 +util_Arrival_Constant_7_PM_7_PM_33,Arrival Constant: 7:00 PM - 7:30 PM (33) ,end==33,coef_Arrival_Constant_7_PM_7_PM_33 +util_Arrival_Constant_7_PM_8_PM_34,Arrival Constant: 7:30 PM - 8:00 PM (34) ,end==34,coef_Arrival_Constant_7_PM_8_PM_34 +util_Arrival_Constant_After_08_PM,Arrival Constant: After 08:00 PM,end>34,coef_Arrival_Constant_After_08_PM +util_Arrival_Constant_Shift_for_every_30_minutes_after_830_pm_Square_root,Arrival Constant: Shift for every 30 minutes after 830 pm - Square root,"@((df.end>35) * (np.maximum(df.end-35,0) ** 0.5))",coef_Arrival_Constant_Shift_for_every_30_minutes_after_830_pm_Square_root +#,#Duration Constants,,coef_Duration_Constants +util_Duration_Constant_Shift_for_every_30_minutes_less_than_8p5_hrs,Duration Constant: Shift for every 30 minutes less than 8.5 hrs - Linear,"@((df.duration<17) * (17-df.duration))",coef_Duration_Constant_Shift_for_every_30_minutes_less_than_8p5_hrs +util_Duration_Constant_Shorter_than_8p5_hrs,Duration Constant: Shorter than 8.5 hrs,duration<17,coef_Duration_Constant_Shorter_than_8p5_hrs +util_Duration_Constant_8p5_hours,Duration Constant: 8.5 hours,duration==17,coef_Duration_Constant_8p5_hours +util_Duration_Constant_9_hours,Duration Constant: 9 hours,duration==18,coef_Duration_Constant_9_hours +util_Duration_Constant_9p5_hours,Duration Constant: 9.5 hours,duration==19,coef_Duration_Constant_9p5_hours +util_Duration_Constant_10_hours,Duration Constant: 10 hours,duration==20,coef_Duration_Constant_10_hours +util_Duration_Constant_10p5_hours,Duration Constant: 10.5 hours,duration==21,coef_Duration_Constant_10p5_hours +util_Duration_Constant_11_hours,Duration Constant: 11 hours,duration==22,coef_Duration_Constant_11_hours +util_Duration_Constant_11p5_hours,Duration Constant: 11.5 hours,duration==23,coef_Duration_Constant_11p5_hours +util_Duration_Constant_12_hours,Duration Constant: 12 hours,duration==24,coef_Duration_Constant_12_hours +util_Duration_Constant_Longer_than_12_hrs,Duration Constant: Longer than 12 hrs,duration>24,coef_Duration_Constant_Longer_than_12_hrs +util_Duration_Constant_Shift_for_every_30_minutes_more_than_12_hrs,Duration Constant: Shift for every 30 minutes more than 12 hrs - Linear,"@((df.duration>24) * (df.duration-24))",coef_Duration_Constant_Shift_for_every_30_minutes_more_than_12_hrs +util_Calibration_constant_Duration_0,Calibration constant: Duration = 0,duration == 0,coef_Calibration_constant_Duration_0 diff --git a/resident/configs/tour_scheduling_work_coeffs.csv b/resident/configs/tour_scheduling_work_coeffs.csv new file mode 100644 index 0000000..e8d9607 --- /dev/null +++ b/resident/configs/tour_scheduling_work_coeffs.csv @@ -0,0 +1,98 @@ +coefficient_name,value,constrain +coef_Mode_Choice_Logsum,0.2,T +coef_Female_Departure_before_7_am,-0.21983073617010354,F +coef_Female_Arrival_after_6_pm,-0.028931718675078898,F +coef_Female_with_preschool_child_Departure_before_7_am,0.0,F +coef_Female_with_preschool_child_Departure_after_7_am,0.0,F +coef_Female_with_preschool_child_Arrival_after_6_pm,0.0,F +coef_Low_income_lt_25000_Departure_before_7_am,0.23142273676542863,F +coef_Low_income_lt_25000_Departure_after_7_am,0.06394624853227485,F +coef_Low_income_lt_25000_Arrival_after_6_pm,-0.2101481937242358,F +coef_Med_income_25k_to_60k_Departure_before_7_am,0.20582925253502693,F +coef_Med_income_25k_to_60k_Arrival_after_6_pm,0.030759330025131475,F +coef_Medhigh_income_60k_to_120k_Departure_before_7_am,0.12339331900370523,F +coef_Age_16_to_18_yrs_Departure_Before_7_am,0.0,F +coef_Age_16_to_18_yrs_Departure_After_7_am,0.1569218088558017,F +coef_Age_19_to_24_yrs_Departure_After_7_am,0.03280621940285348,F +coef_Age_25_to_40_yrs_Departure_Before_7_am,-0.14071003044101923,F +coef_Age_65_plus_yrs_Departure_After_7_am,0.0,F +coef_Age_19_to_24_yrs_Arrival_after_6_pm,0.03801692388513126,F +coef_Age_25_to_40_yrs_Arrival_before_5_pm,-0.028091109013424525,F +coef_Age_56_to_64_yrs_Arrival_after_6_pm,0.0,F +coef_Age_65_plus_yrs_Arrival_before_5_pm,0.0,F +coef_Age_65_plus_yrs_Arrival_after_6_pm,0.0,F +coef_Zero_auto_HH_Departure_before_7_am,0.0,F +coef_Zero_auto_HH_Arrival_after_6_pm,0.054809482440729494,F +coef_Parttime_worker_Departure_before_7_am,-0.465300506980724,F +coef_Parttime_worker_Departure_after_7_am,0.24035601017298427,F +coef_Parttime_worker_Arrival_before_5_pm,0.27263218681666973,F +coef_Parttime_worker_Arrival_after_6_pm,-0.2533496036708649,F +coef_University_student_Departure_after_7_am,0.10708190818422741,F +coef_University_student_Arrival_before_5_pm,0.0,F +coef_University_student_Arrival_after_6_pm,-0.0784130766004373,F +coef_Blue_collar_Departure_before_7_am,0.327242475,F +coef_Blue_collar_Departure_after_7_am,0.047214248,F +coef_Blue_collar_Arrival_before_5_pm,0.04197056,F +coef_Service_Departure_before_7_am,0.117783508,F +coef_Service_Departure_after_7_am,0.081611629,F +coef_Service_Arrival_before_5_pm,0.0,T +coef_Health_Departure_before_7_am,0.135275931,F +coef_Health_Arrival_after_6_pm,0.062010123,F +coef_Retail_and_food_Departure_after_7_am,0.076302969,F +coef_Retail_and_food_Arrival_before_5_pm,0.052905387,F +coef_Retail_and_food_Arrival_after_6_pm,0.0270691939999999,F +coef_Time_SOV_freeflowto_destination_Departure_before_7_am,0.006152588569993294,F +coef_Time_SOV_freeflowto_destination_Departure_after_7_am,-0.002638509520279399,F +coef_Time_SOV_freeflowto_destination_Arrival_before_5_pm,-0.003924739241949497,F +coef_Time_SOV_freeflowto_destination_Arrival_after_6_pm,0.0016423390591779922,F +coef_Presence_of_NonWorking_Adult_in_the_HH_Departure_before_7_am,0.0,F +coef_Presence_of_NonWorking_Adult_in_the_HH_Arrival_before_5_pm,0.0,F +coef_Presence_of_PreDriving_Age_Children_in_the_HH_Departure_before_7_am,-0.08289214126546349,F +coef_Presence_of_PreDriving_Age_Children_in_the_HH_Departure_after_8_am,0.0,F +coef_Presence_of_PreDriving_Age_Children_in_the_HH_Arrival_before_5_pm,0.0,F +coef_Presence_of_PreDriving_Age_Children_in_the_HH_Arrival_after_6_pm,-0.04292555821779037,F +coef_First_of_2_plus_mandatory_tour_Departure_before_7_am,-0.15631713590985458,F +coef_First_of_2_plus_mandatory_tour_Departure_after_7_am,-0.037321682863975456,F +coef_First_of_2_plus_mandatory_tour_Duration_lt_dur_ref,0.3054062365147087,F +coef_First_of_2_plus_mandatory_tour_Duration_gt_dur_ref,0.15794972916369113,F +coef_2nd_or_later_of_2_plus_mandatory_tour_Departure_before_1_pm,0.0,F +coef_2nd_or_later_of_2_plus_mandatory_tour_Departure_after_2_pm,-0.13397035652802908,F +coef_2nd_or_later_of_2_plus_mandatory_tour_Duration_lt_dur_ref,0.45835848769473103,F +coef_2nd_or_later_of_2_plus_mandatory_tour_Duration_gt_dur_ref,0.5272527491008135,F +coef_Departure_Constant_Shift_for_every_30_minutes_before_6_am,-0.7231020452773588,F +coef_Departure_Constant_Before_06_AM,-0.1915960040171813,F +coef_Departure_Constant_06_AM_06_AM_7,-0.5420940244335356,F +coef_Departure_Constant_06_AM_07_AM_8,-0.0814829941363625,F +coef_Departure_Constant_07_AM_07_AM_9,0.0,T +coef_Departure_Constant_07_AM_08_AM_10,-0.12487604386728021,F +coef_Departure_Constant_08_AM_08_AM_11,-0.4486786777966309,F +coef_Departure_Constant_08_AM_09_AM_12,-0.9838612097965408,F +coef_Departure_Constant_After_09_AM,-1.5618817419634867,F +coef_Departure_Constant_Shift_for_every_30_minutes_after_9_am_Square_Root,-0.5783447729979645,F +coef_Arrival_Constant_Shift_for_every_30_minutes_before_3_pm,-0.20009504631293196,F +coef_Arrival_Constant_Before_03_PM,-0.8629231486548913,F +coef_Arrival_Constant_03_PM_04_PM_26,-0.644880302920333,F +coef_Arrival_Constant_04_PM_04_PM_27,-0.3047263117628279,F +coef_Arrival_Constant_04_PM_05_PM_28,-0.1886361914686483,F +coef_Arrival_Constant_05_PM_05_PM_29,0.0,F +coef_Arrival_Constant_05_PM_06_PM_30,0.2500583541484266,T +coef_Arrival_Constant_06_PM_06_PM_31,0.23990107930944812,F +coef_Arrival_Constant_06_PM_7_PM_32,0.15526238473246975,F +coef_Arrival_Constant_7_PM_7_PM_33,0.0,F +coef_Arrival_Constant_7_PM_8_PM_34,-0.18208024865786762,F +coef_Arrival_Constant_After_08_PM,0.0,F +coef_Arrival_Constant_Shift_for_every_30_minutes_after_830_pm_Square_root,0.0,F +coef_Duration_Constant_Shift_for_every_30_minutes_less_than_8p5_hrs,-0.10663128725700759,F +coef_Duration_Constant_Shorter_than_8p5_hrs,0.0,F +coef_Duration_Constant_8p5_hours,-0.2403003279562287,F +coef_Duration_Constant_9_hours,0.0,F +coef_Duration_Constant_9p5_hours,0.0,F +coef_Duration_Constant_10_hours,0.0,F +coef_Duration_Constant_10p5_hours,-0.3088419298533547,T +coef_Duration_Constant_11_hours,-0.6583531336885191,F +coef_Duration_Constant_11p5_hours,-0.8734017033609268,F +coef_Duration_Constant_12_hours,-1.3551244638084843,F +coef_Duration_Constant_Longer_than_12_hrs,-1.0959910217157498,F +coef_Duration_Constant_Shift_for_every_30_minutes_more_than_12_hrs,-0.4918560543397093,F +coef_Calibration_constant_Duration_0,-1.2069106964427703,F +zero_coef,0.0,T diff --git a/resident/configs/transit_pass_ownership.csv b/resident/configs/transit_pass_ownership.csv new file mode 100644 index 0000000..6d7a25c --- /dev/null +++ b/resident/configs/transit_pass_ownership.csv @@ -0,0 +1,27 @@ +Label,Description,Expression,no_pass,pass +util_pass_asc,Constant,1,0,asc_pass +util_age_19_to_34,Age Group - 19 yrs to 34 yrs,@(df.age >= 19) & (df.age < 35),0,coef_age_19_to_34_pass +util_age_35_to_44,Age Group - 35 yrs to 44 yrs,@(df.age >= 35) & (df.age < 45),0,coef_age_35_to_44_pass +util_age_55_to_64,Age Group - 55 yrs to 64 yrs,@(df.age >= 55) & (df.age < 65),0,coef_age_55_to_64_pass +util_age_65_to_79,Age Group - 65 yrs to 79 yrs,@(df.age >= 65) & (df.age < 80),0,coef_age_65_to_79_pass +util_hhinc_less_15,"Income less than 15,000",@df.income<15000,0,coef_hhinc_less_15 +util_hhinc_15_25,"income between 15,000- 24,999","@df.income.between(15000,24999)",0,coef_hhinc_15_25 +util_hhinc_50_100,"income between 50,000 and 99,999","@df.income.between(50000,99999)",0,coef_hhinc_50_100 +util_hhinc_100_200,"income between 100,000 and 199,999","@df.income.between(100000,199999)",0,coef_hhinc_100_200 +util_hhinc_200_plus,"income greater than 250,000",@df.income>=200000,0,coef_hhinc_200_plus +util_persons_0_4_in_hhld,Persons age 0-4 in hhld,@df.num_young_children,0,coef_persons_0_4_in_hhld_pass +util_persons_5_15_in_hhld,Persons age 5-15 in hhld,@df.num_children_5_to_15,0,coef_persons_5_15_in_hhld_pass +util_zero_autos,zero auto household ownership,@df.auto_ownership==0,0,coef_zero_autos_pass +util_auto_deficient,auto deficient household,@((df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),0,coef_auto_deficient_pass +util_subs,Subsidy offered,@df.transit_pass_subsidy,0,coef_subs_pass +util_inc_adj_transit_pass_cost,Income adjusted transit pass cost,"@df.transit_pass_cost_per_trip / (np.maximum(df.income, 1000) ** df.income_exponent) / 60",0,coef_tripmc_cost_coef +util_parking_cost_at_work,Parking cost at work,@df.exp_daily_work,0,coef_parking_cost_at_work_pass +#util_total_acc_transit,Total accessibility (0.66*PK + 0.34*OP) by transit,@(0.66*df.trPkTotal+0.34*df.trOpTotal),0,coef_total_transit_acc_pass +util_is_univ,University student,@df.pstudent == PSTUDENT_UNIVERSITY,0,coef_is_univ +util_ft_ASC,ptype_ft calibration constant,@df.ptype == 1,0,coef_ft_asc +util_pt_ASC,ptype_pt calibration constant,@df.ptype == 2,0,coef_pt_asc +util_un_ASC,ptype_un calibration constant,@df.ptype == 3,0,coef_un_asc +util_na_ASC,ptype_na calibration constant,@df.ptype == 4,0,coef_na_asc +util_da_ASC,ptype_da calibration constant,@df.ptype == 5,0,coef_da_asc +util_nd_ASC,ptype_nd calibration constant,@df.ptype == 6,0,coef_nd_asc +util_ps_ASC,ptype_ps calibration constant,@df.ptype == 7,0,coef_ps_asc diff --git a/resident/configs/transit_pass_ownership.yaml b/resident/configs/transit_pass_ownership.yaml new file mode 100644 index 0000000..9dc3644 --- /dev/null +++ b/resident/configs/transit_pass_ownership.yaml @@ -0,0 +1,13 @@ + +SPEC: transit_pass_ownership.csv +COEFFICIENTS: transit_pass_ownership_coeffs.csv + +#LOGIT_TYPE: NL +LOGIT_TYPE: MNL + +preprocessor: + SPEC: transit_pass_ownership_preprocessor + DF: choosers + TABLES: + - land_use + - persons \ No newline at end of file diff --git a/resident/configs/transit_pass_ownership_coeffs.csv b/resident/configs/transit_pass_ownership_coeffs.csv new file mode 100644 index 0000000..1b29485 --- /dev/null +++ b/resident/configs/transit_pass_ownership_coeffs.csv @@ -0,0 +1,27 @@ +coefficient_name,value,constrain +asc_pass,-4.1,F +coef_age_19_to_34_pass,0.52,F +coef_age_35_to_44_pass,0.346,F +coef_age_55_to_64_pass,0.384,F +coef_age_65_to_79_pass,0.315,F +coef_auto_deficient_pass,1.21,F +coef_hhinc_100_200,1.17,F +coef_hhinc_15_25,0.336,F +coef_hhinc_200_plus,1.17,F +coef_hhinc_50_100,0.233,F +coef_hhinc_less_15,0.492,F +coef_is_univ,1.22,F +coef_parking_cost_at_work_pass,0.0484,F +coef_persons_0_4_in_hhld_pass,-0.551,F +coef_persons_5_15_in_hhld_pass,-0.578,F +coef_subs_pass,1.49,F +coef_total_transit_acc_pass,0.0692,F +coef_tripmc_cost_coef,-1.25,T +coef_zero_autos_pass,3.32,F +coef_ft_asc,1.310,F +coef_pt_asc,1.364,F +coef_un_asc,0.654,F +coef_na_asc,1.650,F +coef_da_asc,1.000,F +coef_nd_asc,-2.0,F +coef_ps_asc,-2.0,F diff --git a/resident/configs/transit_pass_ownership_preprocessor.csv b/resident/configs/transit_pass_ownership_preprocessor.csv new file mode 100644 index 0000000..b570bd3 --- /dev/null +++ b/resident/configs/transit_pass_ownership_preprocessor.csv @@ -0,0 +1,6 @@ +Description,Target,Expression +# transit pass cost converted to cost per trip and multiplied by trip mode choice cost coefficient in model,, +# assumes 22 working days per month and 2 trips per work day. reduced fare for children and seniors half price.,, +,income_exponent,0.6 +,transit_pass_cost_per_trip,monthly_transit_pass_cost / (22 * 2) +,transit_pass_cost_per_trip,"np.where((df.age >= 65) | (df.age <= 18), transit_pass_cost_per_trip / 2, transit_pass_cost_per_trip)" \ No newline at end of file diff --git a/resident/configs/transit_pass_subsidy.csv b/resident/configs/transit_pass_subsidy.csv new file mode 100644 index 0000000..2082f6e --- /dev/null +++ b/resident/configs/transit_pass_subsidy.csv @@ -0,0 +1,23 @@ +Label,Description,Expression,no_subsidy,subsidy +util_pt,Part-time worker,@df.pemploy==PEMPLOY_PART,0,coef_pt +util_un,University/College,@df.ptype==PTYPE_UNIVERSITY,0,coef_un +util_tr_hh_emp,Household transit accessibility to Employment,@df.trPkTotal,0,coef_tr_hh_emp +util_tr_emp_hh,Employment transit access to HHs,@df.trn_wk_access_hh,0,coef_tr_emp_hh +util_pkcost,Peak parking cost (dollars),@df.exp_daily_work,0,coef_pkcost +util_hhinc_less_15,"Income less than 15,000",@df.income<15000,0,coef_hhinc_less_15 +util_hhinc_100_200,"income between 100,000 and 200,000","@df.income.between(100000,199999)",0,coef_hhinc_100_200 +util_ind_bus_srv,Industry type is business services,@df.naics_code==54,0,coef_ind_bus_srv +util_ind_construct,Industry type is construction,@df.naics_code==23,0,coef_ind_construct +util_ind_edu,Industry type is education,@df.naics_code==61,0,coef_ind_edu +util_ind_food_srv,Industry type is food services,@df.naics_code==722,0,coef_ind_food_srv +util_ind_gov,Industry type is government,@df.naics_code==92,0,coef_ind_gov +util_ind_health,Industry type is healthcare,@df.naics_code==62,0,coef_ind_health +util_ind_mgmt_srv,Industry type is management services,@df.naics_code==55,0,coef_ind_mgmt_srv +util_ind_mil,Industry type is military,@df.naics_code==9000,0,coef_ind_mil +util_ind_retail,Industry type is retail,@df.naics_code==721,0,coef_ind_retail +util_is_student,Student status,@df.is_student,0,coef_is_student +utils_sub_asc,Constant,1,0,coef_sub_asc +util_availability,Availability of transit pass,transit_subsidy_available==False,0,coef_unavailable +util_ft_ASC,ptype_ft calibration constant,@df.ptype == 1,0,coef_ft_asc +util_pt_ASC,ptype_pt calibration constant,@df.ptype == 2,0,coef_pt_asc +util_un_ASC,ptype_un calibration constant,@df.ptype == 3,0,coef_un_asc diff --git a/resident/configs/transit_pass_subsidy.yaml b/resident/configs/transit_pass_subsidy.yaml new file mode 100644 index 0000000..a717359 --- /dev/null +++ b/resident/configs/transit_pass_subsidy.yaml @@ -0,0 +1,14 @@ +SPEC: transit_pass_subsidy.csv +COEFFICIENTS: transit_pass_subsidy_coefficients.csv + +#LOGIT_TYPE: NL +LOGIT_TYPE: MNL + +preprocessor: + SPEC: transit_pass_subsidy_preprocessor + DF: persons + TABLES: + - accessibility + - land_use + +CHOOSER_FILTER_COLUMN_NAME: transit_subsidy_available diff --git a/resident/configs/transit_pass_subsidy_coefficients.csv b/resident/configs/transit_pass_subsidy_coefficients.csv new file mode 100644 index 0000000..a7b3539 --- /dev/null +++ b/resident/configs/transit_pass_subsidy_coefficients.csv @@ -0,0 +1,23 @@ +coefficient_name,value,constrain +coef_hhinc_100_200,-0.34,F +coef_hhinc_less_15,-0.866,F +coef_ind_bus_srv,1.07,F +coef_ind_construct,0.904,F +coef_ind_edu,0.635,F +coef_ind_food_srv,-2.11,F +coef_ind_gov,1.46,F +coef_ind_health,0.895,F +coef_ind_mgmt_srv,0.8,F +coef_ind_mil,1.72,F +coef_ind_retail,-1.59,F +coef_is_student,-0.87,F +coef_pkcost,0.063,F +coef_pt,-0.685,F +coef_sub_asc,-4.19,F +coef_tr_emp_hh,0.0431,F +coef_tr_hh_emp,0.0434,F +coef_un,0.508,F +coef_unavailable,-999.0,F +coef_ft_asc,-0.240,F +coef_pt_asc,-0.250,F +coef_un_asc,0.30,F diff --git a/resident/configs/transit_pass_subsidy_preprocessor.csv b/resident/configs/transit_pass_subsidy_preprocessor.csv new file mode 100644 index 0000000..6f213cb --- /dev/null +++ b/resident/configs/transit_pass_subsidy_preprocessor.csv @@ -0,0 +1,5 @@ +Description,Target,Expression +,_transit_ivt_home_to_work_walk_peak,"skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, ('WTW_TIV', 'AM'))" +,_transit_ivt_home_to_work_drive_peak,"skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, ('KTW_TIV', 'AM'))" +,transit_subsidy_available,((_transit_ivt_home_to_work_walk_peak > 0) | (_transit_ivt_home_to_work_drive_peak > 0)) & (persons.workplace_zone_id > 0) +,trn_wk_access_hh,"np.where(persons.workplace_zone_id > 0, reindex(accessibility.trPkHH, persons.workplace_zone_id), 0)" \ No newline at end of file diff --git a/resident/configs/trip_destination.csv b/resident/configs/trip_destination.csv new file mode 100644 index 0000000..db222c6 --- /dev/null +++ b/resident/configs/trip_destination.csv @@ -0,0 +1,51 @@ +Label,Description,Expression,work,univ,school,escort,shopping,othmaint,eatout,social,othdiscr,atwork +local_dist_od,,"_od_DIST@od_skims[('SOV_M_DIST', 'MD')]",1,1,1,1,1,1,1,1,1,1 +local_dist_dp,,"_dp_DIST@dp_skims[('SOV_M_DIST', 'MD')]",1,1,1,1,1,1,1,1,1,1 +loca_dist_op,,"_op_DIST@op_skims[('SOV_M_DIST', 'MD')]",1,1,1,1,1,1,1,1,1,1 +local_dist_nd,,"_nd_DIST@nd_skims[('SOV_M_DIST', 'MD')]",1,1,1,1,1,1,1,1,1,1 +distance_deviation,distance deviation,_dist_dev@(_od_DIST +_dp_DIST - _op_DIST),1,1,1,1,1,1,1,1,1,1 +dist_deviation_logged,logged deviation distance,"_dist_dev_logged@np.where((_dist_dev > 0), np.log(_dist_dev),0)",1,1,1,1,1,1,1,1,1,1 +#,,,,,,,,,,,, +util_sizeterm,size term,"@np.log1p(size_terms.get(df.dest_taz, df.purpose))",coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one +util_Sampleofalternativescorrectionfactor,Sample of alternatives correction factor,"@np.minimum(np.log(df.pick_count/df.prob), 60)",coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one +util_Modechoicelogsum,Mode choice logsum,od_logsum + dp_logsum,coef_mode_choice_logsum_mandatory,coef_mode_choice_logsum_mandatory,coef_mode_choice_logsum_mandatory,coef_mode_choice_logsum_maint,coef_mode_choice_logsum_maint,coef_mode_choice_logsum_maint,coef_mode_choice_logsum_discr,coef_mode_choice_logsum_discr,coef_mode_choice_logsum_discr,coef_mode_choice_logsum_discr +#,,,,,,,,,,,, +util_DistanceDeviationLinearRelative,Distance Deviation Linear - Relative,"@np.minimum((_dist_dev)/_op_DIST, 100)",,,,coef_DistanceDeviationLinearRelative_maint,coef_DistanceDeviationLinearRelative_maint,coef_DistanceDeviationLinearRelative_maint,coef_DistanceDeviationLinearRelative_discr,coef_DistanceDeviationLinearRelative_discr,coef_DistanceDeviationLinearRelative_discr,coef_DistanceDeviationLinearRelative_discr +util_DistanceDeviationLinearAbsolute,Distance Deviation Linear - Absolute,@(_dist_dev),coef_DistanceDeviationLinearAbsolute_mandatory,coef_DistanceDeviationLinearAbsolute_mandatory,coef_DistanceDeviationLinearAbsolute_mandatory,coef_DistanceDeviationLinearAbsolute_maint,coef_DistanceDeviationLinearAbsolute_maint,coef_DistanceDeviationLinearAbsolute_maint,coef_DistanceDeviationLinearAbsolute_discr,coef_DistanceDeviationLinearAbsolute_discr,coef_DistanceDeviationLinearAbsolute_discr,coef_DistanceDeviationLinearAbsolute_discr +util_LogofDistanceRelativeDeviation,Log of Distance Relative Deviation,"@np.where((_dist_dev > 0), np.log(np.minimum((_dist_dev)/_op_DIST, 100)), 0)",,,,coef_LogofDistanceRelativeDeviation_maint,coef_LogofDistanceRelativeDeviation_maint,coef_LogofDistanceRelativeDeviation_maint,coef_LogofDistanceRelativeDeviation_discr,coef_LogofDistanceRelativeDeviation_discr,coef_LogofDistanceRelativeDeviation_discr,coef_LogofDistanceRelativeDeviation_discr +util_LogofDistanceAbsoluteDeviation,Log of Distance Absolute Deviation,"@(_dist_dev_logged)",coef_LogofDistanceAbsoluteDeviation_mandatory,coef_LogofDistanceAbsoluteDeviation_mandatory,coef_LogofDistanceAbsoluteDeviation_mandatory,coef_LogofDistanceAbsoluteDeviation_maint,coef_LogofDistanceAbsoluteDeviation_maint,coef_LogofDistanceAbsoluteDeviation_maint,coef_LogofDistanceAbsoluteDeviation_discr,coef_LogofDistanceAbsoluteDeviation_discr,coef_LogofDistanceAbsoluteDeviation_discr,coef_LogofDistanceAbsoluteDeviation_discr +util_DistanceDeviationsquaredRelative,Distance Deviation squared - Relative,"@np.power(np.minimum((_dist_dev)/_op_DIST, 100),2)",,,,coef_DistanceDeviationsquaredRelative_maint,coef_DistanceDeviationsquaredRelative_maint,coef_DistanceDeviationsquaredRelative_maint,coef_DistanceDeviationsquaredRelative_discr,coef_DistanceDeviationsquaredRelative_discr,coef_DistanceDeviationsquaredRelative_discr,coef_DistanceDeviationsquaredRelative_discr +util_DistanceDeviationsquaredAbsolute,Distance Deviation squared - Absolute,"@np.power(_dist_dev,2)",coef_DistanceDeviationsquaredAbsolute_mandatory,coef_DistanceDeviationsquaredAbsolute_mandatory,coef_DistanceDeviationsquaredAbsolute_mandatory,coef_DistanceDeviationsquaredAbsolute_maint,coef_DistanceDeviationsquaredAbsolute_maint,coef_DistanceDeviationsquaredAbsolute_maint,coef_DistanceDeviationsquaredAbsolute_discr,coef_DistanceDeviationsquaredAbsolute_discr,coef_DistanceDeviationsquaredAbsolute_discr,coef_DistanceDeviationsquaredAbsolute_discr +util_DistanceDeviation2ndstopofhalftour,Distance Deviation - 2nd stop of half tour,@(df.trip_num==2) * (_dist_dev),coef_DistanceDeviation2ndstopofhalftour_mandatory,coef_DistanceDeviation2ndstopofhalftour_mandatory,coef_DistanceDeviation2ndstopofhalftour_mandatory,coef_DistanceDeviation2ndstopofhalftour_maint,coef_DistanceDeviation2ndstopofhalftour_maint,coef_DistanceDeviation2ndstopofhalftour_maint,coef_DistanceDeviation2ndstopofhalftour_discr,coef_DistanceDeviation2ndstopofhalftour_discr,coef_DistanceDeviation2ndstopofhalftour_discr,coef_DistanceDeviation2ndstopofhalftour_discr +util_DistanceDeviation3rdmorestoponhalftour,Distance Deviation - 3rd+ stop on half tour,@(df.trip_num>2) * (_dist_dev),coef_DistanceDeviation3rdmorestoponhalftour_mandatory,coef_DistanceDeviation3rdmorestoponhalftour_mandatory,coef_DistanceDeviation3rdmorestoponhalftour_mandatory,coef_DistanceDeviation3rdmorestoponhalftour_maint,coef_DistanceDeviation3rdmorestoponhalftour_maint,coef_DistanceDeviation3rdmorestoponhalftour_maint,coef_DistanceDeviation3rdmorestoponhalftour_discr,coef_DistanceDeviation3rdmorestoponhalftour_discr,coef_DistanceDeviation3rdmorestoponhalftour_discr,coef_DistanceDeviation3rdmorestoponhalftour_discr +util_DistanceDeviationNumberofStopsonthehalftour,Distance Deviation - Number of Stops on the half-tour,"@np.minimum(_dist_dev,7.0)*df.trip_count",coef_DistanceDeviationNumberofStopsonthehalftour_mandatory,coef_DistanceDeviationNumberofStopsonthehalftour_mandatory,coef_DistanceDeviationNumberofStopsonthehalftour_mandatory,coef_DistanceDeviationNumberofStopsonthehalftour_maint,coef_DistanceDeviationNumberofStopsonthehalftour_maint,coef_DistanceDeviationNumberofStopsonthehalftour_maint,coef_DistanceDeviationNumberofStopsonthehalftour_discr,coef_DistanceDeviationNumberofStopsonthehalftour_discr,coef_DistanceDeviationNumberofStopsonthehalftour_discr,coef_DistanceDeviationNumberofStopsonthehalftour_discr +util_DistanceDeviationwalkbiketour,Distance Deviation - walk/bike tour,nonmotorTour * (_dist_dev),coef_DistanceDeviationwalkbiketour_mandatory,coef_DistanceDeviationwalkbiketour_mandatory,coef_DistanceDeviationwalkbiketour_mandatory,coef_DistanceDeviationwalkbiketour_maint,coef_DistanceDeviationwalkbiketour_maint,coef_DistanceDeviationwalkbiketour_maint,coef_DistanceDeviationwalkbiketour_discr,coef_DistanceDeviationwalkbiketour_discr,coef_DistanceDeviationwalkbiketour_discr,coef_DistanceDeviationwalkbiketour_discr +util_DistanceDeviationworkstoppurpose,Distance Deviation - work stop purpose,workStop *_dist_dev,coef_DistanceDeviationworkstoppurpose_mandatory,coef_DistanceDeviationworkstoppurpose_mandatory,coef_DistanceDeviationworkstoppurpose_mandatory,coef_DistanceDeviationworkstoppurpose_maint,coef_DistanceDeviationworkstoppurpose_maint,coef_DistanceDeviationworkstoppurpose_maint,coef_DistanceDeviationworkstoppurpose_discr,coef_DistanceDeviationworkstoppurpose_discr,coef_DistanceDeviationworkstoppurpose_discr,coef_DistanceDeviationworkstoppurpose_discr +util_DistanceDeviationuniversitystoppurpose,Distance Deviation - university stop purpose,"@df.univStop * np.minimum(_dist_dev,5.0)",coef_DistanceDeviationuniversitystoppurpose_mandatory,coef_DistanceDeviationuniversitystoppurpose_mandatory,coef_DistanceDeviationuniversitystoppurpose_mandatory,coef_DistanceDeviationuniversitystoppurpose_maint,coef_DistanceDeviationuniversitystoppurpose_maint,coef_DistanceDeviationuniversitystoppurpose_maint,coef_DistanceDeviationuniversitystoppurpose_discr,coef_DistanceDeviationuniversitystoppurpose_discr,coef_DistanceDeviationuniversitystoppurpose_discr,coef_DistanceDeviationuniversitystoppurpose_discr +util_DistanceDeviationmaintenancestoppurpose,Distance Deviation - maintenance stop purpose,othmainStop * _dist_dev,coef_DistanceDeviationmaintenancestoppurpose_mandatory,coef_DistanceDeviationmaintenancestoppurpose_mandatory,coef_DistanceDeviationmaintenancestoppurpose_mandatory,coef_DistanceDeviationmaintenancestoppurpose_maint,coef_DistanceDeviationmaintenancestoppurpose_maint,coef_DistanceDeviationmaintenancestoppurpose_maint,coef_DistanceDeviationmaintenancestoppurpose_discr,coef_DistanceDeviationmaintenancestoppurpose_discr,coef_DistanceDeviationmaintenancestoppurpose_discr,coef_DistanceDeviationmaintenancestoppurpose_discr +util_DistanceDeviationdiscretionarystoppurpose,Distance Deviation - discretionary stop purpose,"@df.discStop * np.minimum(_dist_dev,8.0)",coef_DistanceDeviationdiscretionarystoppurpose_mandatory,coef_DistanceDeviationdiscretionarystoppurpose_mandatory,coef_DistanceDeviationdiscretionarystoppurpose_mandatory,coef_DistanceDeviationdiscretionarystoppurpose_maint,coef_DistanceDeviationdiscretionarystoppurpose_maint,coef_DistanceDeviationdiscretionarystoppurpose_maint,coef_DistanceDeviationdiscretionarystoppurpose_discr,coef_DistanceDeviationdiscretionarystoppurpose_discr,coef_DistanceDeviationdiscretionarystoppurpose_discr,coef_DistanceDeviationdiscretionarystoppurpose_discr +util_DistanceDeviationshoppingstoppurpose,Distance Deviation - shopping stop purpose,shopStop *_dist_dev,,,,coef_DistanceDeviationshoppingstoppurpose_maint,coef_DistanceDeviationshoppingstoppurpose_maint,coef_DistanceDeviationshoppingstoppurpose_maint,coef_DistanceDeviationshoppingstoppurpose_discr,coef_DistanceDeviationshoppingstoppurpose_discr,coef_DistanceDeviationshoppingstoppurpose_discr,coef_DistanceDeviationshoppingstoppurpose_discr +util_DistanceDeviationeatoutstoppurpose,Distance Deviation - eatout stop purpose,eatStop *_dist_dev,,,,coef_DistanceDeviationeatoutstoppurpose_maint,coef_DistanceDeviationeatoutstoppurpose_maint,coef_DistanceDeviationeatoutstoppurpose_maint,coef_DistanceDeviationeatoutstoppurpose_discr,coef_DistanceDeviationeatoutstoppurpose_discr,coef_DistanceDeviationeatoutstoppurpose_discr,coef_DistanceDeviationeatoutstoppurpose_discr +util_DistanceDeviationsocialstoppurpose,Distance Deviation - social stop purpose,socStop * _dist_dev,,,,coef_DistanceDeviationsocialstoppurpose_maint,coef_DistanceDeviationsocialstoppurpose_maint,coef_DistanceDeviationsocialstoppurpose_maint,coef_DistanceDeviationsocialstoppurpose_discr,coef_DistanceDeviationsocialstoppurpose_discr,coef_DistanceDeviationsocialstoppurpose_discr,coef_DistanceDeviationsocialstoppurpose_discr +util_DistanceDeviationworkstoppurpose_log,Distance Deviation - work stop purpose,workStop * _dist_dev_logged,coef_DistanceDeviationworkstoppurpose,coef_DistanceDeviationworkstoppurpose,coef_DistanceDeviationworkstoppurpose,,,,,,, +util_DistanceDeviationshoppingstoppurpose_log,Distance Deviation - shopping stop purpose,shopStop * _dist_dev_logged,coef_DistanceDeviationshoppingstoppurpose_mandatory,coef_DistanceDeviationshoppingstoppurpose_mandatory,coef_DistanceDeviationshoppingstoppurpose_mandatory,,,,,,, +util_DistanceDeviationsocialvisitingstoppurpose_log,Distance Deviation - socialvisiting stop purpose,socStop * _dist_dev_logged,coef_DistanceDeviationsocialvisitingstoppurpose_mandatory,coef_DistanceDeviationsocialvisitingstoppurpose_mandatory,coef_DistanceDeviationsocialvisitingstoppurpose_mandatory,,,,,,, +util_DistanceDeviationSchool,Distance Deviation - School Tour purpose,schoolTour * _dist_dev,coef_DistanceDeviationSchool_mandatory,coef_DistanceDeviationSchool_mandatory,coef_DistanceDeviationSchool_mandatory,,,,,,, +util_DistanceDeviationUniversity,Distance Deviation - University Tour purpose,univTour * _dist_dev,coef_DistanceDeviationUniversity_mandatory,coef_DistanceDeviationUniversity_mandatory,coef_DistanceDeviationUniversity_mandatory,,,,,,, +util_DistanceDeviationShopping,Distance Deviation - Shopping Tour purpose,shopTour * _dist_dev,,,,coef_DistanceDeviationothmaint_maint,coef_DistanceDeviationothmaint_maint,coef_DistanceDeviationothmaint_maint,,,, +util_DistanceDeviationOthMaint,Distance Deviation - Other maintenanceTour purpose,othmainTour * _dist_dev,,,,coef_DistanceDeviationshopping_maint,coef_DistanceDeviationshopping_maint,coef_DistanceDeviationshopping_maint,,,, +util_DistanceDeviationDiscr,Distance Deviation - DiscTour purpose,discTour * _dist_dev,,,,,,,coef_DistanceDeviationdiscr_maint,coef_DistanceDeviationdiscr_maint,coef_DistanceDeviationdiscr_maint,coef_DistanceDeviationdiscr_maint +util_DistanceDeviationAtwork,Distance Deviation - AtworkTour purpose,atworkTour * _dist_dev,,,,,,,coef_DistanceDeviationatwork_maint,coef_DistanceDeviationatwork_maint,coef_DistanceDeviationatwork_maint,coef_DistanceDeviationatwork_maint +util_DistanceDeviationIncomeLessthan60k,"Distance Deviation - Income Less than $60,000","@(df.income_trips<60000) * np.minimum(_dist_dev,13.0)",coef_DistanceDeviationIncomeLessthan60k_mandatory,coef_DistanceDeviationIncomeLessthan60k_mandatory,coef_DistanceDeviationIncomeLessthan60k_mandatory,coef_DistanceDeviationIncomeLessthan60k_maint,coef_DistanceDeviationIncomeLessthan60k_maint,coef_DistanceDeviationIncomeLessthan60k_maint,coef_DistanceDeviationIncomeLessthan60k_discr,coef_DistanceDeviationIncomeLessthan60k_discr,coef_DistanceDeviationIncomeLessthan60k_discr,coef_DistanceDeviationIncomeLessthan60k_discr +util_DistanceDeviationFemale,Distance Deviation - Female,(female & (is_joint==0)) * (_dist_dev),coef_DistanceDeviationFemale_mandatory,coef_DistanceDeviationFemale_mandatory,coef_DistanceDeviationFemale_mandatory,coef_DistanceDeviationFemale_maint,coef_DistanceDeviationFemale_maint,coef_DistanceDeviationFemale_maint,coef_DistanceDeviationFemale_discr,coef_DistanceDeviationFemale_discr,coef_DistanceDeviationFemale_discr,coef_DistanceDeviationFemale_discr +util_DistanceDeviationAgebetween35and54years,Distance Deviation - Age between 35 and 54 years,"@(df.age_trips.between(35,54)*df.is_joint==0) * (_dist_dev)",coef_DistanceDeviationAgebetween35and54years_mandatory,coef_DistanceDeviationAgebetween35and54years_mandatory,coef_DistanceDeviationAgebetween35and54years_mandatory,coef_DistanceDeviationAgebetween35and54years_maint,coef_DistanceDeviationAgebetween35and54years_maint,coef_DistanceDeviationAgebetween35and54years_maint,coef_DistanceDeviationAgebetween35and54years_discr,coef_DistanceDeviationAgebetween35and54years_discr,coef_DistanceDeviationAgebetween35and54years_discr,coef_DistanceDeviationAgebetween35and54years_discr +util_DistanceDeviationAgeover54years,Distance Deviation - Age over 54 years,@(df.age_trips>54) * (df.is_joint==0) * (_dist_dev),coef_DistanceDeviationAgeover54years_mandatory,coef_DistanceDeviationAgeover54years_mandatory,coef_DistanceDeviationAgeover54years_mandatory,coef_DistanceDeviationAgeover54years_maint,coef_DistanceDeviationAgeover54years_maint,coef_DistanceDeviationAgeover54years_maint,coef_DistanceDeviationAgeover54years_discr,coef_DistanceDeviationAgeover54years_discr,coef_DistanceDeviationAgeover54years_discr,coef_DistanceDeviationAgeover54years_discr +util_DistanceRatio,Distance Ratio,(_nd_DIST)/ (_nd_DIST + _dp_DIST),coef_DistanceRatio_mandatory,coef_DistanceRatio_mandatory,coef_DistanceRatio_mandatory,coef_DistanceRatio_maint,coef_DistanceRatio_maint,coef_DistanceRatio_maint,coef_DistanceRatio_discr,coef_DistanceRatio_discr,coef_DistanceRatio_discr,coef_DistanceRatio_discr +util_DistanceRatioFirstOutboundStop,Distance Ratio - First Outbound Stop,@(df.trip_num==1)*(df.outbound) * ((_nd_DIST)/ (_nd_DIST + _dp_DIST)),coef_DistanceRatioFirstOutboundStop_mandatory,coef_DistanceRatioFirstOutboundStop_mandatory,coef_DistanceRatioFirstOutboundStop_mandatory,coef_DistanceRatioFirstOutboundStop_maint,coef_DistanceRatioFirstOutboundStop_maint,coef_DistanceRatioFirstOutboundStop_maint,coef_DistanceRatioFirstOutboundStop_discr,coef_DistanceRatioFirstOutboundStop_discr,coef_DistanceRatioFirstOutboundStop_discr,coef_DistanceRatioFirstOutboundStop_discr +util_DistanceRatioFirstInboundStop,Distance Ratio - First Inbound Stop,@(df.trip_num==1)*(df.outbound==0) * ((_nd_DIST)/ (_nd_DIST + _dp_DIST)),coef_DistanceRatioFirstInboundStop_mandatory,coef_DistanceRatioFirstInboundStop_mandatory,coef_DistanceRatioFirstInboundStop_mandatory,coef_DistanceRatioFirstInboundStop_maint,coef_DistanceRatioFirstInboundStop_maint,coef_DistanceRatioFirstInboundStop_maint,coef_DistanceRatioFirstInboundStop_discr,coef_DistanceRatioFirstInboundStop_discr,coef_DistanceRatioFirstInboundStop_discr,coef_DistanceRatioFirstInboundStop_discr +util_DistanceRatioMandatoryOutboundTour,Distance Ratio - Mandatory Outbound Tour,(outbound)* mandTour * ((_nd_DIST)/ (_nd_DIST + _dp_DIST)),coef_DistanceRatioMandatoryOutboundTour_mandatory,coef_DistanceRatioMandatoryOutboundTour_mandatory,coef_DistanceRatioMandatoryOutboundTour_mandatory,,,,,,, +util_DistanceRatioMandatoryInboundTour,Distance Ratio - Mandatory Inbound Tour,(outbound==0) * mandTour * ((_nd_DIST)/ (_nd_DIST + _dp_DIST)),coef_DistanceRatioMandatoryInboundTour_mandatory,coef_DistanceRatioMandatoryInboundTour_mandatory,coef_DistanceRatioMandatoryInboundTour_mandatory,,,,,,, +util_DistanceDeviationSquareWork,Distance Deviation Square - Work Tour purpose,"@df.workTour * np.power(_dist_dev,2)",coef_DistanceDeviationSquareWork_mandatory,coef_DistanceDeviationSquareWork_mandatory,coef_DistanceDeviationSquareWork_mandatory,,,,,,, +util_DistanceDeviationSocial,Distance Deviation SocialTour purpose,socTour * _dist_dev,,,,,,,coef_DistanceDeviationDiscr,coef_DistanceDeviationDiscr,coef_DistanceDeviationDiscr,coef_DistanceDeviationDiscr +util_DistanceDeviationDiscr,Distance Deviation DiscrTour purpose,discTour * _dist_dev,,,,,,,coef_DistanceDeviationDiscr,coef_DistanceDeviationDiscr,coef_DistanceDeviationDiscr,coef_DistanceDeviationDiscr +util_DistanceDeviationAtwork_calibration,Distance Deviation - AtworkTour purpose,atworkTour * _dist_dev,,,,coef_DistanceDeviationatwork_calib,coef_DistanceDeviationatwork_calib,coef_DistanceDeviationatwork_calib,coef_DistanceDeviationatwork_calib,coef_DistanceDeviationatwork_calib,coef_DistanceDeviationatwork_calib,coef_DistanceDeviationatwork_calib diff --git a/resident/configs/trip_destination.yaml b/resident/configs/trip_destination.yaml new file mode 100644 index 0000000..ee6c9fc --- /dev/null +++ b/resident/configs/trip_destination.yaml @@ -0,0 +1,46 @@ +SAMPLE_SPEC: trip_destination_sample.csv +SPEC: trip_destination.csv +COEFFICIENTS: trip_destination_coefficients.csv + +SAMPLE_SIZE: 30 + +DESTINATION_SAMPLE_SPEC: trip_destination_sample.csv +DESTINATION_SPEC: trip_destination.csv + +LOGSUM_SETTINGS: trip_mode_choice.yaml + +# optional (comment out if not desired) +DEST_CHOICE_LOGSUM_COLUMN_NAME: destination_logsum + +# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if saved alt logsum table +DEST_CHOICE_SAMPLE_TABLE_NAME: trip_destination_sample + +# model-specific logsum-related settings +TRIP_ORIGIN: origin +ALT_DEST_COL_NAME: dest_taz +PRIMARY_ORIGIN: tour_leg_origin +PRIMARY_DEST: tour_leg_dest # must be created in preprocessor + +# tour_mode is already in trips table, so we don't need it from tours +# (it is assigned in trip_destination_annotate_trips_preprocessor ) +REDUNDANT_TOURS_MERGED_CHOOSER_COLUMNS: + - tour_mode + + + +preprocessor: + SPEC: trip_destination_annotate_trips_preprocessor + DF: trips + TABLES: + - tours + - persons + - households + - land_use + + +# drop failed trips and cleanup failed trip leg_mates for consistency +# (i.e. adjust trip_count, trip_num, first for missing failed trips) +CLEANUP: False + +# this setting is used by testing code to force failed trip_destination +# fail_some_trips_for_testing: False diff --git a/resident/configs/trip_destination_annotate_trips_preprocessor.csv b/resident/configs/trip_destination_annotate_trips_preprocessor.csv new file mode 100644 index 0000000..6bc7269 --- /dev/null +++ b/resident/configs/trip_destination_annotate_trips_preprocessor.csv @@ -0,0 +1,42 @@ +Description,Target,Expression +#,, +,tour_mode,"reindex(tours.tour_mode, df.tour_id)" +,is_joint,"reindex(tours.tour_category, df.tour_id) == 'joint'" +,tour_leg_origin,"np.where(df.outbound,reindex(tours.origin, df.tour_id), reindex(tours.destination, df.tour_id))" +,tour_leg_dest,"np.where(df.outbound,reindex(tours.destination, df.tour_id), reindex(tours.origin, df.tour_id))" +#,, +,_tod,"np.where(df.outbound,reindex_i(tours.start, df.tour_id),reindex_i(tours.end, df.tour_id))" +,trip_period,network_los.skim_time_period_label(_tod) +#,, +adding _trips to avoid conflict with the variables in the tours_merged,income_trips,"reindex(households.income, df.household_id)" +adding _trips to avoid conflict with the variables in the tours_merged,age_trips,"reindex(persons.age, df.person_id)" +,_ebike_owner_trips,"reindex(households.ebike_owner, df.household_id)" +,ebike_owner_trips,"np.where(_ebike_owner_trips,1,0)" +,female,"reindex(persons.female, df.person_id)" +#,age_55p,"reindex(persons.age_55_p, df.person_id)" +#,age_35_54,"reindex(persons.age_35_to_54, df.person_id)" +,workTour,"np.where(df.primary_purpose == 'work', 1, 0)" +,schoolTour,"np.where(df.primary_purpose == 'school', 1, 0)" +,univTour,"np.where(df.primary_purpose == 'univ', 1, 0)" +,shopTour,"np.where(df.primary_purpose == 'shopping', 1, 0)" +,othmainTour,"np.where(df.primary_purpose == 'othmaint', 1, 0)" +,discTour,"np.where(df.primary_purpose == 'discr', 1, 0)" +,socTour,"np.where(df.primary_purpose == 'social', 1, 0)" +,atworkTour,"np.where(df.primary_purpose == 'atwork', 1, 0)" +,mandTour,"np.where(df.primary_purpose.isin(['work', 'school', 'univ']), 1, 0)" +,workStop,"np.where(df.purpose == 'work', 1, 0)" +,univStop,"np.where(df.purpose == 'univ', 1, 0)" +,othmainStop,"np.where(df.purpose == 'othmaint', 1, 0)" +,discStop,"np.where(df.purpose == 'othdiscr', 1, 0)" +,shopStop,"np.where(df.purpose == 'shopping', 1, 0)" +,eatStop,"np.where(df.purpose == 'eatout', 1, 0)" +,socStop,"np.where(df.purpose == 'social', 1, 0)" +,nonmotorTour,"np.where(tour_mode.isin(['WALK','BIKE']), 1, 0)" +,walkTour,"np.where(tour_mode == 'WALK', 1, 0)" +,bikeTour,"np.where(tour_mode == 'BIKE', 1, 0)" +,microTour,"np.where((tour_mode == 'ESCOOTER') | (tour_mode == 'EBIKE'), 1, 0)" +,ebikeTour,"np.where((tour_mode == 'EBIKE'), 1, 0)" +Micromobility access Time,o_MicroAccessTime,"reindex(land_use.MicroAccessTime,df.origin)" +,max_walk_distance,max_walk_distance +,max_bike_distance,max_bike_distance +,microAccessThreshold,microAccessThreshold \ No newline at end of file diff --git a/resident/configs/trip_destination_coefficients.csv b/resident/configs/trip_destination_coefficients.csv new file mode 100644 index 0000000..39e23a7 --- /dev/null +++ b/resident/configs/trip_destination_coefficients.csv @@ -0,0 +1,89 @@ +coefficient_name,value,constrain +coef_UNAVAILABLE,-999,T +coef_one,1,T +#,, +coef_mode_choice_logsum_mandatory,1.31417,F +coef_DistanceDeviationLinearAbsolute_mandatory,0,F +coef_LogofDistanceAbsoluteDeviation_mandatory,-0.83965,F +coef_DistanceDeviationsquaredAbsolute_mandatory,0,F +coef_DistanceDeviation2ndstopofhalftour_mandatory,-0.06176,F +coef_DistanceDeviation3rdmorestoponhalftour_mandatory,-0.07764,F +coef_DistanceDeviationNumberofStopsonthehalftour_mandatory,0,F +coef_DistanceDeviationwalkbiketour_mandatory,0,F +coef_DistanceDeviationworkstoppurpose_mandatory,-0.10657,F +coef_DistanceDeviationuniversitystoppurpose_mandatory,0.08433,F +coef_DistanceDeviationmaintenancestoppurpose_mandatory,0.02921,F +coef_DistanceDeviationdiscretionarystoppurpose_mandatory,0.07266,F +coef_DistanceDeviationworkstoppurpose,1.02985,F +coef_DistanceDeviationshoppingstoppurpose_mandatory,-0.19942,F +coef_DistanceDeviationsocialvisitingstoppurpose_mandatory,0.65223,F +coef_DistanceDeviationSchool_mandatory,0.11216,F +coef_DistanceDeviationUniversity_mandatory,0.02587,F +coef_DistanceDeviationIncomeLessthan60k_mandatory,0.0227,F +coef_DistanceDeviationFemale_mandatory,-0.07066,F +coef_DistanceDeviationAgebetween35and54years_mandatory,-0.08953,F +coef_DistanceDeviationAgeover54years_mandatory,-0.0987,F +coef_DistanceRatio_mandatory,-1.99261,F +coef_DistanceRatioFirstOutboundStop_mandatory,-0.6487,F +coef_DistanceRatioFirstInboundStop_mandatory,1.49723,F +coef_DistanceRatioMandatoryOutboundTour_mandatory,0,F +coef_DistanceRatioMandatoryInboundTour_mandatory,0.91734,F +coef_DistanceDeviationSquareWork_mandatory,0,F +#,, +coef_mode_choice_logsum_maint,1.00663,F +coef_DistanceDeviationLinearRelative_maint,0,F +coef_DistanceDeviationLinearAbsolute_maint,-0.0615,F +coef_LogofDistanceRelativeDeviation_maint,0,F +coef_LogofDistanceAbsoluteDeviation_maint,-0.94056,F +coef_DistanceDeviationsquaredRelative_maint,0,F +coef_DistanceDeviationsquaredAbsolute_maint,-0.00022,F +coef_DistanceDeviation2ndstopofhalftour_maint,-0.03146,F +coef_DistanceDeviation3rdmorestoponhalftour_maint,-0.0631,F +coef_DistanceDeviationNumberofStopsonthehalftour_maint,0.02045,F +coef_DistanceDeviationwalkbiketour_maint,-1.00489,F +coef_DistanceDeviationworkstoppurpose_maint,0.06367,F +coef_DistanceDeviationuniversitystoppurpose_maint,0.09372,F +coef_DistanceDeviationmaintenancestoppurpose_maint,0.02109,F +coef_DistanceDeviationdiscretionarystoppurpose_maint,0.05744,F +coef_DistanceDeviationshoppingstoppurpose_maint,-0.02269,F +coef_DistanceDeviationeatoutstoppurpose_maint,0.02249,F +coef_DistanceDeviationsocialstoppurpose_maint,0.06855,F +coef_DistanceDeviationothmaint_maint,0.02685,F +coef_DistanceDeviationshopping_maint,0.03415,F +coef_DistanceDeviationIncomeLessthan60k_maint,0.03634,F +coef_DistanceDeviationFemale_maint,-0.02723,F +coef_DistanceDeviationAgebetween35and54years_maint,-0.01022,F +coef_DistanceDeviationAgeover54years_maint,-0.01353,F +coef_DistanceRatio_maint,-0.4485,F +coef_DistanceRatioFirstOutboundStop_maint,-1.51595,F +coef_DistanceRatioFirstInboundStop_maint,1.80508,F +#,, +coef_mode_choice_logsum_discr,1.00663, +coef_DistanceDeviationLinearRelative_discr,0, +coef_DistanceDeviationLinearAbsolute_discr,-0.0615, +coef_LogofDistanceRelativeDeviation_discr,0, +coef_LogofDistanceAbsoluteDeviation_discr,-0.94056, +coef_DistanceDeviationsquaredRelative_discr,0, +coef_DistanceDeviationsquaredAbsolute_discr,-0.00022, +coef_DistanceDeviation2ndstopofhalftour_discr,-0.03146, +coef_DistanceDeviation3rdmorestoponhalftour_discr,-0.0631, +coef_DistanceDeviationNumberofStopsonthehalftour_discr,0.02045, +coef_DistanceDeviationwalkbiketour_discr,-1.00489, +coef_DistanceDeviationworkstoppurpose_discr,0.06367, +coef_DistanceDeviationuniversitystoppurpose_discr,0.09372, +coef_DistanceDeviationmaintenancestoppurpose_discr,0.02109, +coef_DistanceDeviationdiscretionarystoppurpose_discr,0.05744, +coef_DistanceDeviationshoppingstoppurpose_discr,-0.02269, +coef_DistanceDeviationeatoutstoppurpose_discr,0.02249, +coef_DistanceDeviationsocialstoppurpose_discr,0.06855, +coef_DistanceDeviationdiscr_maint,-0.045, +coef_DistanceDeviationatwork_maint,0.08779, +coef_DistanceDeviationIncomeLessthan60k_discr,0.03634, +coef_DistanceDeviationFemale_discr,-0.02723, +coef_DistanceDeviationAgebetween35and54years_discr,-0.01022, +coef_DistanceDeviationAgeover54years_discr,-0.01353, +coef_DistanceRatio_discr,-0.4485, +coef_DistanceRatioFirstOutboundStop_discr,-1.51595, +coef_DistanceRatioFirstInboundStop_discr,1.80508, +coef_DistanceDeviationDiscr,0.02, +coef_DistanceDeviationatwork_calib,-3.400, \ No newline at end of file diff --git a/resident/configs/trip_destination_sample.csv b/resident/configs/trip_destination_sample.csv new file mode 100644 index 0000000..c7e6c7b --- /dev/null +++ b/resident/configs/trip_destination_sample.csv @@ -0,0 +1,19 @@ +Description,Expression,work,univ,school,escort,shopping,eatout,othmaint,social,othdiscr,atwork +,"_od_DIST@od_skims[('SOV_M_DIST', 'MD')]",1,1,1,1,1,1,1,1,1,1 +,"_dp_DIST@dp_skims[('SOV_M_DIST', 'MD')]",1,1,1,1,1,1,1,1,1,1 +,"_op_DIST@op_skims[('SOV_M_DIST', 'MD')]",1,1,1,1,1,1,1,1,1,1 +# next line gets max MAZ micromobility access time in destination TAZ +,"_d_microAccTime@land_use.sort_values(by='MicroAccessTime',ascending=False).drop_duplicates('TAZ',keep='first').set_index('TAZ')['MicroAccessTime'].reindex(df.dest_taz)",1,1,1,1,1,1,1,1,1,1 +#,,,,,,,,,,, +size term,"@np.log1p(size_terms.get(df.dest_taz, df.purpose))",1,1,1,1,1,1,1,1,1,1 +no attractions,"@size_terms.get(df.dest_taz, df.purpose) == 0",-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 +#,,,,,,,,,,, +,"@df.walkTour * (np.where(_od_DIST > df.max_walk_distance,1,0) + np.where(_dp_DIST > df.max_walk_distance,1,0))",-10,-10,-10,-10,-10,-10,-10,-10,-10,-10 +,@df.walkTour * (_od_DIST + _dp_DIST),-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5 +#,,,,,,,,,,, +,"@df.bikeTour * (np.where(_od_DIST > df.max_bike_distance,1,0) + np.where(_dp_DIST > df.max_bike_distance,1,0))",-10,-10,-10,-10,-10,-10,-10,-10,-10,-10 +,@df.bikeTour * (_od_DIST + _dp_DIST - _op_DIST),-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05 +,@df.bikeTour * ((_od_DIST < max_bike_distance) & (_dp_DIST < max_bike_distance)),-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 +#,,,,,,,,,,, +,@(df.nonmotorTour==0) * (_od_DIST + _dp_DIST - _op_DIST),-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05 +,"@(df.microTour * (np.where(_d_microAccTime > df.microAccessThreshold,1,0) + np.where(df.o_MicroAccessTime > df.microAccessThreshold,1,0)) * np.where(df.ebike_owner_trips * df.ebikeTour,0,1))",-10,-10,-10,-10,-10,-10,-10,-10,-10,-10 diff --git a/resident/configs/trip_mode_choice.csv b/resident/configs/trip_mode_choice.csv new file mode 100644 index 0000000..ee28216 --- /dev/null +++ b/resident/configs/trip_mode_choice.csv @@ -0,0 +1,238 @@ +Label,Description,Expression,DRIVEALONE,SHARED2,SHARED3,WALK,BIKE,WALK_TRANSIT,PNR_TRANSIT,KNR_TRANSIT,BIKE_TRANSIT,TAXI,TNC_SINGLE,TNC_SHARED,SCH_BUS,EBIKE,ESCOOTER +#,Drive alone,,,,,,,,,,,,,,,, +util_DRIVEALONEFREE_Unavailable,Drive alone - Unavailable,sov_available == False,-999,,,,,,,,,,,,,, +util_Drive alone - In-vehicle time,Drive alone - In-vehicle time,(da_time_skims) * autoIVTFactor * time_factor,coef_ivt,,,,,,,,,,,,,, +util_Drive alone - cost,Drive alone - cost,"@(df.auto_op_cost * df.autoCPMFactor * df.da_dist_skims + df.da_cost_skims)/(np.maximum(df.income,1000)**df.income_exponent)",coef_income,,,,,,,,,,,,,, +util_Drive alone - Parking cost ,Drive alone - Parking cost ,"@(df.parkingCost * df.autoParkingCostFactor)/(np.maximum(df.income,1000)**df.income_exponent)",coef_income,,,,,,,,,,,,,, +util_Drive alone - Terminal Time,Drive alone - Terminal Time,(oTermTime + dTermTime) * autoTermTimeFactor * time_factor,coef_acctime,,,,,,,,,,,,,, +#,Shared ride2,,,,,,,,,,,,,,,, +util_sr2_available,sr2 available,sr2_available == False,,-999,,,,,,,,,,,,, +util_sr2_tourbike_disadvantage,sr2 tourbike disadvantage,tourBike,,-30,,,,,,,,,,,,, +util_Shared ride 2 - In -vehicle time,Shared ride 2 - In -vehicle time,(s2_time_skims) * autoIVTFactor * time_factor,,coef_ivt,,,,,,,,,,,,, +util_Shared ride 2 - cost,Shared ride 2 - cost,"@((df.auto_op_cost * df.autoCPMFactor * df.s2_dist_skims) + df.s2_cost_skims)/(np.maximum(df.income,1000)**df.income_exponent)",,coef_income,,,,,,,,,,,,, +util_Shared ride 2 - Parking cost ,Shared ride 2 - Parking cost ,"@(df.parkingCost * df.autoParkingCostFactor * np.where(df.is_joint,1,df.costFactorS2))/(np.maximum(df.income,1000)**df.income_exponent)",,coef_income,,,,,,,,,,,,, +util_Shared ride 2 Terminal Time - acc,Shared ride 2 - Terminal Time ,(oTermTime + dTermTime) * autoTermTimeFactor * time_factor,,coef_acctime,,,,,,,,,,,,, +util_Shared ride 2 _Two_person_household,Shared ride 2 - Two person household,@(df.hhsize == 2),,coef_size2_sr2,,,,,,,,,,,,, +util_Shared ride 2 _Three_person_household,Shared ride 2 - Three person household,@(df.hhsize == 3),,coef_size3_sr2,,,,,,,,,,,,, +util_Shared ride 2 _Four+_person_household,Shared ride 2 - Four plus person household,@(df.hhsize > 3),,coef_size4p_sr2,,,,,,,,,,,,, +util_Shared ride 2 - Female,Shared ride 2 - female,@(df.female == 1),,coef_female_sr2,,,,,,,,,,,,, +#,Shared ride 3,,,,,,,,,,,,,,,, +util_sr3_available,sr3 available,sr3_available == False,,,-999,,,,,,,,,,,, +util_sr3_tourbike_disadvantage,sr3 tourbike disadvantage,tourBike,,,-30,,,,,,,,,,,, +util_Shared ride 3 - In -vehicle time,Shared ride 3 - In -vehicle time,(s3_time_skims) * autoIVTFactor * time_factor,,,coef_ivt,,,,,,,,,,,, +util_Shared ride 3 - Cost ,Shared ride 3 - cost,"@(df.auto_op_cost * df.s3_dist_skims * df.autoCPMFactor + df.s3_cost_skims)/(np.maximum(df.income,1000)**df.income_exponent)",,,coef_income,,,,,,,,,,,, +util_Shared ride 3 - Parking cost ,Shared ride 3 - Parking cost ,"@(df.parkingCost * df.autoParkingCostFactor * np.where(df.is_joint,1,df.costFactorS3))/(np.maximum(df.income,1000)**df.income_exponent)",,,coef_income,,,,,,,,,,,, +util_Shared ride 3 - Terminal Time,Shared ride 3 - Terminal Time,(oTermTime + dTermTime) * autoTermTimeFactor * time_factor,,,coef_acctime,,,,,,,,,,,, +util_Shared ride 3 - Two_person_household,Shared ride 3 - Two person household,@(df.hhsize == 2),,,coef_size2_sr3p,,,,,,,,,,,, +util_Shared ride 3 - Three_person_household,Shared ride 3 - Three person household,@(df.hhsize == 3),,,coef_size3_sr3p,,,,,,,,,,,, +util_Shared ride 3 - Four+_person_household,Shared ride 3 - Four plus person household,@(df.hhsize > 3),,,coef_size4p_sr3p,,,,,,,,,,,, +util_Shared ride 3 - Female,Shared ride 3 - female,@(df.female == 1),,,coef_female_sr3p,,,,,,,,,,,, +#,Walk,,,,,,,,,,,,,,,, +#,school escorting can force longer walk trips so not turning off completely if walk tour,,,,,,,,,,,,,,,, +util_Walk - Unavailable,Walk - Unavailable,(walkAvailable == 0) & (tourWalk == 0),,,,-999,,,,,,,,,,, +util_Walk - Unavailable - walkTour,Walk - Unavailable - Walk Tour,(walkAvailable == 0) & (tourWalk == 1),,,,-20,,,,,,,,,,, +util_Walk - Female,Walk - Female,@df.female,,,,coef_female_nmot,,,,,,,,,,, +util_Walk - time,Walk - time - clipped for really long walk trips to avoid zero probs,@(df.walk_time_skims * df.time_factor).clip(upper=1000),,,,coef_walkTime,,,,,,,,,,, +util_Walk - Origin Mix,Walk - Origin Mix,oMGRAMix,,,,coef_oMix_nmot,,,,,,,,,,, +util_Walk - Origin Intersection Density,Walk - Origin Intersection Density,oMGRATotInt,,,,coef_oIntDen_nmot,,,,,,,,,,, +util_Walk - Destination Employment Density,Walk - Destination Employment Density,dMGRAEmpDen,,,,coef_dEmpDen_nmot,,,,,,,,,,, +util_Walk - Age 1-5 (school only),Walk - Age 1-6,"@df.age.between(1,5)",,,,coef_age1to5_nmot,,,,,,,,,,, +util_Walk - Age 6-12 (school only),Walk - Age 6-13,"@df.age.between(6,12)",,,,coef_age6to12_nmot,,,,,,,,,,, +util_Walk - Age 13-15 (school only),Walk - Age 13-16,"@df.age.between(13,15)",,,,coef_age13to15_nmot,,,,,,,,,,, +#,Bike,,,,,,,,,,,,,,,, +util_BIKE_Mode unavailable,Bike - Mode unavailable unless on bike tour,@(df.tourBike == 0)|(df.bike_time <= 0),,,,,-999,,,,,,,,,, +util_BIKE_Female - bike,Bike - Female,@df.female,,,,,coef_female_nmot,,,,,,,,,, +util_BIKE_Bike - logsum,Bike - logsum clipped to allow for long bike trips on school escort tours,@df.bikeLS.clip(lower=-100),,,,,coef_bikeLogsum,,,,,,,,,, +#,WalktoTransit,,,,,,,,,,,,,,,, +util_WalkTransit_Unavailable,WalkTransit_Available,walk_local_available == False,,,,,,-999,,,,,,,,, +util_WALKLOC__In_vehicle_time,WALK_LOC - In-vehicle time,@(odt_skims['WTW_TIV']) * df.time_factor,,,,,,coef_ivt,,,,,,,,, +util_WALK_LOC_iwait_time,WALK_LOC - wait time,@(odt_skims['WTW_FWT']) * df.time_factor,,,,,,coef_wait,,,,,,,,, +util_WALK_LOC_transfer_wait_time,WALK_LOC - transfer wait time,@(odt_skims['WTW_XWT'])* df.time_factor,,,,,,coef_xwait,,,,,,,,, +util_WALK_LOC_Walk_access_time,WALK_LOC - Walk access time,"@np.where(df.nev_local_access_available_in & ~df.outbound, df.nev_local_access_time_in, np.where(df.microtransit_local_access_available_in & ~df.outbound, df.microtransit_local_access_time_in, df.origin_local_time))* df.time_factor",,,,,,coef_acctime,,,,,,,,, +util_WALK_LOC_wait_access_time,WALK_LOC - Access mt/nev wait time,"@np.where(df.nev_local_access_available_in & ~df.outbound, nevWaitTime, np.where(df.microtransit_local_access_available_in & ~df.outbound, microtransitWaitTime, 0))* df.time_factor",,,,,,coef_wait,,,,,,,,, +util_WALK_LOC_Walk_egress_time,WALK_LOC - Walk egress time,"@np.where(df.nev_local_egress_available_out & df.outbound, df.nev_local_egress_time_out, np.where(df.microtransit_local_egress_available_out & df.outbound, df.microtransit_local_egress_time_out, df.dest_local_time))* df.time_factor",,,,,,coef_acctime,,,,,,,,, +util_WALK_LOC_wait_egress_time,WALK_LOC - Egress mt/nev wait time,"@np.where(df.nev_local_egress_available_out & df.outbound, nevWaitTime, np.where(df.microtransit_local_egress_available_out & df.outbound, microtransitWaitTime, 0))* df.time_factor",,,,,,coef_wait,,,,,,,,, +util_WALK_LOC_transfer_walk_time,WALK_LOC - transfer walk time,@(odt_skims['WTW_AUX'])* df.time_factor,,,,,,coef_xwalk,,,,,,,,, +util_WALK_LOC_transfers_penalty,WALK_LOC - number of transfers,"@(-23+23*np.exp(0.414*np.clip(odt_skims['WTW_XFR'] + df.outbound*df.mtnev_egr_xfer_out + ~df.outbound*df.mtnev_acc_xfer_in, a_min=None,a_max=4))) * df.time_factor",,,,,,coef_xfer,,,,,,,,, +# FIXME need transit fares ,,,,,,,,,,,,,,,,, +# util_WTW_FARE,WALK_LOC - Fare,"@df.transitSubsidyPassDiscount*(odt_skims['WTW_FARE'])*100*df.number_of_participants/(np.maximum(df.income,1000)**df.income_exponent)",,,,,,coef_income,,,,,,,,, +util_WALK_LOC - Female,WALK_LOC - Female,@(df.female),,,,,,coef_female_tran,,,,,,,,, +util_WALK_LOC - Origin Mix,WALK_LOC - Origin Mix,oMGRAMix,,,,,,coef_oMix_wTran,,,,,,,,, +util_WALK_LOC - Origin Intersection Density,WALK_LOC - Origin Intersection Density,oMGRATotInt,,,,,,coef_oIntDen_wTran,,,,,,,,, +util_WALK_LOC - Destination Employment Density,WALK_LOC - Destination Employment Density,dMGRAEmpDen,,,,,,coef_dEmpDen_wTran,,,,,,,,, +#,PNR_LOC,,,,,,,,,,,,,,,, +# FIXME need PNR utilties for new PNR treatment if not restricting stops on PNR tours,,,,,,,,,,,,,,,,, +util_is_PNR_transit,Only allowed to take PNR transit on PNR tours,tourPNR,-999,-999,-999,-999,-999,-999,,-999,-999,-999,-999,-999,-999,-999, +# util_PNR_LOC_Unavailable,PNR_LOC - Unavailable,(pnr_local_available == False)|(PNR_available==0),,,,,,,-999,,,,,,,, +# util_PNR_LOC_In_vehicle_time,PNR_LOC - In-vehicle time,@(odt_skims['PNROUT_LOC_TIV'])* df.time_factor * df.outbound,,,,,,,coef_ivt,,,,,,,, +# util_PNR_LOC_iwait_time,PNR_LOC - First iwait time,@(odt_skims['PNROUT_LOC_FWT'])* df.time_factor * df.outbound,,,,,,,coef_wait,,,,,,,, +# util_PNR_LOC_transfer_wait_time,PNR_LOC - transfer wait time,@(odt_skims['PNROUT_LOC_XWT'])* df.time_factor * df.outbound,,,,,,,coef_xwait,,,,,,,, +# util_PNR_LOC_number_of_transfers,PNR_LOC - number of transfers,"@(-23+23*np.exp(0.414*np.clip(odt_skims['PNROUT_LOC_XFR'] + df.mtnev_egr_xfer_out, a_min=None,a_max=4))) * df.time_factor * df.outbound",,,,,,,coef_xferdrive,,,,,,,, +# util_PNR_LOC_PNR_time,PNR_LOC - PNR time,@odt_skims['PNROUT_LOC_ACC']* df.time_factor * df.outbound,,,,,,,coef_acctime,,,,,,,, +# util_PNR_LOC_PNR_cost,PNR_LOC - PNR cost,"@(df.auto_op_cost * (odt_skims['PNROUT_LOC_ACC']/60) *driveSpeed)* df.outbound/(np.maximum(df.income,1000)**df.income_exponent)",,,,,,,coef_income,,,,,,,, +# util_PNRIN_LOC_PNR_cost,PNR_LOC - PNR cost,"@(df.auto_op_cost * (odt_skims['PNRIN_LOC_EGR']/60) *driveSpeed) * ~df.outbound/(np.maximum(df.income,1000)**df.income_exponent)",,,,,,,coef_income,,,,,,,, +# util_PNR_LOC_Walk_egress_time_(at_attraction_end),PNR_LOC - Walk egress time (at attraction end),"@np.where(df.nev_local_egress_available_out, df.nev_local_egress_time_out, np.where(df.microtransit_local_egress_available_out, df.microtransit_local_egress_time_out, df.dest_local_time))* df.time_factor * df.outbound",,,,,,,coef_acctime,,,,,,,, +# util_PNR_LOC_wait_egress_time_(at_attraction_end),PNR_LOC - Egress mt/nev wait time (at attraction end),"@np.where(df.nev_local_egress_available_out, nevWaitTime, np.where(df.microtransit_local_egress_available_out, microtransitWaitTime, 0)) * df.time_factor * df.outbound",,,,,,,coef_wait,,,,,,,, +# util_PNR_LOC_Walk_other_time,PNR_LOC - Walk other time,@odt_skims['PNROUT_LOC_AUX']* df.time_factor * df.outbound,,,,,,,coef_xwalk,,,,,,,, +# util_PNR_LOC_Fare_and_operating_cost,PNR_LOC - Fare ,"@df.transitSubsidyPassDiscount*(odt_skims['PNROUT_LOC_FARE'])*100*df.number_of_participants/(np.maximum(df.income,1000)**df.income_exponent) * df.outbound",,,,,,,coef_income,,,,,,,, +# util_PNRIN_LOC_In_vehicle_time,PNRIN_LOC - In-vehicle time,@(odt_skims['PNRIN_LOC_TIV'])* df.time_factor * ~df.outbound,,,,,,,coef_ivt,,,,,,,, +# util_PNRIN_LOC_iwait_time,PNRIN_LOC - First iwait time,@(odt_skims['PNRIN_LOC_FWT']) * df.time_factor* ~df.outbound,,,,,,,coef_wait,,,,,,,, +# util_PNRIN_LOC_transfer_wait_time,PNRIN_LOC - transfer wait time,@(odt_skims['PNRIN_LOC_XWT'])* ~df.outbound,,,,,,,coef_xwait,,,,,,,, +# util_PNRIN_LOC_number_of_transfers,PNRIN_LOC - number of transfers,"@(-23+23*np.exp(0.414*np.clip(odt_skims['PNRIN_LOC_XFR'] + df.mtnev_acc_xfer_in, a_min=None,a_max=4))) * df.time_factor * ~df.outbound",,,,,,,coef_xferdrive,,,,,,,, +# util_PNRIN_LOC_PNRIN_time,PNRIN_LOC - PNR time,@odt_skims['PNRIN_LOC_EGR'] * df.time_factor * ~df.outbound,,,,,,,coef_acctime,,,,,,,, +# util_PNRIN_LOC_Walk_access_time,PNRIN_LOC - Walk access time,"@np.where(df.nev_local_access_available_in, df.nev_local_access_time_in, np.where(df.microtransit_local_access_available_in, df.microtransit_local_access_time_in, df.origin_local_time)) * df.time_factor * ~df.outbound",,,,,,,coef_acctime,,,,,,,, +# util_PNRIN_LOC_wait_access_time,PNRIN_LOC - Egress mt/nev wait time,"@np.where(df.nev_local_access_available_in, nevWaitTime, np.where(df.microtransit_local_access_available_in, microtransitWaitTime, 0)) * df.time_factor * ~df.outbound",,,,,,,coef_wait,,,,,,,, +# util_PNRIN_LOC_Walk_other_time,PNRIN_LOC - Walk other time,@odt_skims['PNRIN_LOC_AUX']* df.time_factor * ~df.outbound,,,,,,,coef_xwalk,,,,,,,, +# util_PNRIN_LOC_Fare_and_operating_cost,PNRIN_LOC - Fare ,"@df.transitSubsidyPassDiscount*(odt_skims['PNRIN_LOC_FARE'])*100*df.number_of_participants/(np.maximum(df.income,1000)**df.income_exponent) * ~df.outbound",,,,,,,coef_income,,,,,,,, +# util_PNRIN_LOC_Destination_zone_densityIndex,PNRIN_LOC - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,coef_ivt,,,,,,,, +# util_PNR_LOC - Female,PNR_LOC - Female,@(df.female),,,,,,,coef_female_tran,,,,,,,, +# util_PNR_LOC - Destination Employment Density,PNR_LOC - Destination Employment Density,dMGRAEmpDen,,,,,,,coef_dEmpDen_dTran,,,,,,,, +#,,,,,,,,,,,,,,,,, +util_KNR_LOC_Unavailable,KNR_LOC - Unavailable,(knr_local_available == False)|(KNR_available==0),,,,,,,,-999,,,,,,, +util_KNR_LOC_In_vehicle_time,KNR_LOC - In-vehicle time,@(odt_skims['KTW_TIV']) * df.time_factor * df.outbound,,,,,,,,coef_ivt,,,,,,, +util_KNR_LOC_iwait_time,KNR_LOC - First iwait time,@(odt_skims['KTW_FWT']) * df.time_factor * df.outbound,,,,,,,,coef_wait,,,,,,, +util_KNR_LOC_transfer_wait_time,KNR_LOC - transfer wait time,@(odt_skims['KTW_XWT']) * df.time_factor * df.outbound,,,,,,,,coef_xwait,,,,,,, +util_KNR_LOC_number_of_transfers,KNR_LOC - number of transfers,"@(-23+23*np.exp(0.414*np.clip(odt_skims['KTW_XFR'] + df.mtnev_egr_xfer_out, a_min=None,a_max=4))) * df.time_factor * df.outbound",,,,,,,,coef_xferdrive,,,,,,, +util_KNR_LOC_KNR_time,KNR_LOC - KNR time,@(odt_skims['KTW_ACC']) * df.time_factor * df.outbound,,,,,,,,coef_acctime,,,,,,, +util_KNR_LOC_Walk_egress_time_(at_attraction_end),KNR_LOC - Walk egress time (at attraction end),"@np.where(df.nev_local_egress_available_out, df.nev_local_egress_time_out, np.where(df.microtransit_local_egress_available_out, df.microtransit_local_egress_time_out, df.dest_local_time)) * df.time_factor * df.outbound",,,,,,,,coef_acctime,,,,,,, +util_KNR_LOC_wait_egress_time_(at_attraction_end),KNR_LOC - Egress mt/nev wait time (at attraction end),"@np.where(df.nev_local_egress_available_out, nevWaitTime, np.where(df.microtransit_local_egress_available_out, microtransitWaitTime, 0)) * df.time_factor * df.outbound",,,,,,,,coef_wait,,,,,,, +util_KNR_LOC_Walk_other_time,KNR_LOC - Walk other time,@(odt_skims['KTW_AUX']) * df.time_factor * df.outbound,,,,,,,,coef_xwalk,,,,,,, +# FIXME need transit fares,,,,,,,,,,,,,,,,, +# util_KNR_LOC_Fare,KNR_LOC - Fare,"@df.transitSubsidyPassDiscount*(odt_skims['KTW_FARE'])*100*df.number_of_participants/(np.maximum(df.income,1000)**df.income_exponent) * df.outbound",,,,,,,,coef_income,,,,,,, +util_KNR_LOC_KNR_cost,KNR_LOC - KNR cost,"@(df.auto_op_cost * df.autoCPMFactor * (odt_skims['KTW_ACC']/60) *driveSpeed )*df.outbound/(np.maximum(df.income,1000)**df.income_exponent)",,,,,,,,coef_income,,,,,,, +util_KNRIN_LOC_KNR_cost,KNR_LOC - KNR cost,"@(df.auto_op_cost * df.autoCPMFactor * (odt_skims['WTK_EGR']/60) *driveSpeed)*~df.outbound/(np.maximum(df.income,1000)**df.income_exponent)",,,,,,,,coef_income,,,,,,, +util_KNRIN_LOC_In_vehicle_time,KNRIN_LOC - In-vehicle time,@(odt_skims['WTK_TIV']) * df.time_factor * ~df.outbound,,,,,,,,coef_ivt,,,,,,, +util_KNRIN_LOC_iwait_time,KNRIN_LOC - First iwait time,@(odt_skims['WTK_FWT']) * df.time_factor * ~df.outbound,,,,,,,,coef_wait,,,,,,, +util_KNRIN_LOC_transfer_wait_time,KNRIN_LOC - transfer wait time,@(odt_skims['WTK_XWT']) * df.time_factor * ~df.outbound,,,,,,,,coef_xwait,,,,,,, +util_KNRIN_LOC_number_of_transfers,KNRIN_LOC - number of transfers,"@(-23+23*np.exp(0.414*np.clip(odt_skims['WTK_XFR'] + df.mtnev_acc_xfer_in, a_min=None,a_max=4))) * df.time_factor * ~df.outbound",,,,,,,,coef_xferdrive,,,,,,, +util_KNRIN_LOC_KNRIN_time,KNRIN_LOC - KNR time,@odt_skims['WTK_EGR'] * df.time_factor * ~df.outbound,,,,,,,,coef_acctime,,,,,,, +util_KNRIN_LOC_Walk_access_time,KNRIN_LOC - Walk access time,"@np.where(df.nev_local_access_available_in, df.nev_local_access_time_in, np.where(df.microtransit_local_access_available_in, df.microtransit_local_access_time_in, df.origin_local_time)) * df.time_factor * ~df.outbound",,,,,,,,coef_acctime,,,,,,, +util_KNRIN_LOC_wait_access_time,KNRIN_LOC - Egress mt/nev wait time,"@np.where(df.nev_local_access_available_in, nevWaitTime, np.where(df.microtransit_local_access_available_in, microtransitWaitTime, 0)) * df.time_factor * ~df.outbound",,,,,,,,coef_wait,,,,,,, +util_KNRIN_LOC_Walk_other_time,KNRIN_LOC - Walk other time,@(odt_skims['WTK_AUX']) * df.time_factor * ~df.outbound,,,,,,,,coef_xwalk,,,,,,, +# FIXME need transit fares,,,,,,,,,,,,,,,,, +#util_KNRIN_LOC_Fare_and_operating_cost,KNRIN_LOC - Fare ,"@df.transitSubsidyPassDiscount*(odt_skims['WTK_FARE'])*100*df.number_of_participants/(np.maximum(df.income,1000)**df.income_exponent) * ~df.outbound",,,,,,,,coef_income,,,,,,, +util_KNR_LOC - Female,KNR_LOC - Female,@(df.female),,,,,,,,coef_female_tran,,,,,,, +util_KNR_LOC - Destination Employment Density,KNR_LOC - Destination Employment Density,dMGRAEmpDen,,,,,,,,coef_dEmpDen_dTran,,,,,,, +#,BiketoTransit,,,,,,,,,,,,,,,, +util_WalkTransit_Unavailable,WalkTransit_Available,bike_local_available == False | (BNR_available==0),,,,,,,,,-999,,,,,, +util_BIKE_TRANSIT__In_vehicle_time,BIKE_TRANSIT - In-vehicle time,@(odt_skims['WTW_TIV']) * df.time_factor,,,,,,,,,coef_ivt,,,,,, +util_BIKE_TRANSIT_iwait_time,BIKE_TRANSIT - wait time,@(odt_skims['WTW_FWT']) * df.time_factor,,,,,,,,,coef_wait,,,,,, +util_BIKE_TRANSIT_transfer_wait_time,BIKE_TRANSIT - transfer wait time,@(odt_skims['WTW_XWT'])* df.time_factor,,,,,,,,,coef_xwait,,,,,, +util_BIKE_TRANSIT_Walk_access_time,BIKE_TRANSIT - Walk access time,"@np.where(df.nev_local_access_available_in & ~df.outbound, df.nev_local_access_time_in, np.where(df.microtransit_local_access_available_in & ~df.outbound, df.microtransit_local_access_time_in, df.origin_local_time))* df.time_factor",,,,,,,,,coef_acctime,,,,,, +util_BIKE_TRANSIT_wait_access_time,BIKE_TRANSIT - Access mt/nev wait time,"@np.where(df.nev_local_access_available_in & ~df.outbound, nevWaitTime, np.where(df.microtransit_local_access_available_in & ~df.outbound, microtransitWaitTime, 0))* df.time_factor",,,,,,,,,coef_wait,,,,,, +util_BIKE_TRANSIT_Walk_egress_time,BIKE_TRANSIT - Walk egress time,"@np.where(df.nev_local_egress_available_out & df.outbound, df.nev_local_egress_time_out, np.where(df.microtransit_local_egress_available_out & df.outbound, df.microtransit_local_egress_time_out, df.dest_local_time))* df.time_factor",,,,,,,,,coef_acctime,,,,,, +util_BIKE_TRANSIT_wait_egress_time,BIKE_TRANSIT - Egress mt/nev wait time,"@np.where(df.nev_local_egress_available_out & df.outbound, nevWaitTime, np.where(df.microtransit_local_egress_available_out & df.outbound, microtransitWaitTime, 0))* df.time_factor",,,,,,,,,coef_wait,,,,,, +util_BIKE_TRANSIT_transfer_walk_time,BIKE_TRANSIT - transfer walk time,@(odt_skims['WTW_AUX'])* df.time_factor,,,,,,,,,coef_xwalk,,,,,, +util_BIKE_TRANSIT_transfers_penalty,BIKE_TRANSIT - number of transfers,"@(-23+23*np.exp(0.414*np.clip(odt_skims['WTW_XFR'] + df.outbound*df.mtnev_egr_xfer_out + ~df.outbound*df.mtnev_acc_xfer_in, a_min=None,a_max=4))) * df.time_factor",,,,,,,,,coef_xfer,,,,,, +# FIXME need transit fares,,,,,,,,,,,,,,,,, +#util_BIKE_TRANSIT_Fare,BIKE_TRANSIT - Fare,"@df.transitSubsidyPassDiscount*(odt_skims['WTW_FARE'])*100*df.number_of_participants/(np.maximum(df.income,1000)**df.income_exponent)",,,,,,,,,coef_income,,,,,, +util_BIKE_TRANSIT - Female,BIKE_TRANSIT - Female,@(df.female),,,,,,,,,coef_female_tran,,,,,, +util_BIKE_TRANSIT - Origin Mix,BIKE_TRANSIT - Origin Mix,oMGRAMix,,,,,,,,,coef_oMix_wTran,,,,,, +util_BIKE_TRANSIT - Origin Intersection Density,BIKE_TRANSIT - Origin Intersection Density,oMGRATotInt,,,,,,,,,coef_oIntDen_wTran,,,,,, +util_BIKE_TRANSIT - Destination Employment Density,BIKE_TRANSIT - Destination Employment Density,dMGRAEmpDen,,,,,,,,,coef_dEmpDen_wTran,,,,,, +#,Taxi,,,,,,,,,,,,,,,, +util_Taxi_Unavailable,Taxi - Unavailable,RideHail_available==0,,,,,,,,,,-999,,,,, +util_Taxi - In-vehicle time,Taxi - In-vehicle time,(s2_time_skims) * time_factor,,,,,,,,,,coef_ivt,,,,, +util_Taxi - Wait time,Taxi - Wait time,origTaxiWaitTime * time_factor,,,,,,,,,,coef_wait,,,,, +util_Taxi - Fare,Taxi - Fare,"@(((Taxi_baseFare + df.s2_dist_skims * Taxi_costPerMile + df.s2_time_skims * Taxi_costPerMinute) * 100 + df.s2_cost_skims)) / (np.maximum(df.income,1000)**df.income_exponent)",,,,,,,,,,coef_income,,,,, +#,TNC Single,,,,,,,,,,,,,,,, +util_TNC Single_Unavailable,TNC Single - Unavailable,RideHail_available==0,,,,,,,,,,,-999,,,, +util_TNC Single - In-vehicle time,TNC Single - In-vehicle time,(s2_time_skims) * time_factor,,,,,,,,,,,coef_ivt,,,, +util_TNC Single - Wait time,TNC Single - Wait time,origSingleTNCWaitTime * time_factor,,,,,,,,,,,coef_wait,,,, +util_TNC Single - Cost,TNC Single - Cost,"@((np.maximum(TNC_single_baseFare + df.s2_dist_skims * TNC_single_costPerMile + df.s2_time_skims * TNC_single_costPerMinute, TNC_single_costMinimum) * 100 + df.s2_cost_skims)) / (np.maximum(df.income,1000)**df.income_exponent)",,,,,,,,,,,coef_income,,,, +#,TNC Shared,,,,,,,,,,,,,,,, +util_TNC Shared_switch,TNC Shared - switch turn-off (depends on data availability),@(((~df.nev_available) & (~df.microtransit_available) & (scenarioYear==2022)) | (df.RideHail_available==0)),,,,,,,,,,,,-999,,, +util_TNC Shared - In-vehicle time,TNC Shared - In-vehicle time,"@(np.where(df.nev_available, df.nev_time, np.where(df.microtransit_available, df.microtransit_time, df.s3_time_skims * TNC_shared_IVTFactor))) * df.time_factor",,,,,,,,,,,,coef_ivt,,, +util_TNC Shared - Wait time,TNC Shared - Wait time,"@np.where(df.nev_available, nevWaitTime, np.where(df.microtransit_available, microtransitWaitTime, df.origSharedTNCWaitTime)) * df.time_factor",,,,,,,,,,,,coef_wait,,, +util_TNC Shared - Cost,TNC Shared - Cost,"@np.where(df.nev_available, nevCost, np.where(df.microtransit_available, microtransitCost, (np.maximum(TNC_shared_baseFare + df.s3_dist_skims * TNC_shared_costPerMile + df.s3_time_skims * TNC_shared_costPerMinute, TNC_shared_costMinimum) * 100 + df.s3_cost_skims))) / (np.maximum(df.income,1000)**df.income_exponent)",,,,,,,,,,,,coef_income,,, +util_calib_flexfleet,Calibration for flexible fleets,microtransit_available | nev_available,,,,,,,,,,,,coef_calib_flexfleet,,, +#,School bus,,,,,,,,,,,,,,,, +util_School Bus - Unavailable,School Bus - Unavailable,SCHBUS_available==0,,,,,,,,,,,,,-999,, +util_School Bus - In-vehicle Time at 20 miles per hour,School Bus - In-vehicle Time at 20 miles per hour,(da_dist_skims)* 3 * time_factor,,,,,,,,,,,,,coef_ivt,, +util_School Bus - Wait Time (asserted),School Bus - Wait Time (asserted),10 * time_factor,,,,,,,,,,,,,coef_wait,, +util_School Bus - Walk Time (asserted),School Bus - Walk Time (asserted),10 * time_factor,,,,,,,,,,,,,coef_acctime,, +util_School Bus - Age 6 to 12,School Bus - Age 6 to 12,@(df.age > 5) * (df.age < 13),,,,,,,,,,,,,coef_age6to12_schb,, +#,Escooter,,,,,,,,,,,,,,,, +util_Escooter - Unavailable,Escooter - Unavailable,Escooter_available==0,,,,,,,,,,,,,,,-999 +#,Ebike,,,,,,,,,,,,,,,, +util_Ebike - Unavailable,Ebike - Unavailable,Ebike_available==0,,,,,,,,,,,,,,-999, +#,indiv tour ASCs,,,,,,,,,,,,,,,, +util_calib_numberofparticipan,abm 2+ calibration,(numberOfParticipantsInJointTour<=2)&(jointTour==1),,,coef_calib_numberofparticipants_SHARED3,,,,,,,,,,,, +util_calib_KNR Transit - Distance Parameter,abm 2+ calibration,"@df.c_ivt*5*np.maximum((10-df.da_dist_skims),0)*0.5",,,,,,,,coef_calib_civt5max10sovdistski_KNR_TRANSIT,coef_calib_civt5max10sovdistski_TNC_TRANSIT,,,,,, +util_calib_Walk-Transit - Distance Parameter,abm 2+ calibration,"@df.c_ivt*np.maximum((200+(-133*df.da_dist_skims)),0)*0.5",,,,,,coef_calib_civtmax200133sovdist_WALK_TRANSIT,,,,,,,,, +util_calib_PNR-Transit - Distance Parameter,abm 2+ calibration,"@df.c_ivt*np.maximum((45+(-2.5*df.da_dist_skims)),0)*0.5",,,,,,,coef_calib_civtmax4525sovdistsk_PNR_TRANSIT,,,,,,,, +util_calib_esctour1,abm 2+ calibration,tour_type=='escort',,,,coef_calib_esctour1_WALK,,,,,,,,,,, +#,,,,,,,,,,,,,,,,, +util_calib_tourda,abm 2+ calibration,tourDA,,coef_calib_tourda_SHARED2,coef_calib_tourda_SHARED3,coef_calib_tourda_WALK,coef_calib_tourda_BIKE,coef_calib_tourda_WALK_TRANSIT,coef_calib_tourda_PNR_TRANSIT,coef_calib_tourda_KNR_TRANSIT,coef_calib_tourda_TNC_TRANSIT,coef_calib_tourda_TAXI,coef_calib_tourda_TNC_SINGLE,coef_calib_tourda_TNC_SHARED,coef_calib_tourda_SCH_BUS,-999,-999 +util_calib_tours2,abm 2+ calibration,tourS2,coef_calib_tours2_DRIVEALONE,coef_calib_tours2_SHARED2,coef_calib_tours2_SHARED3,coef_calib_tours2_WALK,coef_calib_tours2_BIKE,coef_calib_tours2_WALK_TRANSIT,coef_calib_tours2_PNR_TRANSIT,coef_calib_tours2_KNR_TRANSIT,coef_calib_tours2_TNC_TRANSIT,coef_calib_tours2_TAXI,coef_calib_tours2_TNC_SINGLE,coef_calib_tours2_TNC_SHARED,coef_calib_tours2_SCH_BUS,0,0 +util_calib_tours3,abm 2+ calibration,tourS3,coef_calib_tours3_DRIVEALONE,coef_calib_tours3_SHARED2,coef_calib_tours3_SHARED3,coef_calib_tours3_WALK,coef_calib_tours3_BIKE,coef_calib_tours3_WALK_TRANSIT,coef_calib_tours3_PNR_TRANSIT,coef_calib_tours3_KNR_TRANSIT,coef_calib_tours3_TNC_TRANSIT,coef_calib_tours3_TAXI,coef_calib_tours3_TNC_SINGLE,coef_calib_tours3_TNC_SHARED,coef_calib_tours3_SCH_BUS,0,0 +util_calib_tourwalk,abm 2+ calibration,tourWalk,coef_calib_tourwalk_DRIVEALONE,coef_calib_tourwalk_SHARED2,coef_calib_tourwalk_SHARED3,0,coef_calib_tourwalk_BIKE,coef_calib_tourwalk_WALK_TRANSIT,coef_calib_tourwalk_PNR_TRANSIT,coef_calib_tourwalk_KNR_TRANSIT,coef_calib_tourwalk_TNC_TRANSIT,coef_calib_tourwalk_TAXI,coef_calib_tourwalk_TNC_SINGLE,coef_calib_tourwalk_TNC_SHARED,coef_calib_tourwalk_SCH_BUS,-999,-999 +util_calib_tourbike,abm 2+ calibration,tourBike,coef_calib_tourbike_DRIVEALONE,coef_calib_tourbike_SHARED2,coef_calib_tourbike_SHARED3,coef_calib_tourbike_WALK,coef_calib_tourbike_BIKE,coef_calib_tourbike_WALK_TRANSIT,coef_calib_tourbike_PNR_TRANSIT,coef_calib_tourbike_KNR_TRANSIT,coef_calib_tourbike_TNC_TRANSIT,coef_calib_tourbike_TAXI,coef_calib_tourbike_TNC_SINGLE,coef_calib_tourbike_TNC_SHARED,coef_calib_tourbike_SCH_BUS,-999,-999 +util_calib_tourwtran,abm 2+ calibration,tourWTran,coef_calib_tourwtran_DRIVEALONE,coef_calib_tourwtran_SHARED2,coef_calib_tourwtran_SHARED3,coef_calib_tourwtran_WALK,coef_calib_tourwtran_BIKE,coef_calib_tourwtran_WALK_TRANSIT,coef_calib_tourwtran_PNR_TRANSIT,coef_calib_tourwtran_KNR_TRANSIT,coef_calib_tourwtran_TNC_TRANSIT,coef_calib_tourwtran_TAXI,coef_calib_tourwtran_TNC_SINGLE,coef_calib_tourwtran_TNC_SHARED,coef_calib_tourwtran_SCH_BUS,0,0 +util_calib_tourpnr,abm 2+ calibration,tourPNR,coef_calib_tourpnr_DRIVEALONE,coef_calib_tourpnr_SHARED2,coef_calib_tourpnr_SHARED3,coef_calib_tourpnr_WALK,coef_calib_tourpnr_BIKE,coef_calib_tourpnr_WALK_TRANSIT,coef_calib_tourpnr_PNR_TRANSIT,coef_calib_tourpnr_KNR_TRANSIT,coef_calib_tourpnr_TNC_TRANSIT,coef_calib_tourpnr_TAXI,coef_calib_tourpnr_TNC_SINGLE,coef_calib_tourpnr_TNC_SHARED,coef_calib_tourpnr_SCH_BUS,-999,-999 +util_calib_tourknr,abm 2+ calibration,tourKNR,coef_calib_tourknr_DRIVEALONE,coef_calib_tourknr_SHARED2,coef_calib_tourknr_SHARED3,coef_calib_tourknr_WALK,coef_calib_tourknr_BIKE,coef_calib_tourknr_WALK_TRANSIT,coef_calib_tourknr_PNR_TRANSIT,coef_calib_tourknr_KNR_TRANSIT,coef_calib_tourknr_TNC_TRANSIT,coef_calib_tourknr_TAXI,coef_calib_tourknr_TNC_SINGLE,coef_calib_tourknr_TNC_SHARED,coef_calib_tourknr_SCH_BUS,-999,-999 +util_calib_tourtnr,abm 2+ calibration,tourBNR,coef_calib_tourtnr_DRIVEALONE,0,0,0,coef_calib_tourtnr_BIKE,0,coef_calib_tourtnr_PNR_TRANSIT,0,0,0,0,0,coef_calib_tourtnr_SCH_BUS,-999,-999 +util_calib_tourmaas,abm 2+ calibration,tourMaaS,coef_calib_tourmaas_DRIVEALONE,coef_calib_tourmaas_SHARED2,coef_calib_tourmaas_SHARED3,coef_calib_tourmaas_WALK,coef_calib_tourmaas_BIKE,coef_calib_tourmaas_WALK_TRANSIT,coef_calib_tourmaas_PNR_TRANSIT,coef_calib_tourmaas_KNR_TRANSIT,coef_calib_tourmaas_TNC_TRANSIT,coef_calib_tourmaas_TAXI,coef_calib_tourmaas_TNC_SINGLE,coef_calib_tourmaas_TNC_SHARED,coef_calib_tourmaas_TNC_SCH_BUS,0,0 +util_calib_tourschbus,abm 2+ calibration,tourSchBus,coef_calib_tourschbus_DRIVEALONE,coef_calib_tourschbus_SHARED2,coef_calib_tourschbus_SHARED3,coef_calib_tourschbus_WALK,coef_calib_tourschbus_BIKE,coef_calib_tourschbus_WALK_TRANSIT,coef_calib_tourschbus_PNR_TRANSIT,coef_calib_tourschbus_KNR_TRANSIT,coef_calib_tourschbus_TNC_TRANSIT,coef_calib_tourschbus_TAXI,coef_calib_tourschbus_TNC_SINGLE,coef_calib_tourschbus_TNC_SHARED,0,-999,-999 +util_availability_tourebike,,tourEbike,-999,-999,-999,0,-999,-999,-999,-999,-999,-999,-999,-999,-999,0,-999 +util_availability_tourescooter,,tourEscooter,-999,-999,-999,0,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,0 +#,,,,,,,,,,,,,,,,, +util_calib_jointtour1,abm 2+ calibration,jointTour==1,0,0,0,coef_calib_jointtour1_WALK,0,coef_calib_jointtour1_WALK_TRANSIT,coef_calib_jointtour1_PNR_TRANSIT,coef_calib_jointtour1_KNR_TRANSIT,coef_calib_jointtour1_TNC_TRANSIT,0,0,0,0,, +util_calib_tourbikejointtour0,abm 2+ calibration,tourBike*(jointTour==0),coef_calib_tourbikejointtour0_DRIVEALONE,coef_calib_tourbikejointtour0_SHARED2,coef_calib_tourbikejointtour0_SHARED3,coef_calib_tourbikejointtour0_WALK,coef_calib_tourbikejointtour0_BIKE,coef_calib_tourbikejointtour0_WALK_TRANSIT,coef_calib_tourbikejointtour0_PNR_TRANSIT,coef_calib_tourbikejointtour0_KNR_TRANSIT,coef_calib_tourbikejointtour0_TNC_TRANSIT,coef_calib_tourbikejointtour0_TAXI,coef_calib_tourbikejointtour0_TNC_SINGLE,coef_calib_tourbikejointtour0_TNC_SHARED,0,, +util_calib_tourbikejointtour1,abm 2+ calibration,tourBike*(jointTour==1),0,0,0,0,coef_calib_tourbikejointtour1_BIKE,0,0,0,0,coef_calib_tourbikejointtour1_TAXI,coef_calib_tourbikejointtour1_TNC_SINGLE,coef_calib_tourbikejointtour1_TNC_SHARED,0,, +util_calib_tourdajointtour0,abm 2+ calibration,tourDA*(jointTour==0),coef_calib_tourdajointtour0_DRIVEALONE,coef_calib_tourdajointtour0_SHARED2,coef_calib_tourdajointtour0_SHARED3,coef_calib_tourdajointtour0_WALK,coef_calib_tourdajointtour0_BIKE,coef_calib_tourdajointtour0_WALK_TRANSIT,coef_calib_tourdajointtour0_PNR_TRANSIT,coef_calib_tourdajointtour0_KNR_TRANSIT,coef_calib_tourdajointtour0_TNC_TRANSIT,coef_calib_tourdajointtour0_TAXI,coef_calib_tourdajointtour0_TNC_SINGLE,coef_calib_tourdajointtour0_TNC_SHARED,0,, +util_calib_tourdajointtour1,abm 2+ calibration,tourDA*(jointTour==1),0,coef_calib_tourdajointtour1_SHARED2,coef_calib_tourdajointtour1_SHARED3,coef_calib_tourdajointtour1_WALK,coef_calib_tourdajointtour1_BIKE,coef_calib_tourdajointtour1_WALK_TRANSIT,coef_calib_tourdajointtour1_PNR_TRANSIT,coef_calib_tourdajointtour1_KNR_TRANSIT,coef_calib_tourdajointtour1_TNC_TRANSIT,coef_calib_tourdajointtour1_TAXI,coef_calib_tourdajointtour1_TNC_SINGLE,coef_calib_tourdajointtour1_TNC_SHARED,0,, +util_calib_tourknrjointtour0,abm 2+ calibration,tourKNR*(jointTour==0),coef_calib_tourknrjointtour0_DRIVEALONE,coef_calib_tourknrjointtour0_SHARED2,coef_calib_tourknrjointtour0_SHARED3,coef_calib_tourknrjointtour0_WALK,coef_calib_tourknrjointtour0_BIKE,coef_calib_tourknrjointtour0_WALK_TRANSIT,coef_calib_tourknrjointtour0_PNR_TRANSIT,coef_calib_tourknrjointtour0_KNR_TRANSIT,coef_calib_tourknrjointtour0_TNC_TRANSIT,coef_calib_tourknrjointtour0_TAXI,coef_calib_tourknrjointtour0_TNC_SINGLE,coef_calib_tourknrjointtour0_TNC_SHARED,0,, +util_calib_tourknrjointtour1,abm 2+ calibration,tourKNR*(jointTour==1),coef_calib_tourknrjointtour1_DRIVEALONE,0,0,0,coef_calib_tourknrjointtour1_BIKE,0,coef_calib_tourknrjointtour1_PNR_TRANSIT,0,coef_calib_tourknrjointtour1_TNC_TRANSIT,coef_calib_tourknrjointtour1_TAXI,coef_calib_tourknrjointtour1_TNC_SINGLE,coef_calib_tourknrjointtour1_TNC_SHARED,0,, +util_calib_tourknrtotstops0,abm 2+ calibration,tourKNR*(totStops==0),coef_calib_tourknrtotstops0_DRIVEALONE,coef_calib_tourknrtotstops0_SHARED2,coef_calib_tourknrtotstops0_SHARED3,coef_calib_tourknrtotstops0_WALK,coef_calib_tourknrtotstops0_BIKE,coef_calib_tourknrtotstops0_WALK_TRANSIT,coef_calib_tourknrtotstops0_PNR_TRANSIT,0,0,0,0,0,0,, +util_calib_tourknrtotstops>0,abm 2+ calibration,tourKNR*(totStops>0),coef_calib_tourknrtotstopsm0_DRIVEALONE,0,0,0,coef_calib_tourknrtotstopsm0_BIKE,0,coef_calib_tourknrtotstopsm0_PNR_TRANSIT,0,0,0,0,0,0,, +util_calib_tourknrfirstofmult,abm 2+ calibration,tourKNR*firstOfMultipleTrips,coef_calib_tourknrfirstofmultip_DRIVEALONE,0,0,0,coef_calib_tourknrfirstofmultip_BIKE,0,coef_calib_tourknrfirstofmultip_PNR_TRANSIT,0,0,0,0,0,0,, +util_calib_tourknrlastofmulti,abm 2+ calibration,tourKNR*lastofMultipleTrips,coef_calib_tourknrlastofmultipl_DRIVEALONE,0,0,0,coef_calib_tourknrlastofmultipl_BIKE,0,coef_calib_tourknrlastofmultipl_PNR_TRANSIT,0,0,0,0,0,0,, +util_calib_tourebikejointtour0,,tourEbike*(jointTour==0),,,,coef_calib_tourebikejointtour0_WALK,,,,,,,,,,coef_calib_tourebikejointtour0_EBIKE, +util_calib_tourebikejointtour1,,tourEbike*(jointTour==1),,,,coef_calib_tourebikejointtour1_WALK,,,,,,,,,,coef_calib_tourebikejointtour1_EBIKE, +util_calib_tourescooterjointtour0,,tourEscooter*(jointTour==0),,,,coef_calib_tourescooterjointtour0_WALK,,,,,,,,,,,coef_calib_tourescooterjointtour0_ESCOOTER +util_calib_tourescooterjointtour1,,tourEscooter*(jointTour==1),,,,coef_calib_tourescooterjointtour1_WALK,,,,,,,,,,,coef_calib_tourescooterjointtour1_ESCOOTER +#,,,,,,,,,,,,,,,,, +util_calib_tourmaasjointtour0,abm 2+ calibration,tourMaaS*(jointTour==0),0,coef_calib_tourmaasjointtour0_SHARED2,coef_calib_tourmaasjointtour0_SHARED3,coef_calib_tourmaasjointtour0_WALK,0,0,0,0,0,coef_calib_tourmaasjointtour0_TAXI,coef_calib_tourmaasjointtour0_TNC_SINGLE,coef_calib_tourmaasjointtour0_TNC_SHARED,0,, +util_calib_tourmaasjointtour1,abm 2+ calibration,tourMaaS*(jointTour==1),0,coef_calib_tourmaasjointtour1_SHARED2,coef_calib_tourmaasjointtour1_SHARED3,coef_calib_tourmaasjointtour1_WALK,0,0,0,0,0,0,coef_calib_tourmaasjointtour1_TNC_SINGLE,coef_calib_tourmaasjointtour1_TNC_SHARED,0,, +util_calib_tourpnrjointtour0,abm 2+ calibration,tourPNR*(jointTour==0),coef_calib_tourpnrjointtour0_DRIVEALONE,coef_calib_tourpnrjointtour0_SHARED2,coef_calib_tourpnrjointtour0_SHARED3,coef_calib_tourpnrjointtour0_WALK,coef_calib_tourpnrjointtour0_BIKE,coef_calib_tourpnrjointtour0_WALK_TRANSIT,coef_calib_tourpnrjointtour0_PNR_TRANSIT,coef_calib_tourpnrjointtour0_KNR_TRANSIT,coef_calib_tourpnrjointtour0_TNC_TRANSIT,coef_calib_tourpnrjointtour0_TAXI,coef_calib_tourpnrjointtour0_TNC_SINGLE,coef_calib_tourpnrjointtour0_TNC_SHARED,0,, +util_calib_tourpnrjointtour1,abm 2+ calibration,tourPNR*(jointTour==1),0,0,0,0,coef_calib_tourpnrjointtour1_BIKE,0,0,coef_calib_tourpnrjointtour1_KNR_TRANSIT,coef_calib_tourpnrjointtour1_TNC_TRANSIT,coef_calib_tourpnrjointtour1_TAXI,coef_calib_tourpnrjointtour1_TNC_SINGLE,coef_calib_tourpnrjointtour1_TNC_SHARED,0,, +util_calib_tourpnrtotstops0,abm 2+ calibration,tourPNR*(totStops==0),coef_calib_tourpnrtotstops0_DRIVEALONE,coef_calib_tourpnrtotstops0_SHARED2,coef_calib_tourpnrtotstops0_SHARED3,coef_calib_tourpnrtotstops0_WALK,coef_calib_tourpnrtotstops0_BIKE,coef_calib_tourpnrtotstops0_WALK_TRANSIT,0,coef_calib_tourpnrtotstops0_KNR_TRANSIT,coef_calib_tourpnrtotstops0_TNC_TRANSIT,0,0,0,0,, +util_calib_tourpnrtotstops>0,abm 2+ calibration,tourPNR*(totStops>0),0,0,0,0,coef_calib_tourpnrtotstopsm0_BIKE,coef_calib_tourpnrtotstopsm0_WALK_TRANSIT,0,coef_calib_tourpnrtotstopsm0_KNR_TRANSIT,coef_calib_tourpnrtotstopsm0_TNC_TRANSIT,0,0,0,0,, +util_calib_tourpnrfirstofmult,abm 2+ calibration,tourPNR*firstOfMultipleTrips,0,0,0,0,coef_calib_tourpnrfirstofmultip_BIKE,0,0,coef_calib_tourpnrfirstofmultip_KNR_TRANSIT,coef_calib_tourpnrfirstofmultip_TNC_TRANSIT,0,0,0,0,, +util_calib_tourpnrlastofmulti,abm 2+ calibration,tourPNR*lastofMultipleTrips,0,0,0,0,coef_calib_tourpnrlastofmultipl_BIKE,0,0,coef_calib_tourpnrlastofmultipl_KNR_TRANSIT,coef_calib_tourpnrlastofmultipl_TNC_TRANSIT,0,0,0,0,, +util_calib_tours2jointtour0,abm 2+ calibration,tourS2*(jointTour==0),coef_calib_tours2jointtour0_DRIVEALONE,coef_calib_tours2jointtour0_SHARED2,coef_calib_tours2jointtour0_SHARED3,coef_calib_tours2jointtour0_WALK,coef_calib_tours2jointtour0_BIKE,coef_calib_tours2jointtour0_WALK_TRANSIT,coef_calib_tours2jointtour0_PNR_TRANSIT,coef_calib_tours2jointtour0_KNR_TRANSIT,coef_calib_tours2jointtour0_TNC_TRANSIT,coef_calib_tours2jointtour0_TAXI,coef_calib_tours2jointtour0_TNC_SINGLE,coef_calib_tours2jointtour0_TNC_SHARED,0,, +util_calib_tours2jointtour1,abm 2+ calibration,tourS2*(jointTour==1),coef_calib_tours2jointtour1_DRIVEALONE,coef_calib_tours2jointtour1_SHARED2,coef_calib_tours2jointtour1_SHARED3,coef_calib_tours2jointtour1_WALK,coef_calib_tours2jointtour1_BIKE,coef_calib_tours2jointtour1_WALK_TRANSIT,coef_calib_tours2jointtour1_PNR_TRANSIT,coef_calib_tours2jointtour1_KNR_TRANSIT,coef_calib_tours2jointtour1_TNC_TRANSIT,coef_calib_tours2jointtour1_TAXI,coef_calib_tours2jointtour1_TNC_SINGLE,coef_calib_tours2jointtour1_TNC_SHARED,0,, +#,,,,,,,,,,,,,,,,, +util_calib_tours2totstops0,abm 2+ calibration,tourS2*(totStops==0),coef_calib_tours2totstops0_DRIVEALONE,0,coef_calib_tours2totstops0_SHARED3,coef_calib_tours2totstops0_WALK,coef_calib_tours2totstops0_BIKE,coef_calib_tours2totstops0_WALK_TRANSIT,coef_calib_tours2totstops0_PNR_TRANSIT,coef_calib_tours2totstops0_KNR_TRANSIT,coef_calib_tours2totstops0_TNC_TRANSIT,0,0,0,0,, +util_calib_tours2firstofmulti,abm 2+ calibration,tourS2*firstOfMultipleTrips,coef_calib_tours2firstofmultipl_DRIVEALONE,0,coef_calib_tours2firstofmultipl_SHARED3,coef_calib_tours2firstofmultipl_WALK,coef_calib_tours2firstofmultipl_BIKE,coef_calib_tours2firstofmultipl_WALK_TRANSIT,coef_calib_tours2firstofmultipl_PNR_TRANSIT,coef_calib_tours2firstofmultipl_KNR_TRANSIT,coef_calib_tours2firstofmultipl_TNC_TRANSIT,0,0,0,0,, +util_calib_tours2lastofmultip,abm 2+ calibration,tourS2*lastofMultipleTrips,coef_calib_tours2lastofmultiple_DRIVEALONE,0,coef_calib_tours2lastofmultiple_SHARED3,coef_calib_tours2lastofmultiple_WALK,coef_calib_tours2lastofmultiple_BIKE,coef_calib_tours2lastofmultiple_WALK_TRANSIT,coef_calib_tours2lastofmultiple_PNR_TRANSIT,coef_calib_tours2lastofmultiple_KNR_TRANSIT,coef_calib_tours2lastofmultiple_TNC_TRANSIT,0,0,0,0,, +util_calib_tours3jointtour0,abm 2+ calibration,tourS3*(jointTour==0),coef_calib_tours3jointtour0_DRIVEALONE,coef_calib_tours3jointtour0_SHARED2,coef_calib_tours3jointtour0_SHARED3,coef_calib_tours3jointtour0_WALK,coef_calib_tours3jointtour0_BIKE,coef_calib_tours3jointtour0_WALK_TRANSIT,coef_calib_tours3jointtour0_PNR_TRANSIT,coef_calib_tours3jointtour0_KNR_TRANSIT,coef_calib_tours3jointtour0_TNC_TRANSIT,coef_calib_tours3jointtour0_TAXI,coef_calib_tours3jointtour0_TNC_SINGLE,coef_calib_tours3jointtour0_TNC_SHARED,0,, +util_calib_tours3jointtour1,abm 2+ calibration,tourS3*(jointTour==1),coef_calib_tours3jointtour1_DRIVEALONE,coef_calib_tours3jointtour1_SHARED2,coef_calib_tours3jointtour1_SHARED3,coef_calib_tours3jointtour1_WALK,coef_calib_tours3jointtour1_BIKE,coef_calib_tours3jointtour1_WALK_TRANSIT,coef_calib_tours3jointtour1_PNR_TRANSIT,coef_calib_tours3jointtour1_KNR_TRANSIT,coef_calib_tours3jointtour1_TNC_TRANSIT,coef_calib_tours3jointtour1_TAXI,coef_calib_tours3jointtour1_TNC_SINGLE,coef_calib_tours3jointtour1_TNC_SHARED,0,, +util_calib_tours3totstops0,abm 2+ calibration,tourS3*(totStops==0),coef_calib_tours3totstops0_DRIVEALONE,coef_calib_tours3totstops0_SHARED2,0,coef_calib_tours3totstops0_WALK,coef_calib_tours3totstops0_BIKE,coef_calib_tours3totstops0_WALK_TRANSIT,coef_calib_tours3totstops0_PNR_TRANSIT,coef_calib_tours3totstops0_KNR_TRANSIT,coef_calib_tours3totstops0_TNC_TRANSIT,0,0,0,0,, +util_calib_tours3autodeficien,abm 2+ calibration,tourS3*autoDeficientHH,0,coef_calib_tours3autodeficienth_SHARED2,0,0,0,0,0,0,0,0,0,0,0,, +util_calib_tours3firstofmulti,abm 2+ calibration,tourS3*firstOfMultipleTrips,coef_calib_tours3firstofmultipl_DRIVEALONE,coef_calib_tours3firstofmultipl_SHARED2,0,coef_calib_tours3firstofmultipl_WALK,coef_calib_tours3firstofmultipl_BIKE,coef_calib_tours3firstofmultipl_WALK_TRANSIT,coef_calib_tours3firstofmultipl_PNR_TRANSIT,coef_calib_tours3firstofmultipl_KNR_TRANSIT,coef_calib_tours3firstofmultipl_TNC_TRANSIT,0,0,0,0,, +util_calib_tours3lastofmultip,abm 2+ calibration,tourS3*lastofMultipleTrips,coef_calib_tours3lastofmultiple_DRIVEALONE,coef_calib_tours3lastofmultiple_SHARED2,0,coef_calib_tours3lastofmultiple_WALK,coef_calib_tours3lastofmultiple_BIKE,coef_calib_tours3lastofmultiple_WALK_TRANSIT,coef_calib_tours3lastofmultiple_PNR_TRANSIT,coef_calib_tours3lastofmultiple_KNR_TRANSIT,coef_calib_tours3lastofmultiple_TNC_TRANSIT,0,0,0,0,, +util_calib_tours3zeroautohh,abm 2+ calibration,tourS3*zeroAutoHH,0,coef_calib_tours3zeroautohh_SHARED2,0,0,0,0,0,0,0,0,0,0,0,, +#,,,,,,,,,,,,,,,,, +util_calib_tourwtranjointtour0,abm 2+ calibration,tourWTran*(jointTour==0),coef_calib_tourwtranjointtour0_DRIVEALONE,coef_calib_tourwtranjointtour0_SHARED2,coef_calib_tourwtranjointtour0_SHARED3,coef_calib_tourwtranjointtour0_WALK,coef_calib_tourwtranjointtour0_BIKE,coef_calib_tourwtranjointtour0_WALK_TRANSIT,coef_calib_tourwtranjointtour0_PNR_TRANSIT,coef_calib_tourwtranjointtour0_KNR_TRANSIT,coef_calib_tourwtranjointtour0_TNC_TRANSIT,0,coef_calib_tourwtranjointtour0_TNC_SINGLE,coef_calib_tourwtranjointtour0_TNC_SHARED,0,, +util_calib_tourwtranjointtour1,abm 2+ calibration,tourWTran*(jointTour==1),coef_calib_tourwtranjointtour1_DRIVEALONE,coef_calib_tourwtranjointtour1_SHARED2,coef_calib_tourwtranjointtour1_SHARED3,coef_calib_tourwtranjointtour1_WALK,coef_calib_tourwtranjointtour1_BIKE,coef_calib_tourwtranjointtour1_WALK_TRANSIT,coef_calib_tourwtranjointtour1_PNR_TRANSIT,coef_calib_tourwtranjointtour1_KNR_TRANSIT,coef_calib_tourwtranjointtour1_TNC_TRANSIT,coef_calib_tourwtranjointtour1_TAXI,coef_calib_tourwtranjointtour1_TNC_SINGLE,coef_calib_tourwtranjointtour1_TNC_SHARED,0,, +util_calib_tourwtrantotstops0,abm 2+ calibration,tourWTran*(totStops==0),coef_calib_tourwtrantotstops0_DRIVEALONE,coef_calib_tourwtrantotstops0_SHARED2,coef_calib_tourwtrantotstops0_SHARED3,coef_calib_tourwtrantotstops0_WALK,coef_calib_tourwtrantotstops0_BIKE,0,coef_calib_tourwtrantotstops0_PNR_TRANSIT,coef_calib_tourwtrantotstops0_KNR_TRANSIT,coef_calib_tourwtrantotstops0_TNC_TRANSIT,0,0,0,coef_calib_tourwtrantotstops0_SCH_BUS,, +#,,,,,,,,,,,,,,,,, +util_calib_tourwtranfirstofmu,abm 2+ calibration,tourWTran*firstOfMultipleTrips,coef_calib_tourwtranfirstofmult_DRIVEALONE,coef_calib_tourwtranfirstofmult_SHARED2,coef_calib_tourwtranfirstofmult_SHARED3,coef_calib_tourwtranfirstofmult_WALK,coef_calib_tourwtranfirstofmult_BIKE,0,coef_calib_tourwtranfirstofmult_PNR_TRANSIT,coef_calib_tourwtranfirstofmult_KNR_TRANSIT,coef_calib_tourwtranfirstofmult_TNC_TRANSIT,0,0,0,coef_calib_tourwtranfirstofmult_SCH_BUS,, +util_calib_tourwtranlastofmul,abm 2+ calibration,tourWTran*lastofMultipleTrips,coef_calib_tourwtranlastofmulti_DRIVEALONE,coef_calib_tourwtranlastofmulti_SHARED2,coef_calib_tourwtranlastofmulti_SHARED3,coef_calib_tourwtranlastofmulti_WALK,coef_calib_tourwtranlastofmulti_BIKE,0,coef_calib_tourwtranlastofmulti_PNR_TRANSIT,coef_calib_tourwtranlastofmulti_KNR_TRANSIT,coef_calib_tourwtranlastofmulti_TNC_TRANSIT,0,0,0,coef_calib_tourwtranlastofmulti_SCH_BUS,, +util_calib_tourwtranzeroautoh,abm 2+ calibration,tourWTran*zeroAutoHH,0,0,0,coef_calib_tourwtranzeroautohh_WALK,0,0,0,0,0,0,0,0,0,, +util_calib_tourwalkjointtour0,abm 2+ calibration,tourWalk*(jointTour==0),coef_calib_tourwalkjointtour0_DRIVEALONE,coef_calib_tourwalkjointtour0_SHARED2,coef_calib_tourwalkjointtour0_SHARED3,coef_calib_tourwalkjointtour0_WALK,coef_calib_tourwalkjointtour0_BIKE,coef_calib_tourwalkjointtour0_WALK_TRANSIT,coef_calib_tourwalkjointtour0_PNR_TRANSIT,coef_calib_tourwalkjointtour0_KNR_TRANSIT,coef_calib_tourwalkjointtour0_TNC_TRANSIT,coef_calib_tourwalkjointtour0_TAXI,coef_calib_tourwalkjointtour0_TNC_SINGLE,coef_calib_tourwalkjointtour0_TNC_SHARED,0,, +util_calib_tourwalkjointtour1,abm 2+ calibration,tourWalk*(jointTour==1),coef_calib_tourwalkjointtour1_DRIVEALONE,coef_calib_tourwalkjointtour1_SHARED2,coef_calib_tourwalkjointtour1_SHARED3,0,coef_calib_tourwalkjointtour1_BIKE,coef_calib_tourwalkjointtour1_WALK_TRANSIT,coef_calib_tourwalkjointtour1_PNR_TRANSIT,coef_calib_tourwalkjointtour1_KNR_TRANSIT,coef_calib_tourwalkjointtour1_TNC_TRANSIT,coef_calib_tourwalkjointtour1_TAXI,coef_calib_tourwalkjointtour1_TNC_SINGLE,coef_calib_tourwalkjointtour1_TNC_SHARED,0,, +#,Micromobility,,,,,,,,,,,,,,,, +util_micromobility_long_access,Shut off micromobility if access time > threshold and not micromobility tour,@((df.MicroAccessTime > microAccessThreshold)& ~(df.tourEbike | df.tourEscooter)),,,,,,,,,,,,,,-999,-999 +util_ebike_long_access_microTour,Decrease ebike if access time > threshold but tour is micromobility,@((df.MicroAccessTime > microAccessThreshold) & (df.tourEbike | df.tourEscooter) & ((~df.ebike_owner) | (~df.tourEbike))),,,,,,,,,,,,,,-20, +util_escooter_long_access_microTour,Decrease escooter if access time > threshold but tour is micromobility,@((df.MicroAccessTime > microAccessThreshold) & (df.tourEbike | df.tourEscooter)),,,,,,,,,,,,,,,-20 +#util_micromobility_long_trip,Shut off ebike if distance > threshold,ebikeMaxDistance,,,,,,,,,,,,,,-999, +#util_micromobility_long_trip,Shut off escooter if distance > threshold,escooterMaxDistance,,,,,,,,,,,,,,,-999 +util_ebike_ivt,Ebike utility for in-vehicle time,@(df.ebike_time * df.time_factor),,,,,,,,,,,,,,coef_ivt, +util_ebike_access,Ebike utility for access time time,@((((~df.ebike_owner) | (~df.tourEbike))&(microRentTime + df.MicroAccessTime)))*df.time_factor,,,,,,,,,,,,,,coef_acctime, +util_ebike_cost_inb,Ebike utility for inbound cost,@(((~df.ebike_owner) | (~df.tourEbike)) & ((microFixedCost + microVarCost*df.ebike_time)/df.cost_sensitivity)),,,,,,,,,,,,,,coef_income, +util_escooter_ivt,Escooter utility for in-vehicle time,@(df.escooter_time)*df.time_factor,,,,,,,,,,,,,,,coef_ivt +util_escooter_access,Escooter utility for access time,@(microRentTime + df.MicroAccessTime) *df.time_factor,,,,,,,,,,,,,,,coef_acctime +util_escooter_cost_inb,Escooter utility for inbound cost,@(microFixedCost + microVarCost*df.escooter_time)/df.cost_sensitivity,,,,,,,,,,,,,,,coef_income diff --git a/resident/configs/trip_mode_choice.yaml b/resident/configs/trip_mode_choice.yaml new file mode 100644 index 0000000..7f87b22 --- /dev/null +++ b/resident/configs/trip_mode_choice.yaml @@ -0,0 +1,139 @@ +SPEC: trip_mode_choice.csv +COEFFICIENTS: trip_mode_choice_coefficients.csv +COEFFICIENT_TEMPLATE: trip_mode_choice_coefficients_template.csv + +LOGIT_TYPE: NL + +NESTS: + name: root + coefficient: coef_nest_root + alternatives: + - name: AUTO + coefficient: coef_nest_AUTO + alternatives: + - DRIVEALONE + - SHARED2 + - SHARED3 + - name: NONMOTORIZED + coefficient: coef_nest_NONMOTORIZED + alternatives: + - WALK + - BIKE + - name: MICROMOBILITY + coefficient: coef_nest_MICROMOBILITY + alternatives: + - EBIKE + - ESCOOTER + - name: TRANSIT + coefficient: coef_nest_TRANSIT + alternatives: + - WALK_TRANSIT + - PNR_TRANSIT + - KNR_TRANSIT + - BIKE_TRANSIT + - name: RIDEHAIL + coefficient: coef_nest_RIDEHAIL + alternatives: + - TAXI + - TNC_SINGLE + - TNC_SHARED + - name: SCHOOL_BUS + coefficient: coef_nest_SCHOOL_BUS + alternatives: + - SCH_BUS + +CONSTANTS: + orig_col_name: origin + dest_col_name: destination + + +# so far, we can use the same spec as for non-joint tours +preprocessor: + SPEC: trip_mode_choice_annotate_trips_preprocessor + DF: df + TABLES: + - land_use + - tours + - vehicles + - households + - persons + +# to reduce memory needs filter chooser table to these fields +TOURS_MERGED_CHOOSER_COLUMNS: + - hhsize + - age + - num_adults + - auto_ownership + - number_of_participants + - tour_category + - parent_tour_id + - tour_mode + - duration + - tour_type + - free_parking_at_work + - income_segment + - income + - SEX + - time_factor_work + - time_factor_nonwork + - ptype + - ebike_owner + - start + - end + - transit_pass_subsidy + - transit_pass_ownership + +MODE_CHOICE_LOGSUM_COLUMN_NAME: mode_choice_logsum + +CHOOSER_COLS_TO_KEEP: + - vot_da + - vot_s2 + - vot_s3 + - ebike_owner + - parkingCost + - auto_op_cost + - autoCPMFactor + - autoParkingCostFactor + - autoTermTimeFactor + - costFactorS2 + - costFactorS3 + - transitSubsidyPassDiscount + - origTaxiWaitTime + - origSingleTNCWaitTime + - destSingleTNCWaitTime + - origSharedTNCWaitTime + - da_dist_skims + - s2_time_skims + - s2_dist_skims + - s2_cost_skims + - s3_time_skims + - s3_dist_skims + - s3_cost_skims + - ebike_time + - escooter_time + - microtransit_orig + - microtransit_dest + - microtransit_operating + - microtransit_available + - microtransit_time + - nev_orig + - nev_dest + - nev_operating + - nev_available + - nev_time + - microtransit_local_access_available_out + - nev_local_access_available_out + - microtransit_local_egress_available_out + - nev_local_egress_available_out + - microtransit_local_access_available_in + - nev_local_access_available_in + - microtransit_local_egress_available_in + - nev_local_egress_available_in + - microtransit_local_access_time_out + - nev_local_access_time_out + - microtransit_local_egress_time_out + - nev_local_egress_time_out + - microtransit_local_access_time_in + - nev_local_access_time_in + - microtransit_local_egress_time_in + - nev_local_egress_time_in \ No newline at end of file diff --git a/resident/configs/trip_mode_choice_annotate_trips_preprocessor.csv b/resident/configs/trip_mode_choice_annotate_trips_preprocessor.csv new file mode 100644 index 0000000..a9fc5fb --- /dev/null +++ b/resident/configs/trip_mode_choice_annotate_trips_preprocessor.csv @@ -0,0 +1,249 @@ +Description,Target,Expression +,is_joint,(df.number_of_participants > 1) +,is_indiv,(df.number_of_participants == 1) +#,, +,dest_density_index,"reindex(land_use.density_index, df[dest_col_name])" +#,, +,tour_type,"df.get('tour_type', default='work')" +# cost coef,, +,income_exponent,"np.where(df.tour_type == 'work', 0.6, 0.5)" +,c_cost,"(coef_income) /(df.income.clip(0,1000).pow(income_exponent))" +,cost_sensitivity,"np.maximum(df.income,1000).pow(income_exponent)" +# ivt coef,, +,time_factor,"np.where(df.tour_type=='work', df.time_factor_work, df.time_factor_nonwork)" +,c_ivt,coef_ivt * time_factor +,origin,df.origin if 'origin' in df.columns else df.home_zone_id +,destination,df.destination if 'destination' in df.columns else df.alt_dest +#,, +# cost coef,, +,income_exponent,"np.where(tour_type == 'work', 0.6, 0.5)" +,c_cost,"(coef_income) /(np.maximum(df.income,1000).pow(income_exponent))" +#,, +,vot_da,c_ivt / c_cost * 0.6 +,vot_s2,vot_da / cost_share_s2 +,vot_s3,vot_da / cost_share_s3 +,_vot_bin_da,"np.where(vot_da < vot_threshold_low, 1, np.where(vot_da < vot_threshold_med, 2, 3))" +,_vot_bin_s2,"np.where(vot_s2 < vot_threshold_low, 1, np.where(vot_s2 < vot_threshold_med, 2, 3))" +,_vot_bin_s3,"np.where(vot_s3 < vot_threshold_low, 1, np.where(vot_s3 < vot_threshold_med, 2, 3))" +#vot-indexed skims,, +,da_dist_skims,"(odt_skims['SOV_L_DIST'] * (_vot_bin_da == 1)) + (odt_skims['SOV_M_DIST'] * (_vot_bin_da == 2)) + (odt_skims['SOV_H_DIST'] * (_vot_bin_da == 3))" +,da_cost_skims,"(odt_skims['SOV_L_COST'] * (_vot_bin_da == 1)) + (odt_skims['SOV_M_COST'] * (_vot_bin_da == 2)) + (odt_skims['SOV_H_COST'] * (_vot_bin_da == 3))" +,da_time_skims,"(odt_skims['SOV_L_TIME'] * (_vot_bin_da == 1)) + (odt_skims['SOV_M_TIME'] * (_vot_bin_da == 2)) + (odt_skims['SOV_H_TIME'] * (_vot_bin_da == 3))" +,s2_dist_skims,(((odt_skims['SR2_L_DIST']) * (_vot_bin_s2 == 1)) + ((odt_skims['SR2_M_DIST']) * (_vot_bin_s2 == 2)) + ((odt_skims['SR2_H_DIST']) * (_vot_bin_s2 == 3))) +,s2_cost_skims,(((odt_skims['SR2_L_COST']) * (_vot_bin_s2 == 1)) + ((odt_skims['SR2_M_COST']) * (_vot_bin_s2 == 2)) + ((odt_skims['SR2_H_COST']) * (_vot_bin_s2 == 3))) +,s2_time_skims,(((odt_skims['SR2_L_TIME']) * (_vot_bin_s2 == 1)) + ((odt_skims['SR2_M_TIME']) * (_vot_bin_s2 == 2)) + ((odt_skims['SR2_H_TIME']) * (_vot_bin_s2 == 3))) +,s3_dist_skims,(((odt_skims['SR3_L_DIST']) * (_vot_bin_s3 == 1)) + ((odt_skims['SR3_M_DIST']) * (_vot_bin_s3 == 2)) + ((odt_skims['SR3_H_DIST']) * (_vot_bin_s3 == 3))) +,s3_cost_skims,(((odt_skims['SR3_L_COST']) * (_vot_bin_s3 == 1)) + ((odt_skims['SR3_M_COST']) * (_vot_bin_s3 == 2)) + ((odt_skims['SR3_H_COST']) * (_vot_bin_s3 == 3))) +,s3_time_skims,(((odt_skims['SR3_L_TIME']) * (_vot_bin_s3 == 1)) + ((odt_skims['SR3_M_TIME']) * (_vot_bin_s3 == 2)) + ((odt_skims['SR3_H_TIME']) * (_vot_bin_s3 == 3))) +,walk_time_skims,od_skims['DISTWALK'] / walkSpeed * 60 +#,, +# FIXME no transit subzones so all zones short walk to transit,, +,_walk_transit_origin,True +,_walk_transit_destination,True +,walk_transit_available,_walk_transit_origin & _walk_transit_destination +,drive_transit_available,"np.where(df.outbound, _walk_transit_destination, _walk_transit_origin) & (df.auto_ownership > 0)" +# RIDEHAIL,, +# FIXME population_density replaced PopDenPerSqMi which may not be calculated the same,, +household_density calculated in annotate_landuse in acres and is converted to sq miles here,_origin_density_measure,"reindex(land_use.population_density, df[orig_col_name])" +employment_density calculated in annotate_landuse in acres and is converted to sq miles here,_dest_density_measure,"reindex(land_use.population_density, df[dest_col_name])" +,origin_density,"pd.cut(_origin_density_measure, bins=[-np.inf, 500, 2000, 5000, 15000, np.inf], labels=[5, 4, 3, 2, 1]).astype(int)" +,dest_density,"pd.cut(_dest_density_measure, bins=[-np.inf, 500, 2000, 5000, 15000, np.inf], labels=[5, 4, 3, 2, 1]).astype(int)" +Origin MGRA Dwelling Unit Density,oMGRADUDen,"reindex(land_use.duden,df.origin)" +Origin MGRA Employment Density,oMGRAEmpDen,"reindex(land_use.empden,df.origin)" +Origin MGRA Total Intersections,oMGRATotInt,"reindex(land_use.totint,df.origin)" +Destination MGRA Dwelling Unit Density,dMGRADUDen,"reindex(land_use.duden,df.destination)" +Destination MGRA Employment Density,dMGRAEmpDen,"reindex(land_use.empden,df.destination)" +Destination MGRA Total Intersections,dMGRATotInt,"reindex(land_use.totint,df.destination)" +Origin MGRA Mix,oMGRAMix,"np.where(oMGRADUDen+oMGRAEmpDen > 0,(oMGRADUDen*oMGRAEmpDen)/(oMGRADUDen+oMGRAEmpDen),0)" +Destination MGRA Mix,dMGRAMix,"np.where(dMGRADUDen+dMGRAEmpDen > 0,(dMGRADUDen*dMGRAEmpDen)/(dMGRADUDen+dMGRAEmpDen),0)" +,origin_zone_taxi_wait_time_mean,"origin_density.map({k: v for k, v in Taxi_waitTime_mean.items()})" +,origin_zone_taxi_wait_time_sd,"origin_density.map({k: v for k, v in Taxi_waitTime_sd.items()})" +,dest_zone_taxi_wait_time_mean,"dest_density.map({k: v for k, v in Taxi_waitTime_mean.items()})" +,dest_zone_taxi_wait_time_sd,"dest_density.map({k: v for k, v in Taxi_waitTime_sd.items()})" +# ,, Note that the mean and standard deviation are not the values for the distribution itself +,origTaxiWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_taxi_wait_time_mean, sigma=origin_zone_taxi_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)" +,destTaxiWaitTime,"rng.lognormal_for_df(df, mu=dest_zone_taxi_wait_time_mean, sigma=dest_zone_taxi_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)" +,origin_zone_singleTNC_wait_time_mean,"origin_density.map({k: v for k, v in TNC_single_waitTime_mean.items()})" +,origin_zone_singleTNC_wait_time_sd,"origin_density.map({k: v for k, v in TNC_single_waitTime_sd.items()})" +,dest_zone_singleTNC_wait_time_mean,"dest_density.map({k: v for k, v in TNC_single_waitTime_mean.items()})" +,dest_zone_singleTNC_wait_time_sd,"dest_density.map({k: v for k, v in TNC_single_waitTime_sd.items()})" +,origSingleTNCWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_singleTNC_wait_time_mean, sigma=origin_zone_singleTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)" +,destSingleTNCWaitTime,"rng.lognormal_for_df(df, mu=dest_zone_singleTNC_wait_time_mean, sigma=dest_zone_singleTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)" +,origin_zone_sharedTNC_wait_time_mean,"origin_density.map({k: v for k, v in TNC_shared_waitTime_mean.items()})" +,origin_zone_sharedTNC_wait_time_sd,"origin_density.map({k: v for k, v in TNC_shared_waitTime_sd.items()})" +# wrong? producing bad probs error,dest_zone_sharedTNC_wait_time_mean,"np.maximum((200+(-133 * da_dist_skims)),0) * time_factor" +,dest_zone_sharedTNC_wait_time_mean,"dest_density.map({k: v for k, v in TNC_shared_waitTime_mean.items()})" +,dest_zone_sharedTNC_wait_time_sd,"dest_density.map({k: v for k, v in TNC_shared_waitTime_sd.items()})" +,origSharedTNCWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_sharedTNC_wait_time_mean, sigma=origin_zone_sharedTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)" +,destSharedTNCWaitTime,"rng.lognormal_for_df(df, mu=dest_zone_sharedTNC_wait_time_mean, sigma=dest_zone_sharedTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)" +,totalWaitTaxi,origTaxiWaitTime + destTaxiWaitTime +,totalWaitSingleTNC,origSingleTNCWaitTime + destSingleTNCWaitTime +,totalWaitSharedTNC,origSharedTNCWaitTime + destSharedTNCWaitTime +#,, +Household autos,autos,df.auto_ownership +Number of adults (age 18+) in household,adults,df.num_adults +Household size (number of persons),hhSize,df.hhsize +Person age,age,df.age +Person is female,female,(df.SEX == 2) +# Auto operating costs,, +,selected_tour_vehicle,"reindex(tours.selected_vehicle, df.tour_id)" +,auto_op_cost,"reindex(vehicles.groupby('vehicle_type')['auto_operating_cost'].mean(), pd.Series(selected_tour_vehicle, df.index))" +,auto_op_cost,"np.where(pd.isna(auto_op_cost), costPerMile, auto_op_cost)" +#,, +Does tour use an AV,useAV,selected_tour_vehicle.str.contains('AV') +Auto IVT Factor,autoIVTFactor,"np.where(useAV,autoIVTFactorAV,1)" +Auto Parking Cost Factor,autoParkingCostFactor,"np.where(useAV,autoParkingCostFactorAV,1)" +Auto CPM Factor,autoCPMFactor,"np.where(useAV,autoCostPerMileFactorAV,1)" +Auto Terminal Time Factor,autoTermTimeFactor,"np.where(useAV,autoTerminalTimeFactorAV,1)" +MinimumAgeDriveAlone,minimumAgeDA,"np.where(useAV,minAgeDriveAloneAV,16)" +#,, +Zero auto households,zeroAutoHH,"np.where(autos==0,1,0)" +Auto deficient household (more adults than autos),autoDeficientHH,"np.where(autos=adults,1,0) * (np.where(zeroAutoHH,0,1))" +Joint tour,jointTour,(df.tour_category == 'joint') +Number of participants in joint tour,numberOfParticipantsInJointTour,df.number_of_participants * jointTour +Individual tour,indivTour,1 * (jointTour==0) +Number of stops - outbound direction,outStops,df.trip_count * df.outbound +Number of stops - return direction,retStops,df.trip_count * ~df.outbound +Total stops on tour,totStops,outStops + retStops +First trip of tour,firstTrip,df.trip_num == 1 +Last trip of tour,lastTrip,df.trip_num == df.trip_count +First leg of multi-stop outbound,firstOfMultipleTrips,firstTrip *(outStops>1) +Last leg of multi-stop return,lastofMultipleTrips,lastTrip *( retStops>1) +# Flag for setting availability of auto modes for drive-transit access/egress trip segments,autoAllowedForDriveTransit,df.autoModeAllowedForTripSegment +# Flag for setting availability of walk mode for drive-transit access/egress trip segments,walkAllowedForDriveTransit,df.walkModeAllowedForTripSegment +Tour mode is drive-alone,tourDA,(df.tour_mode == 'DRIVEALONE').astype(int) +Tour mode is shared-2,tourS2,(df.tour_mode == 'SHARED2').astype(int) +Tour mode is shared-3+,tourS3,(df.tour_mode == 'SHARED3').astype(int) +Tour mode is walk,tourWalk,(df.tour_mode == 'WALK').astype(int) +Tour mode is bike,tourBike,(df.tour_mode == 'BIKE').astype(int) +Tour mode is walk-transit,tourWTran,(df.tour_mode == 'WALK_TRANSIT').astype(int) +Tour mode is PNR-transit,tourPNR,(df.tour_mode == 'PNR_TRANSIT').astype(int) +Tour mode is KNR-transit,tourKNR,(df.tour_mode == 'KNR_TRANSIT').astype(int) +Tour mode is Bike-transit,tourBNR,"(df.tour_mode == 'BIKE_TRANSIT').astype(int)" +Tour mode is MaaS,tourMaaS,"(df.tour_mode.isin(['TAXI', 'TNC_SINGLE', 'TNC_SHARED'])).astype(int)" +Tour mode is school bus,tourSchBus,(df.tour_mode == 'SCH_BUS').astype(int) +Tour mode is Ebike,tourEbike,(df.tour_mode == 'EBIKE').astype(int) +Tour mode is Escooter,tourEscooter,(df.tour_mode == 'ESCOOTER').astype(int) +#,#, +,free_parking_available,(df.tour_type == 'work') & df.free_parking_at_work +person has free on-site parking at workplace,freeOnsite,"(free_parking_available)*np.where(is_indiv,1,0)" +new reimbursement amount,reimburseProportion,0 +tour primary destination,tour_dest,"reindex(tours.destination, df.tour_id)" +half tour duration,tourDuration,"reindex(tours.duration, df.tour_id)/2" +new daily parking cost with reimbursement,parkingCostDay,"reindex(land_use.PRKCST_DAY, tour_dest)" +new hourly parking cost with reimbursement,parkingCostHour,"reindex(land_use.PRKCST_HR, tour_dest)" +new monthly parking cost with reimbursement,parkingCostMonth,"reindex(land_use.PRKCST_MNTH, tour_dest)" +Parking area,parkingConstrainedArea,"np.where(reindex(land_use.parking_type, tour_dest) == 1, 1, 0)" +Tour parking cost for full-time workers and university students,_parkingCostBeforeReimb,"df.ptype.isin([1,3]) * is_indiv * np.minimum(parkingCostMonth/22, parkingCostDay)" +Tour parking cost for full-time workers and university students,_parkingCostBeforeReimb,"df.ptype.isin([1,3]) * is_indiv * np.minimum(_parkingCostBeforeReimb, parkingCostHour * tourDuration)" +Tour parking cost for other person types,parkingCostBeforeReimb,"np.where((~df.ptype.isin([1,3]) * is_indiv) | (is_joint), np.minimum(parkingCostDay, parkingCostHour * tourDuration), _parkingCostBeforeReimb)" +Reimbursement applies to this tour purpose,reimbursePurpose,"reindex(tours.tour_type, df.tour_id)=='work'" +Effective parking cost for free parkers,_parkingCost,"0 * np.where(reimbursePurpose*freeOnsite,1,0)" +Effective parking cost for reimbursed parkers,_parkingCost,"np.where(is_indiv*reimbursePurpose*(1-freeOnsite), np.maximum((1-reimburseProportion) * parkingCostBeforeReimb, 0),_parkingCost)" +Effective parking cost applied to tour purpose,parkingCostPrimDest,"np.where(is_joint+is_indiv*(1-reimbursePurpose), parkingCostBeforeReimb,_parkingCost)" +#,, +Total trips on tour,totalTrips,totStops+2 +Indicator for trip origin is tour prim. Destination,tripOrigIsTourDest,~df.outbound*df.trip_num==1 +Indicator for trip destination is tour prim. Destination,tripDestIsTourDest,df.outbound*df.trip_num==df.trip_count +Contribution to trip parking cost from primary destination,parkCostTourDestContrib,parkingCostPrimDest/(totalTrips+2) +Hourly parking cost at trip origin,parkCostTripOrig,"reindex(land_use.PRKCST_HR, origin)*100" +Hourly parking cost at trip destination,parkCostTripDest,"reindex(land_use.PRKCST_HR, destination)*100" +Contribution to trip parking cost from trip origin,parkCostTripOrigContrib,parkCostTripOrig*(1-firstTrip)*(1-tripOrigIsTourDest) +Contribution to trip parking cost from trip origin,parkCostTripOrigContrib,parkingCostPrimDest/(totalTrips+2) * tripOrigIsTourDest +Contribution to trip parking cost from trip destination,parkCostTripDestContrib,parkCostTripDest * (1-lastTrip)*(1-tripDestIsTourDest) +Contribution to trip parking cost from trip destination,parkCostTripDestContrib,parkingCostPrimDest/(totalTrips+2) * tripDestIsTourDest +Final parking cost,parkingCost,parkCostTourDestContrib + parkCostTripOrigContrib + parkCostTripDestContrib +#,, +Origin Terminal Time,oTermTime,"reindex(land_use.TERMTIME,origin)" +Destination Terminal Time,dTermTime,"reindex(land_use.TERMTIME,destination)" +#,, +# FIXME need actual bike logsums and times,, +#bike logsum,bikeLS,od_skims['BIKE_LOGSUM'] +#bike time,bike_time,od_skims['BIKE_TIME'] +bike logsum,bikeLS,0 +bike time,bike_time,od_skims['DISTWALK'] / bikeSpeed * 60 +#,, +"Cost factor for shared 2 tours, 1/(2^0.8)",costFactorS2,0.57 +"Cost factor for shared 3+ tours, 1/(3.5^0.8)",costFactorS3,0.37 +# no sov for 0 autos or age< min drving age,sov_available,(autos>0) * (age>=minimumAgeDA) +no sov for age < min drving age,sov_available,"(age>=minimumAgeDA) * is_indiv * np.where((tourPNR)|(tourKNR)|(tourBNR)|(tourMaaS)|(tourSchBus)|(tourEbike)|(tourEscooter),0,1)" +,sr2_available,"np.where((tourPNR)|(tourKNR)|(tourBNR)|(tourMaaS)|(tourSchBus)|(tourEbike)|(tourEscooter)|(df.number_of_participants>2),0,1)" +,sr3_available,"np.where((tourPNR)|(tourKNR)|(tourBNR)|(tourMaaS)|(tourSchBus)|(tourEbike)|(tourEscooter)|(df.number_of_participants==2),0,1)" +no long walks,walkAvailable,"np.where((walk_time_skims < max_walk_time),1,0) * np.where((tourPNR)|(tourKNR)|(tourBNR)|(tourMaaS)|(tourSchBus)|(tourEbike)|(tourEscooter),0,1)" +,Escooter_available,"(bike_time>0) * np.where((tourDA)|(tourS2)|(tourS3)|(tourWalk)|(tourBike)|(tourWTran)|(tourPNR)|(tourKNR)|(tourBNR)|(tourMaaS)|(tourSchBus)|(tourEbike),0,1)" +,Ebike_available,"(bike_time>0) * np.where((tourDA)|(tourS2)|(tourS3)|(tourWalk)|(tourBike)|(tourWTran)|(tourPNR)|(tourKNR)|(tourBNR)|(tourMaaS)|(tourSchBus)|(tourEscooter),0,1)" +,PNR_available,"(autos>0) * (age>15) * np.where((tourDA)|(tourS2)|(tourS3)|(tourWalk)|(tourBike)|(tourWTran)|(tourKNR)|(tourBNR)|(tourMaaS)|(tourSchBus)|(tourEbike)|(tourEscooter),0,1)" +,KNR_available,"np.where((tourDA)|(tourS2)|(tourS3)|(tourWalk)|(tourBike)|(tourWTran)|(tourPNR)|(tourBNR)|(tourMaaS)|(tourSchBus)|(tourEbike)|(tourEscooter),0,1)" +,BNR_available,"np.where((tourDA)|(tourS2)|(tourS3)|(tourWalk)|(tourBike)|(tourWTran)|(tourPNR)|(tourKNR)|(tourMaaS)|(tourSchBus)|(tourEbike)|(tourEscooter),0,1)" +,RideHail_available,"np.where((tourDA)|(tourS2)|(tourS3)|(tourWalk)|(tourBike)|(tourWTran)|(tourPNR)|(tourKNR)|(tourBNR)|(tourSchBus)|(tourEbike)|(tourEscooter),0,1)" +,SCHBUS_available,"(df.tour_type =='school') & (df.ptype!=3)*np.where((tourDA)|(tourS2)|(tourS3)|(tourWalk)|(tourBike)|(tourWTran)|(tourPNR)|(tourKNR)|(tourBNR)|(tourMaaS)|(tourEbike)|(tourEscooter),0,1)" +#,, +,walk_local_available,(odt_skims['WTW_TIV']>0)&(tourWTran) +# FIXME need updated PNR availability,, +,knr_local_available,((df.outbound & (odt_skims['KTW_TIV']>0)) | (~df.outbound & (odt_skims['WTK_TIV']>0)))&(tourKNR) +,bike_local_available,(odt_skims['WTW_TIV']>0)&(tourBNR) +#access egress distances,, +,origin_local_dist,"reindex(land_use.walk_dist_local_bus, origin)" +,origin_micro_local_dist,"reindex(land_use.micro_dist_local_bus, origin)" +,dest_local_dist,"reindex(land_use.walk_dist_local_bus, destination)" +,dest_micro_local_dist,"reindex(land_use.micro_dist_local_bus, destination)" +,micro_local_dist_tncout,"odt_skims['KTW_ACC']/60 * driveSpeed" +,micro_local_dist_tncin,"odt_skims['WTK_EGR']/60 * driveSpeed" +#access egress times,, +,origin_local_time,origin_local_dist * 60/walkSpeed +,dest_local_time,dest_local_dist * 60/walkSpeed +# added for school escorting model,, +Number of school children in vehicle on trip,num_escortees,df.escort_participants.fillna('').apply(lambda x: len(x.split('_')) if len(x)>0 else 0) +# Micromobility times,, +ebike time,ebike_time,bike_time * bikeSpeed / ebikeSpeed +escooter time,escooter_time,bike_time * bikeSpeed / escooterSpeed +Micromobility access Time,MicroAccessTime,"reindex(land_use.MicroAccessTime,origin)" +ebike max distance availability,ebikeMaxDistance,"(od_skims[('SOV_M_DIST', 'MD')] > ebikeMaxDist)" +escooter max distance availability,escooterMaxDistance,"(od_skims[('SOV_M_DIST', 'MD')] > escooterMaxDist)" +# Microtransit and NEV,, +microtransit available at origin,microtransit_orig,"reindex(land_use.microtransit, df[orig_col_name])" +microtransit available at destination,microtransit_dest,"reindex(land_use.microtransit, df[dest_col_name])" +microtransit operating at time of trip,microtransit_operating,True +microtransit available,microtransit_available,(microtransit_orig > 0) & (microtransit_orig == microtransit_dest) & (s3_dist_skims < microtransitMaxDist) & microtransit_operating +microtransit direct time,microtransit_direct_time,"np.maximum(s3_dist_skims/microtransitSpeed*60, s3_time_skims)" +microtransit total time,microtransit_time,"np.maximum(microtransit_direct_time + microtransitDiversionConstant, microtransitDiversionFactor*microtransit_direct_time)" +nev available at origin,nev_orig,"reindex(land_use.nev, df[orig_col_name])" +nev available at destination,nev_dest,"reindex(land_use.nev, df[dest_col_name])" +nev operating at time of trip,nev_operating,True +nev available,nev_available,(nev_orig > 0) & (nev_orig == nev_dest) & (s3_dist_skims < nevMaxDist) & nev_operating +nev direct time,nev_direct_time,"np.maximum(s3_dist_skims/nevSpeed*60, s3_time_skims)" +nev total time,nev_time,"np.maximum(nev_direct_time + nevDiversionConstant, nevDiversionFactor*nev_direct_time)" +# Microtransit and NEV access to transit,, +outbound microtransit access to local available,microtransit_local_access_available_out,df.outbound & (microtransit_orig>0) & (micro_local_dist_tncout0) & (micro_local_dist_tncin0) & (micro_local_dist_tncout0) & (micro_local_dist_tncin0) & (dest_micro_local_dist>maxWalkIfMTAccessAvailable) & (dest_micro_local_dist0) & (origin_micro_local_dist>maxWalkIfMTAccessAvailable) & (origin_micro_local_dist0) & (dest_micro_local_dist>maxWalkIfMTAccessAvailable) & (dest_micro_local_dist0) & (origin_micro_local_dist>maxWalkIfMTAccessAvailable) & (origin_micro_local_dist= 25) & (df['outbound'])) | ((periods_left >= 34) & (~df['outbound'])), 47, periods_left)" +,tour_purpose,"reindex(tours.tour_type, df.tour_id)" +,tour_purpose_grouped,"np.where(tour_purpose.isin(['work','school','univ']), 'mand', 'non_mand')" +,half_tour_stops_remaining_grouped,(df.trip_count - df.trip_num).clip(upper=1) \ No newline at end of file diff --git a/resident/configs/trip_scheduling_probs_purpose_stops.csv b/resident/configs/trip_scheduling_probs_purpose_stops.csv new file mode 100644 index 0000000..411be00 --- /dev/null +++ b/resident/configs/trip_scheduling_probs_purpose_stops.csv @@ -0,0 +1,245 @@ +periods_left_min,periods_left_max,outbound,tour_purpose_grouped,half_tour_stops_remaining_grouped,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,All +0,0,True,non_mand,0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 +0,0,True,non_mand,1,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 +0,0,True,mand,0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 +0,0,True,mand,1,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 +1,1,True,non_mand,0,0.8189802736716861,0.18101972632831478,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 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+31,31,False,non_mand,1,0.0,0.0,0.0,0.01833810824001649,0.0,0.0,0.0,0.0,0.011516290886021865,0.16308529577680958,0.0,0.0,0.0,0.02547074507105579,0.01652281484738158,0.0,0.04452351185239044,0.009317122261056536,0.19971424002149518,0.013045312623538401,0.0075661778015869464,0.3102328065307183,0.0,0.025003003703219125,0.035055083246462164,0.030069783720618536,0.018526881151892546,0.034928530599363024,0.037084291666373344,0.0,0.0,0.0,0.0,0.0,0.0,1.0 +31,31,False,mand,0,0.0,0.0,0.0,0.0,0.0,0.011660697134411879,0.08359356906747217,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0074351633681512585,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.053515133972031996,0.0,0.046642788537647514,0.06362339255281085,0.004389112388089102,0.04017428022808142,0.4817135990383709,0.20725226371293273,0.0,0.0,0.0,1.0 +31,31,False,mand,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.014153420408759243,0.0,0.0,0.0,0.0,0.023248141345199345,0.008890938931074744,0.05285349575062624,0.04990553163058654,0.16677175839150424,0.11331994669851629,0.1181638409115989,0.07387812106291494,0.15865917983104275,0.031235937800509726,0.06596532918651592,0.035468217376292764,0.013865230438545558,0.033059807910658684,0.03141248412869757,0.006106628878031803,0.003041989318924492,0.0,0.0,0.0,0.0,1.0 +32,32,False,non_mand,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.09030713454422928,0.10225330344102772,0.0,0.0,0.0,0.0,0.34201165614137685,0.10656854103943249,0.35885936483393366,0.0,0.0,1.0 +32,32,False,non_mand,1,0.0,0.03615936143527503,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.11870838866384083,0.0,0.0,0.030664587449557346,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.026266199236874226,0.0,0.0,0.08761524377109123,0.0,0.0,0.7005862194433614,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 +32,32,False,mand,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.05789616370086815,0.1344598373883522,0.3375637056076247,0.2230108052807423,0.24706948802241244,0.0,0.0,1.0 +32,32,False,mand,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.009238420497671889,0.0,0.0,0.0,0.0,0.0,0.0,0.027952902624459142,0.004745183696099721,0.041750935844626774,0.03560104623729895,0.019208273165269348,0.039862390831722126,0.2445054537893148,0.1472889280930601,0.10677793164025022,0.07872994398102684,0.04261569901118799,0.005675051098870241,0.0815413323203732,0.019486477408908912,0.0,0.09502002975985951,0.0,0.0,0.0,0.0,0.0,1.0 +33,33,False,non_mand,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3937868145193479,0.4486503063939636,0.15756287908668853,0.0,1.0 +33,33,False,non_mand,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.020230772694571117,0.0,0.13595146003291136,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.09279893744389224,0.0,0.0,0.07918749737876385,0.0,0.0,0.12358922634696451,0.07179448760198139,0.0,0.0,0.1394804713644242,0.0,0.2868037587900601,0.0,0.0,0.05016338834643131,0.0,0.0,1.0 +33,33,False,mand,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.21087359210326426,0.35509399657354956,0.28936975269124166,0.14466265863194447,0.0,1.0 +33,33,False,mand,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.023247760476100464,0.0,0.024677767802522798,0.0,0.009363181643807422,0.029519156456030052,0.11193010731182607,0.02285317247116739,0.051385963088573655,0.04473753625556975,0.14095762264166245,0.055024985843556244,0.17384148255260748,0.029384651649520036,0.16325933704399054,0.03425760908549876,0.013563487427945331,0.015926159431522906,0.007948502073252565,0.0,0.022039827009254335,0.005005115351559031,0.013479183790602863,0.0,0.007597390593429946,0.0,0.0,1.0 +34,47,False,non_mand,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.018384368573649112,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5577089454113409,0.0,0.4239066860150099,1.0 +34,47,False,non_mand,1,0.016657687671303212,0.02183836474581605,0.0,0.021670440655999233,0.0,0.10173930098123364,0.02498724707017388,0.0,0.14226492691657533,0.004760495563960537,0.0,0.0,0.0,0.0,0.0,0.0,0.02638875495711764,0.0,0.0,0.05929170885647927,0.0,0.0,0.0,0.0,0.0,0.008986741490552959,0.0,0.0,0.22252478651398835,0.033523970436925156,0.0,0.010929820731753599,0.017516567897537586,0.28691918551058354,0.0,1.0 +34,47,False,mand,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0778976250021037,0.030793465479170962,0.0,0.0,0.012379085610171855,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.07427570742104288,0.0,0.0,0.0,0.0,0.10023495355353525,0.26524960444707757,0.1397015192237292,0.29946803926316845,1.0 +34,47,False,mand,1,0.0,0.0,0.0,0.0,0.1018436706346972,0.0,0.0,0.0,0.0,0.002263878168597051,0.0,0.000983293615706626,0.0,0.008218649939220985,0.023737052572553673,0.021264126033222547,0.05518389314660721,0.010993620292826722,0.20415745418381434,0.07475058698068834,0.07116274568401747,0.07424603443354091,0.06495972196633373,0.03217245961981454,0.08352494702875252,0.03514324066332105,0.026534322261271913,0.04879548794140681,0.004334841005982029,0.02812637911830777,0.0,0.0,0.015797438844245334,0.0,0.011806155865071414,1.0 diff --git a/resident/configs/vehicle_allocation.csv b/resident/configs/vehicle_allocation.csv new file mode 100644 index 0000000..7a4bd77 --- /dev/null +++ b/resident/configs/vehicle_allocation.csv @@ -0,0 +1,174 @@ +Label,Description,Expression,veh_num1,veh_num2,veh_num3,veh_num4,non_hh_veh +#,Availability Conditions,,,, +util_alt1_unavail,Household does not own vehicle,"veh_num1.isna() | (veh_num1 == '')",coef_unavail,0,0,0,0 +util_alt2_unavail,Household does not own vehicle,"veh_num2.isna() | (veh_num2 == '')",0,coef_unavail,0,0,0 +util_alt3_unavail,Household does not own vehicle,"veh_num3.isna() | (veh_num3 == '')",0,0,coef_unavail,0,0 +util_alt4_unavail,Household does not own vehicle,"veh_num4.isna() | (veh_num4 == '')",0,0,0,coef_unavail,0 +#,BEV Range,,,, +util_dstgtrng1,Round trip tour distance > BEV range,"@np.where((df.tot_tour_dist > df.Range_1) & (df.fuel_type_1 == 'BEV'), 1, 0)",coef_dstgtrng,0,0,0,0 +util_dstgtrng2,Round trip tour distance > BEV range,"@np.where((df.tot_tour_dist > df.Range_2) & (df.fuel_type_2 == 'BEV'), 1, 0)",0,coef_dstgtrng,0,0,0 +util_dstgtrng3,Round trip tour distance > BEV range,"@np.where((df.tot_tour_dist > df.Range_3) & (df.fuel_type_3 == 'BEV'), 1, 0)",0,0,coef_dstgtrng,0,0 +util_dstgtrng4,Round trip tour distance > BEV range,"@np.where((df.tot_tour_dist > df.Range_4) & (df.fuel_type_4 == 'BEV'), 1, 0)",0,0,0,coef_dstgtrng,0 +#,Vehicles & Driver interactions,,,, +util_vehltdr_nh,Vehicles < Drivers -- Non-Household Vehicle,"hh_veh_lt_drivers",0,0,0,0,coef_vehltdr_nh +util_vehltdr_nh,Vehicles > Drivers -- Non-Household Vehicle,"hh_veh_gt_drivers",0,0,0,0,coef_vehgtdr_nh +util_vehltdr_van1,Vehicles > Drivers -- Van alt 1,"hh_veh_gt_drivers * (body_type_1 == 'Van')",coef_vehltdr_van,0,0,0,0 +util_vehltdr_van2,Vehicles > Drivers -- Van alt 2,"hh_veh_gt_drivers * (body_type_2 == 'Van')",0,coef_vehltdr_van,0,0,0 +util_vehltdr_van3,Vehicles > Drivers -- Van alt 3,"hh_veh_gt_drivers * (body_type_3 == 'Van')",0,0,coef_vehltdr_van,0,0 +util_vehltdr_van4,Vehicles > Drivers -- Van alt 4,"hh_veh_gt_drivers * (body_type_4 == 'Van')",0,0,0,coef_vehltdr_van,0 +util_vehltdr_suv1,Vehicles > Drivers -- SUV alt 1,"hh_veh_gt_drivers * (body_type_1 == 'SUV')",coef_vehltdr_suv,0,0,0,0 +util_vehltdr_suv2,Vehicles > Drivers -- SUV alt 2,"hh_veh_gt_drivers * (body_type_2 == 'SUV')",0,coef_vehltdr_suv,0,0,0 +util_vehltdr_suv3,Vehicles > Drivers -- SUV alt 3,"hh_veh_gt_drivers * (body_type_3 == 'SUV')",0,0,coef_vehltdr_suv,0,0 +util_vehltdr_suv4,Vehicles > Drivers -- SUV alt 4,"hh_veh_gt_drivers * (body_type_4 == 'SUV')",0,0,0,coef_vehltdr_suv,0 +util_vehltdr_pu1,Vehicles > Drivers -- Pickup alt 1,"hh_veh_gt_drivers * (body_type_1 == 'Pickup')",coef_vehltdr_pu,0,0,0,0 +util_vehltdr_pu2,Vehicles > Drivers -- Pickup alt 2,"hh_veh_gt_drivers * (body_type_2 == 'Pickup')",0,coef_vehltdr_pu,0,0,0 +util_vehltdr_pu3,Vehicles > Drivers -- Pickup alt 3,"hh_veh_gt_drivers * (body_type_3 == 'Pickup')",0,0,coef_vehltdr_pu,0,0 +util_vehltdr_pu4,Vehicles > Drivers -- Pickup alt 4,"hh_veh_gt_drivers * (body_type_4 == 'Pickup')",0,0,0,coef_vehltdr_pu,0 +util_vehltdr_mc1,Vehicles > Drivers -- Motorcycle alt 1,"hh_veh_gt_drivers * (body_type_1 == 'Motorcycle')",coef_vehltdr_mc,0,0,0,0 +util_vehltdr_mc2,Vehicles > Drivers -- Motorcycle alt 2,"hh_veh_gt_drivers * (body_type_2 == 'Motorcycle')",0,coef_vehltdr_mc,0,0,0 +util_vehltdr_mc3,Vehicles > Drivers -- Motorcycle alt 3,"hh_veh_gt_drivers * (body_type_3 == 'Motorcycle')",0,0,coef_vehltdr_mc,0,0 +util_vehltdr_mc4,Vehicles > Drivers -- Motorcycle alt 4,"hh_veh_gt_drivers * (body_type_4 == 'Motorcycle')",0,0,0,coef_vehltdr_mc,0 +util_vehltdr_hyb1,Vehicles > Drivers -- Hybrid alt 1,"hh_veh_gt_drivers * (fuel_type_1 == 'Hybrid')",coef_vehltdr_hyb,0,0,0,0 +util_vehltdr_hyb2,Vehicles > Drivers -- Hybrid alt 2,"hh_veh_gt_drivers * (fuel_type_2 == 'Hybrid')",0,coef_vehltdr_hyb,0,0,0 +util_vehltdr_hyb3,Vehicles > Drivers -- Hybrid alt 3,"hh_veh_gt_drivers * (fuel_type_3 == 'Hybrid')",0,0,coef_vehltdr_hyb,0,0 +util_vehltdr_hyb4,Vehicles > Drivers -- Hybrid alt 4,"hh_veh_gt_drivers * (fuel_type_4 == 'Hybrid')",0,0,0,coef_vehltdr_hyb,0 +util_vehltdr_ev1,Vehicles > Drivers -- Hybrid alt 1,"hh_veh_gt_drivers * ((fuel_type_1=='PEV') | (fuel_type_1=='BEV'))",coef_vehltdr_ev,0,0,0,0 +util_vehltdr_ev2,Vehicles > Drivers -- Hybrid alt 2,"hh_veh_gt_drivers * ((fuel_type_2=='PEV') | (fuel_type_2=='BEV'))",0,coef_vehltdr_ev,0,0,0 +util_vehltdr_ev3,Vehicles > Drivers -- Hybrid alt 3,"hh_veh_gt_drivers * ((fuel_type_3=='PEV') | (fuel_type_3=='BEV'))",0,0,coef_vehltdr_ev,0,0 +util_vehltdr_ev4,Vehicles > Drivers -- Hybrid alt 4,"hh_veh_gt_drivers * ((fuel_type_4=='PEV') | (fuel_type_4=='BEV'))",0,0,0,coef_vehltdr_ev,0 +util_vehltdr_age1,Vehicles > Drivers -- Age alt 1,"hh_veh_gt_drivers * age_1",coef_vehltdr_age,0,0,0,0 +util_vehltdr_age2,Vehicles > Drivers -- Age alt 2,"hh_veh_gt_drivers * age_2",0,coef_vehltdr_age,0,0,0 +util_vehltdr_age3,Vehicles > Drivers -- Age alt 3,"hh_veh_gt_drivers * age_3",0,0,coef_vehltdr_age,0,0 +util_vehltdr_age4,Vehicles > Drivers -- Age alt 4,"hh_veh_gt_drivers * age_4",0,0,0,coef_vehltdr_age,0 +#,Occupancy interactions,,,, +util_maxocc_van1,Maximum Occupancy -- Van alt 1,"@occup * (df.body_type_1 == 'Van')",coef_maxocc_van,0,0,0,0 +util_maxocc_van2,Maximum Occupancy -- Van alt 2,"@occup * (df.body_type_2 == 'Van')",0,coef_maxocc_van,0,0,0 +util_maxocc_van3,Maximum Occupancy -- Van alt 3,"@occup * (df.body_type_3 == 'Van')",0,0,coef_maxocc_van,0,0 +util_maxocc_van4,Maximum Occupancy -- Van alt 4,"@occup * (df.body_type_4 == 'Van')",0,0,0,coef_maxocc_van,0 +util_maxocc_suv1,Maximum Occupancy -- SUV alt 1,"@occup * (df.body_type_1 == 'SUV')",coef_maxocc_suv,0,0,0,0 +util_maxocc_suv2,Maximum Occupancy -- SUV alt 2,"@occup * (df.body_type_2 == 'SUV')",0,coef_maxocc_suv,0,0,0 +util_maxocc_suv3,Maximum Occupancy -- SUV alt 3,"@occup * (df.body_type_3 == 'SUV')",0,0,coef_maxocc_suv,0,0 +util_maxocc_suv4,Maximum Occupancy -- SUV alt 4,"@occup * (df.body_type_4 == 'SUV')",0,0,0,coef_maxocc_suv,0 +util_maxocc_pu1,Maximum Occupancy -- Pickup alt 1,"@occup * (df.body_type_1 == 'Pickup')",coef_maxocc_pu,0,0,0,0 +util_maxocc_pu2,Maximum Occupancy -- Pickup alt 2,"@occup * (df.body_type_2 == 'Pickup')",0,coef_maxocc_pu,0,0,0 +util_maxocc_pu3,Maximum Occupancy -- Pickup alt 3,"@occup * (df.body_type_3 == 'Pickup')",0,0,coef_maxocc_pu,0,0 +util_maxocc_pu4,Maximum Occupancy -- Pickup alt 4,"@occup * (df.body_type_4 == 'Pickup')",0,0,0,coef_maxocc_pu,0 +util_maxocc_mc1,Maximum Occupancy -- Motorcycle alt 1,"@occup * (df.body_type_1 == 'Motorcycle')",coef_maxocc_mc,0,0,0,0 +util_maxocc_mc2,Maximum Occupancy -- Motorcycle alt 2,"@occup * (df.body_type_2 == 'Motorcycle')",0,coef_maxocc_mc,0,0,0 +util_maxocc_mc3,Maximum Occupancy -- Motorcycle alt 3,"@occup * (df.body_type_3 == 'Motorcycle')",0,0,coef_maxocc_mc,0,0 +util_maxocc_mc4,Maximum Occupancy -- Motorcycle alt 4,"@occup * (df.body_type_4 == 'Motorcycle')",0,0,0,coef_maxocc_mc,0 +util_maxocc_age1,Maximum Occupancy -- Age alt 1,"@occup * df.age_1",coef_maxocc_age,0,0,0,0 +util_maxocc_age2,Maximum Occupancy -- Age alt 2,"@occup * df.age_2",0,coef_maxocc_age,0,0,0 +util_maxocc_age3,Maximum Occupancy -- Age alt 3,"@occup * df.age_3",0,0,coef_maxocc_age,0,0 +util_maxocc_age4,Maximum Occupancy -- Age alt 4,"@occup * df.age_4",0,0,0,coef_maxocc_age,0 +util_maxocc_nh,Maximum Occupancy -- Age alt 1,"@occup",0,0,0,0,util_maxocc_nh +#,Alternative Specific Constants,,,, +util_non_hh,Non-Household Vehicle Constant,"1",0,0,0,0,coef_non_hh +util_van1,Van ASC alt 1,"(body_type_1 == 'Van')",coef_van,0,0,0,0 +util_van2,Van ASC alt 2,"(body_type_2 == 'Van')",0,coef_van,0,0,0 +util_van3,Van ASC alt 3,"(body_type_3 == 'Van')",0,0,coef_van,0,0 +util_van4,Van ASC alt 4,"(body_type_4 == 'Van')",0,0,0,coef_van,0 +util_suv1,SUV ASC alt 1,"(body_type_1 == 'SUV')",coef_suv,0,0,0,0 +util_suv2,SUV ASC alt 2,"(body_type_2 == 'SUV')",0,coef_suv,0,0,0 +util_suv3,SUV ASC alt 3,"(body_type_3 == 'SUV')",0,0,coef_suv,0,0 +util_suv4,SUV ASC alt 4,"(body_type_4 == 'SUV')",0,0,0,coef_suv,0 +util_pu1,Pickup ASC alt 1,"(body_type_1 == 'Pickup')",coef_pu,0,0,0,0 +util_pu2,Pickup ASC alt 2,"(body_type_2 == 'Pickup')",0,coef_pu,0,0,0 +util_pu3,Pickup ASC alt 3,"(body_type_3 == 'Pickup')",0,0,coef_pu,0,0 +util_pu4,Pickup ASC alt 4,"(body_type_4 == 'Pickup')",0,0,0,coef_pu,0 +util_mc1,Motorcycle ASC alt 1,"(body_type_1 == 'Motorcycle')",coef_mc,0,0,0,0 +util_mc2,Motorcycle ASC alt 2,"(body_type_2 == 'Motorcycle')",0,coef_mc,0,0,0 +util_mc3,Motorcycle ASC alt 3,"(body_type_3 == 'Motorcycle')",0,0,coef_mc,0,0 +util_mc4,Motorcycle ASC alt 4,"(body_type_4 == 'Motorcycle')",0,0,0,coef_mc,0 +util_dsl1,Diesel ASC alt 1,"(fuel_type_1 == 'Diesel')",coef_dsl,0,0,0,0 +util_dsl2,Diesel ASC alt 2,"(fuel_type_2 == 'Diesel')",0,coef_dsl,0,0,0 +util_dsl3,Diesel ASC alt 3,"(fuel_type_3 == 'Diesel')",0,0,coef_dsl,0,0 +util_dsl4,Diesel ASC alt 4,"(fuel_type_4 == 'Diesel')",0,0,0,coef_dsl,0 +util_hyb1,Hybrid ASC alt 1,"(fuel_type_1 == 'Hybrid')",coef_hyb,0,0,0,0 +util_hyb2,Hybrid ASC alt 2,"(fuel_type_2 == 'Hybrid')",0,coef_hyb,0,0,0 +util_hyb3,Hybrid ASC alt 3,"(fuel_type_3 == 'Hybrid')",0,0,coef_hyb,0,0 +util_hyb4,Hybrid ASC alt 4,"(fuel_type_4 == 'Hybrid')",0,0,0,coef_hyb,0 +util_pev1,PEV ASC alt 1,"(fuel_type_1 == 'PEV')",coef_pev,0,0,0,0 +util_pev2,PEV ASC alt 2,"(fuel_type_2 == 'PEV')",0,coef_pev,0,0,0 +util_pev3,PEV ASC alt 3,"(fuel_type_3 == 'PEV')",0,0,coef_pev,0,0 +util_pev4,PEV ASC alt 4,"(fuel_type_4 == 'PEV')",0,0,0,coef_pev,0 +util_bev1,BEV ASC alt 1,"(fuel_type_1 == 'BEV')",coef_bev,0,0,0,0 +util_bev2,BEV ASC alt 2,"(fuel_type_2 == 'BEV')",0,coef_bev,0,0,0 +util_bev3,BEV ASC alt 3,"(fuel_type_3 == 'BEV')",0,0,coef_bev,0,0 +util_bev4,BEV ASC alt 4,"(fuel_type_4 == 'BEV')",0,0,0,coef_bev,0 +util_age2_1,Age 1 alt 1,"(age_1 == 2)",coef_age2,0,0,0,0 +util_age2_2,Age 1 alt 2,"(age_2 == 2)",0,coef_age2,0,0,0 +util_age2_3,Age 1 alt 3,"(age_3 == 2)",0,0,coef_age2,0,0 +util_age2_4,Age 1 alt 4,"(age_4 == 2)",0,0,0,coef_age2,0 +util_age3_1,Age 3 alt 1,"(age_1 == 3)",coef_age3,0,0,0,0 +util_age3_2,Age 3 alt 2,"(age_2 == 3)",0,coef_age3,0,0,0 +util_age3_3,Age 3 alt 3,"(age_3 == 3)",0,0,coef_age3,0,0 +util_age3_4,Age 4 alt 4,"(age_4 == 3)",0,0,0,coef_age3,0 +util_age4_1,Age 4 alt 1,"(age_1 == 4)",coef_age4,0,0,0,0 +util_age4_2,Age 4 alt 2,"(age_2 == 4)",0,coef_age4,0,0,0 +util_age4_3,Age 4 alt 3,"(age_3 == 4)",0,0,coef_age4,0,0 +util_age4_4,Age 3 alt 4,"(age_4 == 4)",0,0,0,coef_age4,0 +util_age5_1,Age 5 alt 1,"(age_1 == 5)",coef_age5,0,0,0,0 +util_age5_2,Age 5 alt 2,"(age_2 == 5)",0,coef_age5,0,0,0 +util_age5_3,Age 5 alt 3,"(age_3 == 5)",0,0,coef_age5,0,0 +util_age5_4,Age 5 alt 4,"(age_4 == 5)",0,0,0,coef_age5,0 +util_age6_1,Age 6 alt 1,"(age_1 == 6)",coef_age6,0,0,0,0 +util_age6_2,Age 6 alt 2,"(age_2 == 6)",0,coef_age6,0,0,0 +util_age6_3,Age 6 alt 3,"(age_3 == 6)",0,0,coef_age6,0,0 +util_age6_4,Age 6 alt 4,"(age_4 == 6)",0,0,0,coef_age6,0 +util_age7_1,Age 7 alt 1,"(age_1 == 7)",coef_age7,0,0,0,0 +util_age7_2,Age 7 alt 2,"(age_2 == 7)",0,coef_age7,0,0,0 +util_age7_3,Age 7 alt 3,"(age_3 == 7)",0,0,coef_age7,0,0 +util_age7_4,Age 7 alt 4,"(age_4 == 7)",0,0,0,coef_age7,0 +util_age8_1,Age 8 alt 1,"(age_1 == 8)",coef_age8,0,0,0,0 +util_age8_2,Age 8 alt 2,"(age_2 == 8)",0,coef_age8,0,0,0 +util_age8_3,Age 8 alt 3,"(age_3 == 8)",0,0,coef_age8,0,0 +util_age8_4,Age 8 alt 4,"(age_4 == 8)",0,0,0,coef_age8,0 +util_age9_1,Age 9 alt 1,"(age_1 == 9)",coef_age9,0,0,0,0 +util_age9_2,Age 9 alt 2,"(age_2 == 9)",0,coef_age9,0,0,0 +util_age9_3,Age 9 alt 3,"(age_3 == 9)",0,0,coef_age9,0,0 +util_age9_4,Age 9 alt 4,"(age_4 == 9)",0,0,0,coef_age9,0 +util_age10_1,Age 10 alt 1,"(age_1 == 10)",coef_age10,0,0,0,0 +util_age10_2,Age 10 alt 2,"(age_2 == 10)",0,coef_age10,0,0,0 +util_age10_3,Age 10 alt 3,"(age_3 == 10)",0,0,coef_age10,0,0 +util_age10_4,Age 10 alt 4,"(age_4 == 10)",0,0,0,coef_age10,0 +util_age11_1,Age 11 alt 1,"(age_1 == 11)",coef_age11,0,0,0,0 +util_age11_2,Age 11 alt 2,"(age_2 == 11)",0,coef_age11,0,0,0 +util_age11_3,Age 11 alt 3,"(age_3 == 11)",0,0,coef_age11,0,0 +util_age11_4,Age 11 alt 4,"(age_4 == 11)",0,0,0,coef_age11,0 +util_age12_1,Age 12 alt 1,"(age_1 == 12)",coef_age12,0,0,0,0 +util_age12_2,Age 12 alt 2,"(age_2 == 12)",0,coef_age12,0,0,0 +util_age12_3,Age 12 alt 3,"(age_3 == 12)",0,0,coef_age12,0,0 +util_age12_4,Age 12 alt 4,"(age_4 == 12)",0,0,0,coef_age12,0 +util_age13_1,Age 13 alt 1,"(age_1 == 13)",coef_age13,0,0,0,0 +util_age13_2,Age 13 alt 2,"(age_2 == 13)",0,coef_age13,0,0,0 +util_age13_3,Age 13 alt 3,"(age_3 == 13)",0,0,coef_age13,0,0 +util_age13_4,Age 13 alt 4,"(age_4 == 13)",0,0,0,coef_age13,0 +util_age14_1,Age 14 alt 1,"(age_1 == 14)",coef_age14,0,0,0,0 +util_age14_2,Age 14 alt 2,"(age_2 == 14)",0,coef_age14,0,0,0 +util_age14_3,Age 14 alt 3,"(age_3 == 14)",0,0,coef_age14,0,0 +util_age14_4,Age 14 alt 4,"(age_4 == 14)",0,0,0,coef_age14,0 +util_age15_1,Age 15 alt 1,"(age_1 == 15)",coef_age15,0,0,0,0 +util_age15_2,Age 15 alt 2,"(age_2 == 15)",0,coef_age15,0,0,0 +util_age15_3,Age 15 alt 3,"(age_3 == 15)",0,0,coef_age15,0,0 +util_age15_4,Age 15 alt 4,"(age_4 == 15)",0,0,0,coef_age15,0 +util_age16_1,Age 16 alt 1,"(age_1 == 16)",coef_age16,0,0,0,0 +util_age16_2,Age 16 alt 2,"(age_2 == 16)",0,coef_age16,0,0,0 +util_age16_3,Age 16 alt 3,"(age_3 == 16)",0,0,coef_age16,0,0 +util_age16_4,Age 16 alt 4,"(age_4 == 16)",0,0,0,coef_age16,0 +util_age17_1,Age 17 alt 1,"(age_1 == 17)",coef_age17,0,0,0,0 +util_age17_2,Age 17 alt 2,"(age_2 == 17)",0,coef_age17,0,0,0 +util_age17_3,Age 17 alt 3,"(age_3 == 17)",0,0,coef_age17,0,0 +util_age17_4,Age 17 alt 4,"(age_4 == 17)",0,0,0,coef_age17,0 +util_age18_1,Age 18 alt 1,"(age_1 == 18)",coef_age18,0,0,0,0 +util_age18_2,Age 18 alt 2,"(age_2 == 18)",0,coef_age18,0,0,0 +util_age18_3,Age 18 alt 3,"(age_3 == 18)",0,0,coef_age18,0,0 +util_age18_4,Age 18 alt 4,"(age_4 == 18)",0,0,0,coef_age18,0 +util_age19_1,Age 19 alt 1,"(age_1 == 19)",coef_age19,0,0,0,0 +util_age19_2,Age 19 alt 2,"(age_2 == 19)",0,coef_age19,0,0,0 +util_age19_3,Age 19 alt 3,"(age_3 == 19)",0,0,coef_age19,0,0 +util_age19_4,Age 19 alt 4,"(age_4 == 19)",0,0,0,coef_age19,0 +util_age20_1,Age 20 alt 1,"(age_1 == 20)",coef_age20,0,0,0,0 +util_age20_2,Age 20 alt 2,"(age_2 == 20)",0,coef_age20,0,0,0 +util_age20_3,Age 20 alt 3,"(age_3 == 20)",0,0,coef_age20,0,0 +util_age20_4,Age 20 alt 4,"(age_4 == 20)",0,0,0,coef_age20,0 diff --git a/resident/configs/vehicle_allocation.yaml b/resident/configs/vehicle_allocation.yaml new file mode 100644 index 0000000..23222fd --- /dev/null +++ b/resident/configs/vehicle_allocation.yaml @@ -0,0 +1,28 @@ +# vehicle_choice.yaml + +# last column in spec needs to be non_hh_veh +SPEC: vehicle_allocation.csv +COEFFICIENTS: vehicle_allocation_coefficients.csv + +LOGIT_TYPE: MNL + +preprocessor: + SPEC: vehicle_allocation_annotate_choosers_preprocessor + DF: choosers + TABLES: + - vehicles + +# will create columns in the tour table selecting a vehicle for each of the following +# occupancy levels. They are named vehicle_occup_1, vehicle_occup_2, ... +# if not supplied, will default to only one occupancy level of 1 +OCCUPANCY_LEVELS: + - 1 + - 2 + - 3.5 + +# optional annotate tours +# annotate_tours: +# SPEC: annotate_tours_vehicle_allocation +# DF: tours +# TABLES: +# - vehicles diff --git a/resident/configs/vehicle_allocation_annotate_choosers_preprocessor.csv b/resident/configs/vehicle_allocation_annotate_choosers_preprocessor.csv new file mode 100644 index 0000000..5113460 --- /dev/null +++ b/resident/configs/vehicle_allocation_annotate_choosers_preprocessor.csv @@ -0,0 +1,20 @@ +Description,Target,Expression +Round Trip Tour Distance,tot_tour_dist,"(odt_skims['SOV_M_DIST'] + dot_skims['SOV_M_DIST'])" +number of vehicles is greater than the number of drivers,hh_veh_gt_drivers,"np.where(df.auto_ownership > df.num_drivers, 1, 0)" +number of vehicles is less than the number of drivers,hh_veh_lt_drivers,"np.where(df.auto_ownership < df.num_drivers, 1, 0)" +Age of vehicle alternative 1,age_1,"df.veh_num1.astype(str).str.split('_').str[1].fillna(0).astype(int)" +Age of vehicle alternative 2,age_2,"df.veh_num2.astype(str).str.split('_').str[1].fillna(0).astype(int)" +Age of vehicle alternative 3,age_3,"df.veh_num3.astype(str).str.split('_').str[1].fillna(0).astype(int)" +Age of vehicle alternative 4,age_4,"df.veh_num4.astype(str).str.split('_').str[1].fillna(0).astype(int)" +body type of vehicle alternative 1,body_type_1,"df.veh_num1.astype(str).str.split('_').str[0]" +body type of vehicle alternative 2,body_type_2,"df.veh_num2.astype(str).str.split('_').str[0]" +body type of vehicle alternative 3,body_type_3,"df.veh_num3.astype(str).str.split('_').str[0]" +body type of vehicle alternative 4,body_type_4,"df.veh_num4.astype(str).str.split('_').str[0]" +fuel type of vehicle alternative 1,fuel_type_1,"df.veh_num1.astype(str).str.split('_').str[2]" +fuel type of vehicle alternative 2,fuel_type_2,"df.veh_num2.astype(str).str.split('_').str[2]" +fuel type of vehicle alternative 3,fuel_type_3,"df.veh_num3.astype(str).str.split('_').str[2]" +fuel type of vehicle alternative 4,fuel_type_4,"df.veh_num4.astype(str).str.split('_').str[2]" +Range of vehicle alternative 1,Range_1,"reindex(vehicles.groupby('vehicle_type')['Range'].mean(), df.veh_num1)" +Range of vehicle alternative 2,Range_2,"reindex(vehicles.groupby('vehicle_type')['Range'].mean(), df.veh_num2)" +Range of vehicle alternative 3,Range_3,"reindex(vehicles.groupby('vehicle_type')['Range'].mean(), df.veh_num3)" +Range of vehicle alternative 4,Range_4,"reindex(vehicles.groupby('vehicle_type')['Range'].mean(), df.veh_num4)" diff --git a/resident/configs/vehicle_allocation_coefficients.csv b/resident/configs/vehicle_allocation_coefficients.csv new file mode 100644 index 0000000..3a1a8fa --- /dev/null +++ b/resident/configs/vehicle_allocation_coefficients.csv @@ -0,0 +1,46 @@ +coefficient_name,value,constrain +coef_unavail,-999,F +coef_dstgtrng,-3.454,F +coef_vehltdr_nh,0.172,F +coef_vehgtdr_nh,-0.212,F +coef_vehltdr_van,0.048,F +coef_vehltdr_suv,0.104,F +coef_vehltdr_pu,-0.011,F +coef_vehltdr_mc,-0.749,F +coef_vehltdr_hyb,0.156,F +coef_vehltdr_ev,0.207,F +coef_vehltdr_age,-0.048,F +coef_maxocc_van,0.570,F +coef_maxocc_suv,0.316,F +coef_maxocc_pu,-0.077,F +coef_maxocc_mc,-0.938,F +coef_maxocc_age,-0.026,F +util_maxocc_nh,-0.212,F +coef_non_hh,-4.549,F +coef_van,-0.942,F +coef_suv,-0.429,F +coef_pu,-0.048,F +coef_mc,-0.470,F +coef_dsl,-0.227,F +coef_hyb,0.024,F +coef_pev,-0.168,F +coef_bev,-0.105,F +coef_age2,0.070,F +coef_age3,0.104,F +coef_age4,0.136,F +coef_age5,0.149,F +coef_age6,0.161,F +coef_age7,0.223,F +coef_age8,0.272,F +coef_age9,0.291,F +coef_age10,0.256,F +coef_age11,0.318,F +coef_age12,0.334,F +coef_age13,0.338,F +coef_age14,0.348,F +coef_age15,0.307,F +coef_age16,0.377,F +coef_age17,0.324,F +coef_age18,0.237,F +coef_age19,0.292,F +coef_age20,-0.181,F diff --git a/resident/configs/vehicle_type_choice.yaml b/resident/configs/vehicle_type_choice.yaml new file mode 100644 index 0000000..010da1f --- /dev/null +++ b/resident/configs/vehicle_type_choice.yaml @@ -0,0 +1,133 @@ +# vehicle_type_choice.yaml + +SPEC: vehicle_type_choice_op4.csv +COEFFICIENTS: vehicle_type_choice_op4_coefficients.csv + +# SPEC: vehicle_type_choice_op2.csv +# COEFFICIENTS: vehicle_type_choice_op2_coefficients.csv +# ALTS: vehicle_type_choice_op2_alternatives.csv +# # probs must have body_type and vehicle_year columns +# # probs spec has vehicle age calculated from FLEET_YEAR +# PROBS_SPEC: vehicle_type_choice_op2_fuel_type_probs.csv + +LOGIT_TYPE: MNL + +# options: simple_simulate or interaction_simulate +# if interaction_simulate, will build alteratives from combinatorial_alts below +# if simple_simulate, alternatives need to be specified in the columns of the model spec +SIMULATION_TYPE: interaction_simulate + +# additional vehicle_type_data merged to the alternatives for use in utility expressions +# need to have columns body_type, fuel_type, and vehicle_year +# entries in the data need to match the combinations in combinatorial_alts below +# VEHICLE_TYPE_DATA_FILE: vehicle_type_data.csv +VEHICLE_TYPE_DATA_FILE: vehicle_type_data_extended.csv + +REQUIRE_DATA_FOR_ALL_ALTS: False + +# age is computed as (1 + FLEET_YEAR - vehicle_year) +# FLEET_YEAR: 2017 +FLEET_YEAR: 2022 + +# if PROBS_SPEC is supplied, auto operating cost will not be +# merged until after a fuel type is selected +COLS_TO_INCLUDE_IN_VEHICLE_TABLE: + - auto_operating_cost + - Range + - MPG + - vehicle_year + +# If PROBS_SPEC is supplied, fuel_type will be ignored +combinatorial_alts: + body_type: + - Car + - Car-AV + - Van + - Van-AV + - SUV + - SUV-AV + - Pickup + - Pickup-AV + - Motorcycle + age: + - 1 + - 2 + - 3 + - 4 + - 5 + - 6 + - 7 + - 8 + - 9 + - 10 + - 11 + - 12 + - 13 + - 14 + - 15 + - 16 + - 17 + - 18 + - 19 + - 20 + fuel_type: + - Gas + - Diesel + - Hybrid + - PEV + - BEV + +# will write out the created vehicle_type_model_alternatives.csv file +# to the base configs directory. Helpful for debugging alternaties. +WRITE_OUT_ALTS_FILE: False + +preprocessor: + SPEC: vehicle_type_choice_annotate_choosers_preprocessor + DF: choosers + TABLES: + - land_use + - vehicles + - persons + - households + +alts_preprocessor: + SPEC: vehicle_type_choice_annotate_alts_preprocessor + DF: alts_wide + +COLS_TO_INCLUDE_IN_ALTS_TABLE: + - age + - fuel_type_num_coded + - body_type_num_coded + - Range + - MPG + - is_av + - logged_models + - logged_makes + - logged_chargers_per_capita + - SAN + - NewPrice + +# annotate_persons: +# SPEC: annotate_persons_vehicle_type +# DF: persons +# TABLES: +# - vehicles +# +annotate_households: + SPEC: annotate_households_vehicle_type + DF: households + TABLES: + - vehicles +# +# annotate_vehicles: +# SPEC: annotate_vehicles_vehicle_type +# DF: vehicles +# TABLES: + +CONSTANTS: + # calculated using charger count / population + CHARGERS_PER_CAP: 0.00034592 + # chargers per cap used in vehicle type model estimation + # CHARGERS_PER_CAP: 0.000721205 + scenarioYear: 2022 + chargerSensitivityDecayFactor: -0.08245202 diff --git a/resident/configs/vehicle_type_choice_annotate_alts_preprocessor.csv b/resident/configs/vehicle_type_choice_annotate_alts_preprocessor.csv new file mode 100644 index 0000000..bcee198 --- /dev/null +++ b/resident/configs/vehicle_type_choice_annotate_alts_preprocessor.csv @@ -0,0 +1,9 @@ +Description,Target,Expression +mapping fuel type to integers,fuel_type_num_coded,"df.fuel_type.map({'BEV': 1, 'Diesel': 2, 'Gas': 3, 'Hybrid': 4, 'PEV': 5}).astype(int)" +mapping body type to integers,body_type_num_coded,"df.body_type.map({'Car': 1, 'Motorcycle': 2, 'Pickup': 3, 'SUV': 4, 'Van': 5, 'Car-AV': 1, 'Motorcycle-AV': 2, 'Pickup-AV': 3, 'SUV-AV': 4, 'Van-AV': 5}).astype(int)" +flag indicating if vehicle is AV,is_av,"df.body_type.str.contains('AV')" +flag for SAN-specific contants,SAN,True +# moving log terms to preprocessor to avoid expensive log calculations,, +log number of models available,logged_models,"np.log(1 + df.NumModels)" +log number of makes available,logged_makes,"np.log(1 + df.NumMakes)" +logged chargers per capita,logged_chargers_per_capita,"np.log(1 + CHARGERS_PER_CAP)" \ No newline at end of file diff --git a/resident/configs/vehicle_type_choice_annotate_choosers_preprocessor.csv b/resident/configs/vehicle_type_choice_annotate_choosers_preprocessor.csv new file mode 100644 index 0000000..e55e502 --- /dev/null +++ b/resident/configs/vehicle_type_choice_annotate_choosers_preprocessor.csv @@ -0,0 +1,16 @@ +Description,Target,Expression +total household distance to work,total_hh_dist_to_work,persons.groupby('household_id')['distance_to_work'].sum().reindex(df.household_id).fillna(0) +total household distance to work capped at 100 mi per worker,total_hh_dist_to_work_cap,"np.where(total_hh_dist_to_work > (100*df.num_workers.values), 100*df.num_workers.values, total_hh_dist_to_work)" +average household distance to work,avg_hh_dist_to_work,persons.groupby('household_id')['distance_to_work'].mean().reindex(df.household_id).fillna(0) +number of vehicles is greater than the number of drivers,hh_veh_gt_drivers,"np.where(df.auto_ownership > df.num_drivers, 1, 0)" +number of household vehicles owned,num_hh_veh_owned,df[df['already_owned_veh'].str.len() > 0].groupby('household_id').size().reindex(df.household_id).fillna(0) +number of household Vans,num_hh_Van,"df[df['already_owned_veh'].str.contains('Van', na=False)].groupby('household_id').size().reindex(df.household_id).fillna(0)" +number of household SUVs,num_hh_SUV,"df[df['already_owned_veh'].str.contains('SUV', na=False)].groupby('household_id').size().reindex(df.household_id).fillna(0)" +number of household Pickups,num_hh_Pickup,"df[df['already_owned_veh'].str.contains('Pickup', na=False)].groupby('household_id').size().reindex(df.household_id).fillna(0)" +number of household Motorcycles,num_hh_Motorcycle,"df[df['already_owned_veh'].str.contains('Motorcycle', na=False)].groupby('household_id').size().reindex(df.household_id).fillna(0)" +number of household Hybrid,num_hh_Hybrid,"df[df['already_owned_veh'].str.contains('Hybrid', na=False)].groupby('household_id').size().reindex(df.household_id).fillna(0)" +number of household BEV,num_hh_BEV,"df[df['already_owned_veh'].str.contains('BEV', na=False)].groupby('household_id').size().reindex(df.household_id).fillna(0)" +number of household PEV,num_hh_PEV,"df[df['already_owned_veh'].str.contains('PEV', na=False)].groupby('household_id').size().reindex(df.household_id).fillna(0)" +number of household EVs,num_hh_EV,"num_hh_BEV + num_hh_PEV" +# FIXME is this correction factor correct for Metro base year?? -- should probably move to constants file,, +household income adjusted to 2017$,income17,"np.where(df.income < 0, df.income, df.income/1.217)" \ No newline at end of file diff --git a/resident/configs/vehicle_type_choice_op4.csv b/resident/configs/vehicle_type_choice_op4.csv new file mode 100644 index 0000000..c6f54c7 --- /dev/null +++ b/resident/configs/vehicle_type_choice_op4.csv @@ -0,0 +1,151 @@ +Label,Description,Expression,Coefficient +util_ln_nmods,number of models available,"logged_models",coef_ln_nmods +util_ln_nmakes,number of makes available,"logged_makes",coef_ln_nmakes +util_mpg,miles per gallon (or equivalent),"@df.MPG",coef_mpg +util_crange,Range for BEV (mi),"@df.Range",coef_crange +util_crangeltwk,range less than average round trip distance to work,"(Range < (avg_hh_dist_to_work * 2)) & (fuel_type_num_coded==1)",coef_crangeltwk +util_ln_chpc_ev,ln(1+number of chargers per capita in MSA/state),"@df.logged_chargers_per_capita * ((df.fuel_type_num_coded==5) | (df.fuel_type_num_coded==1)) * np.exp(chargerSensitivityDecayFactor*(scenarioYear-2022))",coef_ln_chpc_ev +#,autonomous vehicle related variables,, +util_must_select_av,Must select autonomous vehicle if hh owns one,av_ownership & ~is_av & (num_hh_veh_owned == 0),coef_unavail +util_must_select_av,Cannot select AV if hh does not own one,~av_ownership & is_av,coef_unavail +#,income related variables,, +util_cprice0,New Purchase Price (2017$) Segmented by Income,"((income17 < 25000) & (income17 > -1)) * NewPrice",coef_cprice0 +util_cprice25,New Purchase Price (2017$) Segmented by Income,"((income17 < 50000) & (income17 >= 25000)) * NewPrice",coef_cprice25 +util_cprice50,New Purchase Price (2017$) Segmented by Income,"((income17 < 100000) & (income17 >=50000)) * NewPrice",coef_cprice50 +util_cprice100,New Purchase Price (2017$) Segmented by Income,"((income17 < 150000) & (income17 >= 100000)) * NewPrice",coef_cprice100 +util_cprice150,New Purchase Price (2017$) Segmented by Income,"(income17 > 150000) * NewPrice",coef_cprice150 +util_cpricem,New Purchase Price (2017$) Missing Income,"((income17 < 0) | income17.isna()) * NewPrice",coef_cpricem +util_i025_age,Vehicle Age Segmented by Income,"((income17 < 25000) & (income17 > -1)) * age",coef_i025_age +util_i2550_age,Vehicle Age Segmented by Income,"((income17 < 50000) & (income17 >= 25000)) * age",coef_i2550_age +util_i100_age,Vehicle Age Segmented by Income,"((income17 < 150000) & (income17 >= 100000)) * age",coef_i100_age +util_i150p_age,Vehicle Age Segmented by Income,"((income17 < 150000) & (income17 >= 100000)) * age",coef_i150p_age +util_imiss_age,Vehicle Age Missing Income,"((income17 < 0) | income17.isna()) * age",coef_imiss_age +#,household density variables,, +util_den3_van,500-999 HH / sq mi Van,"((household_density < 1000) & (household_density >= 500)) & (body_type_num_coded==5)",coef_den3_van +util_den4_van,1000-1999 HH / sq mi Van,"((household_density < 2000) & (household_density >= 1000)) & (body_type_num_coded==5)",coef_den4_van +util_den5_van,2000-3999 HH / sq mi Van,"((household_density < 4000) & (household_density >= 2000)) & (body_type_num_coded==5)",coef_den5_van +util_den6_van,4000-9999 HH / sq mi Van,"((household_density < 10000) & (household_density >= 4000)) & (body_type_num_coded==5)",coef_den6_van +util_den7_van,10000-24999 HH / sq mi Van,"((household_density < 25000) & (household_density >= 10000)) & (body_type_num_coded==5)",coef_den7_van +util_den8_van,25000+ HH / sq mi Van,"(household_density >= 25000) & (body_type_num_coded==5)",coef_den8_van +util_den3_suv,500-999 HH / sq mi SUV,"((household_density < 1000) & (household_density >= 500)) & (body_type_num_coded==4)",coef_den3_suv +util_den4_suv,1000-1999 HH / sq mi SUV,"((household_density < 2000) & (household_density >= 1000)) & (body_type_num_coded==4)",coef_den4_suv +util_den5_suv,2000-3999 HH / sq mi SUV,"((household_density < 4000) & (household_density >= 2000)) & (body_type_num_coded==4)",coef_den5_suv +util_den6_suv,4000-9999 HH / sq mi SUV,"((household_density < 10000) & (household_density >= 4000)) & (body_type_num_coded==4)",coef_den6_suv +util_den7_suv,10000-24999 HH / sq mi SUV,"((household_density < 25000) & (household_density >= 10000)) & (body_type_num_coded==4)",coef_den7_suv +util_den8_suv,25000+ HH / sq mi SUV,"(household_density >= 25000) & (body_type_num_coded==4)",coef_den8_suv +util_den3_pu,500-999 HH / sq mi Pickup,"((household_density < 1000) & (household_density >= 500)) & (body_type_num_coded==3)",coef_den3_pu +util_den4_pu,1000-1999 HH / sq mi Pickup,"((household_density < 2000) & (household_density >= 1000)) & (body_type_num_coded==3)",coef_den4_pu +util_den5_pu,2000-3999 HH / sq mi Pickup,"((household_density < 4000) & (household_density >= 2000)) & (body_type_num_coded==3)",coef_den5_pu +util_den6_pu,4000-9999 HH / sq mi Pickup,"((household_density < 10000) & (household_density >= 4000)) & (body_type_num_coded==3)",coef_den6_pu +util_den7_pu,10000-24999 HH / sq mi Pickup,"((household_density < 25000) & (household_density >= 10000)) & (body_type_num_coded==3)",coef_den7_pu +util_den8_mc,25000+ HH / sq mi Pickup,"(household_density >= 25000) & (body_type_num_coded==3)",coef_den8_pu +util_den3_mc,500-999 HH / sq mi Motorcycle,"((household_density < 1000) & (household_density >= 500)) & (body_type_num_coded==2)",coef_den3_mc +util_den4_mc,1000-1999 HH / sq mi Motorcycle,"((household_density < 2000) & (household_density >= 1000)) & (body_type_num_coded==2)",coef_den4_mc +util_den5_mc,2000-3999 HH / sq mi Motorcycle,"((household_density < 4000) & (household_density >= 2000)) & (body_type_num_coded==2)",coef_den5_mc +util_den6_mc,4000-9999 HH / sq mi Motorcycle,"((household_density < 10000) & (household_density >= 4000)) & (body_type_num_coded==2)",coef_den6_mc +util_den7_mc,10000-24999 HH / sq mi Motorcycle,"((household_density < 25000) & (household_density >= 10000)) & (body_type_num_coded==2)",coef_den7_mc +util_den8_mc,25000+ HH / sq mi Motorcycle,"(household_density >= 25000) & (body_type_num_coded==2)",coef_den8_mc +util_den3_hyb,500-999 HH / sq mi Hybrid,"((household_density < 1000) & (household_density >= 500)) & (fuel_type_num_coded==4)",coef_den3_hyb +util_den4_hyb,1000-1999 HH / sq mi Hybrid,"((household_density < 2000) & (household_density >= 1000)) & (fuel_type_num_coded==4)",coef_den4_hyb +util_den5_hyb,2000-3999 HH / sq mi Hybrid,"((household_density < 4000) & (household_density >= 2000)) & (fuel_type_num_coded==4)",coef_den5_hyb +util_den6_hyb,4000-9999 HH / sq mi Hybrid,"((household_density < 10000) & (household_density >= 4000)) & (fuel_type_num_coded==4)",coef_den6_hyb +util_den7_hyb,10000-24999 HH / sq mi Hybrid,"((household_density < 25000) & (household_density >= 10000)) & (fuel_type_num_coded==4)",coef_den7_hyb +util_den8_hyb,25000+ HH / sq mi Hybrid,"(household_density >= 25000) & (fuel_type_num_coded==4)",coef_den8_hyb +util_den34_ev,500-1999 HH / sq mi Electric,"((household_density < 2000) & (household_density >= 500)) & ((fuel_type_num_coded==5) | (fuel_type_num_coded==1))",coef_den34_ev +util_den5_ev,2000-3999 HH / sq mi Electric,"((household_density < 4000) & (household_density >= 2000)) & ((fuel_type_num_coded==5) | (fuel_type_num_coded==1))",coef_den5_ev +util_den6_ev,4000-9999 HH / sq mi Electric,"((household_density < 10000) & (household_density >= 4000)) & ((fuel_type_num_coded==5) | (fuel_type_num_coded==1))",coef_den6_ev +util_den78_ev,10000+ HH / sq mi Electric,"(household_density >= 10000) & ((fuel_type_num_coded==5) | (fuel_type_num_coded==1))",coef_den78_ev +#,household drivers,, +util_oneveh_age,Household owns only one vehicle * vehicle age,"(auto_ownership == 1) * age",coef_oneveh_age +util_vhgtdr_van,Household vehicles gt drivers for Van,hh_veh_gt_drivers * (body_type_num_coded==5),coef_vhgtdr_van +util_vhgtdr_suv,Household vehicles gt drivers for SUV,hh_veh_gt_drivers * (body_type_num_coded==4),coef_vhgtdr_suv +util_vhgtdr_pu,Household vehicles gt drivers for Pickup,hh_veh_gt_drivers * (body_type_num_coded==3),coef_vhgtdr_pu +util_vhgtdr_mc,Household vehicles gt drivers for Motorcycle,hh_veh_gt_drivers * (body_type_num_coded==2),coef_vhgtdr_mc +util_vhgtdr_hy,Household vehicles gt drivers for Hybrid,hh_veh_gt_drivers * (fuel_type_num_coded==4),coef_vhgtdr_hy +util_vhgtdr_ev,Household vehicles gt drivers for EVs,hh_veh_gt_drivers * ((fuel_type_num_coded==5) | (fuel_type_num_coded==1)),coef_vhgtdr_ev +util_vhgtdr_age,Household vehicles gt drivers for age,hh_veh_gt_drivers * age,coef_vhgtdr_age +#,interacting with number of children,, +util_nchld_van,Number of children max 3 * Van,num_children.clip(upper=3) * (body_type_num_coded==5),coef_nchld_van +util_nchld_suv,Number of children max 3 * SUV,num_children.clip(upper=3) * (body_type_num_coded==4),coef_nchld_suv +util_nchld_pu,Number of children max 3 * Pickup,num_children.clip(upper=3) * (body_type_num_coded==3),coef_nchld_pu +util_nchld_mc,Number of children max 3 * Motorcycle,num_children.clip(upper=3) * (body_type_num_coded==2),coef_nchld_mc +util_nchld_hy,Number of children max 3 * Hybrid,num_children.clip(upper=3) * (fuel_type_num_coded==4),coef_nchld_hy +util_nchld_ev,Number of children max 3 * EVs,num_children.clip(upper=3) * ((fuel_type_num_coded==5) | (fuel_type_num_coded==1)),coef_nchld_ev +util_nchld_age,Number of children max 3 * age,num_children.clip(upper=3) * age,coef_nchld_age +util_dstwkt_age,Total distance to work * age,total_hh_dist_to_work_cap * age,coef_dstwkt_age +#,household already owning vehicles,, +util_van_van,Household already owns a Van -- Van,"(num_hh_Van > 0) & (body_type_num_coded==5)",coef_van_van +util_van_suv,Household already owns a Van -- SUV,"(num_hh_Van > 0) & (body_type_num_coded==4)",coef_van_suv +util_van_pu,Household already owns a Van -- Pickup,"(num_hh_Van > 0) & (body_type_num_coded==3)",coef_van_pu +util_van_mc,Household already owns a Van -- Motorcycle,"(num_hh_Van > 0) & (body_type_num_coded==2)",coef_van_mc +util_van_suv,Household already owns an SUV -- Van (symmetrical with above),"(num_hh_SUV > 0) & (body_type_num_coded==5)",coef_van_suv +util_suv_suv,Household already owns an SUV -- SUV,"(num_hh_SUV > 0) & (body_type_num_coded==4)",coef_suv_suv +util_suv_pu,Household already owns an SUV -- Pickup,"(num_hh_SUV > 0) & (body_type_num_coded==3)",coef_suv_pu +util_suv_mc,Household already owns an SUV -- Motorcycle,"(num_hh_SUV > 0) & (body_type_num_coded==2)",coef_suv_mc +util_van_pu,Household already owns a Pickup -- Van (symmetrical with above),"(num_hh_Pickup > 0) & (body_type_num_coded==5)",coef_van_pu +util_suv_pu,Household already owns a Pickup -- SUV (symmetrical with above),"(num_hh_Pickup > 0) & (body_type_num_coded==4)",coef_suv_pu +util_pu_pu,Household already owns a Pickup -- Pickup,"(num_hh_Pickup > 0) & (body_type_num_coded==3)",coef_pu_pu +util_pu_mc,Household already owns a Pickup -- Motorcycle,"(num_hh_Pickup > 0) & (body_type_num_coded==2)",coef_pu_mc +util_van_mc,Household already owns a Motorcycle -- Van (symmetrical with above),"(num_hh_Motorcycle > 0) & (body_type_num_coded==5)",coef_van_mc +util_suv_mc,Household already owns a Motorcycle -- SUV (symmetrical with above),"(num_hh_Motorcycle > 0) & (body_type_num_coded==4)",coef_suv_mc +util_pu_mc,Household already owns a Motorcycle -- Pickup (symmetrical with above),"(num_hh_Motorcycle > 0) & (body_type_num_coded==3)",coef_pu_mc +util_mc_mc,Household already owns a Motorcycle -- Motorcycle,"(num_hh_Motorcycle > 0) & (body_type_num_coded==2)",coef_mc_mc +util_hyb_hyb,Houeshold already owns a Hybrid -- Hybrid,"(num_hh_Hybrid > 0) & (fuel_type_num_coded==4)",coef_hyb_hyb +util_hyb_ev,Houeshold already owns a Hybrid -- EV,"(num_hh_Hybrid > 0) & ((fuel_type_num_coded==5) | (fuel_type_num_coded==1))",coef_hyb_ev +util_hyb_ev,Houeshold already owns an EV -- Hybrid (symmetrical with above),"(num_hh_EV> 0) & (fuel_type_num_coded==4)",coef_hyb_ev +util_only_van,Household only owns Vans,"(num_hh_Van > 0) & (num_hh_Van == num_hh_veh_owned) & (body_type_num_coded==5)",coef_only_van +util_only_suv,Household only owns SUVs,"(num_hh_SUV > 0) & (num_hh_SUV == num_hh_veh_owned) & (body_type_num_coded==4)",coef_only_suv +util_only_pu,Household only owns Pickups,"(num_hh_Pickup > 0) & (num_hh_Pickup == num_hh_veh_owned) & (body_type_num_coded==3)",coef_only_pu +util_only_mc,Household only owns Motorcycles,"(num_hh_Motorcycle > 0) & (num_hh_Motorcycle == num_hh_veh_owned) & (body_type_num_coded==2)",coef_only_mc +util_only_bev,Household only owns BEVs,"(num_hh_BEV > 0) & (num_hh_BEV == num_hh_veh_owned) & (fuel_type_num_coded==1)",coef_only_bev +#,constants,, +util_van,Van ASC,(body_type_num_coded==5),coef_van +util_suv,SUV ASC,(body_type_num_coded==4),coef_suv +util_pu,Pickup ASC,(body_type_num_coded==3),coef_pu +util_mc,Motorcycle ASC,(body_type_num_coded==2),coef_mc +util_dsl,Diesel ASC,(fuel_type_num_coded==2),coef_dsl +util_hyb,Hybrid ASC,(fuel_type_num_coded==4),coef_hyb +util_pev,PEV ASC,(fuel_type_num_coded==5),coef_pev +util_bev,BEV ASC,(fuel_type_num_coded==1),coef_bev +util_age2,Age 2 ASC,(age==2),coef_age2 +util_age3,Age 3 ASC,(age==3),coef_age3 +util_age4,Age 4 ASC,(age==4),coef_age4 +util_age5,Age 5 ASC,(age==5),coef_age5 +util_age6,Age 6 ASC,(age==6),coef_age6 +util_age7,Age 7 ASC,(age==7),coef_age7 +util_age8,Age 8 ASC,(age==8),coef_age8 +util_age9,Age 9 ASC,(age==9),coef_age9 +util_age10,Age 10 ASC,(age==10),coef_age10 +util_age11,Age 11 ASC,(age==11),coef_age11 +util_age12,Age 12 ASC,(age==12),coef_age12 +util_age13,Age 13 ASC,(age==13),coef_age13 +util_age14,Age 14 ASC,(age==14),coef_age14 +util_age15,Age 15 ASC,(age==15),coef_age15 +util_age16,Age 16 ASC,(age==16),coef_age16 +util_age17,Age 17 ASC,(age==17),coef_age17 +util_age18,Age 18 ASC,(age==18),coef_age18 +util_age19,Age 19 ASC,(age==19),coef_age19 +util_age20,Age 20 ASC,(age==20),coef_age20 +#,rural household coefficients,, +util_rural_van,Household is in Rural Area - Van,"home_is_rural & (body_type_num_coded==5)",coef_rural_van +util_rural_suv,Household is in Rural Area - SUV,"home_is_rural & (body_type_num_coded==4)",coef_rural_suv +util_rural_pu,Household is in Rural Area - Pickup,"home_is_rural & (body_type_num_coded==3)",coef_rural_pu +util_rural_mc,Household is in Rural Area - Motorcycle,"home_is_rural & (body_type_num_coded==2)",coef_rural_mc +util_rural_hyb,Household is in Rural Area - Hybrid,"home_is_rural & (fuel_type_num_coded==4)",coef_rural_hyb +util_rural_ev,Household is in Rural Area - Electric,"home_is_rural & ((fuel_type_num_coded==5) | (fuel_type_num_coded==1))",coef_rural_ev +util_rural_age,Household is in Rural Area - Age,"home_is_rural * age",coef_rural_age +#,Region Specific Constants,, +util_san_van,San Diego - Van,"@(df.SAN) & (df.body_type_num_coded==5)",coef_san_van +util_san_suv,San Diego - SUV,"@(df.SAN) & (df.body_type_num_coded==4)",coef_san_suv +util_san_pu,San Diego - Pickup,"@(df.SAN) & (df.body_type_num_coded==3)",coef_san_pu +util_san_mc,San Diego - Motorcycle,"@(df.SAN) & (df.body_type_num_coded==2)",coef_san_mc +util_san_hyb,San Diego - Hybrid,"@(df.SAN) & (df.fuel_type_num_coded==4)",coef_san_hyb +util_san_ev,San Diego - Electric,"@(df.SAN) & ((df.fuel_type_num_coded==5) | (df.fuel_type_num_coded==1))",coef_san_ev +util_san_age,San Diego - Age,"@(df.SAN) * df.age",coef_san_age +#,EV calibration constants,, +util_calib_pev_2022,PEV calibration factor for 2022,"@(df.fuel_type_num_coded==5)*np.maximum(1 + (2022-scenarioYear)/(2035-2022), 0)",coef_calib_pev_2022 +util_calib_bev_2022,BEV calibration factor for 2022,"@(df.fuel_type_num_coded==1)*np.maximum(1 + (2022-scenarioYear)/(2035-2022), 0)",coef_calib_bev_2022 +util_calib_pev_2035,PEV calibration factor for 2035,"@(df.fuel_type_num_coded==5)*np.minimum((scenarioYear-2022)/(2035-2022), 1 + (2035-scenarioYear)/(2050-2035))",coef_calib_pev_2035 +util_calib_bev_2035,BEV calibration factor for 2035,"@(df.fuel_type_num_coded==1)*np.minimum((scenarioYear-2022)/(2035-2022), 1 + (2035-scenarioYear)/(2050-2035))",coef_calib_bev_2035 +util_calib_pev_2050,PEV calibration factor for 2050,"@(df.fuel_type_num_coded==5)*np.maximum((scenarioYear-2035)/(2050-2035), 0)",coef_calib_pev_2050 +util_calib_bev_2050,BEV calibration factor for 2050,"@(df.fuel_type_num_coded==1)*np.maximum((scenarioYear-2035)/(2050-2035), 0)",coef_calib_bev_2050 diff --git a/resident/configs/vehicle_type_choice_op4_coefficients.csv b/resident/configs/vehicle_type_choice_op4_coefficients.csv new file mode 100644 index 0000000..1b70509 --- /dev/null +++ b/resident/configs/vehicle_type_choice_op4_coefficients.csv @@ -0,0 +1,196 @@ +coefficient_name,value,constrain +coef_unavail,-999,F +coef_ln_nmods,.612,F +coef_ln_nmakes,.247,F +coef_mpg,0.007,F +coef_crange,0.018,F +coef_crangeltwk,-0.781,F +coef_ln_chpc_ev,354.347,F +coef_cprice0,-0.00010,F +coef_cprice25,-0.00009,F +coef_cprice50,-0.00008,F +coef_cprice100,-0.00007,F +coef_cprice150,-0.00005,F +coef_cpricem,-0.00007,F +coef_i025_age,0.107,F +coef_i2550_age,0.047,F +coef_i100_age,-0.037,F +coef_i150p_age,-0.071,F +coef_imiss_age,0.001,F +coef_den3_van,-0.019,F +coef_den4_van,-0.089,F +coef_den5_van,-0.193,F +coef_den6_van,-0.413,F +coef_den7_van,-0.710,F +coef_den8_van,-0.509,F +coef_den3_suv,0.005,F +coef_den4_suv,-0.084,F +coef_den5_suv,-0.208,F +coef_den6_suv,-0.422,F +coef_den7_suv,-0.465,F +coef_den8_suv,-0.463,F +coef_den3_pu,-0.167,F +coef_den4_pu,-0.219,F +coef_den5_pu,-0.313,F +coef_den6_pu,-0.720,F +coef_den7_pu,-1.140,F +coef_den8_pu,-2.006,F +coef_den3_mc,-0.030,F +coef_den4_mc,0.157,F +coef_den5_mc,0.185,F +coef_den6_mc,0.436,F +coef_den7_mc,1.106,F +coef_den8_mc,1.533,F +coef_den3_hyb,-0.018,F +coef_den4_hyb,0.070,F +coef_den5_hyb,0.239,F +coef_den6_hyb,0.290,F +coef_den7_hyb,0.406,F +coef_den8_hyb,0.445,F +coef_den34_ev,0.107,F +coef_den5_ev,0.323,F +coef_den6_ev,0.272,F +coef_den78_ev,0.030,F +coef_oneveh_age,-0.028,F +coef_vhgtdr_van,-.108,F +coef_vhgtdr_suv,-0.126,F +coef_vhgtdr_pu,0.287,F +coef_vhgtdr_mc,2.408,F +coef_vhgtdr_hy,-0.168,F +coef_vhgtdr_ev,0.165,F +coef_vhgtdr_age,0.068,F +coef_nchld_van,0.560,F +coef_nchld_suv,0.193,F +coef_nchld_pu,0.016,F +coef_nchld_mc,0.028,F +coef_nchld_hy,-0.045,F +coef_nchld_ev,0.174,F +coef_nchld_age,-0.001,F +coef_dstwkt_age,-0.00013,F +coef_van_van,0.028,F +coef_van_suv,-0.477,F +coef_van_pu,-0.075,F +coef_van_mc,0.527,F +coef_suv_suv,-0.115,F +coef_suv_pu,0.194,F +coef_suv_mc,1.048,F +coef_pu_pu,-0.164,F +coef_pu_mc,1.360,F +coef_mc_mc,3.469,F +coef_hyb_hyb,1.611,F +coef_hyb_ev,0.940,F +coef_only_van,-0.259,F +coef_only_suv,-0.102,F +coef_only_pu,-0.781,F +coef_only_mc,-1.233,F +coef_only_bev,-0.491,F +coef_van,-0.629,F +coef_suv,0.413,F +coef_pu,0.224,F +coef_mc,-5.259,F +coef_dsl,-0.649,F +coef_hyb,-1.055,F +coef_pev,-1.406,F +coef_bev,-4.640,F +coef_age2,0.163,F +coef_age3,0.107,F +coef_age4,0.009,F +coef_age5,-0.079,F +coef_age6,-0.307,F +coef_age7,-0.216,F +coef_age8,-0.346,F +coef_age9,-0.133,F +coef_age10,-0.163,F +coef_age11,-0.060,F +coef_age12,-0.032,F +coef_age13,-0.248,F +coef_age14,-0.350,F +coef_age15,-0.510,F +coef_age16,-0.658,F +coef_age17,-0.772,F +coef_age18,-0.885,F +coef_age19,-1.187,F +coef_age20,0.484,F +coef_rural_van,0.234,F +coef_rural_suv,0.150,F +coef_rural_pu,0.824,F +coef_rural_mc,-0.150,F +coef_rural_hyb,-0.280,F +coef_rural_ev,-0.483,F +coef_rural_age,0.004,F +coef_smsa_van,0.130,F +coef_smsa_suv,0.003,F +coef_smsa_pu,0.357,F +coef_smsa_mc,0.011,F +coef_smsa_hyb,-0.063,F +coef_smsa_ev,-0.341,F +coef_smsa_age,0.011,F +coef_sfo_van,0.047,F +coef_sfo_suv,-0.386,F +coef_sfo_pu,-0.527,F +coef_sfo_mc,-0.021,F +coef_sfo_hyb,0.942,F +coef_sfo_ev,1.497,F +coef_sfo_age,0.058,F +coef_san_van,0.055,F +coef_san_suv,-0.132,F +coef_san_pu,-0.187,F +coef_san_mc,-0.129,F +coef_san_hyb,0.0629,F +coef_san_ev,0.0922,F +coef_san_age,0.030,F +coef_atl_van,-0.158,F +coef_atl_suv,-0.072,F +coef_atl_pu,-0.107,F +coef_atl_mc,-0.284,F +coef_atl_hyb,-0.180,F +coef_atl_ev,0.547,F +coef_atl_age,0.016,F +coef_sea_van,0.088,F +coef_sea_suv,-0.134,F +coef_sea_pu,-0.131,F +coef_sea_mc,-0.275,F +coef_sea_hyb,0.421,F +coef_sea_ev,0.706,F +coef_sea_age,0.060,F +coef_det_van,0.703,F +coef_det_suv,0.260,F +coef_det_pu,-0.150,F +coef_det_mc,0.427,F +coef_det_hyb,-0.982,F +coef_det_ev,0.531,F +coef_det_age,-0.045,F +coef_msp_van,0.263,F +coef_msp_suv,-0.052,F +coef_msp_pu,-0.087,F +coef_msp_mc,0.083,F +coef_msp_hyb,-0.111,F +coef_msp_ev,-0.733,F +coef_msp_age,0.008,F +coef_dca_van,0.062,F +coef_dca_suv,0.273,F +coef_dca_pu,-0.411,F +coef_dca_mc,0.062,F +coef_dca_hyb,-0.428,F +coef_dca_ev,-1.605,F +coef_dca_age,-0.040,F +coef_oreg_van,0.037,F +coef_oreg_suv,-0.121,F +coef_oreg_pu,0.185,F +coef_oreg_mc,0.044,F +coef_oreg_hyb,0.819,F +coef_oreg_ev,0.278,F +coef_oreg_age,0.056,F +coef_ohio_van,0.170,F +coef_ohio_suv,-0.147,F +coef_ohio_pu,-0.355,F +coef_ohio_mc,0.140,F +coef_ohio_hyb,-0.142,F +coef_ohio_ev,-1.590,F +coef_ohio_age,-0.008,F +coef_calib_pev_2022,0.56468,F +coef_calib_bev_2022,1.42228,F +coef_calib_pev_2035,0.86656,F +coef_calib_bev_2035,0.98343,F +coef_calib_pev_2050,0.36963,F +coef_calib_bev_2050,-0.64752,F diff --git a/resident/configs/vehicle_type_data.csv b/resident/configs/vehicle_type_data.csv new file mode 100644 index 0000000..7831b52 --- /dev/null +++ b/resident/configs/vehicle_type_data.csv @@ -0,0 +1,501 @@ +body_type,fuel_type,vehicle_year,NumMakes,NumModels,MPG,Range,NewPrice,auto_operating_cost,co2gpm +Car,Gas,2017,39,738,24,0,32926.61346,20.17,388.85 +Car,Gas,2016,39,734,23.7,0,33383.61537,20.32822785,392.27 +Car,Gas,2015,39,740,23.4,0,33369.12233,20.49051282,398.43 +Car,Gas,2014,41,717,23.2,0,32571.33539,20.60103448,402.86 +Car,Gas,2013,40,702,23.1,0,32976.62731,20.65701299,402.94 +Car,Gas,2012,42,668,22.4,0,32959.56219,21.06285714,417.59 +Car,Gas,2011,42,617,21.7,0,34409.88113,21.49488479,428.48 +Car,Gas,2010,44,641,21.5,0,35279.71318,21.62348837,434.37 +Car,Gas,2009,45,637,21,0,35638.52401,21.95571429,444.46 +Car,Gas,2008,46,649,20.5,0,35745.78309,22.30414634,454.02 +Car,Gas,2007,43,594,20.3,0,37613.76774,22.44832512,454.23 +Car,Gas,2006,43,595,20.2,0,38581.65596,22.52148515,456.18 +Car,Gas,2005,41,661,20.4,0,37537.36453,22.37588235,452.05 +Car,Gas,2004,43,628,20.6,0,37988.14511,22.2331068,445.25 +Car,Gas,2003,42,573,20.5,0,37597.37624,22.30414634,450.5 +Car,Gas,2002,42,543,20.8,0,37380.8733,22.09307692,439.76 +Car,Gas,2001,39,505,21,0,37361.09207,21.95571429,437.72 +Car,Gas,2000,39,473,21,0,38856.86312,21.95571429,437.7 +Car,Gas,1999,40,483,21.2,0,38447.21363,21.8209434,435.35 +Car,Gas,1998,42,464,21.3,0,38952.55256,21.75450704,433.86 +Car,Diesel,2017,3,9,35.2,0,38787.1746,16.19272727,290 +Car,Diesel,2016,2,8,31.4,0,39325.51763,17.22414013,325.13 +Car,Diesel,2015,4,23,32.8,0,39308.445,16.81634146,311.57 +Car,Diesel,2014,4,20,31.5,0,38368.66109,17.19380952,322.3 +Car,Diesel,2013,2,12,31.8,0,38846.09034,17.10396226,319.17 +Car,Diesel,2012,2,8,32.3,0,38825.98783,16.9579257,314.55 +Car,Diesel,2011,3,8,31.4,0,40534.44697,17.22414013,324.25 +Car,Diesel,2010,3,8,31.4,0,41559.0992,17.22414013,324.25 +Car,Diesel,2009,3,6,30.2,0,41981.77425,17.60377483,339.1 +Car,Diesel,2008,1,1,26,0,42108.12422,19.20846154,391.54 +Car,Diesel,2007,1,1,26,0,44308.58881,19.20846154,391.54 +Car,Diesel,2006,2,7,32.3,0,45448.75007,16.9579257,317.06 +Car,Diesel,2005,2,13,32,0,44218.5867,17.045,321.07 +Car,Diesel,2004,1,10,32.6,0,44749.60108,16.87245399,315.61 +Car,Diesel,2003,1,8,35.6,0,44289.27981,16.09696629,287.33 +Car,Diesel,2002,1,8,35.6,0,44034.24182,16.09696629,287.33 +Car,Diesel,2001,1,6,35.5,0,44010.93975,16.12070423,288.19 +Car,Diesel,2000,1,6,35.5,0,45772.94097,16.12070423,288.19 +Car,Diesel,1999,2,8,34.5,0,45290.37855,16.36565217,299.48 +Car,Diesel,1998,2,8,33.8,0,45885.66203,16.54573964,306.26 +Car,Hybrid,2017,11,29,40.4,0,34769.7517,15.09574257,242.55 +Car,Hybrid,2016,14,31,36.8,0,35252.33528,15.82217391,253.65 +Car,Hybrid,2015,12,33,35.5,0,35237.03096,16.12070423,261.42 +Car,Hybrid,2014,15,40,34.8,0,34394.58617,16.29068966,270.95 +Car,Hybrid,2013,15,34,35.3,0,34822.56518,16.16858357,265.21 +Car,Hybrid,2012,12,25,34.2,0,34804.54481,16.44192982,280.1 +Car,Hybrid,2011,11,20,33.6,0,36336.04847,16.59857143,285.2 +Car,Hybrid,2010,8,13,34.5,0,37254.57126,16.36565217,276.5 +Car,Hybrid,2009,6,8,32.3,0,37633.4673,16.9579257,293.75 +Car,Hybrid,2008,6,8,31.8,0,37746.73043,17.10396226,299.43 +Car,Hybrid,2007,5,7,33.3,0,39719.27956,16.67900901,281.75 +Car,Hybrid,2006,2,5,42.4,0,40741.3474,14.7454717,224.05 +Car,Hybrid,2005,2,8,41.9,0,39638.59951,14.82990453,218.55 +Car,Hybrid,2004,2,7,43.9,0,40114.61351,14.50371298,204.44 +Car,Hybrid,2003,2,7,43.3,0,39701.9705,14.59840647,207.34 +Car,Hybrid,2002,2,3,47,0,39473.34834,14.05297872,191.17 +Car,Hybrid,2001,2,4,47,0,39452.45982,14.05297872,190.65 +Car,Hybrid,2000,1,1,53,0,41031.95988,13.33037736,167.68 +Car,Hybrid,1999,0,0,0,0,40599.37938,13.33037736,0 +Car,Hybrid,1998,0,0,0,0,41133.00574,13.33037736,0 +Car,PEV,2017,9,14,35.1,0,56882.64178,16.21700855,138.64 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+Motorcycle,BEV,2009,1,1,240,80,11382.15714,8.08,0 +Motorcycle,BEV,2008,1,1,240,80,11382.15714,8.08,0 +Motorcycle,BEV,2007,0,0,0,0,11382.15714,8.08,0 +Motorcycle,BEV,2006,0,0,0,0,11382.15714,8.08,0 +Motorcycle,BEV,2005,0,0,0,0,11382.15714,8.08,0 +Motorcycle,BEV,2004,0,0,0,0,11382.15714,8.08,0 +Motorcycle,BEV,2003,0,0,0,0,11382.15714,8.08,0 +Motorcycle,BEV,2002,0,0,0,0,11382.15714,8.08,0 +Motorcycle,BEV,2001,0,0,0,0,11382.15714,8.08,0 +Motorcycle,BEV,2000,0,0,0,0,11382.15714,8.08,0 +Motorcycle,BEV,1999,0,0,0,0,11382.15714,8.08,0 +Motorcycle,BEV,1998,0,0,0,0,11382.15714,8.08,0 diff --git a/resident/configs/vehicle_type_data_extended.csv b/resident/configs/vehicle_type_data_extended.csv new file mode 100644 index 0000000..c996ee7 --- /dev/null +++ b/resident/configs/vehicle_type_data_extended.csv @@ -0,0 +1,1116 @@ +body_type,fuel_type,vehicle_year,MPG,NewPrice,NumMakes,NumModels,Range,auto_operating_cost,co2gpm +Car,BEV,2011,93.8,44331.89559,3.0,4.0,73.0,10.86829424,0.0 +Car,BEV,2012,95.6,54291.33743,5.0,5.0,113.0,10.80807531,0.0 +Car,BEV,2013,104.3,59928.48966,9.0,12.0,105.0,10.54631831,0.0 +Car,BEV,2014,106.1,50547.30465,10.0,13.0,115.0,10.49752121,0.0 +Car,BEV,2015,103.2,63109.22167,10.0,18.0,157.0,10.57697674,0.0 +Car,BEV,2016,103.1,74787.61065,11.0,25.0,173.0,10.57979631,0.0 +Car,BEV,2017,108.7,65091.20275,13.0,23.0,169.0,10.4298896,0.0 +Car,BEV,2018,115.03098584261784,65202.70828428572,13.923076923076923,44.30769230769231,145.639398695536,10.36546280178571, +Car,BEV,2019,116.75645063025709,65202.70828428572,14.846153846153847,65.61538461538461,147.82398967596902,10.302922147738087, +Car,BEV,2020,118.50779738971093,65202.70828428572,15.76923076923077,86.92307692307692,150.04134952110854,10.240381493690478, +Car,BEV,2021,120.28541435055658,65202.70828428572,16.692307692307693,108.23076923076923,152.29196976392515,10.177840839642855, +Car,BEV,2022,122.08969556581492,65202.70828428572,17.615384615384617,129.53846153846155,154.57634931038402,10.115300185595231, +Car,BEV,2023,123.92104099930214,65202.70828428572,18.53846153846154,150.84615384615384,156.89499455003977,10.052759531547622, +Car,BEV,2024,125.77985661429163,65202.70828428572,19.46153846153846,172.15384615384613,159.24841946829034,9.990218877499998, +Car,BEV,2025,127.66655446350602,65202.70828428572,20.384615384615387,193.46153846153845,161.6371457603147,9.927678223452375, +Car,BEV,2026,129.5815527804586,65202.70828428572,21.307692307692307,214.76923076923077,164.06170294671938,9.865137569404752, +Car,BEV,2027,131.52527607216544,65202.70828428572,22.230769230769234,236.07692307692307,166.52262849092014,9.802596915357142, +Car,BEV,2028,133.4981552132479,65202.70828428572,23.153846153846153,257.38461538461536,169.02046791828394,9.740056261309519, +Car,BEV,2029,135.50062754144662,65202.70828428572,24.076923076923077,278.6923076923077,171.55577493705817,9.677515607261896, +Car,BEV,2030,137.53313695456833,65202.70828428572,25.0,300.0,174.12911156111406,9.614974953214286, +Car,BEV,2031,139.59613400888682,65202.70828428572,25.25,297.5,176.74104823453075,9.552434299166663, +Car,BEV,2032,141.6900760190201,65202.70828428572,25.5,295.0,179.39216395804866,9.48989364511904, +Car,BEV,2033,143.8154271593054,65202.70828428572,25.75,292.5,182.08304641741938,9.42735299107143, +Car,BEV,2034,145.97265856669497,65202.70828428572,26.0,290.0,184.81429211368066,9.364812337023807, +Car,BEV,2035,148.16224844519536,65202.70828428572,26.25,287.5,187.58650649538583,9.302271682976183, +Car,BEV,2036,150.3846821718733,65202.70828428572,26.5,285.0,190.4003040928166,9.23973102892856, +Car,BEV,2037,152.64045240445137,65202.70828428572,26.75,282.5,193.25630865420885,9.17719037488095, +Car,BEV,2038,154.93005919051814,65202.70828428572,27.0,280.0,196.15515328402196,9.114649720833327, +Car,BEV,2039,157.2540100783759,65202.70828428572,27.25,277.5,199.09748058328228,9.052109066785704, +Car,BEV,2040,159.61282022955152,65202.70828428572,27.5,275.0,202.08394279203148,8.989568412738095, +Car,BEV,2041,162.00701253299476,65202.70828428572,27.75,272.5,205.11520193391195,8.927027758690471, +Car,BEV,2042,164.43711772098968,65202.70828428572,28.0,270.0,208.1919299629206,8.864487104642848, +Car,BEV,2043,166.9036744868045,65202.70828428572,28.25,267.5,211.3148089123644,8.801946450595239, +Car,BEV,2044,169.40722960410656,65202.70828428572,28.5,265.0,214.48453104604982,8.739405796547615, +Car,BEV,2045,171.94833804816815,65202.70828428572,28.75,262.5,217.70179901174058,8.676865142499992, +Car,BEV,2046,174.52756311889064,65202.70828428572,29.0,260.0,220.96732599691666,8.614324488452368, +Car,BEV,2047,177.145476565674,65202.70828428572,29.25,257.5,224.28183588687037,8.551783834404745, +Car,BEV,2048,179.8026587141591,65202.70828428572,29.5,255.0,227.64606342517342,8.489243180357136, +Car,BEV,2049,182.49969859487146,65202.70828428572,29.75,252.5,231.060754376551,8.426702526309526, +Car,BEV,2050,185.2371940737945,65202.70828428572,30.0,250.0,234.52666569219926,8.364161872261889, +Car,Diesel,1998,33.8,45885.66203,2.0,8.0,0.0,16.54573964,306.26 +Car,Diesel,1999,34.5,45290.37855,2.0,8.0,0.0,16.36565217,299.48 +Car,Diesel,2000,35.5,45772.94097,1.0,6.0,0.0,16.12070423,288.19 +Car,Diesel,2001,35.5,44010.93975,1.0,6.0,0.0,16.12070423,288.19 +Car,Diesel,2002,35.6,44034.24182,1.0,8.0,0.0,16.09696629,287.33 +Car,Diesel,2003,35.6,44289.27981,1.0,8.0,0.0,16.09696629,287.33 +Car,Diesel,2004,32.6,44749.60108,1.0,10.0,0.0,16.87245399,315.61 +Car,Diesel,2005,32.0,44218.5867,2.0,13.0,0.0,17.045,321.07 +Car,Diesel,2006,32.3,45448.75007,2.0,7.0,0.0,16.9579257,317.06 +Car,Diesel,2007,26.0,44308.58881,1.0,1.0,0.0,19.20846154,391.54 +Car,Diesel,2008,26.0,42108.12422,1.0,1.0,0.0,19.20846154,391.54 +Car,Diesel,2009,30.2,41981.77425,3.0,6.0,0.0,17.60377483,339.1 +Car,Diesel,2010,31.4,41559.0992,3.0,8.0,0.0,17.22414013,324.25 +Car,Diesel,2011,31.4,40534.44697,3.0,8.0,0.0,17.22414013,324.25 +Car,Diesel,2012,32.3,38825.98783,2.0,8.0,0.0,16.9579257,314.55 +Car,Diesel,2013,31.8,38846.09034,2.0,12.0,0.0,17.10396226,319.17 +Car,Diesel,2014,31.5,38368.66109,4.0,20.0,0.0,17.19380952,322.3 +Car,Diesel,2015,32.8,39308.445,4.0,23.0,0.0,16.81634146,311.57 +Car,Diesel,2016,31.4,39325.51763,2.0,8.0,0.0,17.22414013,325.13 +Car,Diesel,2017,35.2,38787.1746,3.0,9.0,0.0,16.19272727,290.0 +Car,Diesel,2018,41.48711268918141,45079.34609428571,2.769230769230769,8.307692307692308,0.0,16.5874271385714, +Car,Diesel,2019,42.109419379519125,45079.34609428571,2.5384615384615383,7.615384615384615,0.0,16.497489097142847, +Car,Diesel,2020,42.74106067021191,45079.34609428571,2.3076923076923075,6.923076923076923,0.0,16.407551055714265, +Car,Diesel,2021,43.38217658026508,45079.34609428571,2.0769230769230766,6.230769230769231,0.0,16.317613014285683, +Car,Diesel,2022,44.032909228969054,45079.34609428571,1.846153846153846,5.538461538461538,0.0,16.22767497285713, +Car,Diesel,2023,44.69340286740359,45079.34609428571,1.6153846153846154,4.846153846153847,0.0,16.13773693142855, +Car,Diesel,2024,45.36380391041463,45079.34609428571,1.3846153846153846,4.153846153846154,0.0,16.047798889999967, +Car,Diesel,2025,46.044260969070855,45079.34609428571,1.1538461538461537,3.4615384615384617,0.0,15.957860848571414, +Car,Diesel,2026,46.73492488360691,45079.34609428571,0.9230769230769229,2.769230769230769,0.0,15.867922807142833, +Car,Diesel,2027,47.435948756861,45079.34609428571,0.6923076923076921,2.0769230769230766,0.0,15.77798476571428, +Car,Diesel,2028,48.14748798821392,45079.34609428571,0.46153846153846123,1.384615384615385,0.0,15.688046724285698, +Car,Diesel,2029,48.86970030803712,45079.34609428571,0.23076923076923084,0.6923076923076934,0.0,15.598108682857116, +Car,Gas,1998,21.3,38952.55256,42.0,464.0,0.0,21.75450704,433.86 +Car,Gas,1999,21.2,38447.21363,40.0,483.0,0.0,21.8209434,435.35 +Car,Gas,2000,21.0,38856.86312,39.0,473.0,0.0,21.95571429,437.7 +Car,Gas,2001,21.0,37361.09207,39.0,505.0,0.0,21.95571429,437.72 +Car,Gas,2002,20.8,37380.8733,42.0,543.0,0.0,22.09307692,439.76 +Car,Gas,2003,20.5,37597.37624,42.0,573.0,0.0,22.30414634,450.5 +Car,Gas,2004,20.6,37988.14511,43.0,628.0,0.0,22.2331068,445.25 +Car,Gas,2005,20.4,37537.36453,41.0,661.0,0.0,22.37588235,452.05 +Car,Gas,2006,20.2,38581.65596,43.0,595.0,0.0,22.52148515,456.18 +Car,Gas,2007,20.3,37613.76774,43.0,594.0,0.0,22.44832512,454.23 +Car,Gas,2008,20.5,35745.78309,46.0,649.0,0.0,22.30414634,454.02 +Car,Gas,2009,21.0,35638.52401,45.0,637.0,0.0,21.95571429,444.46 +Car,Gas,2010,21.5,35279.71318,44.0,641.0,0.0,21.62348837,434.37 +Car,Gas,2011,21.7,34409.88113,42.0,617.0,0.0,21.49488479,428.48 +Car,Gas,2012,22.4,32959.56219,42.0,668.0,0.0,21.06285714,417.59 +Car,Gas,2013,23.1,32976.62731,40.0,702.0,0.0,20.65701299,402.94 +Car,Gas,2014,23.2,32571.33539,41.0,717.0,0.0,20.60103448,402.86 +Car,Gas,2015,23.4,33369.12233,39.0,740.0,0.0,20.49051282,398.43 +Car,Gas,2016,23.7,33383.61537,39.0,734.0,0.0,20.32822785,392.27 +Car,Gas,2017,24.0,32926.61346,39.0,738.0,0.0,20.17,388.85 +Car,Gas,2018,29.449734763848717,38268.06719428572,36.94444444444444,699.7777777777778,0.0,19.85096401428575, +Car,Gas,2019,29.891480785306445,38268.06719428572,34.888888888888886,661.5555555555555,0.0,19.639288838571474, +Car,Gas,2020,30.339852997086037,38268.06719428572,32.833333333333336,623.3333333333334,0.0,19.42761366285714, +Car,Gas,2021,30.794950792042325,38268.06719428572,30.77777777777778,585.1111111111111,0.0,19.215938487142864, 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+Car,Gas,2031,35.73879759880567,38268.06719428572,10.222222222222225,202.8888888888889,0.0,17.099186730000042, +Car,Gas,2032,36.274879562787746,38268.06719428572,8.166666666666671,164.66666666666663,0.0,16.887511554285766, +Car,Gas,2033,36.81900275622956,38268.06719428572,6.111111111111114,126.44444444444446,0.0,16.675836378571432, +Car,Gas,2034,37.371287797573,38268.06719428572,4.055555555555557,88.22222222222229,0.0,16.464161202857156, +Car,Gas,2035,37.931857114536584,38268.06719428572,2.0,50.0,0.0,16.25248602714288, +Car,Gas,2036,38.50083497125463,38268.06719428572,2.0,50.0,0.0,16.040810851428603, +Car,Gas,2037,39.07834749582345,38268.06719428572,2.0,50.0,0.0,15.829135675714326, +Car,Gas,2038,39.6645227082608,38268.06719428572,2.0,50.0,0.0,15.61746050000005, +Car,Gas,2039,40.259490548884706,38268.06719428572,2.0,50.0,0.0,15.405785324285716, +Car,Gas,2040,40.86338290711797,38268.06719428572,2.0,50.0,0.0,15.19411014857144, 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+Pickup,Diesel,2014,22.5,39406.01202,1.0,2.0,0.0,21.66333333,450.5 +Pickup,Diesel,2015,23.0,40371.20431,1.0,3.0,0.0,21.37347826,443.67 +Pickup,Diesel,2016,23.6,40388.73853,3.0,7.0,0.0,21.04186441,434.71 +Pickup,Diesel,2017,23.0,39835.84063,3.0,7.0,0.0,21.37347826,448.43 +Pickup,Diesel,2018,18.039974136761973,46298.12986714286,2.9444444444444446,6.888888888888889,0.0,20.536679650357087, +Pickup,Diesel,2019,18.3105737488134,46298.12986714286,2.888888888888889,6.777777777777778,0.0,20.227085658214264, +Pickup,Diesel,2020,18.585232355045598,46298.12986714286,2.8333333333333335,6.666666666666667,0.0,19.91749166607144, +Pickup,Diesel,2021,18.86401084037128,46298.12986714286,2.7777777777777777,6.555555555555555,0.0,19.607897673928505, +Pickup,Diesel,2022,19.146971002976848,46298.12986714286,2.7222222222222223,6.444444444444445,0.0,19.298303681785683, +Pickup,Diesel,2023,19.434175568021498,46298.12986714286,2.6666666666666665,6.333333333333333,0.0,18.98870968964286, 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+Pickup,Gas,2001,16.0,37667.36471,8.0,126.0,0.0,27.08,566.42 +Pickup,Gas,2002,15.7,37687.3081,9.0,127.0,0.0,27.43828025,577.81 +Pickup,Gas,2003,15.8,37905.58585,8.0,127.0,0.0,27.31734177,571.99 +Pickup,Gas,2004,16.3,38299.55811,7.0,140.0,0.0,26.73490798,557.48 +Pickup,Gas,2005,16.4,37845.08219,7.0,127.0,0.0,26.62268293,556.04 +Pickup,Gas,2006,16.2,38897.93434,12.0,151.0,0.0,26.84851852,561.0 +Pickup,Gas,2007,16.1,37922.11173,12.0,162.0,0.0,26.96354037,558.22 +Pickup,Gas,2008,16.3,36038.81401,14.0,148.0,0.0,26.73490798,556.44 +Pickup,Gas,2009,16.8,35930.67567,11.0,138.0,0.0,26.18714286,534.88 +Pickup,Gas,2010,17.2,35568.92344,10.0,108.0,0.0,25.77186047,525.8 +Pickup,Gas,2011,17.3,34691.96081,8.0,110.0,0.0,25.67104046,522.85 +Pickup,Gas,2012,17.3,33229.7527,8.0,100.0,0.0,25.67104046,520.49 +Pickup,Gas,2013,16.9,33246.9577,7.0,69.0,0.0,26.08147929,532.93 +Pickup,Gas,2014,17.1,32838.34335,8.0,65.0,0.0,25.87385965,531.12 +Pickup,Gas,2015,18.6,33642.67026,6.0,66.0,0.0,24.45903226,485.79 +Pickup,Gas,2016,18.5,33657.28211,7.0,72.0,0.0,24.54621622,488.18 +Pickup,Gas,2017,18.5,33196.53386,8.0,84.0,0.0,24.54621622,488.67 +Pickup,Gas,2018,22.755150249225185,38581.77489,7.722222222222222,80.44444444444444,0.0,24.360774814999957, +Pickup,Gas,2019,23.09647750296356,38581.77489,7.444444444444445,76.88888888888889,0.0,24.14592630083331, +Pickup,Gas,2020,23.44292466550801,38581.77489,7.166666666666667,73.33333333333333,0.0,23.931077786666663, +Pickup,Gas,2021,23.794568535490626,38581.77489,6.888888888888889,69.77777777777777,0.0,23.71622927249996, +Pickup,Gas,2022,24.151487063522985,38581.77489,6.611111111111111,66.22222222222223,0.0,23.501380758333312, +Pickup,Gas,2023,24.513759369475828,38581.77489,6.333333333333333,62.66666666666667,0.0,23.286532244166665, +Pickup,Gas,2024,24.88146576001796,38581.77489,6.055555555555555,59.111111111111114,0.0,23.07168372999996, +Pickup,Gas,2025,25.254687746418227,38581.77489,5.777777777777778,55.55555555555556,0.0,22.856835215833314, 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+Pickup,Gas,2035,29.30909615298073,38581.77489,3.0,20.0,0.0,20.708350074166617, +Pickup,Gas,2036,29.74873259527544,38581.77489,3.0,20.0,0.0,20.49350155999997, +Pickup,Gas,2037,30.194963584204565,38581.77489,3.0,20.0,0.0,20.278653045833323, +Pickup,Gas,2038,30.64788803796763,38581.77489,3.0,20.0,0.0,20.06380453166662, +Pickup,Gas,2039,31.107606358537144,38581.77489,3.0,20.0,0.0,19.848956017499972, +Pickup,Gas,2040,31.574220453915196,38581.77489,3.0,20.0,0.0,19.634107503333325, +Pickup,Gas,2041,32.04783376072392,38581.77489,3.0,20.0,0.0,19.41925898916662, +Pickup,Gas,2042,32.52855126713478,38581.77489,3.0,20.0,0.0,19.204410474999975, +Pickup,Gas,2043,33.0164795361418,38581.77489,3.0,20.0,0.0,18.989561960833328, +Pickup,Gas,2044,33.51172672918392,38581.77489,3.0,20.0,0.0,18.774713446666624, +Pickup,Gas,2045,34.01440263012168,38581.77489,3.0,20.0,0.0,18.559864932499977, +Pickup,Gas,2046,34.5246186695735,38581.77489,3.0,20.0,0.0,18.34501641833333, +Pickup,Gas,2047,35.0424879496171,38581.77489,3.0,20.0,0.0,18.130167904166626, +Pickup,Gas,2048,35.56812526886135,38581.77489,3.0,20.0,0.0,17.91531938999998, +Pickup,Gas,2049,36.10164714789427,38581.77489,3.0,20.0,0.0,17.700470875833332, +Pickup,Gas,2050,36.643171855112676,38581.77489,3.0,20.0,0.0,17.48562236166663, +Pickup,Hybrid,2004,16.5,42129.51392,2.0,4.0,0.0,26.51181818,539.1 +Pickup,Hybrid,2005,16.5,41629.5904,2.0,4.0,0.0,26.51181818,539.1 +Pickup,Hybrid,2006,16.5,42787.72777,2.0,4.0,0.0,26.51181818,539.1 +Pickup,Hybrid,2007,16.5,41714.3229,2.0,4.0,0.0,26.51181818,539.1 +Pickup,Hybrid,2009,20.5,39523.74323,2.0,4.0,0.0,22.96414634,433.77 +Pickup,Hybrid,2010,21.5,39125.81578,2.0,4.0,0.0,22.28348837,413.57 +Pickup,Hybrid,2011,21.0,38161.1569,2.0,4.0,0.0,22.61571429,423.19 +Pickup,Hybrid,2012,21.0,36552.72796,2.0,4.0,0.0,22.61571429,423.19 +Pickup,Hybrid,2013,21.0,36571.65347,2.0,4.0,0.0,22.61571429,420.5 +Pickup,Hybrid,2016,19.0,37023.01032,2.0,4.0,0.0,24.11947368,468.0 +Pickup,Hybrid,2017,19.0,36516.18724,2.0,4.0,0.0,24.11947368,467.5 +Pickup,Hybrid,2018,19.71362026541786,42439.95237571428,2.0,4.461538461538462,0.0,24.73958708500004, +Pickup,Hybrid,2019,20.009324569399123,42439.95237571428,2.0,4.923076923076923,0.0,25.053702938333345, +Pickup,Hybrid,2020,20.309464437940107,42439.95237571428,2.0,5.384615384615385,0.0,25.36781879166665, +Pickup,Hybrid,2021,20.614106404509208,42439.95237571428,2.0,5.846153846153847,0.0,25.681934644999956, +Pickup,Hybrid,2022,20.923318000576845,42439.95237571428,2.0,6.307692307692308,0.0,25.996050498333375, +Pickup,Hybrid,2023,21.237167770585494,42439.95237571428,2.0,6.769230769230769,0.0,26.31016635166668, +Pickup,Hybrid,2024,21.55572528714427,42439.95237571428,2.0,7.230769230769231,0.0,26.624282204999986, +Pickup,Hybrid,2025,21.879061166451436,42439.95237571428,2.0,7.6923076923076925,0.0,26.93839805833329, +Pickup,Hybrid,2026,22.207247083948204,42439.95237571428,2.0,8.153846153846153,0.0,27.25251391166671, 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+Van-AV,PEV,2049,53.61630764538753,800000.0,2.9000000000000004,3.85,0.0,17.0, +Van-AV,PEV,2050,54.42055226006834,800000.0,3.0,4.0,0.0,17.0, diff --git a/resident/configs/work_from_home.csv b/resident/configs/work_from_home.csv new file mode 100644 index 0000000..0b9fa07 --- /dev/null +++ b/resident/configs/work_from_home.csv @@ -0,0 +1,24 @@ +Label,Description,Expression,work_at_home,work_away_from_home +util_work_from_home_constant,Constant for Working from home,1,coef_work_from_home_constant, +util_part_time_worker,Part time worker,@df.ptype==2,coef_part_time_worker, +util_access_to_workplaces,Accessibility to workplaces of the home mgra,@df.workplace_location_accessibility,,coef_access_to_workplaces +util_2016,Year 2016 survey,@PRE_COVID,coef_2016, +util_age_35_to_44,Age Group - 35 yrs to 44 yrs,"@df.age.between(35, 44)",coef_age_35_to_44, +util_age_45_to_54,Age Group - 45 yrs to 54 yrs,"@df.age.between(45, 54)",coef_age_45_to_54, +util_age_55_to_64,Age Group - 55 yrs to 64 yrs,"@df.age.between(55, 64)",coef_age_55_to_64, +util_age_65_79,Age 65-79,"@df.age.between(65, 79)",coef_age_65_79, +util_age_80_plus,Age 80 plus,@df.age > 79,coef_age_80_plus, +util_inc_lt_15,Household income less than 15k,@df.income<15000,coef_inc_lt_15, +util_inc_150_250,Household income between 150k-249999k,"@df.income.between(150000,249999)",coef_inc_150_250, +util_ind_accom,Industry type is accomodation,@df.naics_code==721,coef_ind_accom, +util_ind_bus_srv,Industry type is business services,@df.naics_code==54,coef_ind_bus_srv, +util_ind_construct,Industry type is construction,@df.naics_code==23,coef_ind_construct, +util_ind_edu,Industry type is education,@df.naics_code==61,coef_ind_edu, +util_ind_enter,Industry type is entertainment,@df.naics_code==71,coef_ind_enter, +util_ind_food_srv,Industry type is food services,@df.naics_code==722,coef_ind_food_srv, +util_ind_gov,Industry type is government,@df.naics_code==92,coef_ind_gov, +util_ind_health,Industry type is healthcare,@df.naics_code==62,coef_ind_health, +util_ind_manu,Industry type is manufacturing,"@df.naics_code.isin([31,32,33])",coef_ind_manu, +util_ind_mgmt_srv,Industry type is management services,@df.naics_code==55,coef_ind_mgmt_srv, +util_ind_mil,Industry type is military,@df.naics_code==9000,coef_ind_mil, +util_regional_calibration, Regional calibration factor,1,coef_regional_calibration, diff --git a/resident/configs/work_from_home.yaml b/resident/configs/work_from_home.yaml new file mode 100644 index 0000000..c4f265c --- /dev/null +++ b/resident/configs/work_from_home.yaml @@ -0,0 +1,12 @@ + +# borrowed from free parking model + +SPEC: work_from_home.csv +COEFFICIENTS: work_from_home_coeffs.csv + +#LOGIT_TYPE: NL +LOGIT_TYPE: MNL + +WORK_FROM_HOME_ALT: 0 + +DEST_CHOICE_COLUMN_NAME: workplace_zone_id diff --git a/resident/configs/work_from_home_coeffs.csv b/resident/configs/work_from_home_coeffs.csv new file mode 100644 index 0000000..31ed2a9 --- /dev/null +++ b/resident/configs/work_from_home_coeffs.csv @@ -0,0 +1,31 @@ +coefficient_name,value,constrain +coef_work_from_home_constant,-0.386629856,F +coef_part_time_worker,-1.4513809,F +coef_access_to_workplaces,0.1,T +coef_age_35_to_44,0.353530241,F +coef_age_45_to_54,0.468133972,F +coef_age_55_to_64,0.495172418,F +coef_2016,-1.06294934,F +coef_age_65_79,1.243578944,F +coef_age_80_plus,0.770722144,F +coef_inc_lt_15,0.613583177,F +coef_inc_150_250,0.419020695,F +coef_ind_accom,-1.639185004,F +coef_ind_bus_srv,0.721333596,F +coef_ind_construct,-0.738399459,F +coef_ind_edu,-0.508510677,F +coef_ind_enter,1.027744147,F +coef_ind_food_srv,-1.204685952,F +coef_ind_gov,-0.75445486,F +coef_ind_health,-0.441707723,F +coef_ind_manu,-0.566600437,F +coef_ind_mgmt_srv,0.386847827,F +coef_ind_mil,-2.257002339,F +coef_external_worker_identification,-999.0,T +coef_regional_calibration,-1.03,T +coef_cbd,0.8653139314986638,F +coef_central,0.45025084346579664,F +coef_SSuburb,-0.4,F +coef_NWCounty,-0.38900940018141394,F +coef_NCounty,-0.7103700573089826,F +coef_ECounty,-5.0,F diff --git a/resident/configs/workplace_location.csv b/resident/configs/workplace_location.csv new file mode 100644 index 0000000..a976ade --- /dev/null +++ b/resident/configs/workplace_location.csv @@ -0,0 +1,31 @@ +Label,Description,Expression,coefficient +local_dist,,"_DIST@skims[('SOV_M_DIST', 'MD')]",1 +alt_dest_pmsa,,"_ALT_PMSA@reindex(land_use.pseudomsa, df.alt_dest)",1 +#,,, +util_dist,Distance,@_DIST,coef_dist +util_dist_sqrt,Square root of distance,@_DIST**0.5,coef_dist_sqrt +util_dist_sqrd,Distance squared,@_DIST**2,coef_dist_sqrd +util_dist_cubed,Distance cubed,@_DIST**3,coef_dist_cubed +util_dist_lowincome,Distance - low income,@_DIST * (df.income<=2),coef_dist_lowincme +util_dist_sqrt_lowincome,Distance sqrt - low income,@(_DIST**0.5) * (df.income<=2),coef_dist_sqrt_lowincme +util_dist_sqrd_lowincome,Distance squared - low income,@(_DIST**2) * (df.income<=2),coef_dist_sqrd__lowincme +util_dist_highincome,Distance - high income,@_DIST * (df.income>=4),coef_dist_highincme +util_dist_sqrd_highincome,Distance squared - high income,@(_DIST**2) * (df.income>=4),coef_dist_sqrd_highincme +util_dist_pt,Distance - parttime worker,@_DIST * (df.ptype==2),coef_dist_ptworker +util_dist_sqrd_pt,Distance squared - parttime worker,@(_DIST**2) * (df.ptype==2),coef_dist_sqrd_ptworker +util_dist_femaleworker,Distance - female worker,@_DIST * (df.ptype<3) * (df.female==1),coef_dist_femaleworker +#,,, +util_size_variable,Size variable,@df['size_term'].apply(np.log1p),1 +util_size_unavailable,Size variable,@df['size_term']==0,-999 +util_sp_utility_adjustment,shadow price utility adjustment,@df['shadow_price_utility_adjustment'],1 +#,,, +util_mode_logsum,Mode choice logsum,mode_choice_logsum,coef_mode_logsum +#,,, +util_sample_of_corrections_factor,Sample of alternatives correction factor,"@np.minimum(np.log(df.pick_count/df.prob), 60)",1 +#calibration constants,,, +util_Calibration 0-2 - miles,Calibration 0-2 - miles,@_DIST<=2,coef_distance_0_2miles +util_Calibration 2-5 - miles,Calibration 2-5 - miles,@(_DIST>2) * (_DIST<=5),coef_distance_2_5miles +util_Calibration 5-10 - miles,Calibration 5-10 - miles,@(_DIST>5) * (_DIST<=10),coef_distance_5_10miles +util_ABM2 calibration 10-20 - miles,ABM2 calibration 10-20 - miles,@(_DIST>10) * (_DIST<=20),coef_distance_10_20miles +util_ABM2 calibration 20-30 - miles,ABM2 calibration 20-30 - miles,@(_DIST>20) * (_DIST<=30),coef_distance_20_30miles +util_ABM2 calibration >30 - miles,ABM2 calibration >30 - miles,@(_DIST>30),coef_distance_30plusmiles diff --git a/resident/configs/workplace_location.yaml b/resident/configs/workplace_location.yaml new file mode 100644 index 0000000..59dcc0b --- /dev/null +++ b/resident/configs/workplace_location.yaml @@ -0,0 +1,77 @@ +SAMPLE_SIZE: 30 +ESTIMATION_SAMPLE_SIZE: 10 + +SIMULATE_CHOOSER_COLUMNS: + - income_segment + - home_zone_id + - income + - age + - ptype + - female + +SAMPLE_SPEC: workplace_location_sample.csv +SPEC: workplace_location.csv +COEFFICIENTS: workplace_location_coefficients.csv + +LOGSUM_SETTINGS: tour_mode_choice.yaml +LOGSUM_PREPROCESSOR: nontour_preprocessor +LOGSUM_TOUR_PURPOSE: work + +# model-specific logsum-related settings +CHOOSER_ORIG_COL_NAME: home_zone_id +ALT_DEST_COL_NAME: alt_dest +IN_PERIOD: 17 +OUT_PERIOD: 8 + +DEST_CHOICE_COLUMN_NAME: workplace_zone_id +# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if not desired in persons table +DEST_CHOICE_LOGSUM_COLUMN_NAME: workplace_location_logsum +# comment out MODE_CHOICE_LOGSUM_COLUMN_NAME if not desired in persons table +MODE_CHOICE_LOGSUM_COLUMN_NAME: workplace_modechoice_logsum + +# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if saved alt logsum table +DEST_CHOICE_SAMPLE_TABLE_NAME: workplace_location_sample + + +annotate_persons: + SPEC: annotate_persons_workplace + DF: persons + TABLES: + - land_use + +# - shadow pricing + +# income_segment is in households, but we want to count persons +CHOOSER_TABLE_NAME: persons_merged + +# size_terms model_selector +MODEL_SELECTOR: workplace + +# we can't use use household income_segment as this will also be set for non-workers +CHOOSER_SEGMENT_COLUMN_NAME: occupation #income_segment + +# boolean column to filter choosers (True means keep) +CHOOSER_FILTER_COLUMN_NAME: is_internal_worker + +# FIXME - these are assigned to persons in annotate_persons. we need a better way to manage this +# FIXME - these are not needed for this model and should be re/factored out +SEGMENT_IDS: + mngt_busi_scic_arts: mngt_busi_scic_arts + services: services + health: health + sales_office: sales_office + prod_trans_move: prod_trans_move + constr_maint: constr_maint + military: military + # work_low: 1 # INCOME_SEGMENT_LOW + # work_med: 2 # INCOME_SEGMENT_MED + # work_high: 3 # INCOME_SEGMENT_HIGH + # work_veryhigh: 4 # INCOME_SEGMENT_VERYHIGH + + +# model adds these tables (informational - not added if commented out) +SHADOW_PRICE_TABLE: workplace_shadow_prices +MODELED_SIZE_TABLE: workplace_modeled_size + +# not loaded if commented out +SAVED_SHADOW_PRICE_TABLE_NAME: workplace_shadow_prices.csv diff --git a/resident/configs/workplace_location_coefficients.csv b/resident/configs/workplace_location_coefficients.csv new file mode 100644 index 0000000..26d9dcf --- /dev/null +++ b/resident/configs/workplace_location_coefficients.csv @@ -0,0 +1,21 @@ +coefficient_name,value,constrain +coef_dist,0.285,F +coef_dist_sqrt,-1.604,F +coef_dist_sqrd,-0.004362,F +coef_dist_cubed,0.00002365,F +coef_dist_lowincme,0.1941,F +coef_dist_sqrt_lowincme,-0.8721,F +coef_dist_sqrd__lowincme,-0.002409,F +coef_dist_highincme,0,F +coef_dist_sqrd_highincme,0.0001919,F +coef_dist_ptworker,-0.116,F +coef_dist_sqrd_ptworker,0.0003934,F +coef_dist_femaleworker,-0.02515,F +coef_mode_logsum,0.5468,F +#work distance calibration,, +coef_distance_0_2miles,-0.770,F +coef_distance_2_5miles,-0.870,F +coef_distance_5_10miles,0.0270,F +coef_distance_10_20miles,0.720,F +coef_distance_20_30miles,0.852,F +coef_distance_30plusmiles,0.231,F diff --git a/resident/configs/workplace_location_sample.csv b/resident/configs/workplace_location_sample.csv new file mode 100644 index 0000000..f844721 --- /dev/null +++ b/resident/configs/workplace_location_sample.csv @@ -0,0 +1,19 @@ +Label,Description,Expression,coefficient +local_dist,,"_DIST@skims[('SOV_M_DIST', 'MD')]",1 +#,,, +util_dist,Distance,@_DIST,coef_dist +util_dist_sqrt,Square root of distance,@_DIST**0.5,coef_dist_sqrt +util_dist_sqrd,Distance squared,@_DIST**2,coef_dist_sqrd +util_dist_cubed,Distance cubed,@_DIST**3,coef_dist_cubed +util_dist_lowincome,Distance - low income,@_DIST * (df.income<=2),coef_dist_lowincme +util_dist_sqrt_lowincome,Distance sqrt - low income,@(_DIST**0.5) * (df.income<=2),coef_dist_sqrt_lowincme +util_dist_sqrd_lowincome,Distance squared - low income,@(_DIST**2) * (df.income<=2),coef_dist_sqrd__lowincme +util_dist_highincome,Distance - high income,@_DIST * (df.income>=4),coef_dist_highincme +util_dist_sqrd_highincome,Distance squared - high income,@(_DIST**2) * (df.income>=4),coef_dist_sqrd_highincme +util_dist_pt,Distance - parttime worker,@_DIST * (df.ptype==2),coef_dist_ptworker +util_dist_sqrd_pt,Distance squared - parttime worker,@(_DIST**2) * (df.ptype==2),coef_dist_sqrd_ptworker +util_dist_femaleworker,Distance - female worker,@_DIST * (df.ptype<3) * (df.female==1),coef_dist_femaleworker +#,,, +util_size_variable,Size variable,@df['size_term'].apply(np.log1p),1 +util_size_unavailable,Size variable,@df['size_term']==0,-999 +util_sp_utility_adjustment,shadow price utility adjustment,@df['shadow_price_utility_adjustment'],1 \ No newline at end of file diff --git a/resident/configs/write_data_dictionary.yaml b/resident/configs/write_data_dictionary.yaml new file mode 100644 index 0000000..6d6ed39 --- /dev/null +++ b/resident/configs/write_data_dictionary.yaml @@ -0,0 +1,13 @@ + + +txt_format: data_dict.txt +csv_format: data_dict.csv + +tables: + - land_use + - accessibility + - households + - persons + - tours + - trips + - joint_tour_participants diff --git a/resident/configs/write_trip_matrices.yaml b/resident/configs/write_trip_matrices.yaml new file mode 100644 index 0000000..b99145b --- /dev/null +++ b/resident/configs/write_trip_matrices.yaml @@ -0,0 +1,406 @@ +# read trips table post preprocessor and run expressions to code +# additional data fields, with one data fields for each matrix specified below + +preprocessor: + SPEC: write_trip_matrices_annotate_trips_preprocessor + DF: trips + TABLES: + - tours + - households + - land_use + +# divide trip counts by household expansion factor +HH_EXPANSION_WEIGHT_COL: sample_rate # added when households read in + +# save preprocessed trips table to pipeline if desired +SAVE_TRIPS_TABLE: True + +MATRICES: + - file_name: autotrips_EA_low.omx + tables: + - name: SOV_EA + data_field: DRIVEALONE_EA_LOW + - name: SR2_EA + data_field: SHARED2_EA_LOW + - name: SR3_EA + data_field: SHARED3_EA_LOW + - file_name: autotrips_EA_med.omx + tables: + - name: SOV_EA + data_field: DRIVEALONE_EA_MED + - name: SR2_EA + data_field: SHARED2_EA_MED + - name: SR3_EA + data_field: SHARED3_EA_MED + - file_name: autotrips_EA_high.omx + tables: + - name: SOV_EA + data_field: DRIVEALONE_EA_HIGH + - name: SR2_EA + data_field: SHARED2_EA_HIGH + - name: SR3_EA + data_field: SHARED3_EA_HIGH + - file_name: autotrips_AM_low.omx + tables: + - name: SOV_AM + data_field: DRIVEALONE_AM_LOW + - name: SR2_AM + data_field: SHARED2_AM_LOW + - name: SR3_AM + data_field: SHARED3_AM_LOW + - file_name: autotrips_AM_med.omx + tables: + - name: SOV_AM + data_field: DRIVEALONE_AM_MED + - name: SR2_AM + data_field: SHARED2_AM_MED + - name: SR3_AM + data_field: SHARED3_AM_MED + - file_name: autotrips_AM_high.omx + tables: + - name: SOV_AM + data_field: DRIVEALONE_AM_HIGH + - name: SR2_AM + data_field: SHARED2_AM_HIGH + - name: SR3_AM + data_field: SHARED3_AM_HIGH + - file_name: autotrips_MD_low.omx + tables: + - name: SOV_MD + data_field: DRIVEALONE_MD_LOW + - name: SR2_MD + data_field: SHARED2_MD_LOW + - name: SR3_MD + data_field: SHARED3_MD_LOW + - file_name: autotrips_MD_med.omx + tables: + - name: SOV_MD + data_field: DRIVEALONE_MD_MED + - name: SR2_MD + data_field: SHARED2_MD_MED + - name: SR3_MD + data_field: SHARED3_MD_MED + - file_name: autotrips_MD_high.omx + tables: + - name: SOV_MD + data_field: DRIVEALONE_MD_HIGH + - name: SR2_MD + data_field: SHARED2_MD_HIGH + - name: SR3_MD + data_field: SHARED3_MD_HIGH + - file_name: autotrips_PM_low.omx + tables: + - name: SOV_PM + data_field: DRIVEALONE_PM_LOW + - name: SR2_PM + data_field: SHARED2_PM_LOW + - name: SR3_PM + data_field: SHARED3_PM_LOW + - file_name: autotrips_PM_med.omx + tables: + - name: SOV_PM + data_field: DRIVEALONE_PM_MED + - name: SR2_PM + data_field: SHARED2_PM_MED + - name: SR3_PM + data_field: SHARED3_PM_MED + - file_name: autotrips_PM_high.omx + tables: + - name: SOV_PM + data_field: DRIVEALONE_PM_HIGH + - name: SR2_PM + data_field: SHARED2_PM_HIGH + - name: SR3_PM + data_field: SHARED3_PM_HIGH + - file_name: autotrips_EV_low.omx + tables: + - name: SOV_EV + data_field: DRIVEALONE_EV_LOW + - name: SR2_EV + data_field: SHARED2_EV_LOW + - name: SR3_EV + data_field: SHARED3_EV_LOW + - file_name: autotrips_EV_med.omx + tables: + - name: SOV_EV + data_field: DRIVEALONE_EV_MED + - name: SR2_EV + data_field: SHARED2_EV_MED + - name: SR3_EV + data_field: SHARED3_EV_MED + - file_name: autotrips_EV_high.omx + tables: + - name: SOV_EV + data_field: DRIVEALONE_EV_HIGH + - name: SR2_EV + data_field: SHARED2_EV_HIGH + - name: SR3_EV + data_field: SHARED3_EV_HIGH + - file_name: trantrips_EA.omx + tables: + - name: WALK_SET_set1_EA + data_field: WALK_LOC_EA + - name: KNROUT_SET_set1_EA + data_field: KNR_LOCOUT_EA + - name: PNROUT_SET_set1_EA + data_field: PNR_LOCOUT_EA + - name: TNCOUT_SET_set1_EA + data_field: TNC_LOCOUT_EA + - name: KNRIN_SET_set1_EA + data_field: KNR_LOCIN_EA + - name: PNRIN_SET_set1_EA + data_field: PNR_LOCIN_EA + - name: TNCIN_SET_set1_EA + data_field: TNC_LOCIN_EA + - name: WALK_SET_set2_EA + data_field: WALK_PRM_EA + - name: KNROUT_SET_set2_EA + data_field: KNR_PRMOUT_EA + - name: PNROUT_SET_set2_EA + data_field: PNR_PRMOUT_EA + - name: TNCOUT_SET_set2_EA + data_field: TNC_PRMOUT_EA + - name: KNRIN_SET_set2_EA + data_field: KNR_PRMIN_EA + - name: PNRIN_SET_set2_EA + data_field: PNR_PRMIN_EA + - name: TNCIN_SET_set2_EA + data_field: TNC_PRMIN_EA + - name: WALK_SET_set3_EA + data_field: WALK_MIX_EA + - name: KNROUT_SET_set3_EA + data_field: KNR_MIXOUT_EA + - name: PNROUT_SET_set3_EA + data_field: PNR_MIXOUT_EA + - name: TNCOUT_SET_set3_EA + data_field: TNC_MIXOUT_EA + - name: KNRIN_SET_set3_EA + data_field: KNR_MIXIN_EA + - name: PNRIN_SET_set3_EA + data_field: PNR_MIXIN_EA + - name: TNCIN_SET_set3_EA + data_field: TNC_MIXIN_EA + - file_name: trantrips_AM.omx + tables: + - name: WALK_SET_set1_AM + data_field: WALK_LOC_AM + - name: KNROUT_SET_set1_AM + data_field: KNR_LOCOUT_AM + - name: PNROUT_SET_set1_AM + data_field: PNR_LOCOUT_AM + - name: TNCOUT_SET_set1_AM + data_field: TNC_LOCOUT_AM + - name: KNRIN_SET_set1_AM + data_field: KNR_LOCIN_AM + - name: PNRIN_SET_set1_AM + data_field: PNR_LOCIN_AM + - name: TNCIN_SET_set1_AM + data_field: TNC_LOCIN_AM + - name: WALK_SET_set2_AM + data_field: WALK_PRM_AM + - name: KNROUT_SET_set2_AM + data_field: KNR_PRMOUT_AM + - name: PNROUT_SET_set2_AM + data_field: PNR_PRMOUT_AM + - name: TNCOUT_SET_set2_AM + data_field: TNC_PRMOUT_AM + - name: KNRIN_SET_set2_AM + data_field: KNR_PRMIN_AM + - name: PNRIN_SET_set2_AM + data_field: PNR_PRMIN_AM + - name: TNCIN_SET_set2_AM + data_field: TNC_PRMIN_AM + - name: WALK_SET_set3_AM + data_field: WALK_MIX_AM + - name: KNROUT_SET_set3_AM + data_field: KNR_MIXOUT_AM + - name: PNROUT_SET_set3_AM + data_field: PNR_MIXOUT_AM + - name: TNCOUT_SET_set3_AM + data_field: TNC_MIXOUT_AM + - name: KNRIN_SET_set3_AM + data_field: KNR_MIXIN_AM + - name: PNRIN_SET_set3_AM + data_field: PNR_MIXIN_AM + - name: TNCIN_SET_set3_AM + data_field: TNC_MIXIN_AM + - file_name: trantrips_MD.omx + tables: + - name: WALK_SET_set1_MD + data_field: WALK_LOC_MD + - name: KNROUT_SET_set1_MD + data_field: KNR_LOCOUT_MD + - name: PNROUT_SET_set1_MD + data_field: PNR_LOCOUT_MD + - name: TNCOUT_SET_set1_MD + data_field: TNC_LOCOUT_MD + - name: KNRIN_SET_set1_MD + data_field: KNR_LOCIN_MD + - name: PNRIN_SET_set1_MD + data_field: PNR_LOCIN_MD + - name: TNCIN_SET_set1_MD + data_field: TNC_LOCIN_MD + - name: WALK_SET_set2_MD + data_field: WALK_PRM_MD + - name: KNROUT_SET_set2_MD + data_field: KNR_PRMOUT_MD + - name: PNROUT_SET_set2_MD + data_field: PNR_PRMOUT_MD + - name: TNCOUT_SET_set2_MD + data_field: TNC_PRMOUT_MD + - name: KNRIN_SET_set2_MD + data_field: KNR_PRMIN_MD + - name: PNRIN_SET_set2_MD + data_field: PNR_PRMIN_MD + - name: TNCIN_SET_set2_MD + data_field: TNC_PRMIN_MD + - name: WALK_SET_set3_MD + data_field: WALK_MIX_MD + - name: KNROUT_SET_set3_MD + data_field: KNR_MIXOUT_MD + - name: PNROUT_SET_set3_MD + data_field: PNR_MIXOUT_MD + - name: TNCOUT_SET_set3_MD + data_field: TNC_MIXOUT_MD + - name: KNRIN_SET_set3_MD + data_field: KNR_MIXIN_MD + - name: PNRIN_SET_set3_MD + data_field: PNR_MIXIN_MD + - name: TNCIN_SET_set3_MD + data_field: TNC_MIXIN_MD + - file_name: trantrips_PM.omx + tables: + - name: WALK_SET_set1_PM + data_field: WALK_LOC_PM + - name: KNROUT_SET_set1_PM + data_field: KNR_LOCOUT_PM + - name: PNROUT_SET_set1_PM + data_field: PNR_LOCOUT_PM + - name: TNCOUT_SET_set1_PM + data_field: TNC_LOCOUT_PM + - name: KNRIN_SET_set1_PM + data_field: KNR_LOCIN_PM + - name: PNRIN_SET_set1_PM + data_field: PNR_LOCIN_PM + - name: TNCIN_SET_set1_PM + data_field: TNC_LOCIN_PM + - name: WALK_SET_set2_PM + data_field: WALK_PRM_PM + - name: KNROUT_SET_set2_PM + data_field: KNR_PRMOUT_PM + - name: PNROUT_SET_set2_PM + data_field: PNR_PRMOUT_PM + - name: TNCOUT_SET_set2_PM + data_field: TNC_PRMOUT_PM + - name: KNRIN_SET_set2_PM + data_field: KNR_PRMIN_PM + - name: PNRIN_SET_set2_PM + data_field: PNR_PRMIN_PM + - name: TNCIN_SET_set2_PM + data_field: TNC_PRMIN_PM + - name: WALK_SET_set3_PM + data_field: WALK_MIX_PM + - name: KNROUT_SET_set3_PM + data_field: KNR_MIXOUT_PM + - name: PNROUT_SET_set3_PM + data_field: PNR_MIXOUT_PM + - name: TNCOUT_SET_set3_PM + data_field: TNC_MIXOUT_PM + - name: KNRIN_SET_set3_PM + data_field: KNR_MIXIN_PM + - name: PNRIN_SET_set3_PM + data_field: PNR_MIXIN_PM + - name: TNCIN_SET_set3_PM + data_field: TNC_MIXIN_PM + - file_name: trantrips_EV.omx + tables: + - name: WALK_SET_set1_EV + data_field: WALK_LOC_EV + - name: KNROUT_SET_set1_EV + data_field: KNR_LOCOUT_EV + - name: PNROUT_SET_set1_EV + data_field: PNR_LOCOUT_EV + - name: TNCOUT_SET_set1_EV + data_field: TNC_LOCOUT_EV + - name: KNRIN_SET_set1_EV + data_field: KNR_LOCIN_EV + - name: PNRIN_SET_set1_EV + data_field: PNR_LOCIN_EV + - name: TNCIN_SET_set1_EV + data_field: TNC_LOCIN_EV + - name: WALK_SET_set2_EV + data_field: WALK_PRM_EV + - name: KNROUT_SET_set2_EV + data_field: KNR_PRMOUT_EV + - name: PNROUT_SET_set2_EV + data_field: PNR_PRMOUT_EV + - name: TNCOUT_SET_set2_EV + data_field: TNC_PRMOUT_EV + - name: KNRIN_SET_set2_EV + data_field: KNR_PRMIN_EV + - name: PNRIN_SET_set2_EV + data_field: PNR_PRMIN_EV + - name: TNCIN_SET_set2_EV + data_field: TNC_PRMIN_EV + - name: WALK_SET_set3_EV + data_field: WALK_MIX_EV + - name: KNROUT_SET_set3_EV + data_field: KNR_MIXOUT_EV + - name: PNROUT_SET_set3_EV + data_field: PNR_MIXOUT_EV + - name: TNCOUT_SET_set3_EV + data_field: TNC_MIXOUT_EV + - name: KNRIN_SET_set3_EV + data_field: KNR_MIXIN_EV + - name: PNRIN_SET_set3_EV + data_field: PNR_MIXIN_EV + - name: TNCIN_SET_set3_EV + data_field: TNC_MIXIN_EV + - file_name: nmottrips_EA.omx + tables: + - name: WALK_EA + data_field: WALK_EA + - name: BIKE_EA + data_field: BIKE_EA + - file_name: nmottrips_AM.omx + tables: + - name: WALK_AM + data_field: WALK_AM + - name: BIKE_AM + data_field: BIKE_AM + - file_name: nmottrips_MD.omx + tables: + - name: WALK_MD + data_field: WALK_MD + - name: BIKE_MD + data_field: BIKE_MD + - file_name: nmottrips_PM.omx + tables: + - name: WALK_PM + data_field: WALK_PM + - name: BIKE_PM + data_field: BIKE_PM + - file_name: nmottrips_EV.omx + tables: + - name: WALK_EV + data_field: WALK_EV + - name: BIKE_EV + data_field: BIKE_EV + +CONSTANTS: + time_periods: + EA: + first_hour: 1 + last_hour: 6 + AM: + first_hour: 7 + last_hour: 12 + MD: + first_hour: 13 + last_hour: 25 + PM: + first_hour: 26 + last_hour: 32 + EV: + first_hour: 33 + last_hour: 48 \ No newline at end of file diff --git a/resident/configs/write_trip_matrices_annotate_trips_preprocessor.csv b/resident/configs/write_trip_matrices_annotate_trips_preprocessor.csv new file mode 100644 index 0000000..2857d4e --- /dev/null +++ b/resident/configs/write_trip_matrices_annotate_trips_preprocessor.csv @@ -0,0 +1,182 @@ +Description,Target,Expression +# add additional fields,, +,tour_participants,trips.tour_id.map(tours.number_of_participants) +# code time periods,, +,is_ea,"trips.depart.between(time_periods['EA']['first_hour'], time_periods['EA']['last_hour'])" +,is_am,"trips.depart.between(time_periods['AM']['first_hour'], time_periods['AM']['last_hour'])" +,is_md,"trips.depart.between(time_periods['MD']['first_hour'], time_periods['MD']['last_hour'])" +,is_pm,"trips.depart.between(time_periods['PM']['first_hour'], time_periods['PM']['last_hour'])" +,is_ev,"trips.depart.between(time_periods['EV']['first_hour'], time_periods['EV']['last_hour'])" +,_vot_bin_da,"np.where(trips.trip_mode.isin(['DRIVEALONE']),np.where(trips.vot_da < vot_threshold_low, 1,np.where(trips.vot_da < vot_threshold_med, 2, 3)),0)" +,_vot_bin_s2,"np.where(trips.trip_mode.isin(['SHARED2','TAXI','TNC_SINGLE']),np.where(trips.vot_s2 < vot_threshold_low,1,np.where(trips.vot_s2 < vot_threshold_med, 2, 3)),0)" +,_vot_bin_s3,"np.where(trips.trip_mode.isin(['SHARED3','TNC_SHARED']),np.where(trips.vot_s3 < vot_threshold_low,1,np.where(trips.vot_s3 < vot_threshold_med, 2, 3)),0)" +,vot1,"np.where((_vot_bin_da == 1) |( _vot_bin_s2 == 1) |( _vot_bin_s3 ==1),1,0)" +,vot2,"np.where((_vot_bin_da == 2 )|( _vot_bin_s2 == 2) |( _vot_bin_s3 ==2),1,0)" +,vot3,"np.where((_vot_bin_da == 3) | (_vot_bin_s2 == 3) | (_vot_bin_s3 ==3),1,0)" +,outbound,trips.outbound +,inbound,~trips.outbound +# ea trips,, +,DRIVEALONE_EA_LOW,((trips.trip_mode == 'DRIVEALONE') & is_ea & vot1) * tour_participants +,SHARED2_EA_LOW,(trips.trip_mode.isin(['SHARED2'])/OCC_SHARED2)* is_ea * vot1 * tour_participants +,SHARED3_EA_LOW,(trips.trip_mode.isin(['SHARED3'])/OCC_SHARED3)* is_ea * vot1 * tour_participants +,DRIVEALONE_EA_MED,((trips.trip_mode == 'DRIVEALONE') & is_ea & vot2) * tour_participants +,SHARED2_EA_MED,(trips.trip_mode.isin(['SHARED2'])/OCC_SHARED2)* is_ea * vot2 * tour_participants +,SHARED3_EA_MED,(trips.trip_mode.isin(['SHARED3'])/OCC_SHARED3)* is_ea * vot2 * tour_participants +,DRIVEALONE_EA_HIGH,((trips.trip_mode == 'DRIVEALONE') & is_ea & vot3) * tour_participants +,SHARED2_EA_HIGH,(trips.trip_mode.isin(['SHARED2'])/OCC_SHARED2)* is_ea * vot3 * tour_participants +,SHARED3_EA_HIGH,(trips.trip_mode.isin(['SHARED3'])/OCC_SHARED3)* is_ea * vot3 * tour_participants +,WALK_LOC_EA,((trips.trip_mode == 'WALK_LOC') & is_ea) * tour_participants +,WALK_PRM_EA,((trips.trip_mode == 'WALK_PRM') & is_ea) * tour_participants +,WALK_MIX_EA,((trips.trip_mode == 'WALK_MIX') & is_ea) * tour_participants +,PNR_LOCOUT_EA,((trips.trip_mode == 'PNR_LOC') & is_ea) * tour_participants * outbound +,PNR_PRMOUT_EA,((trips.trip_mode == 'PNR_PRM') & is_ea) * tour_participants * outbound +,PNR_MIXOUT_EA,((trips.trip_mode == 'PNR_MIX') & is_ea) * tour_participants * outbound +,KNR_LOCOUT_EA,((trips.trip_mode == 'KNR_LOC') & is_ea) * tour_participants * outbound +,KNR_PRMOUT_EA,((trips.trip_mode == 'KNR_PRM') & is_ea) * tour_participants * outbound +,KNR_MIXOUT_EA,((trips.trip_mode == 'KNR_MIX') & is_ea) * tour_participants * outbound +,TNC_LOCOUT_EA,((trips.trip_mode == 'TNC_LOC') & is_ea) * tour_participants * outbound +,TNC_PRMOUT_EA,((trips.trip_mode == 'TNC_PRM') & is_ea) * tour_participants * outbound +,TNC_MIXOUT_EA,((trips.trip_mode == 'TNC_MIX') & is_ea) * tour_participants * outbound +,PNR_LOCIN_EA,((trips.trip_mode == 'PNR_LOC') & is_ea) * tour_participants * inbound +,PNR_PRMIN_EA,((trips.trip_mode == 'PNR_PRM') & is_ea) * tour_participants * inbound +,PNR_MIXIN_EA,((trips.trip_mode == 'PNR_MIX') & is_ea) * tour_participants * inbound +,KNR_LOCIN_EA,((trips.trip_mode == 'KNR_LOC') & is_ea) * tour_participants * inbound +,KNR_PRMIN_EA,((trips.trip_mode == 'KNR_PRM') & is_ea) * tour_participants * inbound +,KNR_MIXIN_EA,((trips.trip_mode == 'KNR_MIX') & is_ea) * tour_participants * inbound +,TNC_LOCIN_EA,((trips.trip_mode == 'TNC_LOC') & is_ea) * tour_participants * inbound +,TNC_PRMIN_EA,((trips.trip_mode == 'TNC_PRM') & is_ea) * tour_participants * inbound +,TNC_MIXIN_EA,((trips.trip_mode == 'TNC_MIX') & is_ea) * tour_participants * inbound +,BIKE_EA,((trips.trip_mode == 'BIKE') & is_ea) * tour_participants +,WALK_EA,((trips.trip_mode == 'WALK') & is_ea) * tour_participants +# am trips,, +,DRIVEALONE_AM_LOW,((trips.trip_mode == 'DRIVEALONE') & is_am & vot1) * tour_participants +,SHARED2_AM_LOW,(trips.trip_mode.isin(['SHARED2'])/OCC_SHARED2)* is_am * vot1 * tour_participants +,SHARED3_AM_LOW,(trips.trip_mode.isin(['SHARED3'])/OCC_SHARED3)* is_am * vot1 * tour_participants +,DRIVEALONE_AM_MED,((trips.trip_mode == 'DRIVEALONE') & is_am & vot2) * tour_participants +,SHARED2_AM_MED,(trips.trip_mode.isin(['SHARED2'])/OCC_SHARED2)* is_am * vot2 * tour_participants +,SHARED3_AM_MED,(trips.trip_mode.isin(['SHARED3'])/OCC_SHARED3)* is_am * vot2 * tour_participants +,DRIVEALONE_AM_HIGH,((trips.trip_mode == 'DRIVEALONE') & is_am & vot3) * tour_participants +,SHARED2_AM_HIGH,(trips.trip_mode.isin(['SHARED2'])/OCC_SHARED2)* is_am * vot3 * tour_participants +,SHARED3_AM_HIGH,(trips.trip_mode.isin(['SHARED3'])/OCC_SHARED3)* is_am * vot3 * tour_participants +,WALK_LOC_AM,((trips.trip_mode == 'WALK_LOC') & is_am) * tour_participants +,WALK_PRM_AM,((trips.trip_mode == 'WALK_PRM') & is_am) * tour_participants +,WALK_MIX_AM,((trips.trip_mode == 'WALK_MIX') & is_am) * tour_participants +,PNR_LOCOUT_AM,((trips.trip_mode == 'PNR_LOC') & is_am) * tour_participants * outbound +,PNR_PRMOUT_AM,((trips.trip_mode == 'PNR_PRM') & is_am) * tour_participants * outbound +,PNR_MIXOUT_AM,((trips.trip_mode == 'PNR_MIX') & is_am) * tour_participants * outbound +,KNR_LOCOUT_AM,((trips.trip_mode == 'KNR_LOC') & is_am) * tour_participants * outbound +,KNR_PRMOUT_AM,((trips.trip_mode == 'KNR_PRM') & is_am) * tour_participants * outbound +,KNR_MIXOUT_AM,((trips.trip_mode == 'KNR_MIX') & is_am) * tour_participants * outbound +,TNC_LOCOUT_AM,((trips.trip_mode == 'TNC_LOC') & is_am) * tour_participants * outbound +,TNC_PRMOUT_AM,((trips.trip_mode == 'TNC_PRM') & is_am) * tour_participants * outbound +,TNC_MIXOUT_AM,((trips.trip_mode == 'TNC_MIX') & is_am) * tour_participants * outbound +,PNR_LOCIN_AM,((trips.trip_mode == 'PNR_LOC') & is_am) * tour_participants * inbound +,PNR_PRMIN_AM,((trips.trip_mode == 'PNR_PRM') & is_am) * tour_participants * inbound +,PNR_MIXIN_AM,((trips.trip_mode == 'PNR_MIX') & is_am) * tour_participants * inbound +,KNR_LOCIN_AM,((trips.trip_mode == 'KNR_LOC') & is_am) * tour_participants * inbound +,KNR_PRMIN_AM,((trips.trip_mode == 'KNR_PRM') & is_am) * tour_participants * inbound +,KNR_MIXIN_AM,((trips.trip_mode == 'KNR_MIX') & is_am) * tour_participants * inbound +,TNC_LOCIN_AM,((trips.trip_mode == 'TNC_LOC') & is_am) * tour_participants * inbound +,TNC_PRMIN_AM,((trips.trip_mode == 'TNC_PRM') & is_am) * tour_participants * inbound +,TNC_MIXIN_AM,((trips.trip_mode == 'TNC_MIX') & is_am) * tour_participants * inbound +,BIKE_AM,((trips.trip_mode == 'BIKE') & is_am) * tour_participants +,WALK_AM,((trips.trip_mode == 'WALK') & is_am) * tour_participants +# md trips,, +,DRIVEALONE_MD_LOW,((trips.trip_mode == 'DRIVEALONE') & is_md & vot1) * tour_participants +,SHARED2_MD_LOW,(trips.trip_mode.isin(['SHARED2'])/OCC_SHARED2)* is_md * vot1 * tour_participants +,SHARED3_MD_LOW,(trips.trip_mode.isin(['SHARED3'])/OCC_SHARED3)* is_md * vot1 * tour_participants +,DRIVEALONE_MD_MED,((trips.trip_mode == 'DRIVEALONE') & is_md & vot2) * tour_participants +,SHARED2_MD_MED,(trips.trip_mode.isin(['SHARED2'])/OCC_SHARED2)* is_md * vot2 * tour_participants +,SHARED3_MD_MED,(trips.trip_mode.isin(['SHARED3'])/OCC_SHARED3)* is_md * vot2 * tour_participants +,DRIVEALONE_MD_HIGH,((trips.trip_mode == 'DRIVEALONE') & is_md & vot3) * tour_participants +,SHARED2_MD_HIGH,(trips.trip_mode.isin(['SHARED2'])/OCC_SHARED2)* is_md * vot3 * tour_participants +,SHARED3_MD_HIGH,(trips.trip_mode.isin(['SHARED3'])/OCC_SHARED3)* is_md * vot3 * tour_participants +,WALK_LOC_MD,((trips.trip_mode == 'WALK_LOC') & is_md) * tour_participants +,WALK_PRM_MD,((trips.trip_mode == 'WALK_PRM') & is_md) * tour_participants +,WALK_MIX_MD,((trips.trip_mode == 'WALK_MIX') & is_md) * tour_participants +,PNR_LOCOUT_MD,((trips.trip_mode == 'PNR_LOC') & is_md) * tour_participants * outbound +,PNR_PRMOUT_MD,((trips.trip_mode == 'PNR_PRM') & is_md) * tour_participants * outbound +,PNR_MIXOUT_MD,((trips.trip_mode == 'PNR_MIX') & is_md) * tour_participants * outbound +,KNR_LOCOUT_MD,((trips.trip_mode == 'KNR_LOC') & is_md) * tour_participants * outbound +,KNR_PRMOUT_MD,((trips.trip_mode == 'KNR_PRM') & is_md) * tour_participants * outbound +,KNR_MIXOUT_MD,((trips.trip_mode == 'KNR_MIX') & is_md) * tour_participants * outbound +,TNC_LOCOUT_MD,((trips.trip_mode == 'TNC_LOC') & is_md) * tour_participants * outbound +,TNC_PRMOUT_MD,((trips.trip_mode == 'TNC_PRM') & is_md) * tour_participants * outbound +,TNC_MIXOUT_MD,((trips.trip_mode == 'TNC_MIX') & is_md) * tour_participants * outbound +,PNR_LOCIN_MD,((trips.trip_mode == 'PNR_LOC') & is_md) * tour_participants * inbound +,PNR_PRMIN_MD,((trips.trip_mode == 'PNR_PRM') & is_md) * tour_participants * inbound +,PNR_MIXIN_MD,((trips.trip_mode == 'PNR_MIX') & is_md) * tour_participants * inbound +,KNR_LOCIN_MD,((trips.trip_mode == 'KNR_LOC') & is_md) * tour_participants * inbound +,KNR_PRMIN_MD,((trips.trip_mode == 'KNR_PRM') & is_md) * tour_participants * inbound +,KNR_MIXIN_MD,((trips.trip_mode == 'KNR_MIX') & is_md) * tour_participants * inbound +,TNC_LOCIN_MD,((trips.trip_mode == 'TNC_LOC') & is_md) * tour_participants * inbound +,TNC_PRMIN_MD,((trips.trip_mode == 'TNC_PRM') & is_md) * tour_participants * inbound +,TNC_MIXIN_MD,((trips.trip_mode == 'TNC_MIX') & is_md) * tour_participants * inbound +,BIKE_MD,((trips.trip_mode == 'BIKE') & is_md) * tour_participants +,WALK_MD,((trips.trip_mode == 'WALK') & is_md) * tour_participants +# pm trips,, +,DRIVEALONE_PM_LOW,((trips.trip_mode == 'DRIVEALONE') & is_pm & vot1) * tour_participants +,SHARED2_PM_LOW,(trips.trip_mode.isin(['SHARED2'])/OCC_SHARED2)* is_pm * vot1 * tour_participants +,SHARED3_PM_LOW,(trips.trip_mode.isin(['SHARED3'])/OCC_SHARED3)* is_pm * vot1 * tour_participants +,DRIVEALONE_PM_MED,((trips.trip_mode == 'DRIVEALONE') & is_pm & vot2) * tour_participants +,SHARED2_PM_MED,(trips.trip_mode.isin(['SHARED2'])/OCC_SHARED2)* is_pm * vot2 * tour_participants +,SHARED3_PM_MED,(trips.trip_mode.isin(['SHARED3'])/OCC_SHARED3)* is_pm * vot2 * tour_participants +,DRIVEALONE_PM_HIGH,((trips.trip_mode == 'DRIVEALONE') & is_pm & vot3) * tour_participants +,SHARED2_PM_HIGH,(trips.trip_mode.isin(['SHARED2'])/OCC_SHARED2)* is_pm * vot3 * tour_participants +,SHARED3_PM_HIGH,(trips.trip_mode.isin(['SHARED3'])/OCC_SHARED3)* is_pm * vot3 * tour_participants +,WALK_LOC_PM,((trips.trip_mode == 'WALK_LOC') & is_pm) * tour_participants +,WALK_PRM_PM,((trips.trip_mode == 'WALK_PRM') & is_pm) * tour_participants +,WALK_MIX_PM,((trips.trip_mode == 'WALK_MIX') & is_pm) * tour_participants +,PNR_LOCOUT_PM,((trips.trip_mode == 'PNR_LOC') & is_pm) * tour_participants * outbound +,PNR_PRMOUT_PM,((trips.trip_mode == 'PNR_PRM') & is_pm) * tour_participants * outbound +,PNR_MIXOUT_PM,((trips.trip_mode == 'PNR_MIX') & is_pm) * tour_participants * outbound +,KNR_LOCOUT_PM,((trips.trip_mode == 'KNR_LOC') & is_pm) * tour_participants * outbound +,KNR_PRMOUT_PM,((trips.trip_mode == 'KNR_PRM') & is_pm) * tour_participants * outbound +,KNR_MIXOUT_PM,((trips.trip_mode == 'KNR_MIX') & is_pm) * tour_participants * outbound +,TNC_LOCOUT_PM,((trips.trip_mode == 'TNC_LOC') & is_pm) * tour_participants * outbound +,TNC_PRMOUT_PM,((trips.trip_mode == 'TNC_PRM') & is_pm) * tour_participants * outbound +,TNC_MIXOUT_PM,((trips.trip_mode == 'TNC_MIX') & is_pm) * tour_participants * outbound +,PNR_LOCIN_PM,((trips.trip_mode == 'PNR_LOC') & is_pm) * tour_participants * inbound +,PNR_PRMIN_PM,((trips.trip_mode == 'PNR_PRM') & is_pm) * tour_participants * inbound +,PNR_MIXIN_PM,((trips.trip_mode == 'PNR_MIX') & is_pm) * tour_participants * inbound +,KNR_LOCIN_PM,((trips.trip_mode == 'KNR_LOC') & is_pm) * tour_participants * inbound +,KNR_PRMIN_PM,((trips.trip_mode == 'KNR_PRM') & is_pm) * tour_participants * inbound +,KNR_MIXIN_PM,((trips.trip_mode == 'KNR_MIX') & is_pm) * tour_participants * inbound +,TNC_LOCIN_PM,((trips.trip_mode == 'TNC_LOC') & is_pm) * tour_participants * inbound +,TNC_PRMIN_PM,((trips.trip_mode == 'TNC_PRM') & is_pm) * tour_participants * inbound +,TNC_MIXIN_PM,((trips.trip_mode == 'TNC_MIX') & is_pm) * tour_participants * inbound +,BIKE_PM,((trips.trip_mode == 'BIKE') & is_pm) * tour_participants +,WALK_PM,((trips.trip_mode == 'WALK') & is_pm) * tour_participants +# ev trips,, +,DRIVEALONE_EV_LOW,((trips.trip_mode == 'DRIVEALONE') & is_ev & vot1) * tour_participants +,SHARED2_EV_LOW,(trips.trip_mode.isin(['SHARED2'])/OCC_SHARED2)* is_ev * vot1 * tour_participants +,SHARED3_EV_LOW,(trips.trip_mode.isin(['SHARED3'])/OCC_SHARED3)* is_ev * vot1 * tour_participants +,DRIVEALONE_EV_MED,((trips.trip_mode == 'DRIVEALONE') & is_ev & vot2) * tour_participants +,SHARED2_EV_MED,(trips.trip_mode.isin(['SHARED2'])/OCC_SHARED2)* is_ev * vot2 * tour_participants +,SHARED3_EV_MED,(trips.trip_mode.isin(['SHARED3'])/OCC_SHARED3)* is_ev * vot2 * tour_participants +,DRIVEALONE_EV_HIGH,((trips.trip_mode == 'DRIVEALONE') & is_ev & vot3) * tour_participants +,SHARED2_EV_HIGH,(trips.trip_mode.isin(['SHARED2'])/OCC_SHARED2)* is_ev * vot3 * tour_participants +,SHARED3_EV_HIGH,(trips.trip_mode.isin(['SHARED3'])/OCC_SHARED3)* is_ev * vot3 * tour_participants +,WALK_LOC_EV,((trips.trip_mode == 'WALK_LOC') & is_ev) * tour_participants +,WALK_PRM_EV,((trips.trip_mode == 'WALK_PRM') & is_ev) * tour_participants +,WALK_MIX_EV,((trips.trip_mode == 'WALK_MIX') & is_ev) * tour_participants +,PNR_LOCOUT_EV,((trips.trip_mode == 'PNR_LOC') & is_ev) * tour_participants * outbound +,PNR_PRMOUT_EV,((trips.trip_mode == 'PNR_PRM') & is_ev) * tour_participants * outbound +,PNR_MIXOUT_EV,((trips.trip_mode == 'PNR_MIX') & is_ev) * tour_participants * outbound +,KNR_LOCOUT_EV,((trips.trip_mode == 'KNR_LOC') & is_ev) * tour_participants * outbound +,KNR_PRMOUT_EV,((trips.trip_mode == 'KNR_PRM') & is_ev) * tour_participants * outbound +,KNR_MIXOUT_EV,((trips.trip_mode == 'KNR_MIX') & is_ev) * tour_participants * outbound +,TNC_LOCOUT_EV,((trips.trip_mode == 'TNC_LOC') & is_ev) * tour_participants * outbound +,TNC_PRMOUT_EV,((trips.trip_mode == 'TNC_PRM') & is_ev) * tour_participants * outbound +,TNC_MIXOUT_EV,((trips.trip_mode == 'TNC_MIX') & is_ev) * tour_participants * outbound +,PNR_LOCIN_EV,((trips.trip_mode == 'PNR_LOC') & is_ev) * tour_participants * inbound +,PNR_PRMIN_EV,((trips.trip_mode == 'PNR_PRM') & is_ev) * tour_participants * inbound +,PNR_MIXIN_EV,((trips.trip_mode == 'PNR_MIX') & is_ev) * tour_participants * inbound +,KNR_LOCIN_EV,((trips.trip_mode == 'KNR_LOC') & is_ev) * tour_participants * inbound +,KNR_PRMIN_EV,((trips.trip_mode == 'KNR_PRM') & is_ev) * tour_participants * inbound +,KNR_MIXIN_EV,((trips.trip_mode == 'KNR_MIX') & is_ev) * tour_participants * inbound +,TNC_LOCIN_EV,((trips.trip_mode == 'TNC_LOC') & is_ev) * tour_participants * inbound +,TNC_PRMIN_EV,((trips.trip_mode == 'TNC_PRM') & is_ev) * tour_participants * inbound +,TNC_MIXIN_EV,((trips.trip_mode == 'TNC_MIX') & is_ev) * tour_participants * inbound +,BIKE_EV,((trips.trip_mode == 'BIKE') & is_ev) * tour_participants +,WALK_EV,((trips.trip_mode == 'WALK') & is_ev) * tour_participants \ No newline at end of file diff --git a/resident/extensions/__init__.py b/resident/extensions/__init__.py new file mode 100644 index 0000000..57fdec8 --- /dev/null +++ b/resident/extensions/__init__.py @@ -0,0 +1,4 @@ +from . import av_ownership +from . import external_identification +from . import external_location_choice +from . import adjust_auto_operating_cost diff --git a/resident/extensions/adjust_auto_operating_cost.py b/resident/extensions/adjust_auto_operating_cost.py new file mode 100644 index 0000000..0020258 --- /dev/null +++ b/resident/extensions/adjust_auto_operating_cost.py @@ -0,0 +1,34 @@ +import logging + +import numpy as np +import pandas as pd + +from activitysim.core import workflow + +logger = logging.getLogger(__name__) + + +@workflow.step +def adjust_auto_operating_cost(state: workflow.State, vehicles: pd.DataFrame): + """ + Adjusts the `auto_operating_cost` field in the vehicles table + so that the average is a desired value set as costPerMile in the + settings + + Parameters + ---------- + vehicles : pd.DataFrame + """ + target_auto_operating_cost = state.get_global_constants()["costPerMile"] + + adjustment_factor = ( + target_auto_operating_cost / vehicles["auto_operating_cost"].mean() + ) + logger.info( + "Adjusting auto operating costs in vehicles table by a factor of {}".format( + adjustment_factor + ) + ) + vehicles["auto_operating_cost"] *= adjustment_factor + + state.add_table("vehicles", vehicles) diff --git a/resident/extensions/av_ownership.py b/resident/extensions/av_ownership.py new file mode 100644 index 0000000..7b7d74c --- /dev/null +++ b/resident/extensions/av_ownership.py @@ -0,0 +1,207 @@ +# ActivitySim +# See full license in LICENSE.txt. +import logging + +import numpy as np +import pandas as pd + +from activitysim.core import ( + config, + expressions, + estimation, + simulate, + tracing, + workflow, +) +from activitysim.core.configuration.base import PreprocessorSettings, PydanticReadable +from activitysim.core.configuration.logit import LogitComponentSettings + +logger = logging.getLogger("activitysim") + + +class AVOwnershipSettings(LogitComponentSettings, extra="forbid"): + """ + Settings for the `transit_pass_subsidy` component. + """ + + preprocessor: PreprocessorSettings | None = None + """Setting for the preprocessor.""" + + AV_OWNERSHIP_ALT: int = 0 + """The column index number of the spec file for owning an autonomous vehicle.""" + + # iterative what-if analysis example + # omit these settings to not iterate + AV_OWNERSHIP_ITERATIONS: int | None = 1 + """Maximum number of auto-calibration iterations to run.""" + AV_OWNERSHIP_TARGET_PERCENT: float | None = 0.0 + """Target percent of households owning an autonomous vehicle.""" + AV_OWNERSHIP_TARGET_PERCENT_TOLERANCE: float | None = 0.01 + """ + Tolerance for the target percent of households owning an autonomous vehicle. + Auto-calibration iterations will stop after achieving tolerance or hitting the max number. + """ + AV_OWNERSHIP_COEFFICIENT_CONSTANT: str | None = "coef_av_target_share" + """Name of the coefficient to adjust in each auto-calibration iteration.""" + + +@workflow.step +def av_ownership( + state: workflow.State, + households_merged: pd.DataFrame, + households: pd.DataFrame, + model_settings: AVOwnershipSettings | None = None, + model_settings_file_name: str = "av_ownership.yaml", + trace_label: str = "av_ownership", + trace_hh_id: bool = False, +) -> None: + """ + This model predicts whether a household owns an autonomous vehicle. + The output from this model is TRUE or FALSE. + """ + + if model_settings is None: + model_settings = AVOwnershipSettings.read_settings_file( + state.filesystem, + model_settings_file_name, + ) + + choosers = households_merged + logger.info("Running %s with %d households", trace_label, len(choosers)) + + estimator = estimation.manager.begin_estimation(state, "av_ownership") + + constants = config.get_model_constants(model_settings) + av_ownership_alt = model_settings.AV_OWNERSHIP_ALT + + # - preprocessor + preprocessor_settings = model_settings.preprocessor + if preprocessor_settings: + + locals_d = {} + if constants is not None: + locals_d.update(constants) + + expressions.assign_columns( + state, + df=choosers, + model_settings=preprocessor_settings, + locals_dict=locals_d, + trace_label=trace_label, + ) + + model_spec = state.filesystem.read_model_spec(file_name=model_settings.SPEC) + coefficients_df = state.filesystem.read_model_coefficients(model_settings) + nest_spec = config.get_logit_model_settings(model_settings) + + if estimator: + estimator.write_model_settings(model_settings, model_settings_file_name) + estimator.write_spec(model_settings) + estimator.write_coefficients(coefficients_df) + estimator.write_choosers(choosers) + + # - iterative single process what-if adjustment if specified + iterations = model_settings.AV_OWNERSHIP_ITERATIONS + iterations_coefficient_constant = model_settings.AV_OWNERSHIP_COEFFICIENT_CONSTANT + iterations_target_percent = model_settings.AV_OWNERSHIP_TARGET_PERCENT + iterations_target_percent_tolerance = ( + model_settings.AV_OWNERSHIP_TARGET_PERCENT_TOLERANCE + ) + + # check to make sure all required settings are specified + assert ( + iterations_coefficient_constant is not None if (iterations > 0) else True + ), "AV_OWNERSHIP_COEFFICIENT_CONSTANT required if AV_OWNERSHIP_ITERATIONS is specified" + assert ( + iterations_target_percent is not None if (iterations > 0) else True + ), "AV_OWNERSHIP_TARGET_PERCENT required if AV_OWNERSHIP_ITERATIONS is specified" + assert ( + iterations_target_percent_tolerance is not None if (iterations > 0) else True + ), "AV_OWNERSHIP_TARGET_PERCENT_TOLERANCE required if AV_OWNERSHIP_ITERATIONS is specified" + + for iteration in range(iterations): + + logger.info( + "Running %s with %d households iteration %d", + trace_label, + len(choosers), + iteration, + ) + + # re-read spec to reset substitution + model_spec = state.filesystem.read_model_spec(file_name=model_settings.SPEC) + model_spec = simulate.eval_coefficients( + state, model_spec, coefficients_df, estimator + ) + + choices = simulate.simple_simulate( + state, + choosers=choosers, + spec=model_spec, + nest_spec=nest_spec, + locals_d=constants, + trace_label=trace_label, + trace_choice_name="av_ownership", + estimator=estimator, + compute_settings=model_settings.compute_settings, + ) + + if iterations_target_percent is not None: + + current_percent = (choices == av_ownership_alt).sum() / len(choosers) + logger.info( + "Running %s iteration %i choosers %i current percent %f target percent %f", + trace_label, + iteration, + len(choosers), + current_percent, + iterations_target_percent, + ) + + if current_percent <= ( + iterations_target_percent + iterations_target_percent_tolerance + ) and current_percent >= ( + iterations_target_percent - iterations_target_percent_tolerance + ): + logger.info( + "Running %s iteration %i converged with coefficient %f", + trace_label, + iteration, + coefficients_df.value[iterations_coefficient_constant], + ) + break + + else: + new_value = ( + np.log( + iterations_target_percent / np.maximum(current_percent, 0.0001) + ) + + coefficients_df.value[iterations_coefficient_constant] + ) + coefficients_df.value[iterations_coefficient_constant] = new_value + logger.info( + "Running %s iteration %i new coefficient for next iteration %f", + trace_label, + iteration, + new_value, + ) + iteration = iteration + 1 + + choices = choices == av_ownership_alt + + if estimator: + estimator.write_choices(choices) + choices = estimator.get_survey_values(choices, "households", "av_ownership") + estimator.write_override_choices(choices) + estimator.end_estimation() + + households["av_ownership"] = ( + choices.reindex(households.index).fillna(0).astype(bool) + ) + + state.add_table("households", households) + + tracing.print_summary("av_ownership", households.av_ownership, value_counts=True) + + if trace_hh_id: + state.tracing.trace_df(households, label=trace_label, warn_if_empty=True) diff --git a/resident/extensions/external_identification.py b/resident/extensions/external_identification.py new file mode 100644 index 0000000..3800a04 --- /dev/null +++ b/resident/extensions/external_identification.py @@ -0,0 +1,420 @@ +# ActivitySim +# See full license in LICENSE.txt. +import logging + +import numpy as np +import pandas as pd + +from pydantic import validator + +from activitysim.core import ( + config, + expressions, + los, + estimation, + simulate, + tracing, + workflow, +) +from activitysim.core.configuration.logit import LogitComponentSettings +from activitysim.core.configuration.base import PreprocessorSettings + +logger = logging.getLogger(__name__) + + +class ExternalIdentificationSettings(LogitComponentSettings, extra="forbid"): + """ + Settings for the `external_identification` component. + """ + + CHOOSER_FILTER_COLUMN_NAME: str | None = None + """Column name which selects choosers.""" + + EXTERNAL_COL_NAME: str | None = None + """Adds this column and set to True if model selects external""" + + INTERNAL_COL_NAME: str | None = None + """Column name set to True if not external but CHOOSER_FILTER_COLUMN_NAME is True""" + + preprocessor: PreprocessorSettings | None = None + + +def determine_closest_external_station( + state, choosers, skim_dict, origin_col="home_zone_id" +): + + unique_origin_zones = choosers[origin_col].unique() + landuse = state.get_table("land_use") + ext_zones = landuse[landuse.EXTERNAL > 0].index.to_numpy() + + choosers["closest_external_zone"] = -1 + choosers["dist_to_external_zone"] = 0.0 + + for origin_zone in unique_origin_zones: + # in_skim check in skim_dictionary.py requires orig and dests to be the same shape in lookup + orig_zones = np.full(shape=len(ext_zones), fill_value=origin_zone, dtype=int) + zone_distances = skim_dict.lookup(orig_zones, ext_zones, ("SOV_M_DIST", "MD")) + + closest_zone_idx = np.argmin(zone_distances) + closest_zone = int(ext_zones[closest_zone_idx]) + dist_to_closest_zone = zone_distances[closest_zone_idx] + + choosers.loc[ + choosers[origin_col] == origin_zone, "closest_external_zone" + ] = closest_zone + choosers.loc[ + choosers[origin_col] == origin_zone, "dist_to_external_zone" + ] = dist_to_closest_zone + + return choosers + + +def external_identification( + state, + model_settings, + estimator, + choosers, + network_los, + model_settings_file_name, + trace_label, +): + + constants = config.get_model_constants(model_settings) + + locals_d = {} + if constants is not None: + locals_d.update(constants) + locals_d.update({"land_use": state.get_table("land_use")}) + + skim_dict = network_los.get_default_skim_dict() + choosers = determine_closest_external_station(state, choosers, skim_dict) + + # - preprocessor + preprocessor_settings = model_settings.preprocessor + if preprocessor_settings: + expressions.assign_columns( + state, + df=choosers, + model_settings=preprocessor_settings, + locals_dict=locals_d, + trace_label=trace_label, + ) + + model_spec = state.filesystem.read_model_spec(file_name=model_settings.SPEC) + coefficients_df = state.filesystem.read_model_coefficients(model_settings) + model_spec = simulate.eval_coefficients( + state, model_spec, coefficients_df, estimator + ) + + nest_spec = config.get_logit_model_settings(model_settings) + + if estimator: + estimator.write_model_settings(model_settings, model_settings_file_name) + estimator.write_spec(model_settings) + estimator.write_coefficients(coefficients_df, model_settings) + estimator.write_choosers(choosers) + + choices = simulate.simple_simulate( + state, + choosers=choosers, + spec=model_spec, + nest_spec=nest_spec, + locals_d=locals_d, + trace_label=trace_label, + trace_choice_name=trace_label, + estimator=estimator, + compute_settings=model_settings.compute_settings, + ) + + return choices + + +@workflow.step +def external_worker_identification( + state: workflow.State, + persons: pd.DataFrame, + persons_merged: pd.DataFrame, + network_los: los.Network_LOS, + model_settings: ExternalIdentificationSettings | None = None, + model_settings_file_name: str = "external_worker_identification.yaml", + trace_label: str = "external_worker_identification", + trace_hh_id: bool = False, +) -> None: + """ + This model predicts the whether a worker has an external work location. + The output from this model is TRUE (if external) or FALSE (if internal). + + The main interface to the external worker model is the external_worker_identification() function. + This function is registered as an orca step in the example Pipeline. + """ + if model_settings is None: + model_settings = ExternalIdentificationSettings.read_settings_file( + state.filesystem, + model_settings_file_name, + ) + + estimator = estimation.manager.begin_estimation(state, trace_label) + + filter_col = model_settings.CHOOSER_FILTER_COLUMN_NAME + if filter_col is None: + choosers = persons_merged + else: + choosers = persons_merged[persons_merged[filter_col]] + logger.info("Running %s with %d persons", trace_label, len(choosers)) + + choices = external_identification( + state, + model_settings, + estimator, + choosers, + network_los, + model_settings_file_name, + trace_label, + ) + + external_col_name = model_settings.EXTERNAL_COL_NAME + internal_col_name = model_settings.INTERNAL_COL_NAME + + if estimator: + estimator.write_choices(choices) + choices = estimator.get_survey_values(choices, "persons", trace_label) + estimator.write_override_choices(choices) + estimator.end_estimation() + + if external_col_name is not None: + persons[external_col_name] = ( + (choices == 0).reindex(persons.index).fillna(False).astype(bool) + ) + if internal_col_name is not None: + persons[internal_col_name] = persons[filter_col] & ~persons[external_col_name] + + state.add_table("persons", persons) + + tracing.print_summary( + external_col_name, persons[external_col_name], value_counts=True + ) + + if trace_hh_id: + state.tracing.trace_df(persons, label=trace_label, warn_if_empty=True) + + +@workflow.step +def external_student_identification( + state: workflow.State, + persons: pd.DataFrame, + persons_merged: pd.DataFrame, + network_los: los.Network_LOS, + model_settings: ExternalIdentificationSettings | None = None, + model_settings_file_name: str = "external_student_identification.yaml", + trace_label: str = "external_student_identification", + trace_hh_id: bool = False, +) -> None: + """ + This model predicts the whether a student has an external work location. + The output from this model is TRUE (if external) or FALSE (if internal). + + The main interface to the external student model is the external_student_identification() function. + This function is registered as an orca step in the example Pipeline. + """ + + if model_settings is None: + model_settings = ExternalIdentificationSettings.read_settings_file( + state.filesystem, + model_settings_file_name, + ) + + estimator = estimation.manager.begin_estimation(state, trace_label) + + filter_col = model_settings.CHOOSER_FILTER_COLUMN_NAME + if filter_col is None: + choosers = persons_merged + else: + choosers = persons_merged[persons_merged[filter_col]] + logger.info("Running %s with %d persons", trace_label, len(choosers)) + + choices = external_identification( + state, + model_settings, + estimator, + choosers, + network_los, + model_settings_file_name, + trace_label, + ) + + external_col_name = model_settings.EXTERNAL_COL_NAME + internal_col_name = model_settings.INTERNAL_COL_NAME + + if estimator: + estimator.write_choices(choices) + choices = estimator.get_survey_values(choices, "persons", trace_label) + estimator.write_override_choices(choices) + estimator.end_estimation() + + if external_col_name is not None: + persons[external_col_name] = ( + (choices == 0).reindex(persons.index).fillna(False).astype(bool) + ) + if internal_col_name is not None: + persons[internal_col_name] = persons[filter_col] & ~persons[external_col_name] + + state.add_table("persons", persons) + + tracing.print_summary( + external_col_name, persons[external_col_name], value_counts=True + ) + + if trace_hh_id: + state.tracing.trace_df(persons, label=trace_label, warn_if_empty=True) + + +def set_external_tour_variables(state, tours, choices, model_settings, trace_label): + """ + Set the internal and external tour indicator columns in the tours file + """ + external_col_name = model_settings.EXTERNAL_COL_NAME + internal_col_name = model_settings.INTERNAL_COL_NAME + + if external_col_name is not None: + tours[external_col_name] = ( + (choices == 0).reindex(tours.index).fillna(False).astype(bool) + ) + if internal_col_name is not None: + tours[internal_col_name] = ( + (choices == 1).reindex(tours.index).fillna(True).astype(bool) + ) + + # - annotate tours table + if "annotate_tours" in model_settings: + expressions.assign_columns( + state, + df=tours, + model_settings=model_settings.get("annotate_tours"), + trace_label=tracing.extend_trace_label(trace_label, "annotate_tours"), + ) + + return tours + + +@workflow.step +def external_non_mandatory_identification( + state: workflow.State, + tours: pd.DataFrame, + tours_merged: pd.DataFrame, + network_los: los.Network_LOS, + model_settings: ExternalIdentificationSettings | None = None, + model_settings_file_name: str = "external_non_mandatory_identification.yaml", + trace_label: str = "external_non_mandatory_identification", + trace_hh_id: bool = False, +) -> None: + """ + This model predicts the whether a non-mandatory tour is external. + The output from this model is TRUE (if external) or FALSE (if internal). + + The main interface to the external student model is the external_nonmandatory_identification() function. + This function is registered as an orca step in the example Pipeline. + """ + if model_settings is None: + model_settings = ExternalIdentificationSettings.read_settings_file( + state.filesystem, + model_settings_file_name, + ) + + estimator = estimation.manager.begin_estimation(state, trace_label) + + choosers = tours_merged[tours_merged["tour_category"] == "non_mandatory"] + + choices = external_identification( + state, + model_settings, + estimator, + choosers, + network_los, + model_settings_file_name, + trace_label, + ) + + if estimator: + estimator.write_choices(choices) + choices = estimator.get_survey_values(choices, "tours", trace_label) + estimator.write_override_choices(choices) + estimator.end_estimation() + + tours = set_external_tour_variables( + state, tours, choices, model_settings, trace_label + ) + + state.add_table("tours", tours) + + external_col_name = model_settings.EXTERNAL_COL_NAME + tracing.print_summary( + external_col_name, tours[external_col_name], value_counts=True + ) + + if trace_hh_id: + state.tracing.trace_df(tours, label=trace_label, warn_if_empty=True) + + +@workflow.step +def external_joint_tour_identification( + state: workflow.State, + tours: pd.DataFrame, + tours_merged: pd.DataFrame, + network_los: los.Network_LOS, + model_settings: ExternalIdentificationSettings | None = None, + model_settings_file_name: str = "external_joint_tour_identification.yaml", + trace_label: str = "external_joint_tour_identification", + trace_hh_id: bool = False, +) -> None: + """ + This model predicts the whether a joint tour is external. + The output from this model is TRUE (if external) or FALSE (if internal). + + The main interface to the external student model is the external_nonmandatory_identification() function. + This function is registered as an orca step in the example Pipeline. + """ + if model_settings is None: + model_settings = ExternalIdentificationSettings.read_settings_file( + state.filesystem, + model_settings_file_name, + ) + + estimator = estimation.manager.begin_estimation(state, trace_label) + + choosers = tours_merged[tours_merged["tour_category"] == "joint"] + + # - if no choosers + if choosers.shape[0] > 0: + choices = external_identification( + state, + model_settings, + estimator, + choosers, + network_los, + model_settings_file_name, + trace_label, + ) + else: + # everything is internal, still want to set internal or external columns in df + choices = pd.Series(1, index=choosers.index) + tracing.no_results(trace_label) + + if estimator: + estimator.write_choices(choices) + choices = estimator.get_survey_values(choices, "tours", trace_label) + estimator.write_override_choices(choices) + estimator.end_estimation() + + tours = set_external_tour_variables( + state, tours, choices, model_settings, trace_label + ) + + state.add_table("tours", tours) + + external_col_name = model_settings.EXTERNAL_COL_NAME + tracing.print_summary( + external_col_name, tours[external_col_name], value_counts=True + ) + + if trace_hh_id: + state.tracing.trace_df(tours, label=trace_label, warn_if_empty=True) diff --git a/resident/extensions/external_location_choice.py b/resident/extensions/external_location_choice.py new file mode 100644 index 0000000..0b3ee8e --- /dev/null +++ b/resident/extensions/external_location_choice.py @@ -0,0 +1,325 @@ +# ActivitySim +# See full license in LICENSE.txt. +from __future__ import annotations + +import logging + +import numpy as np +import pandas as pd + +from activitysim.abm.models.util import logsums as logsum +from activitysim.abm.models.util import tour_destination +from activitysim.abm.tables import shadow_pricing +from activitysim.core import ( + config, + expressions, + los, + estimation, + simulate, + tracing, + workflow, +) +from activitysim.core.configuration.logit import ( + TourLocationComponentSettings, + TourModeComponentSettings, +) +from activitysim.abm.models.location_choice import ( + write_estimation_specs, + iterate_location_choice, +) +from activitysim.core.util import assign_in_place + +logger = logging.getLogger(__name__) + + +@workflow.step +def external_school_location( + state: workflow.State, + persons_merged: pd.DataFrame, + persons: pd.DataFrame, + households: pd.DataFrame, + network_los: los.Network_LOS, + locutor: bool, + model_settings: TourLocationComponentSettings | None = None, + model_settings_file_name: str = "external_school_location.yaml", + trace_label: str = "external_school_location", +): + """ + External school location choice model + + iterate_location_choice adds location choice column and annotations to persons table + """ + if model_settings is None: + model_settings = TourLocationComponentSettings.read_settings_file( + state.filesystem, + model_settings_file_name, + ) + + estimator = estimation.manager.begin_estimation(state, "external_school_location") + if estimator: + write_estimation_specs(estimator, model_settings, model_settings_file_name) + + persons_df = iterate_location_choice( + state=state, + model_settings=model_settings, + persons_merged=persons_merged, + persons=persons, + households=households, + network_los=network_los, + estimator=estimator, + chunk_size=state.settings.chunk_size, + locutor=locutor, + trace_label=trace_label, + ) + + state.add_table("persons", persons_df) + + if estimator: + estimator.end_estimation() + + +@workflow.step +def external_workplace_location( + state: workflow.State, + persons_merged: pd.DataFrame, + persons: pd.DataFrame, + households: pd.DataFrame, + network_los: los.Network_LOS, + locutor: bool, + model_settings: TourLocationComponentSettings | None = None, + model_settings_file_name: str = "external_workplace_location.yaml", + trace_label: str = "external_workplace_location", +): + """ + External workplace location choice model + + iterate_location_choice adds location choice column and annotations to persons table + """ + + if model_settings is None: + model_settings = TourLocationComponentSettings.read_settings_file( + state.filesystem, + model_settings_file_name, + ) + + estimator = estimation.manager.begin_estimation( + state, "external_workplace_location" + ) + if estimator: + write_estimation_specs(estimator, model_settings, model_settings_file_name) + + persons_df = iterate_location_choice( + state=state, + model_settings=model_settings, + persons_merged=persons_merged, + persons=persons, + households=households, + network_los=network_los, + estimator=estimator, + chunk_size=state.settings.chunk_size, + locutor=locutor, + trace_label=trace_label, + ) + + state.add_table("persons", persons_df) + + if estimator: + estimator.end_estimation() + + +@workflow.step +def external_non_mandatory_destination( + state: workflow.State, + tours: pd.DataFrame, + persons_merged: pd.DataFrame, + network_los: los.Network_LOS, + model_settings: TourLocationComponentSettings | None = None, + model_settings_file_name: str = "external_non_mandatory_destination.yaml", + trace_label: str = "external_non_mandatory_destination", +): + """ + Given the tour generation from the above, each tour needs to have a + destination, so in this case tours are the choosers (with the associated + person that's making the tour) + """ + if model_settings is None: + model_settings = TourLocationComponentSettings.read_settings_file( + state.filesystem, + model_settings_file_name, + ) + + logsum_column_name = model_settings.DEST_CHOICE_LOGSUM_COLUMN_NAME + want_logsums = logsum_column_name is not None + + sample_table_name = model_settings.DEST_CHOICE_SAMPLE_TABLE_NAME + want_sample_table = ( + state.settings.want_dest_choice_sample_tables and sample_table_name is not None + ) + + # choosers are tours - in a sense tours are choosing their destination + non_mandatory_ext_tours = tours[ + (tours.tour_category == "non_mandatory") & (tours.is_external_tour) + ] + + # if no external non-mandatory tours + if non_mandatory_ext_tours.shape[0] == 0: + tracing.no_results(trace_label) + return + + estimator = estimation.manager.begin_estimation( + state, "external_non_mandatory_destination" + ) + if estimator: + estimator.write_coefficients(model_settings=model_settings) + # estimator.write_spec(model_settings, tag='SAMPLE_SPEC') + estimator.write_spec(model_settings, tag="SPEC") + estimator.set_alt_id(model_settings.ALT_DEST_COL_NAME) + estimator.write_table( + state.get_injectable("size_terms"), "size_terms", append=False + ) + estimator.write_table(state.get_table("land_use"), "landuse", append=False) + estimator.write_model_settings(model_settings, model_settings_file_name) + + choices_df, save_sample_df = tour_destination.run_tour_destination( + state, + non_mandatory_ext_tours, + persons_merged, + want_logsums, + want_sample_table, + model_settings, + network_los, + estimator, + trace_label, + ) + + if estimator: + estimator.write_choices(choices_df.choice) + choices_df.choice = estimator.get_survey_values( + choices_df.choice, "tours", "destination" + ) + estimator.write_override_choices(choices_df.choice) + estimator.end_estimation() + + non_mandatory_ext_tours["destination"] = choices_df.choice + + assign_in_place(tours, non_mandatory_ext_tours[["destination"]]) + + if want_logsums: + non_mandatory_ext_tours[logsum_column_name] = choices_df["logsum"] + assign_in_place(tours, non_mandatory_ext_tours[[logsum_column_name]]) + + state.add_table("tours", tours) + + if want_sample_table: + assert len(save_sample_df.index.get_level_values(0).unique()) == len(choices_df) + # save_sample_df.set_index(model_settings['ALT_DEST_COL_NAME'], append=True, inplace=True) + state.extend_table(sample_table_name, save_sample_df) + + if state.settings.trace_hh_id: + state.tracing.trace_df( + tours[tours.tour_category == "non_mandatory"], + label="external_non_mandatory_destination", + slicer="person_id", + index_label="tour", + columns=None, + warn_if_empty=True, + ) + + +@workflow.step +def external_joint_tour_destination( + state: workflow.State, + tours: pd.DataFrame, + persons_merged: pd.DataFrame, + network_los: los.Network_LOS, + model_settings: TourLocationComponentSettings | None = None, + model_settings_file_name: str = "external_joint_tour_destination.yaml", + trace_label: str = "external_joint_tour_destination", +): + """ + Given the tour generation from the above, each tour needs to have a + destination, so in this case tours are the choosers (with the associated + person that's making the tour) + """ + if model_settings is None: + model_settings = TourLocationComponentSettings.read_settings_file( + state.filesystem, + model_settings_file_name, + ) + + logsum_column_name = model_settings.DEST_CHOICE_LOGSUM_COLUMN_NAME + want_logsums = logsum_column_name is not None + + sample_table_name = model_settings.DEST_CHOICE_SAMPLE_TABLE_NAME + want_sample_table = ( + state.settings.want_dest_choice_sample_tables and sample_table_name is not None + ) + + joint_ext_tours = tours[ + (tours.tour_category == "joint") + & (tours.get("is_external_tour", False) == True) + ] # get needed incase no joint tours + + # if no external joint tours + if joint_ext_tours.shape[0] == 0: + tracing.no_results(trace_label) + return + + estimator = estimation.manager.begin_estimation( + state, "external_joint_tour_destination" + ) + if estimator: + estimator.write_coefficients(model_settings=model_settings) + # estimator.write_spec(model_settings, tag='SAMPLE_SPEC') + estimator.write_spec(model_settings, tag="SPEC") + estimator.set_alt_id(model_settings.ALT_DEST_COL_NAME) + estimator.write_table( + state.get_injectable("size_terms"), "size_terms", append=False + ) + estimator.write_table(state.get_table("land_use"), "landuse", append=False) + estimator.write_model_settings(model_settings, model_settings_file_name) + + choices_df, save_sample_df = tour_destination.run_tour_destination( + state, + joint_ext_tours, + persons_merged, + want_logsums, + want_sample_table, + model_settings, + network_los, + estimator, + trace_label, + ) + + if estimator: + estimator.write_choices(choices_df.choice) + choices_df.choice = estimator.get_survey_values( + choices_df.choice, "tours", "destination" + ) + estimator.write_override_choices(choices_df.choice) + estimator.end_estimation() + + joint_ext_tours["destination"] = choices_df.choice + + assign_in_place(tours, joint_ext_tours[["destination"]]) + + if want_logsums: + joint_ext_tours[logsum_column_name] = choices_df["logsum"] + assign_in_place(tours, joint_ext_tours[[logsum_column_name]]) + + state.add_table("tours", tours) + + if want_sample_table: + assert len(save_sample_df.index.get_level_values(0).unique()) == len(choices_df) + # save_sample_df.set_index(model_settings['ALT_DEST_COL_NAME'], append=True, inplace=True) + state.extend_table(sample_table_name, save_sample_df) + + if state.settings.trace_hh_id: + state.tracing.trace_df( + tours[tours.tour_category == "non_mandatory"], + label="external_joint_tour_destination", + slicer="person_id", + index_label="tour", + columns=None, + warn_if_empty=True, + ) diff --git a/resident/model_data/metro/crop_metro_data.ipynb b/resident/model_data/metro/crop_metro_data.ipynb new file mode 100644 index 0000000..f417d23 --- /dev/null +++ b/resident/model_data/metro/crop_metro_data.ipynb @@ -0,0 +1,803 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "93a5ee20", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import os\n", + "import secrets\n", + "import openmatrix as omx" + ] + }, + { + "cell_type": "markdown", + "id": "4de81361", + "metadata": {}, + "source": [ + "## File cropping for testing\n", + "The idea here is for us to be able to quickly iterate through the model using a small subset of the data for easier development and testing. Set the files and zones below and run the notebook." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "b9c8494b", + "metadata": {}, + "outputs": [], + "source": [ + "# read full input data\n", + "input_folder = r\".\\metro_data\"\n", + "output_folder = r\".\\metro_data_cropped\"\n", + "full_households = pd.read_csv(os.path.join(input_folder, 'households.csv'))\n", + "full_persons = pd.read_csv(os.path.join(input_folder, 'persons.csv'))\n", + "full_landuse = pd.read_csv(os.path.join(input_folder, 'land_use.csv'))\n", + "\n", + "input_skim_folder = r\".\\metro_data\\skims\"\n", + "skim_list = [\n", + " \"autoSkims__EA.omx\",\n", + " \"autoSkims__AM.omx\",\n", + " \"autoSkims__MD.omx\",\n", + " \"autoSkims__PM.omx\",\n", + " \"autoSkims__EV.omx\",\n", + " \"transitSkims__EA.omx\",\n", + " \"transitSkims__AM.omx\",\n", + " \"transitSkims__MD.omx\",\n", + " \"transitSkims__PM.omx\",\n", + " \"transitSkims__EV.omx\",\n", + " \"walkSkim.omx\"\n", + "]\n", + "maz_skim_file = \"maz_maz_walk.csv\"\n", + "\n", + "# optional since this can take a while (actually only about 1 minute!)\n", + "CROP_SKIMS = True\n", + "# downtown and a little bit of the northwest + some externals in NE Vancouver\n", + "tazs_to_crop = list(range(1, 51)) + list(range(2000, 2010))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "9bf1237a", + "metadata": {}, + "outputs": [], + "source": [ + "cropped_landuse = full_landuse[full_landuse['TAZ'].isin(tazs_to_crop)].copy()\n", + "cropped_households = full_households[full_households['TAZ'].isin(tazs_to_crop)].copy()\n", + "cropped_persons = full_persons[full_persons['household_id'].isin(cropped_households['household_id'])].copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "0400758d", + "metadata": {}, + "outputs": [], + "source": [ + "# obfuscate the employment data\n", + "emp_colummns = [col for col in cropped_landuse.columns if col.startswith('EMP')]\n", + "for col in emp_colummns:\n", + " # scale employment by +/- 0-50% \n", + " cropped_landuse[col] = cropped_landuse[col].apply(\n", + " lambda x: x * (0.5 + secrets.randbelow(10000) / 10000)\n", + " ).fillna(secrets.randbelow(100)).astype(int) " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "cf90dca7", + "metadata": {}, + "outputs": [], + "source": [ + "# check there is some enrollment and external counts in the cropped dataset\n", + "for col in ['ENROLL_K8','ENROLL_912','ENROLL_COLL', \"external_work\", \"external_nonwork\"]:\n", + " if cropped_landuse[col].sum() == 0:\n", + " raise ValueError(f\"Column {col} has no data in the cropped land use dataset.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "b362c8d0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cropped land use dataset has 712 MAZs\n", + "Cropped households dataset has 35986 households\n", + "Cropped persons dataset has 59632 persons\n" + ] + } + ], + "source": [ + "print(f\"Cropped land use dataset has {len(cropped_landuse)} MAZs\")\n", + "print(f\"Cropped households dataset has {len(cropped_households)} households\")\n", + "print(f\"Cropped persons dataset has {len(cropped_persons)} persons\")\n", + "# save the cropped datasets\n", + "cropped_landuse.to_csv(os.path.join(output_folder, 'land_use.csv'), index=False)\n", + "cropped_households.to_csv(os.path.join(output_folder, 'households.csv'), index=False)\n", + "cropped_persons.to_csv(os.path.join(output_folder, 'persons.csv'), index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "516172fc", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "omx_in shape (2162, 2162)\n", + "SOV_H_COST__EA (60, 60)\n", + "SOV_H_DIST__EA (60, 60)\n", + "SOV_H_TIME__EA (60, 60)\n", + "SOV_L_COST__EA (60, 60)\n", + "SOV_L_DIST__EA (60, 60)\n", + "SOV_L_TIME__EA (60, 60)\n", + "SOV_M_COST__EA (60, 60)\n", + "SOV_M_DIST__EA (60, 60)\n", + "SOV_M_TIME__EA (60, 60)\n", + "SR2_H_COST__EA (60, 60)\n", + "SR2_H_DIST__EA (60, 60)\n", + "SR2_H_TIME__EA (60, 60)\n", + "SR2_L_COST__EA (60, 60)\n", + "SR2_L_DIST__EA (60, 60)\n", + "SR2_L_TIME__EA (60, 60)\n", + "SR2_M_COST__EA (60, 60)\n", + "SR2_M_DIST__EA (60, 60)\n", + "SR2_M_TIME__EA (60, 60)\n", + "SR3_H_COST__EA (60, 60)\n", + "SR3_H_DIST__EA (60, 60)\n", + "SR3_H_TIME__EA (60, 60)\n", + "SR3_L_COST__EA (60, 60)\n", + "SR3_L_DIST__EA (60, 60)\n", + "SR3_L_TIME__EA (60, 60)\n", + "SR3_M_COST__EA (60, 60)\n", + "SR3_M_DIST__EA (60, 60)\n", + "SR3_M_TIME__EA (60, 60)\n", + "omx_in shape (2162, 2162)\n", + "SOV_H_COST__AM (60, 60)\n", + "SOV_H_DIST__AM (60, 60)\n", + "SOV_H_TIME__AM (60, 60)\n", + "SOV_L_COST__AM (60, 60)\n", + "SOV_L_DIST__AM (60, 60)\n", + "SOV_L_TIME__AM (60, 60)\n", + "SOV_M_COST__AM (60, 60)\n", + "SOV_M_DIST__AM (60, 60)\n", + "SOV_M_TIME__AM (60, 60)\n", + "SR2_H_COST__AM (60, 60)\n", + "SR2_H_DIST__AM (60, 60)\n", + "SR2_H_TIME__AM (60, 60)\n", + "SR2_L_COST__AM (60, 60)\n", + "SR2_L_DIST__AM (60, 60)\n", + "SR2_L_TIME__AM (60, 60)\n", + "SR2_M_COST__AM (60, 60)\n", + "SR2_M_DIST__AM (60, 60)\n", + "SR2_M_TIME__AM (60, 60)\n", + "SR3_H_COST__AM (60, 60)\n", + "SR3_H_DIST__AM (60, 60)\n", + "SR3_H_TIME__AM (60, 60)\n", + "SR3_L_COST__AM (60, 60)\n", + "SR3_L_DIST__AM (60, 60)\n", + "SR3_L_TIME__AM (60, 60)\n", + "SR3_M_COST__AM (60, 60)\n", + "SR3_M_DIST__AM (60, 60)\n", + "SR3_M_TIME__AM (60, 60)\n", + "omx_in shape (2162, 2162)\n", + "SOV_H_COST__MD (60, 60)\n", + "SOV_H_DIST__MD (60, 60)\n", + "SOV_H_TIME__MD (60, 60)\n", + "SOV_L_COST__MD (60, 60)\n", + "SOV_L_DIST__MD (60, 60)\n", + "SOV_L_TIME__MD (60, 60)\n", + "SOV_M_COST__MD (60, 60)\n", + "SOV_M_DIST__MD (60, 60)\n", + "SOV_M_TIME__MD (60, 60)\n", + "SR2_H_COST__MD (60, 60)\n", + "SR2_H_DIST__MD (60, 60)\n", + "SR2_H_TIME__MD (60, 60)\n", + "SR2_L_COST__MD (60, 60)\n", + "SR2_L_DIST__MD (60, 60)\n", + "SR2_L_TIME__MD (60, 60)\n", + "SR2_M_COST__MD (60, 60)\n", + 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2162)\n", + "KTW_ACC__PM (60, 60)\n", + "KTW_AST__PM (60, 60)\n", + "KTW_AUX__PM (60, 60)\n", + "KTW_BRT__PM (60, 60)\n", + "KTW_BUS__PM (60, 60)\n", + "KTW_CRT__PM (60, 60)\n", + "KTW_EGR__PM (60, 60)\n", + "KTW_FWT__PM (60, 60)\n", + "KTW_LRT__PM (60, 60)\n", + "KTW_RST__PM (60, 60)\n", + "KTW_SCR__PM (60, 60)\n", + "KTW_TIV__PM (60, 60)\n", + "KTW_VTC__PM (60, 60)\n", + "KTW_XFR__PM (60, 60)\n", + "KTW_XWT__PM (60, 60)\n", + "WTK_ACC__PM (60, 60)\n", + "WTK_AST__PM (60, 60)\n", + "WTK_AUX__PM (60, 60)\n", + "WTK_BRT__PM (60, 60)\n", + "WTK_BUS__PM (60, 60)\n", + "WTK_CRT__PM (60, 60)\n", + "WTK_EGR__PM (60, 60)\n", + "WTK_FWT__PM (60, 60)\n", + "WTK_LRT__PM (60, 60)\n", + "WTK_RST__PM (60, 60)\n", + "WTK_SCR__PM (60, 60)\n", + "WTK_TIV__PM (60, 60)\n", + "WTK_VTC__PM (60, 60)\n", + "WTK_XFR__PM (60, 60)\n", + "WTK_XWT__PM (60, 60)\n", + "WTW_ACC__PM (60, 60)\n", + "WTW_AST__PM (60, 60)\n", + "WTW_AUX__PM (60, 60)\n", + "WTW_BRT__PM (60, 60)\n", + "WTW_BUS__PM (60, 60)\n", + 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(60, 60)\n", + "WTK_SCR__EV (60, 60)\n", + "WTK_TIV__EV (60, 60)\n", + "WTK_VTC__EV (60, 60)\n", + "WTK_XFR__EV (60, 60)\n", + "WTK_XWT__EV (60, 60)\n", + "WTW_ACC__EV (60, 60)\n", + "WTW_AST__EV (60, 60)\n", + "WTW_AUX__EV (60, 60)\n", + "WTW_BRT__EV (60, 60)\n", + "WTW_BUS__EV (60, 60)\n", + "WTW_CRT__EV (60, 60)\n", + "WTW_EGR__EV (60, 60)\n", + "WTW_FWT__EV (60, 60)\n", + "WTW_LRT__EV (60, 60)\n", + "WTW_RST__EV (60, 60)\n", + "WTW_SCR__EV (60, 60)\n", + "WTW_TIV__EV (60, 60)\n", + "WTW_VTC__EV (60, 60)\n", + "WTW_XFR__EV (60, 60)\n", + "WTW_XWT__EV (60, 60)\n", + "omx_in shape (2162, 2162)\n", + "WLK_DIST (60, 60)\n" + ] + } + ], + "source": [ + "if CROP_SKIMS:\n", + " # crop omx skim files\n", + " taz_labels = cropped_landuse.sort_values(\"TAZ\")['TAZ'].unique() # TAZ zone_ids in omx index order\n", + " tazs_indexes = taz_labels - 1 # index of TAZ in skim (zero-based, no mapping)\n", + "\n", + " for omx_infile_name in skim_list:\n", + " skim_data_type = np.float32\n", + "\n", + " omx_in = omx.open_file(os.path.join(input_skim_folder, omx_infile_name))\n", + " omx_out = omx.open_file(os.path.join(output_folder, omx_infile_name), \"w\")\n", + " print(f\"omx_in shape {omx_in.shape()}\")\n", + "\n", + " omx_out.create_mapping(\"ZONE\", taz_labels)\n", + "\n", + " for mat_name in omx_in.list_matrices():\n", + "\n", + " # make sure we have a vanilla numpy array, not a CArray\n", + " m = np.asanyarray(omx_in[mat_name]).astype(skim_data_type)\n", + " m = m[tazs_indexes, :][:, tazs_indexes]\n", + " print(f\"{mat_name} {m.shape}\")\n", + "\n", + " omx_out[mat_name] = m\n", + "\n", + " omx_in.close()\n", + " omx_out.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "if CROP_SKIMS:\n", + " # crop the maz-maz walk skim\n", + " maz_maz_walk = pd.read_csv(os.path.join(input_skim_folder, maz_skim_file))\n", + " cropped_maz_maz_walk = maz_maz_walk[maz_maz_walk['OMAZ'].isin(cropped_landuse.MAZ) & maz_maz_walk['DMAZ'].isin(cropped_landuse.MAZ)]\n", + " # FIXME this should be renamed in the maz-maz walk creation script\n", + " maz_maz_walk.rename(columns={'DISTWALK': 'WLK_DIST'}, inplace=True)\n", + " cropped_maz_maz_walk.to_csv(os.path.join(output_folder, maz_skim_file), index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "bc86f9a1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['KTW_ACC__AM', 'KTW_AST__AM', 'KTW_AUX__AM', 'KTW_BRT__AM', 'KTW_BUS__AM', 'KTW_CRT__AM', 'KTW_EGR__AM', 'KTW_FWT__AM', 'KTW_LRT__AM', 'KTW_RST__AM', 'KTW_SCR__AM', 'KTW_TIV__AM', 'KTW_VTC__AM', 'KTW_XFR__AM', 'KTW_XWT__AM', 'WTK_ACC__AM', 'WTK_AST__AM', 'WTK_AUX__AM', 'WTK_BRT__AM', 'WTK_BUS__AM', 'WTK_CRT__AM', 'WTK_EGR__AM', 'WTK_FWT__AM', 'WTK_LRT__AM', 'WTK_RST__AM', 'WTK_SCR__AM', 'WTK_TIV__AM', 'WTK_VTC__AM', 'WTK_XFR__AM', 'WTK_XWT__AM', 'WTW_ACC__AM', 'WTW_AST__AM', 'WTW_AUX__AM', 'WTW_BRT__AM', 'WTW_BUS__AM', 'WTW_CRT__AM', 'WTW_EGR__AM', 'WTW_FWT__AM', 'WTW_LRT__AM', 'WTW_RST__AM', 'WTW_SCR__AM', 'WTW_TIV__AM', 'WTW_VTC__AM', 'WTW_XFR__AM', 'WTW_XWT__AM']\n" + ] + } + ], + "source": [ + "skm = omx.open_file(os.path.join(output_folder, 'transitSkims__AM.omx'), \"r\")\n", + "print(skm.list_matrices())\n", + "skm.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "2a837f63", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tazs[tazs['NO'].isin(tazs_to_crop)].explore()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c5ae32df", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "rsg_activitysim", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.15" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/resident/model_data/metro/ctramp_to_asim_conversion/MAZ_Transformation_Walkacross.xlsx b/resident/model_data/metro/ctramp_to_asim_conversion/MAZ_Transformation_Walkacross.xlsx new file mode 100644 index 0000000..54949f0 Binary files /dev/null and b/resident/model_data/metro/ctramp_to_asim_conversion/MAZ_Transformation_Walkacross.xlsx differ diff --git a/resident/model_data/metro/ctramp_to_asim_conversion/SyntheticPop_Processing.ipynb b/resident/model_data/metro/ctramp_to_asim_conversion/SyntheticPop_Processing.ipynb new file mode 100644 index 0000000..f0ab6fb --- /dev/null +++ b/resident/model_data/metro/ctramp_to_asim_conversion/SyntheticPop_Processing.ipynb @@ -0,0 +1,533 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "5e394cdd", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import pandas as pd\n", + "import numpy as np\n", + "import openmatrix as omx\n", + "import geopandas as gpd" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "fc2d5cd4", + "metadata": {}, + "outputs": [], + "source": [ + "input_data_folder = r'C:\\projects\\odot_joint_estimation\\metro_data_prep\\PopulationSim_to_RSG\\HH_GQ_combined'\n", + "output_data_folder = r'C:\\projects\\odot_joint_estimation\\pnr\\SimOR\\resident\\model_data\\metro\\data_full'\n", + "\n", + "assert os.path.exists(input_data_folder), f\"Input data folder {input_data_folder} does not exist.\"\n", + "assert os.path.exists(output_data_folder), f\"Output data folder {output_data_folder} does not exist.\"" + ] + }, + { + "cell_type": "markdown", + "id": "411e2af0", + "metadata": {}, + "source": [ + "## Households Data Processing" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e953c8c2", + "metadata": {}, + "outputs": [], + "source": [ + "in_households = pd.read_csv(os.path.join(input_data_folder, 'households_all.csv'))\n", + "\n", + "hh_renaming_dict = {\n", + " 'hhid': 'household_id',\n", + " # 'serialno': 'SERIALNO', # (Optional, excluding for now)\n", + " 'maz': 'MAZ',\n", + " 'taz': 'TAZ',\n", + " 'htype': 'TYPE',\n", + " 'np': 'NP',\n", + " 'hht': 'HHT',\n", + " 'hhincadj': 'HHINCADJ',\n", + " 'nwrkrs_esr': 'WORKERS'\n", + "}\n", + "\n", + "out_households = in_households[hh_renaming_dict.keys()].copy()\n", + "out_households.rename(columns=hh_renaming_dict, inplace=True)\n", + "\n", + "out_households.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "b9261619", + "metadata": {}, + "outputs": [], + "source": [ + "# out_households.to_csv(os.path.join(output_data_folder, 'households.csv'), index=False)" + ] + }, + { + "cell_type": "markdown", + "id": "d066739c", + "metadata": {}, + "source": [ + "## Persons Data Processing" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6ec763e5", + "metadata": {}, + "outputs": [], + "source": [ + "# Import Persons Table\n", + "in_persons = pd.read_csv(os.path.join(input_data_folder, 'persons_all.csv'))\n", + "\n", + "renaming_dict = {\n", + " 'hhid': 'household_id',\n", + " 'sporder': 'PERSON_NUM',\n", + " 'PERID': 'person_id',\n", + " 'PUMA': 'PUMA_GEOID',\n", + " # 'taz': 'TAZ', # optional, excluding for now\n", + " # 'maz': 'MAZ', # optional, excluding for now\n", + " 'agep': 'AGEP',\n", + " 'sex': 'SEX',\n", + " 'wkhp': 'WKHP',\n", + " 'wkw': 'WKW',\n", + " 'esr': 'ESR',\n", + " 'schg': 'SCHG',\n", + " 'mil': 'MIL',\n", + " 'occp': 'OCCCAT'\n", + "}\n", + "\n", + "final_order = ['household_id','PERSON_NUM','person_id','PUMA_GEOID','AGEP','SEX','WKHP','WKW','ESR','SCHG','MIL','NAICSP','INDP',\n", + "'OCCP','OCCCAT','DDRS','DEAR','DEYE','DOUT','DPHY','DREM','TOLLFACTOR','FAREFACTOR']\n", + "\n", + "# Grab data and rename columns\n", + "out_persons = in_persons[renaming_dict.keys()].copy()\n", + "\n", + "out_persons.rename(columns=renaming_dict, inplace=True)\n", + "\n", + "# fill in missing columns with empty strings\n", + "missing_cols = set(final_order) - set(out_persons.columns)\n", + "print(f\"Filling missing columns in out_persons with empty string: {missing_cols}\")\n", + "\n", + "# Add new column headers to ActivitySim\n", + "out_persons[list(missing_cols)] = ''\n", + "\n", + "# Reorder fields again\n", + "out_persons = out_persons[final_order]\n", + "\n", + "out_persons.head()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e1a035c9", + "metadata": {}, + "outputs": [], + "source": [ + "(~out_persons.household_id.isin(out_households.household_id)).sum()" + ] + }, + { + "cell_type": "markdown", + "id": "925eda14", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "id": "fea3a764", + "metadata": {}, + "source": [ + "## Land Use Processing" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "67d6d78b", + "metadata": {}, + "outputs": [], + "source": [ + "landuse = pd.read_csv(os.path.join(input_data_folder, 'CTRAMP_land_use.csv'))\n", + "\n", + "# CT_RampFields to ActivitySimFields mapping taken from MAZ_Transformation_Walkacross.xlsx\n", + "landuse_rename = {\n", + " 'MAZ': 'MAZ',\n", + " 'TAZ': 'TAZ',\n", + " 'BG': 'BLKGRP',\n", + " 'TRACT': 'TRACT',\n", + " 'PUMA': 'PUMA',\n", + " 'COUNTY': 'COUNTY',\n", + " # '--': 'DISTRICTXX', # No direct source, create empty or calculated if needed\n", + " # The following are calculated fields:\n", + " # 'HH_SF+HH_MF+HH_MH+HH_DUPLEX': 'TOTNGQHHS',\n", + " # 'HH_GQ_CIV+HH_GQ_MIL+HH_GQ_UNIV': 'TOTGQHHS',\n", + " 'HH': 'TOTHHS',\n", + " 'POP': 'TOTPOP',\n", + " 'EMP_AMF': 'EMP_NRM',\n", + " 'EMP_CON': 'EMP_CON',\n", + " 'EMP_MFG': 'EMP_NHTMFG',\n", + " 'EMP_MHT': 'EMP_HTMFG',\n", + " 'EMP_WT': 'EMP_WT',\n", + " 'EMP_TWU': 'EMP_TWU',\n", + " 'EMP_RCS': 'EMP_RET',\n", + " 'EMP_PBS': 'EMP_IFRPBS',\n", + " 'EMP_HSS': 'EMP_HCS',\n", + " 'EMP_OSV': 'EMP_OSV',\n", + " 'EMP_GOV': 'EMP_GOV',\n", + " 'EMP_EDU': 'EMP_EDU',\n", + " 'EMP_AER': 'EMP_AER',\n", + " # '0': 'EMP_ACC', # Set to 0\n", + " 'EMP_FSD': 'EMP_FSD',\n", + " 'EMP_TOTAL': 'EMP_TOT',\n", + " 'ENROLLK_8': 'ENROLL_K8',\n", + " 'ENROLL9_12': 'ENROLL_912',\n", + " 'ENROLLCOLL': 'ENROLL_COLL',\n", + " # 'TERMTIME (set to 0)': 'TERMTIME', # Set to 0\n", + " # 'HPARKCOST*100 (convert to cents)': 'PRKCST_HR', # Needs conversion\n", + " # 'DPARKCOST*100 (convert to cents)': 'PRKCST_DAY', # Needs conversion\n", + " # 'MPARKCOST*100*22 (convert to cents and to monthly from daily)': 'PRKCST_MNTH', # Needs conversion\n", + " # 'MAX(HSTALLSSAM+DSTALLSSAM+MSTALLSSAM+HSTALLSOTH+DSTALLSOTH+MSTALLSOTH)': 'PRKSPACES', # Needs calculation\n", + " # 'calculated from shapefile': 'ACRES', # Needs calculation\n", + " 'PARKACRES': 'ACTIVE_ACRES',\n", + " # '0': 'OSPC_ACRES', # Set to 0\n", + " # 'NOT AVAILABLE IN CTRAMP': 'ESCOOACCTIME', # Set to NaN or 0\n", + " # 'NOT AVAILABLE IN CTRAMP': 'EBIKEACCTIME', # Set to NaN or 0\n", + " # '0': 'EXTERNAL', # Set to 0\n", + " # '0': 'EXT_WORK_SIZE', # Set to 0\n", + " # '0': 'EXT_NWRK_SIZE', # Set to 0\n", + " # '--': 'TOTINT', # Set to NaN or 0\n", + " # '--': 'EMPDEN',\n", + " # '--': 'RETDEN',\n", + " # '--': 'HHDEN',\n", + " # '--': 'POPDEN',\n", + " # '--': 'POPEMPDEN',\n", + " # '--': 'WLKTIME_BUS',\n", + " # '--': 'WLKTIME_BRT',\n", + " # '--': 'WLKTIME_LRT',\n", + " # '--': 'WLKTIME_CR',\n", + " # '--': 'EXPPRK_MNTH',\n", + " # '--': 'EXPPRK_DAY',\n", + " # '--': 'EXPPRK_HR',\n", + " 'PARKAREA': 'PARKAREA',\n", + " # '--': 'Built Form (not sure on naming)'\n", + "}\n", + "\n", + "# Rename columns\n", + "landuse = landuse.rename(columns=landuse_rename)\n", + "\n", + "# Create/calculated fields\n", + "landuse['TOTNGQHHS'] = landuse[['HH_SF', 'HH_MF', 'HH_MH', 'HH_DUPLEX']].sum(axis=1)\n", + "landuse['TOTGQHHS'] = landuse[['HH_GQ_CIV', 'HH_GQ_MIL', 'HH_GQ_UNIV']].sum(axis=1)\n", + "landuse['PRKCST_HR'] = landuse['HPARKCOST'] * 100 # converting to cents\n", + "landuse['PRKCST_DAY'] = landuse['DPARKCOST'] * 100\n", + "landuse['PRKCST_MNTH'] = landuse['MPARKCOST'] * 100 * 22\n", + "landuse['PRKSPACES'] = landuse[['HSTALLSSAM', 'DSTALLSSAM', 'MSTALLSSAM', 'HSTALLSOTH', 'DSTALLSOTH', 'MSTALLSOTH']].max(axis=1)\n", + "\n", + "# FIXME the following fields need to be calculated\n", + "landuse['EMP_ACC'] = 0 # Accomodations employment not available in CTRAMP\n", + "landuse['TERMTIME'] = 0 # terminal time\n", + "landuse['ACRES'] = 1 # Placeholder, needs actual calculation from shapefile\n", + "landuse['OSPC_ACRES'] = 0 # Open space acres\n", + "landuse['ESCOOACCTIME'] = 0 # need calculation for e-scooter and e-bike access times\n", + "landuse['EBIKEACCTIME'] = 0\n", + "landuse['EXTERNAL'] = 0 # no external zones in landuse file? -- needs to be added\n", + "landuse['EXT_WORK_SIZE'] = 0 # total resident work tours from SWIM at external station\n", + "landuse['EXT_NWRK_SIZE'] = 0 # non-work resident tours from SWIM at external station\n", + "\n", + "# If you need to add empty columns for fields not available in CT_RampFields:\n", + "for col in ['DISTRICTXX','TOTINT','EMPDEN','RETDEN','HHDEN','POPDEN','POPEMPDEN','WLKTIME_BUS','WLKTIME_BRT','WLKTIME_LRT','WLKTIME_CR','EXPPRK_MNTH','EXPPRK_DAY','EXPPRK_HR','Built Form']:\n", + " if col not in landuse.columns:\n", + " landuse[col] = ''\n", + "\n", + "# PARKAREA recoding:\n", + "# 1 -> 1, 2/3 -> 2, 4 -> 3\n", + "# 1: Constrained parking area (destinations in area have an expected cost and parking location choice model is applied. \n", + "# 2: Buffer area (free parking in area is included in expected cost for destinations in PARKAREA 1, but destinations in area do not have an expected cost and parking location choice model not applied), \n", + "# 3: Not parking constrained or buffer area (free unconstrained parking).  \n", + "landuse['PARKAREA'] = landuse['PARKAREA'].replace({2: 2, 3: 2, 4: 3})\n", + "\n", + "# Final output columns for ActivitySim\n", + "activitysim_fields = [\n", + " 'MAZ','TAZ','BLKGRP','TRACT','PUMA','COUNTY','DISTRICTXX','TOTNGQHHS','TOTGQHHS','TOTHHS','TOTPOP',\n", + " 'EMP_NRM','EMP_CON','EMP_NHTMFG','EMP_HTMFG','EMP_WT','EMP_TWU','EMP_RET','EMP_IFRPBS','EMP_HCS','EMP_OSV',\n", + " 'EMP_GOV','EMP_EDU','EMP_AER','EMP_ACC','EMP_FSD','EMP_TOT','ENROLL_K8','ENROLL_912','ENROLL_COLL','TERMTIME',\n", + " 'PRKCST_HR','PRKCST_DAY','PRKCST_MNTH','PRKSPACES','ACRES','ACTIVE_ACRES','OSPC_ACRES','ESCOOACCTIME','EBIKEACCTIME',\n", + " 'EXTERNAL','EXT_WORK_SIZE','EXT_NWRK_SIZE','TOTINT','EMPDEN','RETDEN','HHDEN','POPDEN','POPEMPDEN','WLKTIME_BUS',\n", + " 'WLKTIME_BRT','WLKTIME_LRT','WLKTIME_CR','EXPPRK_MNTH','EXPPRK_DAY','EXPPRK_HR','PARKAREA','Built Form'\n", + "]\n", + "landuse = landuse.reindex(columns=activitysim_fields)\n", + "\n", + "landuse.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cd2acf86", + "metadata": {}, + "outputs": [], + "source": [ + "# need to read in maz to stop file and merge with landuse\n", + "maz_stop_walk = pd.read_csv(os.path.join(output_data_folder, 'maz_stop_walk.csv')).rename(columns={'maz': 'MAZ'})\n", + "maz_stop_walk.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "f0233278", + "metadata": {}, + "outputs": [], + "source": [ + "landuse = landuse.merge(maz_stop_walk, on='MAZ', how='left', validate='one_to_one')" + ] + }, + { + "cell_type": "markdown", + "id": "af0053b3", + "metadata": {}, + "source": [ + "#### Need to have landuse numbers match the skim numbers" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7d0c7233", + "metadata": {}, + "outputs": [], + "source": [ + "skim_file = omx.open_file(os.path.join(output_data_folder, 'autoSkims__AM.omx'), 'r')\n", + "skim_taz_numbers = range(1, skim_file.shape()[0] + 1)\n", + "print(len(skim_taz_numbers))\n", + "skim_file.close()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "64966ead", + "metadata": {}, + "outputs": [], + "source": [ + "landuse.TAZ.nunique()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4d06fbd6", + "metadata": {}, + "outputs": [], + "source": [ + "# creating additional landuse rows for the missing TAZs\n", + "missing_tazs = list(set(skim_taz_numbers) - set(landuse.TAZ))\n", + "\n", + "missing_landuse = pd.DataFrame(columns=landuse.columns, index=missing_tazs).fillna(0)\n", + "missing_landuse['TAZ'] = missing_landuse.index\n", + "missing_landuse['MAZ'] = landuse.MAZ.max() + range(len(missing_landuse)) # Giving dummy MAZ numbers for these TAZs\n", + "missing_landuse['EXTERNAL'] = 1 # Assuming these missing TAZs are external, set EXTERNAL to 1\n", + "landuse = pd.concat([landuse, missing_landuse], ignore_index=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "5fcbc355", + "metadata": {}, + "outputs": [], + "source": [ + "# FIXME additional columns needed for current SANDAG implementation\n", + "# expect these to be removed or changed in future versions\n", + "landuse['micro_dist_local_bus'] = landuse['walk_dist_local_bus'] \n", + "landuse['micro_dist_premium_transit'] = landuse['walk_dist_premium_transit']\n", + "landuse['MicroAccessTime'] = 0.5\n", + "landuse['nev'] = 0\n", + "landuse['microtransit'] = 0\n", + "landuse['external_work'] = np.where(landuse['EXTERNAL'], 10, 0) # adding non-zero size for external models so model won't crash\n", + "landuse['external_nonwork'] = np.where(landuse['EXTERNAL'], 10, 0) # adding non-zero size for external models so model won't crash\n", + "landuse['external_MAZ'] = np.where(landuse['EXTERNAL'], 1, 0)\n", + "landuse['totint'] = 10 # total intersections" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "527e057b", + "metadata": {}, + "outputs": [], + "source": [ + "assert landuse.TAZ.nunique() == len(skim_taz_numbers), \"Landuse and skim file TAZ counts do not match.\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8b6960f0", + "metadata": {}, + "outputs": [], + "source": [ + "len(skim_taz_numbers)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3544cc07", + "metadata": {}, + "outputs": [], + "source": [ + "landuse[landuse['MAZ'].duplicated()]" + ] + }, + { + "cell_type": "markdown", + "id": "124d9822", + "metadata": {}, + "source": [ + "#### Park and Ride Data" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "f25c8748", + "metadata": {}, + "outputs": [], + "source": [ + "pnr = pd.read_excel(os.path.join(input_data_folder, '../../2025_PnR_2162_TAZs.xlsx'), sheet_name=0)\n", + "\n", + "# number of official parking spaces provided by the transit agency in PNR zones in the MAZ\n", + "# since the data was available only at the TAZ level, the parking spaces are all assigned to the first MAZ listed in land_use.csv within the TAZ\n", + "landuse_unique_taz = landuse.drop_duplicates(subset=['TAZ'], keep='first')\n", + "landuse_unique_taz = landuse_unique_taz.merge(pnr[['TAZ', 'CAPACITY']], on='TAZ', how='left')\n", + "landuse = landuse.merge(landuse_unique_taz[['MAZ', 'CAPACITY']], on='MAZ', how='left')\n", + "landuse['PNR_CAP_FORMAL'] = landuse['CAPACITY'].fillna(0).astype(int)\n", + "landuse.drop(columns=['CAPACITY'], inplace=True)\n", + "\n", + "# number of informal parking spaces in PNR zones in the MAZ, e.g., surface street\n", + "# sample a random 10 percent of zones with PNR lots\n", + "landuse['PNR_CAP_INFORMAL'] = np.where(np.random.rand(len(landuse)) < 0.1, (landuse['PNR_CAP_FORMAL'] > 0) * np.random.randint(50, 200), 0)\n", + "\n", + "# cost of parking in the formal PNR lot (daily in 2024 dollars)\n", + "# sample a random 50 percent of zones\n", + "landuse['PNR_PRKCST'] = np.where(np.random.rand(len(landuse)) < 0.5, (landuse['PNR_CAP_FORMAL'] > 0) * np.random.randint(5, 20), 0)\n", + "\n", + "# average time in minutes to get from the formal parking lot to the transit stop\n", + "# add dummy values to all PNR lots\n", + "landuse['PNR_TERMINALTIME'] = (landuse['PNR_CAP_FORMAL'] > 0) * np.round(landuse['PNR_CAP_FORMAL'] / 50, decimals=0)" + ] + }, + { + "cell_type": "markdown", + "id": "b414e208", + "metadata": {}, + "source": [ + "### Checking for consistency and saving to CSV" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "baa9c278", + "metadata": {}, + "outputs": [], + "source": [ + "# removing duplicated MAZ rows, keeping the first occurrence\n", + "landuse = landuse.drop_duplicates(subset=['MAZ'], keep='last')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a59bac70", + "metadata": {}, + "outputs": [], + "source": [ + "# checking to make sure all households have a valid MAZ in landuse\n", + "invalid_maz_hh = out_households[~out_households.MAZ.isin(landuse.MAZ)]\n", + "print(f\"Removing {len(invalid_maz_hh)} households with invalid MAZs not found in landuse data.\")\n", + "print(f\"MAZs and TAZ of these households:\\n{invalid_maz_hh[['MAZ', 'TAZ']].drop_duplicates().to_string(index=False)}\")\n", + "out_households = out_households[out_households.MAZ.isin(landuse.MAZ)]\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "516fb68b", + "metadata": {}, + "outputs": [], + "source": [ + "# checking to make sure all persons have a valid household_id in households\n", + "invalid_hh_persons = out_persons[~out_persons.household_id.isin(out_households.household_id)]\n", + "print(f\"Removing {len(invalid_hh_persons)} persons with invalid household_id not found in households data.\")\n", + "out_persons = out_persons[out_persons.household_id.isin(out_households.household_id)]" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "a744d439", + "metadata": {}, + "outputs": [], + "source": [ + "assert landuse.TAZ.nunique() == len(skim_taz_numbers), \"Landuse and skim file TAZ counts do not match.\"" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "cdd7aae8", + "metadata": {}, + "outputs": [], + "source": [ + "# Save to CSV\n", + "landuse.to_csv(os.path.join(output_data_folder, 'land_use.csv'), index=False)\n", + "out_persons.to_csv(os.path.join(output_data_folder, 'persons.csv'), index=False)\n", + "out_households.to_csv(os.path.join(output_data_folder, 'households.csv'), index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2d04c646", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "asim_v14", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.17" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/resident/model_data/metro/ctramp_to_asim_conversion/SyntheticPop_Transformation_Walkacross.xlsx b/resident/model_data/metro/ctramp_to_asim_conversion/SyntheticPop_Transformation_Walkacross.xlsx new file mode 100644 index 0000000..71dbd19 Binary files /dev/null and b/resident/model_data/metro/ctramp_to_asim_conversion/SyntheticPop_Transformation_Walkacross.xlsx differ diff --git a/resident/model_data/metro/data_cropped/autoSkims__AM.omx b/resident/model_data/metro/data_cropped/autoSkims__AM.omx new file mode 100644 index 0000000..9c76fb8 Binary files /dev/null and b/resident/model_data/metro/data_cropped/autoSkims__AM.omx differ diff --git a/resident/model_data/metro/data_cropped/autoSkims__EA.omx b/resident/model_data/metro/data_cropped/autoSkims__EA.omx new file mode 100644 index 0000000..440beae Binary files /dev/null and b/resident/model_data/metro/data_cropped/autoSkims__EA.omx differ diff --git a/resident/model_data/metro/data_cropped/autoSkims__EV.omx b/resident/model_data/metro/data_cropped/autoSkims__EV.omx new file mode 100644 index 0000000..fe17402 Binary files /dev/null and b/resident/model_data/metro/data_cropped/autoSkims__EV.omx differ diff --git a/resident/model_data/metro/data_cropped/autoSkims__MD.omx b/resident/model_data/metro/data_cropped/autoSkims__MD.omx new file mode 100644 index 0000000..7102f31 Binary files /dev/null and b/resident/model_data/metro/data_cropped/autoSkims__MD.omx differ diff --git a/resident/model_data/metro/data_cropped/autoSkims__PM.omx b/resident/model_data/metro/data_cropped/autoSkims__PM.omx new file mode 100644 index 0000000..8248bd2 Binary files /dev/null and b/resident/model_data/metro/data_cropped/autoSkims__PM.omx differ diff --git a/resident/model_data/metro/data_cropped/households.csv b/resident/model_data/metro/data_cropped/households.csv new file mode 100644 index 0000000..1278aaa --- /dev/null +++ b/resident/model_data/metro/data_cropped/households.csv @@ -0,0 +1,35987 @@ +household_id,MAZ,TAZ,TYPE,NP,HHT,HHINCADJ,WORKERS +192515,7268,38,3,2,1,365601.606126576,1 +192516,7268,38,3,1,4,8640.80427784154,0 +192517,7268,38,1,6,3,251975.342810858,5 +192518,7268,38,1,4,1,118415.298536071,3 +192519,7268,38,1,4,1,102911.203909186,3 +192520,7268,38,1,4,1,56281.0354258892,2 +192521,7268,38,1,3,2,94807.9886978985,1 +192522,7268,38,1,3,5,92726.0502073748,1 +192523,7268,38,1,2,1,308002.862161771,1 +192524,7268,38,1,2,1,266291.4660861,1 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a/resident/model_data/metro/data_cropped/walkSkim.omx b/resident/model_data/metro/data_cropped/walkSkim.omx new file mode 100644 index 0000000..b2f66bf Binary files /dev/null and b/resident/model_data/metro/data_cropped/walkSkim.omx differ diff --git a/resident/simulation.py b/resident/simulation.py new file mode 100644 index 0000000..5d31553 --- /dev/null +++ b/resident/simulation.py @@ -0,0 +1,24 @@ +# ActivitySim +# See full license in LICENSE.txt. + +import sys +import argparse +import os +import pandas as pd +import warnings + +from activitysim.cli.run import add_run_args, run + +import extensions + +if __name__ == "__main__": + + warnings.simplefilter(action="ignore", category=pd.errors.PerformanceWarning) + + parser = argparse.ArgumentParser() + add_run_args(parser) + args = parser.parse_args() + + os.environ["MKL_NUM_THREADS"] = "1" + + sys.exit(run(args)) diff --git a/resident/test/test_cropped_dataset.py b/resident/test/test_cropped_dataset.py new file mode 100644 index 0000000..8a7068f --- /dev/null +++ b/resident/test/test_cropped_dataset.py @@ -0,0 +1,24 @@ +import subprocess +import os +import sys + +def test_cropped_example(): + + file_path = os.path.join(os.path.dirname(__file__), "../simulation.py") + + run_args = [ + "-c", "configs", # Config directory + "-d", "model_data/metro/data_cropped", # Input data directory + "-o", "outputs/test" # Output directory + ] + + if os.environ.get("GITHUB_ACTIONS") == "true": + subprocess.run(["coverage", "run", "-a", file_path] + run_args, check=True) + else: + subprocess.run([sys.executable, file_path] + run_args, check=True) + + return True + + +if __name__ == "__main__": + test_cropped_example() \ No newline at end of file diff --git a/skimming_and_assignment/maz_maz_stop_skims/.gitignore b/skimming_and_assignment/maz_maz_stop_skims/.gitignore new file mode 100644 index 0000000..f4f7aec --- /dev/null +++ b/skimming_and_assignment/maz_maz_stop_skims/.gitignore @@ -0,0 +1,4 @@ +# Folders +input/ +output/ +preprocessing/ \ No newline at end of file diff --git a/skimming_and_assignment/maz_maz_stop_skims/2zoneSkim.py b/skimming_and_assignment/maz_maz_stop_skims/2zoneSkim.py new file mode 100644 index 0000000..13b3e72 --- /dev/null +++ b/skimming_and_assignment/maz_maz_stop_skims/2zoneSkim.py @@ -0,0 +1,607 @@ +import pandas as pd +import pandana as pdna +import numpy as np +import yaml +import os +import geopandas as gpd +from datetime import datetime +from typing import Tuple, Dict, List +from dataclasses import dataclass +import logging + +@dataclass +class SkimParameters: + """Configuration parameters for skim generation read from YAML file""" + max_maz_maz_walk_dist_feet: int + max_maz_maz_bike_dist_feet: int + max_maz_local_bus_stop_walk_dist_feet: int + max_maz_premium_transit_stop_walk_dist_feet: int + walk_speed_mph: float + drive_speed_mph: float + + @classmethod + def from_yaml(cls, yaml_data: dict) -> 'SkimParameters': + """Create parameters from YAML configuration""" + mmms = yaml_data['mmms'] + return cls( + max_maz_maz_walk_dist_feet=int(mmms['max_maz_maz_walk_dist_feet']), + max_maz_maz_bike_dist_feet=int(mmms['max_maz_maz_bike_dist_feet']), + max_maz_local_bus_stop_walk_dist_feet=int(mmms['max_maz_local_bus_stop_walk_dist_feet']), + max_maz_premium_transit_stop_walk_dist_feet=int(mmms['max_maz_premium_transit_stop_walk_dist_feet']), + walk_speed_mph=float(mmms['walk_speed_mph']), + drive_speed_mph=float(mmms['drive_speed_mph']) + ) + +class NetworkBuilder: + """ + Handles network construction, and node assignment, and transit stop processing for transportation networks. + + This class processes raw node and link data to create a network and processes transit stops. It handles coordinate projections, network topology, + and centroid connections. + """ + + def __init__(self, nodes: gpd.GeoDataFrame, links: gpd.GeoDataFrame, + stops: pd.DataFrame, routes: pd.DataFrame, config: dict): + """ + Initialize the NetworkBuilder with nodes, links, and configuration. + + Args: + nodes (gpd.GeoDataFrame): GeoDataFrame containing node geometries and attributes. + Must include columns for node ID and coordinates. + links (gpd.GeoDataFrame): GeoDataFrame containing link geometries and attributes. + Must include columns for from/to nodes and length. + config (dict): Configuration dictionary containing: + - mmms (dict): Network construction parameters + - mmms_link_ref_id (str): Column name for link start node + - mmms_link_nref_id (str): Column name for link end node + - mmms_link_len (str): Column name for link length + + Note: + The network is built immediately upon initialization using _build_network(). + """ + self.nodes = nodes + self.links = links + self.stops = stops + self.routes = routes + self.config = config + self.network = self._build_network(nodes, links, config) + + @classmethod + def from_files(cls, model_inputs: str, config: dict) -> 'NetworkBuilder': + """ + Create a NetworkBuilder instance by reading nodes and links from shapefiles. + + This factory method handles reading and processing the raw input files to create + a properly configured NetworkBuilder instance. + + Args: + model_inputs (str): Path to directory containing input shapefiles + config (dict): Configuration dictionary containing: + - mmms (dict): File and processing parameters + - shapefile_node_name (str): Filename for nodes shapefile + - shapefile_name (str): Filename for links shapefile + + Returns: + NetworkBuilder: A fully initialized NetworkBuilder instance with processed + nodes and links. + """ + # Read and process nodes + nodes = gpd.read_file(os.path.join(model_inputs, config['mmms']['shapefile_node_name'])) + nodes = cls._process_nodes(nodes, config) + + # Read and process links + links = gpd.read_file(os.path.join(model_inputs, config['mmms']['shapefile_name'])) + links = cls._process_links(links, config) + + # Read and process stops and routes + stops = pd.read_csv(os.path.join(model_inputs, config['stop_attributes']['file'])) + routes = pd.read_csv(os.path.join(model_inputs, config['route_attributes']['file'])) + cols = [config['route_attributes']['rid_field'], + config['route_attributes']['mode']] + routes = routes.filter(cols) + stops = stops.merge(routes, + left_on=config['stop_attributes']['rid_field'], + right_on=config['route_attributes']['rid_field']) + network = cls._build_network(nodes, links, config) + stops = cls.process_transit_stops(stops, network, nodes, config) + + return cls(nodes, links, stops, routes, config) + + @staticmethod + def _process_nodes(nodes: gpd.GeoDataFrame, config: dict) -> gpd.GeoDataFrame: + """ + Process raw nodes GeoDataFrame by projecting and adding coordinates. + + Performs the following operations: + 1. Projects the geometries to project projected crs + 2. Sets NodeLev_ID as the index + 3. Extracts X and Y coordinates from the geometry + + Args: + nodes (gpd.GeoDataFrame): Raw nodes GeoDataFrame with geometry column + + Returns: + gpd.GeoDataFrame: Processed nodes with: + - Projected coordinates + - NodeLev_ID as index + - X and Y columns containing coordinates + """ + crs = config['settings']['epsg'] + nodes = nodes.to_crs(crs).set_index(config['stop_attributes']['id_field']) + nodes['X'] = nodes.geometry.x + nodes['Y'] = nodes.geometry.y + return nodes + + @staticmethod + def _process_links(links: gpd.GeoDataFrame, config: dict) -> gpd.GeoDataFrame: + """ + Process raw links GeoDataFrame. + + Args: + links: Raw links GeoDataFrame + + Returns: + Processed links GeoDataFrame + """ + return links.to_crs(config['settings']['epsg']) + + @classmethod + def _build_network(cls, nodes: gpd.GeoDataFrame, links: gpd.GeoDataFrame, config: dict) -> pdna.Network: + """Build pandana network from nodes and links""" + mmms = config['mmms'] + # nodes.index = nodes.index.astype('int32') + # links.index = links.index.astype('int32') + # links[mmms['mmms_link_ref_id']] = links[mmms['mmms_link_ref_id']].astype('int32') + # links[mmms['mmms_link_nref_id']] = links[mmms['mmms_link_nref_id']].astype('int32') + + # links[mmms['mmms_link_ref_id']] = links[mmms['mmms_link_ref_id']].astype('float64') + # links[mmms['mmms_link_nref_id']] = links[mmms['mmms_link_nref_id']].astype('float64') + + # print(links[mmms['mmms_link_ref_id']].dtype) + # print(links[mmms['mmms_link_nref_id']].dtype) + # print(links[mmms['mmms_link_len']].dtype) + # print("Before: ", links.index.dtype) + # links.index = links.index.astype('int32') + # print("After: ", links.index.dtype) + # print("NaN's:", links.isna().sum()) + + # links[mmms['mmms_link_len']] = links[mmms['mmms_link_len']].astype('int32') + + return pdna.Network( + nodes.X, + nodes.Y, + links[mmms['mmms_link_ref_id']], + links[mmms['mmms_link_nref_id']], + links[[mmms['mmms_link_len']]] / 5280.0, + twoway=mmms['two_way_network'] + ) + + @classmethod + def get_closest_net_node_to_MGRA(cls, nodes: gpd.GeoDataFrame, links: gpd.GeoDataFrame, + config: dict) -> pd.DataFrame: + """ + Gets closest network nodes to MAZ centroids. This is used to assign network nodes to MAZ centroids. This assigned nodes is then used to calculate skims. + + Args: + nodes: Processed nodes GeoDataFrame + links: Processed links GeoDataFrame + config: Configuration dictionary + + Returns: + DataFrame containing centroid information + """ + # Get closest network nodes for MAZ centroids, e.g. 'centroid connector' start and end nodes + maz_closest_network_node_id = cls._get_closest_network_nodes(nodes, links, config) + + # Create centroids DataFrame + centroids = cls._create_centroids_df(nodes, config) + + # Merge with closest network nodes. This will add the end node of the connector as the associated network node for the MGRA + return cls._merge_centroids_with_connector_end_nodes(centroids, maz_closest_network_node_id, + nodes, config) + + @staticmethod + def _get_closest_network_nodes(nodes: gpd.GeoDataFrame, + links: gpd.GeoDataFrame, + config: dict) -> pd.DataFrame: + """Get closest network nodes for MAZs""" + mmms = config['mmms'] + maz_id = mmms['mmms_node_maz_id'] + + # Return the 'centroid connector' start and end nodes + return links[links[mmms["mmms_link_ref_id"]].isin( + list(nodes[nodes[maz_id]!=0].index) + )][[mmms["mmms_link_ref_id"], mmms["mmms_link_nref_id"]]] + + @staticmethod + def _create_centroids_df(nodes: gpd.GeoDataFrame, config: dict) -> pd.DataFrame: + """Create initial centroids DataFrame""" + maz_nodes = nodes[nodes[config['mmms']['mmms_node_maz_id']]!=0] + return pd.DataFrame({ + 'X': maz_nodes.X, + 'Y': maz_nodes.Y, + 'MAZ': maz_nodes.MAZ, + 'MAZ_centroid_id': maz_nodes.index + }) + + @staticmethod + def _merge_centroids_with_connector_end_nodes(centroids: pd.DataFrame, + closest_nodes: pd.DataFrame, + nodes: gpd.GeoDataFrame, + config: dict) -> pd.DataFrame: + """Merge centroids with their closest network nodes at the nd of the connector""" + mmms = config['mmms'] + + centroids = pd.merge( + centroids, + closest_nodes, + left_on='MAZ_centroid_id', + right_on=mmms["mmms_link_ref_id"], + how='left' + ) + + centroids = centroids.rename(columns={mmms["mmms_link_nref_id"]: 'network_node_id'}) + + # Add network node coordinates + centroids["network_node_x"] = nodes["X"].loc[centroids["network_node_id"]].tolist() + centroids["network_node_y"] = nodes["Y"].loc[centroids["network_node_id"]].tolist() + + return centroids + + @staticmethod + def _process_stop_geometry(stops: pd.DataFrame, config: dict) -> gpd.GeoDataFrame: + """ + Convert stops to GeoDataFrame and project coordinates. + + Args: + stops (pd.DataFrame): Stops DataFrame with Longitude/Latitude + + Returns: + gpd.GeoDataFrame: Projected stops with updated coordinates + """ + crs = config['settings']['epsg'] + pd.set_option('display.float_format', lambda x: '%.9f' % x) + + gpd_stops = gpd.GeoDataFrame( + stops, + geometry=gpd.points_from_xy(stops.Longitude, stops.Latitude, crs='epsg:4326') + ) + gpd_stops = gpd_stops.to_crs(crs) + + gpd_stops['Longitude'] = gpd_stops['geometry'].x + gpd_stops['Latitude'] = gpd_stops['geometry'].y + + return gpd_stops + + @staticmethod + def _assign_network_nodes_to_stops( + stops: pd.DataFrame, + gpd_stops: gpd.GeoDataFrame, + net: pdna.Network, + nodes: pd.DataFrame + ) -> pd.DataFrame: + """ + Assign network nodes to stops. + + Args: + stops (pd.DataFrame): Stops DataFrame + gpd_stops (gpd.GeoDataFrame): Projected stops + net (pdna.Network): Network object + nodes (pd.DataFrame): Network nodes + + Returns: + pd.DataFrame: Stops with assigned network nodes and coordinates + """ + stops["network_node_id"] = net.get_node_ids(gpd_stops['Longitude'], gpd_stops['Latitude']) + stops["network_node_x"] = nodes["X"].loc[stops["network_node_id"]].tolist() + stops["network_node_y"] = nodes["Y"].loc[stops["network_node_id"]].tolist() + return stops + + @staticmethod + def _assign_transit_modes(stops: pd.DataFrame, config: dict) -> pd.DataFrame: + # FIXME: Add mode dict to configs + """ + Assign simplified transit modes to stops. + + Modes: + - L: Local + - E: Premium + - N: None (should not occur) + + Args: + stops (pd.DataFrame): Stops DataFrame with Mode column + + Returns: + pd.DataFrame: Stops with assigned simplified modes + """ + stops['Mode'] = np.where( + stops['Mode'].isin(config['modes']['local_modes']), 'L', + np.where(stops['Mode'].isin(config['modes']['prm_modes']), 'E', 'N') + ) + return stops + + @classmethod + def process_transit_stops(cls, stops: pd.DataFrame, network: pdna.Network, + nodes: pd.DataFrame, config: dict) -> pd.DataFrame: + """Process transit stops using provided network.""" + gpd_stops = cls._process_stop_geometry(stops, config) + stops = cls._assign_network_nodes_to_stops(stops, gpd_stops, network, nodes) + stops = cls._assign_transit_modes(stops, config) + return stops + +class SkimGenerator: + """Main class for generating walk, bike, and stop skims""" + + def __init__(self, network_builder: NetworkBuilder, params: SkimParameters, output_path: str): + self.network_builder = network_builder + self.params = params + self.output_path = output_path + self.net_centroids = self._get_closest_net_node(network_builder.nodes, network_builder.links, network_builder.config) + self.stops = self.network_builder.stops + + def _get_closest_net_node(self, nodes: gpd.GeoDataFrame, links: gpd.GeoDataFrame, config: dict) -> pd.DataFrame: + """Get centroids DataFrame""" + return self.network_builder.get_closest_net_node_to_MGRA(nodes, links, config) + + def generate_maz_maz_walk_skim(self) -> pd.DataFrame: + """Generate MAZ to MAZ walk skims""" + maz_pairs = self._create_maz_pairs(self.net_centroids) + walk_skim = self._get_walk_distances(maz_pairs, self.params.max_maz_maz_walk_dist_feet) + # Add intrazonal distances + walk_skim = self._add_intrazonal_distances(walk_skim) + walk_skim = self._convert_columns_to_type(walk_skim, {'OMAZ': 'uint16', 'DMAZ': 'uint16', 'i': 'uint16', 'j': 'uint16'}) + return walk_skim + + def generate_maz_maz_bike_skim(self) -> pd.DataFrame: + """Generate MAZ to MAZ bike skims""" + maz_pairs = self._create_maz_pairs(self.net_centroids) + bike_skim = self._get_bike_distances(maz_pairs, self.params.max_maz_maz_bike_dist_feet) + bike_skim = self._convert_columns_to_type(bike_skim, {'OMAZ': 'uint16', 'DMAZ': 'uint16'}) + return bike_skim + + def generate_maz_stop_walk_skim(self) -> pd.DataFrame: + """Generate MAZ to stop walk skims""" + maz_stop_pairs, maz_stop_output = self._create_maz_stop_pairs(self.net_centroids, self.stops) + stop_skim = self._get_stop_distances(maz_stop_pairs) + stop_skim = self._process_stop_skims_by_mode(stop_skim, maz_stop_output) + + return stop_skim.sort_values('maz') + + def _create_maz_pairs(self, centroids: pd.DataFrame) -> pd.DataFrame: + """Create all possible MAZ to MAZ pairs with their network nodes""" + o_m = np.repeat(centroids['MAZ'].tolist(), len(centroids)) + d_m = np.tile(centroids['MAZ'].tolist(), len(centroids)) + + pairs = pd.DataFrame({ + "OMAZ": o_m, + "DMAZ": d_m, + "OMAZ_NODE": np.repeat(centroids['network_node_id'].tolist(), len(centroids)), + "DMAZ_NODE": np.tile(centroids['network_node_id'].tolist(), len(centroids)), + "OMAZ_NODE_X": np.repeat(centroids['network_node_x'].tolist(), len(centroids)), + "OMAZ_NODE_Y": np.repeat(centroids['network_node_y'].tolist(), len(centroids)), + "DMAZ_NODE_X": np.tile(centroids['network_node_x'].tolist(), len(centroids)), + "DMAZ_NODE_Y": np.tile(centroids['network_node_y'].tolist(), len(centroids)) + }) + + pairs["DISTWALK"] = pairs.eval("(((OMAZ_NODE_X-DMAZ_NODE_X)**2 + (OMAZ_NODE_Y-DMAZ_NODE_Y)**2)**0.5) / 5280.0") + return pairs[pairs["OMAZ"] != pairs["DMAZ"]] + + def _create_maz_stop_pairs(self, centroids: pd.DataFrame, stops: pd.DataFrame, ) -> Tuple[pd.DataFrame, pd.DataFrame]: + """ + Build a table of MAZ to transit stop connections with initial distances. + + Creates a cross-join between MAZs and stops, calculating the straight-line + distance between each MAZ-stop pair. Distances are converted to miles. + + + """ + # Create cross product of MAZs and stops + o_m = np.repeat(centroids['MAZ'].tolist(), len(stops)) + o_m_nn = np.repeat(centroids['network_node_id'].tolist(), len(stops)) + d_t = np.tile(stops['NO'].tolist(), len(centroids)) + d_t_nn = np.tile(stops['network_node_id'].tolist(), len(centroids)) + o_m_x = np.repeat(centroids['network_node_x'].tolist(), len(stops)) + o_m_y = np.repeat(centroids['network_node_y'].tolist(), len(stops)) + d_t_x = np.tile(stops['network_node_x'].tolist(), len(centroids)) + d_t_y = np.tile(stops['network_node_y'].tolist(), len(centroids)) + mode = np.tile(stops['Mode'].tolist(), len(centroids)) + + # Create DataFrame + pairs = pd.DataFrame({ + "MAZ": o_m, + "stop": d_t, + "OMAZ_NODE": o_m_nn, + "DSTOP_NODE": d_t_nn, + "OMAZ_NODE_X": o_m_x, + "OMAZ_NODE_Y": o_m_y, + "DSTOP_NODE_X": d_t_x, + "DSTOP_NODE_Y": d_t_y, + "MODE": mode + }) + + # Calculate distances in miles + pairs["DISTANCE"] = pairs.eval( + "(((OMAZ_NODE_X-DSTOP_NODE_X)**2 + (OMAZ_NODE_Y-DSTOP_NODE_Y)**2)**0.5) / 5280.0" + ) + + #create stop distances output file + maz_stop_output = pd.DataFrame(centroids['MAZ']) + maz_stop_output = maz_stop_output.rename(columns = {'MAZ': 'maz'}) + maz_stop_output['maz'] = maz_stop_output['maz'].astype('int') + maz_stop_output.sort_values(by=['maz'], inplace=True) + + return pairs, maz_stop_output + + def _get_walk_distances(self, pairs: pd.DataFrame, max_dist: float) -> pd.DataFrame: + """Process walking distances between MAZ pairs + This finction also adds i, j ,and actual columns that are required by Java code for TNC routing""" + filtered = pairs[pairs["DISTWALK"] <= max_dist / 5280.0].copy().reset_index(drop=True) + filtered["DISTWALK"] = self.network_builder.network.shortest_path_lengths( + filtered["OMAZ_NODE"], filtered["DMAZ_NODE"]) + result = filtered[filtered["DISTWALK"] <= max_dist / 5280.0] + + # Add missing MAZs + result = self._add_missing_mazs(self.net_centroids, result, pairs, 'DISTWALK') + + # Add required fields for TNC routing + result[['i', 'j']] = result[['OMAZ', 'DMAZ']] + result['actual'] = result['DISTWALK'] / self.params.walk_speed_mph * 60.0 + + return result + + def _get_bike_distances(self, pairs: pd.DataFrame, max_dist: float) -> pd.DataFrame: + """Process bike distances between MAZ pairs""" + filtered = pairs[pairs["DISTWALK"] <= max_dist / 5280.0].copy() + filtered["DISTBIKE"] = self.network_builder.network.shortest_path_lengths( + filtered["OMAZ_NODE"], filtered["DMAZ_NODE"]) + result = filtered[filtered["DISTBIKE"] <= max_dist / 5280.0] + + # Add missing MAZs + result = self._add_missing_mazs(self.net_centroids, result, pairs, 'DISTBIKE') + + return result + + def _get_stop_distances(self, pairs: pd.DataFrame) -> pd.DataFrame: + """Process stop distances between MAZ pairs""" + filtered = pairs[(pairs["DISTANCE"] <= self.params.max_maz_local_bus_stop_walk_dist_feet / 5280.0) & (pairs['MODE'] == 'L') | + (pairs["DISTANCE"] <= self.params.max_maz_premium_transit_stop_walk_dist_feet / 5280.0) & (pairs['MODE'] == 'E')] + filtered.reset_index(drop=True, inplace=True) + filtered["DISTWALK"] = self.network_builder.network.shortest_path_lengths( + filtered["OMAZ_NODE"], filtered["DSTOP_NODE"]) + + result = filtered[(filtered["DISTWALK"] <= self.params.max_maz_local_bus_stop_walk_dist_feet / 5280.0) & (filtered['MODE'] == 'L') | + (filtered["DISTWALK"] <= self.params.max_maz_premium_transit_stop_walk_dist_feet / 5280.0) & (filtered['MODE'] == 'E')] + + return result + + def _process_stop_skims_by_mode(self, stop_skims: pd.DataFrame, maz_stop_output: pd.DataFrame) -> pd.DataFrame: + """ + Process stop skims by transit mode and merge with output DataFrame. + + Args: + stop_skims (pd.DataFrame): DataFrame containing stop skim data with columns: + - MODE: Transit mode ('L' or 'E') + - MAZ: MAZ ID + - DISTWALK: Walk distance + maz_stop_output (pd.DataFrame): Base DataFrame to merge results into + + Returns: + pd.DataFrame: Processed DataFrame with walk distances by mode: + - maz: MAZ ID + - walk_dist_local_bus: Walk distance to nearest local bus stop + - walk_dist_premium_transit: Walk distance to nearest premium transit stop + """ + modes = {"L": "local_bus", "E": "premium_transit"} + + for mode, mode_descr in modes.items(): + stop_skims_by_mode = ( + stop_skims[stop_skims.MODE == mode] + .groupby("MAZ")["DISTWALK"] + .min() + .reset_index() + ) + + stop_skims_by_mode = stop_skims_by_mode.rename( + { + "MAZ": "maz", + "DISTWALK": f"walk_dist_{mode_descr}", + }, + axis="columns", + ) + + maz_stop_output = maz_stop_output.merge( + stop_skims_by_mode, + on="maz", + how="outer" + ) + maz_stop_output[f"walk_dist_{mode_descr}"].fillna(999999, inplace=True) + + return maz_stop_output + + def _add_intrazonal_distances(self, skim: pd.DataFrame) -> pd.DataFrame: + """Add intrazonal distances based on 3 nearest neighbors""" + skim = skim.sort_values(['OMAZ', 'DISTWALK']) + skim.set_index(['OMAZ', 'DMAZ'], inplace=True) + unique_omaz = skim.index.get_level_values(0).unique() + # find the average of the closest 3 zones + means = skim.loc[(unique_omaz, slice(None)), 'DISTWALK'].groupby(level=0).head(3).groupby(level=0).mean() + intra_skims = pd.DataFrame({ + 'OMAZ': unique_omaz, + 'DMAZ': unique_omaz, + 'DISTWALK': means.values/2, + 'i': unique_omaz, + 'j': unique_omaz, + 'actual': (means.values/self.params.walk_speed_mph * 60.0) / 2 + }).set_index(['OMAZ', 'DMAZ']) + + return pd.concat([skim, intra_skims], axis=0).reset_index() + + def _add_missing_mazs(self, centroids: pd.DataFrame, skim_table: pd.DataFrame, + cost_table: pd.DataFrame, dist_col: str = 'DISTWALK') -> pd.DataFrame: + """Add missing MAZs to skim table since some MAZs will not be within distance of a stop or each other. + This will make sure we will have skims for all MAZs in the region.""" + missing_maz = centroids[~centroids['MAZ'].isin(skim_table['OMAZ'])][['MAZ']] + missing_maz = missing_maz.rename(columns={'MAZ': 'OMAZ'}) + + filtered_cost = cost_table[cost_table['OMAZ'] != cost_table['DMAZ']] + if dist_col != 'DISTWALK': + filtered_cost = filtered_cost.rename(columns={'DISTWALK': dist_col}) + sorted_cost = filtered_cost.sort_values(dist_col) + grouped_cost = sorted_cost.groupby('OMAZ').agg({ + 'DMAZ': 'first', + dist_col: 'first' + }).reset_index() + + missing_maz = missing_maz.merge(grouped_cost, on='OMAZ', how='left') + skim_table = pd.concat([skim_table[["OMAZ", "DMAZ", dist_col]], missing_maz]).sort_values(["OMAZ", "DMAZ"]) + + return skim_table + + def _convert_columns_to_type(self, df: pd.DataFrame, columns: Dict[str, str]) -> pd.DataFrame: + """ + Convert specified columns in a DataFrame to a given data type. + + Args: + df (pd.DataFrame): The DataFrame to convert. + columns (Dict[str, str]): A dictionary where keys are column names and values are the target data types. + + Returns: + pd.DataFrame: The DataFrame with converted columns. + """ + return df.astype(columns) + +def main(path: str): + """Main execution function""" + # Load configuration + with open(os.path.join(path, "2zoneSkim_params.yaml"), 'r') as f: + config = yaml.safe_load(f) + + params = SkimParameters.from_yaml(config) + model_inputs = os.path.join(path, "input") + output_path = os.path.join(path, "output") + + # Create network builder using class method + network_builder = NetworkBuilder.from_files(model_inputs, config) + + # Initialize skim generator + skim_generator = SkimGenerator(network_builder, params, output_path) + + # Generate and save skims + if config['settings']['run_maz_maz_walk']: + print(f"{datetime.now().strftime('%H:%M:%S')} Generating MAZ-MAZ walk skims...") + walk_skim = skim_generator.generate_maz_maz_walk_skim() + walk_skim.to_csv(os.path.join(output_path, config['settings']['maz_maz_walk_output']), index=False) + + if config['settings']['run_maz_maz_bike']: + print(f"{datetime.now().strftime('%H:%M:%S')} Generating MAZ-MAZ bike skims...") + bike_skim = skim_generator.generate_maz_maz_bike_skim() + bike_skim.to_csv(os.path.join(output_path, config['settings']['maz_maz_bike_output']), index=False) + + if config['settings']['run_maz_stop_walk']: + print(f"{datetime.now().strftime('%H:%M:%S')} Generating MAZ-stop walk skims...") + stop_skim = skim_generator.generate_maz_stop_walk_skim() + stop_skim.to_csv(os.path.join(output_path, config['settings']['maz_stop_walk_output']), index=False) + +if __name__ == "__main__": + import sys + main(sys.argv[1]) \ No newline at end of file diff --git a/skimming_and_assignment/maz_maz_stop_skims/2zoneSkim_params.yaml b/skimming_and_assignment/maz_maz_stop_skims/2zoneSkim_params.yaml new file mode 100644 index 0000000..92eba3b --- /dev/null +++ b/skimming_and_assignment/maz_maz_stop_skims/2zoneSkim_params.yaml @@ -0,0 +1,54 @@ +# Recommend to use SANDAG's python environment: +# https://github.com/SANDAG/ABM/blob/main/src/asim/scripts/environment.yml + +# Project settings +settings: + run_maz_maz_walk: False + run_maz_maz_bike: False + run_maz_stop_walk: True + maz_maz_walk_output: "maz_maz_walk.csv" + maz_maz_bike_output: "maz_maz_bike.csv" + maz_stop_walk_output: "maz_stop_walk.csv" + input_dir: input + output_dir: output + epsg: 32126 # projected crs of project + +# Stop attributes CSV, expectd in input_dir +# One stop (row) per transit route +stop_attributes: + file: "stops.csv" + id_field: "NO" + x_field: "Latitude" + y_field: "Longitude" + rid_field: "Route_ID" + +# Route attributes CSV, expected in input_dir +route_attributes: + file: "routes.csv" + rid_field: "Route_ID" + mode: "Mode" + +# Modes +modes: + prm_modes: ['a', 'l', 'r'] # premium + local_modes: ['b', 'e'] + +# Network and nodes shp, expected in input_dir +# Network must include connector links +# Nodes must include connector centroids (MAZ centroids) as nodes +# Node id must be consistent across network and node files +mmms: + shapefile_name: "links.shp" + shapefile_node_name: "nodes.shp" + two_way_network: False # True if links are bidirectional, False if one link per direction + mmms_link_ref_id: "FROMNODENO" + mmms_link_nref_id: "TONODENO" + mmms_link_id: "NO" + mmms_link_len: "length" # feet + mmms_node_maz_id: "MAZ" + max_maz_maz_walk_dist_feet: 15840 # int + max_maz_maz_bike_dist_feet: 26400 # int + max_maz_local_bus_stop_walk_dist_feet: 3960 # int + max_maz_premium_transit_stop_walk_dist_feet: 6600 # int + walk_speed_mph: 3.0 # float + drive_speed_mph: 25.0 # float \ No newline at end of file diff --git a/skimming_and_assignment/visum/.gitattributes b/skimming_and_assignment/visum/.gitattributes new file mode 100644 index 0000000..dd16bb2 --- /dev/null +++ b/skimming_and_assignment/visum/.gitattributes @@ -0,0 +1,3 @@ +# Auto detect text files and perform LF normalization +* text=auto +*.ver filter=lfs diff=lfs merge=lfs -text diff --git a/skimming_and_assignment/visum/.gitignore b/skimming_and_assignment/visum/.gitignore new file mode 100644 index 0000000..3865438 --- /dev/null +++ b/skimming_and_assignment/visum/.gitignore @@ -0,0 +1,2 @@ +*__pycache__* +*.ver diff --git a/skimming_and_assignment/visum/config/CapacitySettingBasedOnEMME.xml b/skimming_and_assignment/visum/config/CapacitySettingBasedOnEMME.xml new file mode 100644 index 0000000..9b9c736 --- /dev/null +++ b/skimming_and_assignment/visum/config/CapacitySettingBasedOnEMME.xml @@ -0,0 +1,871 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 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+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/skimming_and_assignment/visum/config/visum_metro/SkimSequence_1.xml b/skimming_and_assignment/visum/config/visum_metro/SkimSequence_1.xml new file mode 100644 index 0000000..231fb4b --- /dev/null +++ b/skimming_and_assignment/visum/config/visum_metro/SkimSequence_1.xml @@ -0,0 +1,2716 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 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+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/skimming_and_assignment/visum/filters/OP_Transit.fil b/skimming_and_assignment/visum/filters/OP_Transit.fil new file mode 100644 index 0000000..1ace9d6 --- /dev/null +++ b/skimming_and_assignment/visum/filters/OP_Transit.fil @@ -0,0 +1,42 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/skimming_and_assignment/visum/filters/PK_Transit.fil b/skimming_and_assignment/visum/filters/PK_Transit.fil new file mode 100644 index 0000000..f53febb --- /dev/null +++ b/skimming_and_assignment/visum/filters/PK_Transit.fil @@ -0,0 +1,42 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/skimming_and_assignment/visum/filters/asn_linkApproach.fil b/skimming_and_assignment/visum/filters/asn_linkApproach.fil new file mode 100644 index 0000000..bae2c95 --- /dev/null +++ b/skimming_and_assignment/visum/filters/asn_linkApproach.fil @@ -0,0 +1,34 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/skimming_and_assignment/visum/filters/asn_linkCBD.fil b/skimming_and_assignment/visum/filters/asn_linkCBD.fil new file mode 100644 index 0000000..d54f004 --- /dev/null +++ b/skimming_and_assignment/visum/filters/asn_linkCBD.fil @@ -0,0 +1,17 @@ + + + + + + + + + + + + + + + + + diff --git a/skimming_and_assignment/visum/filters/asn_linkFreeway.fil b/skimming_and_assignment/visum/filters/asn_linkFreeway.fil new file mode 100644 index 0000000..4a55adb --- /dev/null +++ b/skimming_and_assignment/visum/filters/asn_linkFreeway.fil @@ -0,0 +1,20 @@ + + + + + + + + + + + + + + + + + + + + diff --git a/skimming_and_assignment/visum/filters/asn_linkHOV.fil b/skimming_and_assignment/visum/filters/asn_linkHOV.fil new file mode 100644 index 0000000..840bb69 --- /dev/null +++ b/skimming_and_assignment/visum/filters/asn_linkHOV.fil @@ -0,0 +1,14 @@ + + + + + + + + + + + + + + diff --git a/skimming_and_assignment/visum/filters/asn_linkNonFreeway.fil b/skimming_and_assignment/visum/filters/asn_linkNonFreeway.fil new file mode 100644 index 0000000..5339b79 --- /dev/null +++ b/skimming_and_assignment/visum/filters/asn_linkNonFreeway.fil @@ -0,0 +1,20 @@ + + + + + + + + + + + + + + + + + + + + diff --git a/skimming_and_assignment/visum/filters/asn_linkRamp.fil b/skimming_and_assignment/visum/filters/asn_linkRamp.fil new file mode 100644 index 0000000..e9323c4 --- /dev/null +++ b/skimming_and_assignment/visum/filters/asn_linkRamp.fil @@ -0,0 +1,14 @@ + + + + + + + + + + + + + + diff --git a/skimming_and_assignment/visum/filters/asn_nodeMetNetIntersection.fil b/skimming_and_assignment/visum/filters/asn_nodeMetNetIntersection.fil new file mode 100644 index 0000000..5e1e169 --- /dev/null +++ b/skimming_and_assignment/visum/filters/asn_nodeMetNetIntersection.fil @@ -0,0 +1,28 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/skimming_and_assignment/visum/filters/asn_turnHOV.fil b/skimming_and_assignment/visum/filters/asn_turnHOV.fil new file mode 100644 index 0000000..ea16b28 --- /dev/null +++ b/skimming_and_assignment/visum/filters/asn_turnHOV.fil @@ -0,0 +1,22 @@ + + + + + + + + + + + + + + + + + + + + + + diff --git a/skimming_and_assignment/visum/filters/linkMetNet.fil b/skimming_and_assignment/visum/filters/linkMetNet.fil new file mode 100644 index 0000000..31c8fa4 --- /dev/null +++ b/skimming_and_assignment/visum/filters/linkMetNet.fil @@ -0,0 +1,14 @@ + + + + + + + + + + + + + + diff --git a/skimming_and_assignment/visum/filters/linkMetered.fil b/skimming_and_assignment/visum/filters/linkMetered.fil new file mode 100644 index 0000000..3e1caba --- /dev/null +++ b/skimming_and_assignment/visum/filters/linkMetered.fil @@ -0,0 +1,31 @@ + + + + + + + + + + diff --git a/skimming_and_assignment/visum/filters/linkRamps_AM.fil b/skimming_and_assignment/visum/filters/linkRamps_AM.fil new file mode 100644 index 0000000..130aaff --- /dev/null +++ b/skimming_and_assignment/visum/filters/linkRamps_AM.fil @@ -0,0 +1,17 @@ + + + + + + + + diff --git a/skimming_and_assignment/visum/filters/linkRamps_MD.fil b/skimming_and_assignment/visum/filters/linkRamps_MD.fil new file mode 100644 index 0000000..9f47e4f --- /dev/null +++ b/skimming_and_assignment/visum/filters/linkRamps_MD.fil @@ -0,0 +1,17 @@ + + + + + + + + diff --git a/skimming_and_assignment/visum/filters/linkRamps_PM.fil b/skimming_and_assignment/visum/filters/linkRamps_PM.fil new file mode 100644 index 0000000..259e687 --- /dev/null +++ b/skimming_and_assignment/visum/filters/linkRamps_PM.fil @@ -0,0 +1,17 @@ + + + + + + + + diff --git a/skimming_and_assignment/visum/filters/linkTruckPath1.fil b/skimming_and_assignment/visum/filters/linkTruckPath1.fil new file mode 100644 index 0000000..83ce163 --- /dev/null +++ b/skimming_and_assignment/visum/filters/linkTruckPath1.fil @@ -0,0 +1,17 @@ + + + + + + + + diff --git a/skimming_and_assignment/visum/filters/linkTruckPath2.fil b/skimming_and_assignment/visum/filters/linkTruckPath2.fil new file mode 100644 index 0000000..1a46bd9 --- /dev/null +++ b/skimming_and_assignment/visum/filters/linkTruckPath2.fil @@ -0,0 +1,17 @@ + + + + + + + + diff --git a/skimming_and_assignment/visum/filters/linkTruckPath4.fil b/skimming_and_assignment/visum/filters/linkTruckPath4.fil new file mode 100644 index 0000000..6288d76 --- /dev/null +++ b/skimming_and_assignment/visum/filters/linkTruckPath4.fil @@ -0,0 +1,17 @@ + + + + + + + + diff --git a/skimming_and_assignment/visum/outputs/assignment/feedback/.gitignore b/skimming_and_assignment/visum/outputs/assignment/feedback/.gitignore new file mode 100644 index 0000000..86d0cb2 --- /dev/null +++ b/skimming_and_assignment/visum/outputs/assignment/feedback/.gitignore @@ -0,0 +1,4 @@ +# Ignore everything in this directory +* +# Except this file +!.gitignore \ No newline at end of file diff --git a/skimming_and_assignment/visum/outputs/assignment/final_assign/.gitignore b/skimming_and_assignment/visum/outputs/assignment/final_assign/.gitignore new file mode 100644 index 0000000..86d0cb2 --- /dev/null +++ b/skimming_and_assignment/visum/outputs/assignment/final_assign/.gitignore @@ -0,0 +1,4 @@ +# Ignore everything in this directory +* +# Except this file +!.gitignore \ No newline at end of file diff --git a/skimming_and_assignment/visum/outputs/reports/.gitignore b/skimming_and_assignment/visum/outputs/reports/.gitignore new file mode 100644 index 0000000..86d0cb2 --- /dev/null +++ b/skimming_and_assignment/visum/outputs/reports/.gitignore @@ -0,0 +1,4 @@ +# Ignore everything in this directory +* +# Except this file +!.gitignore \ No newline at end of file diff --git a/skimming_and_assignment/visum/outputs/skims/.gitignore b/skimming_and_assignment/visum/outputs/skims/.gitignore new file mode 100644 index 0000000..86d0cb2 --- /dev/null +++ b/skimming_and_assignment/visum/outputs/skims/.gitignore @@ -0,0 +1,4 @@ +# Ignore everything in this directory +* +# Except this file +!.gitignore \ No newline at end of file diff --git a/skimming_and_assignment/visum/outputs/trips/logs/.gitignore b/skimming_and_assignment/visum/outputs/trips/logs/.gitignore new file mode 100644 index 0000000..86d0cb2 --- /dev/null +++ b/skimming_and_assignment/visum/outputs/trips/logs/.gitignore @@ -0,0 +1,4 @@ +# Ignore everything in this directory +* +# Except this file +!.gitignore \ No newline at end of file diff --git a/skimming_and_assignment/visum/outputs/trips/model/peak/.gitignore b/skimming_and_assignment/visum/outputs/trips/model/peak/.gitignore new file mode 100644 index 0000000..86d0cb2 --- /dev/null +++ b/skimming_and_assignment/visum/outputs/trips/model/peak/.gitignore @@ -0,0 +1,4 @@ +# Ignore everything in this directory +* +# Except this file +!.gitignore \ No newline at end of file diff --git a/skimming_and_assignment/visum/outputs/trips/model/reports/.gitignore b/skimming_and_assignment/visum/outputs/trips/model/reports/.gitignore new file mode 100644 index 0000000..86d0cb2 --- /dev/null +++ b/skimming_and_assignment/visum/outputs/trips/model/reports/.gitignore @@ -0,0 +1,4 @@ +# Ignore everything in this directory +* +# Except this file +!.gitignore \ No newline at end of file diff --git a/skimming_and_assignment/visum/outputs/trips/model_hbo/.gitignore b/skimming_and_assignment/visum/outputs/trips/model_hbo/.gitignore new file mode 100644 index 0000000..86d0cb2 --- /dev/null +++ b/skimming_and_assignment/visum/outputs/trips/model_hbo/.gitignore @@ -0,0 +1,4 @@ +# Ignore everything in this directory +* +# Except this file +!.gitignore \ No newline at end of file diff --git a/skimming_and_assignment/visum/outputs/trips/model_hbr/.gitignore b/skimming_and_assignment/visum/outputs/trips/model_hbr/.gitignore new file mode 100644 index 0000000..86d0cb2 --- /dev/null +++ b/skimming_and_assignment/visum/outputs/trips/model_hbr/.gitignore @@ -0,0 +1,4 @@ +# Ignore everything in this directory +* +# Except this file +!.gitignore \ No newline at end of file diff --git a/skimming_and_assignment/visum/outputs/trips/model_hbs/.gitignore b/skimming_and_assignment/visum/outputs/trips/model_hbs/.gitignore new file mode 100644 index 0000000..86d0cb2 --- /dev/null +++ b/skimming_and_assignment/visum/outputs/trips/model_hbs/.gitignore @@ -0,0 +1,4 @@ +# Ignore everything in this directory +* +# Except this file +!.gitignore \ No newline at end of file diff --git a/skimming_and_assignment/visum/outputs/trips/model_hbw/.gitignore b/skimming_and_assignment/visum/outputs/trips/model_hbw/.gitignore new file mode 100644 index 0000000..86d0cb2 --- /dev/null +++ b/skimming_and_assignment/visum/outputs/trips/model_hbw/.gitignore @@ -0,0 +1,4 @@ +# Ignore everything in this directory +* +# Except this file +!.gitignore \ No newline at end of file diff --git a/skimming_and_assignment/visum/outputs/trips/model_nh/.gitignore b/skimming_and_assignment/visum/outputs/trips/model_nh/.gitignore new file mode 100644 index 0000000..86d0cb2 --- /dev/null +++ b/skimming_and_assignment/visum/outputs/trips/model_nh/.gitignore @@ -0,0 +1,4 @@ +# Ignore everything in this directory +* +# Except this file +!.gitignore \ No newline at end of file diff --git a/skimming_and_assignment/visum/outputs/trips/model_sc/.gitignore b/skimming_and_assignment/visum/outputs/trips/model_sc/.gitignore new file mode 100644 index 0000000..86d0cb2 --- /dev/null +++ b/skimming_and_assignment/visum/outputs/trips/model_sc/.gitignore @@ -0,0 +1,4 @@ +# Ignore everything in this directory +* +# Except this file +!.gitignore \ No newline at end of file diff --git a/skimming_and_assignment/visum/scripts/AssignmentMetrics_Export.py b/skimming_and_assignment/visum/scripts/AssignmentMetrics_Export.py new file mode 100644 index 0000000..9b43438 --- /dev/null +++ b/skimming_and_assignment/visum/scripts/AssignmentMetrics_Export.py @@ -0,0 +1,837 @@ +# SRTC: Full script to handle Assignment Summary, Model Reporting, and Results export. Uses same timestamped folder for all outputs +# Adapted to LCOG, only outputs Assignment Summary + +""" +created 5/13/2025 + +@author: luke.gordon + +""" + +# Libraries +import VisumPy.helpers +import VisumPy.excel +import pandas as pd +import numpy as np +import csv +from datetime import datetime +import math +import os.path + + +# Pull timestamp for folder name from Visum network attribute +date = Visum.Net.AttValue("output_date") + +# Read user inputs from Visum +proj_dir = Visum.GetPath(2) + +# Formatting functions +# Formatting for columns +# Create formatting function for large numbers (no decimals and thousand commas) +def format_commas(column): + return column.apply(lambda x: '{:,.0f}'.format(x) if pd.notna(x) else None) +# Create formatting function for percentages (2 decimals and percent symbol, also multiplies by 100) +def format_percent(column): + return column.apply(lambda x: '{:.2%}'.format(x) if pd.notna(x) else None) +# Create formatting function for small numbers (2 decimals) +def format_twoplaces(column): + return column.apply(lambda x: '{:.2f}'.format(x) if pd.notna(x) else None) +# Create formatting function for small numbers (1 decimal) +def format_oneplace(column): + return column.apply(lambda x: '{:.1f}'.format(x) if pd.notna(x) else None) +# Create formatting function for small numbers (0 decimals) +def format_zeroplaces(column): + return column.apply(lambda x: '{:.0f}'.format(x) if pd.notna(x) else None) + + +# Formatting for single cells +# Create formatting function for large numbers (no decimals and thousand commas) +def format_commas_cell(cell_value): + return '{:,.0f}'.format(cell_value) if pd.notna(cell_value) else None +# Create formatting function for percentages (2 decimals and percent symbol, also multiplies by 100) +def format_percent_cell(cell_value): + return '{:.2%}'.format(cell_value) if pd.notna(cell_value) else None +# Create formatting function for small numbers (2 decimals) +def format_twoplaces_cell(cell_value): + return '{:.2f}'.format(cell_value) if pd.notna(cell_value) else None +# Create formatting function for small numbers (0 decimals) +def format_zeroplaces_cell(cell_value): + return '{:.0f}'.format(cell_value) if pd.notna(cell_value) else None + + +# Round to nearest even number function. Used in Volume corridor reporting +def round_to_nearest_even(number): + rounded = round(number) + return rounded + (rounded % 2 == 1) + +# Create Percent Error function +def pct_error(count , flow): + error = ((sum(flow)/len(flow)) - (sum(count)/len(count))) / (sum(count)/len(count)) + + return error + +# Create Percent RMSE function +def pct_rmse(count , sqerror): + rmse = math.sqrt(sum(sqerror)/len(sqerror))/(sum(count)/len(count)) + + return rmse + +# Create VMT function (needs to pull data from all links, not just ones with counts) +def vmt(flow , length): + vmt = sum(flow*length) + + return vmt + + +# Create VHT function (needs to pull data from all links and periods, not just ones with counts) + # Need to have logic to handle daily vs. a single period +def vht_dly(am_flow, am_time, pm_flow, pm_time, op_flow, op_time): + vht = sum(am_flow*(am_time/3600)) + sum(pm_flow*(pm_time/3600)) + sum(op_flow*(op_time/3600)) + return vht +def vht_per(flow, time): + vht = sum(flow*time) + return vht + + + +# Assignment summary function +def assignment_summary(auto_count, sut_count, mut_count, all_count, auto_flow, sut_flow, mut_flow, all_flow): #, cong_auto_time, cong_trk_time, period): + # DAILY + # Percent Error and Percent RMSE + # Import ID fields and fields with Counts and Flows + # Link ID fields + NO = VisumPy.helpers.GetMulti(Visum.Net.Links,"No", activeOnly = True) + FCLASS = VisumPy.helpers.GetMulti(Visum.Net.Links,"TYPENO", activeOnly = True) + LENGTH = VisumPy.helpers.GetMulti(Visum.Net.Links,"Length", activeOnly = True) + SCRNLINE = VisumPy.helpers.GetMulti(Visum.Net.Links,r"CONCATENATE:SCREENLINES\CODE", activeOnly = True) + + # Counts + Auto_Count = VisumPy.helpers.GetMulti(Visum.Net.Links,auto_count, activeOnly = True) + SUT_Count = VisumPy.helpers.GetMulti(Visum.Net.Links,sut_count, activeOnly = True) + MUT_Count = VisumPy.helpers.GetMulti(Visum.Net.Links,mut_count, activeOnly = True) + Tot_Count = VisumPy.helpers.GetMulti(Visum.Net.Links,all_count, activeOnly = True) + AADT = VisumPy.helpers.GetMulti(Visum.Net.Links,'AADT', activeOnly = True) + # Link Daily Flows + Auto_Flow = VisumPy.helpers.GetMulti(Visum.Net.Links,auto_flow, activeOnly = True) + SUT_Flow = VisumPy.helpers.GetMulti(Visum.Net.Links,sut_flow, activeOnly = True) + MUT_Flow = VisumPy.helpers.GetMulti(Visum.Net.Links,mut_flow, activeOnly = True) + Tot_Flow = VisumPy.helpers.GetMulti(Visum.Net.Links,all_flow, activeOnly = True) + + # Screenline Names for Report + SL_NAME = VisumPy.helpers.GetMulti(Visum.Net.Screenlines,"Name", activeOnly = True) + SL_NAME = list(dict.fromkeys(SL_NAME)) + + # Make Visum list with link data + summary_list = [NO, FCLASS, SCRNLINE, LENGTH, Auto_Flow, SUT_Flow, MUT_Flow, Tot_Flow, Auto_Count, SUT_Count, MUT_Count, Tot_Count, AADT] + + # Put Visum link list into dataframe + df = pd.DataFrame(np.column_stack(summary_list), columns = ['NO', 'FCLASS', 'SCRNLINE', 'LENGTH', + 'Auto_Flow', 'SUT_Flow', 'MUT_Flow', 'Tot_Flow', + 'Auto_Count', 'SUT_Count', 'MUT_Count', 'Tot_Count','AADT']) + + # Break out SCRNLINE field to separate by commas into individual columns + df[['SCRNLINE']] = df[['SCRNLINE']].astype(str) + df = pd.concat([df,df['SCRNLINE'].str.split(',', expand = True)], axis = 1) + # Change Screenline field names + if 1 not in df: + df[1] = None + df = df.rename(columns = {0:'SCRNLINE1',1:'SCRNLINE2'}) + # Replace null values with 0 in the screenline fields + df['SCRNLINE1'] = df['SCRNLINE1'].replace('',np.nan).fillna(0) + df['SCRNLINE2'] = df['SCRNLINE2'].replace('',np.nan).fillna(0) + + # Define custom_sum function to maintain null values when aggregating Counts and Flows by LinkNO + def custom_sum(series): + # If all values are null, return null; otherwise, return the sum of the values + return series.sum() if series.notna().any() else None + ## GROUP EACH DATAFRAME BY 'NO' COLUMN TO COMBINE COUNTS ON EACH LINK INTO BOTH DIRECTIONS + df = df.groupby('NO').agg( + FCLASS =('FCLASS', 'max'), + LENGTH =('LENGTH', 'max'), + SCRNLINE1 =('SCRNLINE1', 'first'), + SCRNLINE2 =('SCRNLINE2', 'first'), + Auto_Flow =('Auto_Flow', custom_sum), + SUT_Flow =('SUT_Flow', custom_sum), + MUT_Flow =('MUT_Flow', custom_sum), + Tot_Flow =('Tot_Flow', custom_sum), + Auto_Count =('Auto_Count', custom_sum), + SUT_Count =('SUT_Count', custom_sum), + MUT_Count =('MUT_Count', custom_sum), + Tot_Count =('Tot_Count', custom_sum), + AADT =('AADT', custom_sum)).reset_index() + + # Drop AADT from df and join in AADT from df_aadt (to have 2-way AADT by link) + #df = df.drop('AADT', axis=1) + #df = pd.merge(df, df_aadt, on='NO', how='left') + + # Convert FCLASS to integer + df[['FCLASS']] = df[['FCLASS']].astype(int) + + # Convert SCRNLINE1 and SCRNLINE2 to Integer + df[['SCRNLINE1','SCRNLINE2']] = df[['SCRNLINE1','SCRNLINE2']].astype(float) + df[['SCRNLINE1','SCRNLINE2']] = df[['SCRNLINE1','SCRNLINE2']].astype(int) + + + # Build results dictionary to use as results dataframe to save summary stats for group stats + results = {"Segment":['Auto', + 'Auto: AADT <5k','Auto: AADT 5-10k','Auto: AADT 10-15k','Auto: AADT 15-20k','Auto: AADT 20-30k','Auto: AADT 30-40k','Auto: AADT 40-50k', + 'Auto: LinkType 1', 'Auto: LinkType 2', 'Auto: LinkType 3', 'Auto: LinkType 4', 'Auto: LinkType 5', 'Auto: LinkType 6', 'Auto: LinkType 7', + 'Auto: LinkType 8', 'Auto: LinkType 9', 'Auto: LinkType 10', 'Auto: LinkType 11', 'Auto: LinkType 12', 'Auto: LinkType 30', 'Auto: LinkType 32', + 'Auto: SL '+SL_NAME[0] ,'Auto: SL '+SL_NAME[1] ,'Auto: SL '+SL_NAME[2] ,'Auto: SL '+SL_NAME[3] ,'Auto: SL '+SL_NAME[4] ,'Auto: SL '+SL_NAME[5] ,'Auto: SL '+SL_NAME[6] , + 'Auto: SL '+SL_NAME[7] ,'Auto: SL '+SL_NAME[8] ,'Auto: SL '+SL_NAME[9] ,'Auto: SL '+SL_NAME[10],'Auto: SL '+SL_NAME[11],'Auto: SL '+SL_NAME[12], + 'SUT', + 'SUT: AADT <5k','SUT: AADT 5-10k','SUT: AADT 10-15k','SUT: AADT 15-20k','SUT: AADT 20-30k','SUT: AADT 30-40k','SUT: AADT 40-50k', + 'SUT: LinkType 1', 'SUT: LinkType 2', 'SUT: LinkType 3', 'SUT: LinkType 4', 'SUT: LinkType 5', 'SUT: LinkType 6', 'SUT: LinkType 7', + 'SUT: LinkType 8', 'SUT: LinkType 9', 'SUT: LinkType 10', 'SUT: LinkType 11', 'SUT: LinkType 12', 'SUT: LinkType 30', 'SUT: LinkType 32', + 'SUT: SL '+SL_NAME[0] ,'SUT: SL '+SL_NAME[1] ,'SUT: SL '+SL_NAME[2] ,'SUT: SL '+SL_NAME[3] ,'SUT: SL '+SL_NAME[4] ,'SUT: SL '+SL_NAME[5] ,'SUT: SL '+SL_NAME[6] , + 'SUT: SL '+SL_NAME[7] ,'SUT: SL '+SL_NAME[8] ,'SUT: SL '+SL_NAME[9] ,'SUT: SL '+SL_NAME[10],'SUT: SL '+SL_NAME[11],'SUT: SL '+SL_NAME[12], + 'MUT', + 'MUT: AADT <5k','MUT: AADT 5-10k','MUT: AADT 10-15k','MUT: AADT 15-20k','MUT: AADT 20-30k','MUT: AADT 30-40k','MUT: AADT 40-50k', + 'MUT: LinkType 1', 'MUT: LinkType 2', 'MUT: LinkType 3', 'MUT: LinkType 4', 'MUT: LinkType 5', 'MUT: LinkType 6', 'MUT: LinkType 7', + 'MUT: LinkType 8', 'MUT: LinkType 9', 'MUT: LinkType 10', 'MUT: LinkType 11', 'MUT: LinkType 12', 'MUT: LinkType 30', 'MUT: LinkType 32', + 'MUT: SL '+SL_NAME[0] ,'MUT: SL '+SL_NAME[1] ,'MUT: SL '+SL_NAME[2] ,'MUT: SL '+SL_NAME[3] ,'MUT: SL '+SL_NAME[4] ,'MUT: SL '+SL_NAME[5] ,'MUT: SL '+SL_NAME[6] , + 'MUT: SL '+SL_NAME[7],'MUT: SL '+SL_NAME[8] ,'MUT: SL '+SL_NAME[9] ,'MUT: SL '+SL_NAME[10],'MUT: SL '+SL_NAME[11],'MUT: SL '+SL_NAME[12], + 'All Modes', + 'All Modes: AADT <5k','All Modes: AADT 5-10k','All Modes: AADT 10-15k','All Modes: AADT 15-20k','All Modes: AADT 20-30k','All Modes: AADT 30-40k','All Modes: AADT 40-50k', + 'All Modes: LinkType 1', 'All Modes: LinkType 2', 'All Modes: LinkType 3', 'All Modes: LinkType 4', 'All Modes: LinkType 5', 'All Modes: LinkType 6', 'All Modes: LinkType 7', + 'All Modes: LinkType 8', 'All Modes: LinkType 9', 'All Modes: LinkType 10', 'All Modes: LinkType 11', 'All Modes: LinkType 12', 'All Modes: LinkType 30', 'All Modes: LinkType 32', + 'All Modes: SL '+SL_NAME[0] ,'All Modes: SL '+SL_NAME[1] ,'All Modes: SL '+SL_NAME[2] ,'All Modes: SL '+SL_NAME[3] ,'All Modes: SL '+SL_NAME[4] ,'All Modes: SL '+SL_NAME[5] , + 'All Modes: SL '+SL_NAME[6] ,'All Modes: SL '+SL_NAME[7] ,'All Modes: SL '+SL_NAME[8] ,'All Modes: SL '+SL_NAME[9] ,'All Modes: SL '+SL_NAME[10],'All Modes: SL '+SL_NAME[11], + 'All Modes: SL '+SL_NAME[12] + ]} + + + + # Plug results dictionary into results_df dataframe + results_df = pd.DataFrame(data = results) + + # Add stats columns + results_df['Percent Error'] = None + results_df['Percent RMSE'] = None + results_df['Total VMT'] = None + results_df['Total VHT'] = None + results_df['Number of Observations'] = None + results_df['Sum of Counts'] = None + results_df['Mean of Counts'] = None + results_df['Median of Counts'] = None + results_df['Count VMT, Links with Counts'] = None + results_df['Modeled VMT, Links with Counts'] = None + + + # For links with counts only, used for Pct. Error and Pct. RMSE + # Filter out links where count is null or 0 and by each condition + # All Links with Auto Counts + count_df = df[df['Auto_Count'].notna()] + + # By AADT Volume + under_5k_df = count_df[(count_df['AADT'] < 5000)] + btwn_5_10k_df = count_df[(count_df['AADT'] >= 5000) & (count_df['AADT'] < 10000)] + btwn_10_15k_df = count_df[(count_df['AADT'] >= 10000) & (count_df['AADT'] < 15000)] + btwn_15_20k_df = count_df[(count_df['AADT'] >= 15000) & (count_df['AADT'] < 20000)] + btwn_20_30k_df = count_df[(count_df['AADT'] >= 20000) & (count_df['AADT'] < 30000)] + btwn_30_40k_df = count_df[(count_df['AADT'] >= 30000) & (count_df['AADT'] < 40000)] + btwn_40_50k_df = count_df[(count_df['AADT'] >= 40000) & (count_df['AADT'] < 50000)] + #over_50k_df = count_df[(count_df['AADT'] >= 50000)] + # By Functional Class + fc1_df = count_df[(count_df['FCLASS'] == 1)] + fc2_df = count_df[(count_df['FCLASS'] == 2)] + fc3_df = count_df[(count_df['FCLASS'] == 3)] + fc4_df = count_df[(count_df['FCLASS'] == 4)] + fc5_df = count_df[(count_df['FCLASS'] == 5)] + fc6_df = count_df[(count_df['FCLASS'] == 6)] + fc7_df = count_df[(count_df['FCLASS'] == 7)] + fc8_df = count_df[(count_df['FCLASS'] == 8)] + fc9_df = count_df[(count_df['FCLASS'] == 9)] + fc10_df = count_df[(count_df['FCLASS'] == 10)] + fc11_df = count_df[(count_df['FCLASS'] == 11)] + fc12_df = count_df[(count_df['FCLASS'] == 12)] + fc30_df = count_df[(count_df['FCLASS'] == 30)] + fc32_df = count_df[(count_df['FCLASS'] == 32)] + + # By Screenline + sl_1_df = count_df[(count_df['SCRNLINE1'] == 1) | (count_df['SCRNLINE2'] == 1)] + sl_2_df = count_df[(count_df['SCRNLINE1'] == 2) | (count_df['SCRNLINE2'] == 2)] + sl_3_df = count_df[(count_df['SCRNLINE1'] == 3) | (count_df['SCRNLINE2'] == 3)] + sl_4_df = count_df[(count_df['SCRNLINE1'] == 4) | (count_df['SCRNLINE2'] == 4)] + sl_5_df = count_df[(count_df['SCRNLINE1'] == 5) | (count_df['SCRNLINE2'] == 5)] + sl_6_df = count_df[(count_df['SCRNLINE1'] == 6) | (count_df['SCRNLINE2'] == 6)] + sl_7_df = count_df[(count_df['SCRNLINE1'] == 7) | (count_df['SCRNLINE2'] == 7)] + sl_8_df = count_df[(count_df['SCRNLINE1'] == 8) | (count_df['SCRNLINE2'] == 8)] + sl_9_df = count_df[(count_df['SCRNLINE1'] == 9) | (count_df['SCRNLINE2'] == 9)] + sl_10_df = count_df[(count_df['SCRNLINE1'] == 10) | (count_df['SCRNLINE2'] == 10)] + sl_11_df = count_df[(count_df['SCRNLINE1'] == 11) | (count_df['SCRNLINE2'] == 11)] + sl_12_df = count_df[(count_df['SCRNLINE1'] == 12) | (count_df['SCRNLINE2'] == 12)] + sl_13_df = count_df[(count_df['SCRNLINE1'] == 13) | (count_df['SCRNLINE2'] == 13)] + #sl_14_df = count_df[(count_df['SCRNLINE1'] == 14) | (count_df['SCRNLINE2'] == 14)] + #sl_15_df = count_df[(count_df['SCRNLINE1'] == 15) | (count_df['SCRNLINE2'] == 15)] + #sl_16_df = count_df[(count_df['SCRNLINE1'] == 16) | (count_df['SCRNLINE2'] == 16)] + #sl_17_df = count_df[(count_df['SCRNLINE1'] == 17) | (count_df['SCRNLINE2'] == 17)] + #sl_18_df = count_df[(count_df['SCRNLINE1'] == 18) | (count_df['SCRNLINE2'] == 18)] + #sl_19_df = count_df[(count_df['SCRNLINE1'] == 19) | (count_df['SCRNLINE2'] == 19)] + #sl_20_df = count_df[(count_df['SCRNLINE1'] == 20) | (count_df['SCRNLINE2'] == 20)] + #sl_21_df = count_df[(count_df['SCRNLINE1'] == 21) | (count_df['SCRNLINE2'] == 21)] + #sl_22_df = count_df[(count_df['SCRNLINE1'] == 22) | (count_df['SCRNLINE2'] == 22)] + #sl_23_df = count_df[(count_df['SCRNLINE1'] == 23) | (count_df['SCRNLINE2'] == 23)] + #sl_24_df = count_df[(count_df['SCRNLINE1'] == 24) | (count_df['SCRNLINE2'] == 24)] + #sl_25_df = count_df[(count_df['SCRNLINE1'] == 25) | (count_df['SCRNLINE2'] == 25)] + #sl_26_df = count_df[(count_df['SCRNLINE1'] == 26) | (count_df['SCRNLINE2'] == 26)] + + # Build list of dataframes to loop thru + auto_df_list = [count_df,#internal_df,external_df, + under_5k_df,btwn_5_10k_df,btwn_10_15k_df,btwn_15_20k_df,btwn_20_30k_df,btwn_30_40k_df,btwn_40_50k_df, + fc1_df,fc2_df,fc3_df,fc4_df,fc5_df,fc6_df,fc7_df,fc8_df,fc9_df,fc10_df,fc11_df,fc12_df,fc30_df,fc32_df, + sl_1_df,sl_2_df,sl_3_df,sl_4_df,sl_5_df,sl_6_df,sl_7_df,sl_8_df,sl_9_df,sl_10_df,sl_11_df,sl_12_df,sl_13_df] + + + + # All Links with SUT Counts + count_df = df[df['SUT_Count'].notna()] + # By AADT Volume + under_5k_df = count_df[(count_df['AADT'] < 5000)] + btwn_5_10k_df = count_df[(count_df['AADT'] >= 5000) & (count_df['AADT'] < 10000)] + btwn_10_15k_df = count_df[(count_df['AADT'] >= 10000) & (count_df['AADT'] < 15000)] + btwn_15_20k_df = count_df[(count_df['AADT'] >= 15000) & (count_df['AADT'] < 20000)] + btwn_20_30k_df = count_df[(count_df['AADT'] >= 20000) & (count_df['AADT'] < 30000)] + btwn_30_40k_df = count_df[(count_df['AADT'] >= 30000) & (count_df['AADT'] < 40000)] + btwn_40_50k_df = count_df[(count_df['AADT'] >= 40000) & (count_df['AADT'] < 50000)] + #over_50k_df = count_df[(count_df['AADT'] >= 50000)] + # By Functional Class + fc1_df = count_df[(count_df['FCLASS'] == 1)] + fc2_df = count_df[(count_df['FCLASS'] == 2)] + fc3_df = count_df[(count_df['FCLASS'] == 3)] + fc4_df = count_df[(count_df['FCLASS'] == 4)] + fc5_df = count_df[(count_df['FCLASS'] == 5)] + fc6_df = count_df[(count_df['FCLASS'] == 6)] + fc7_df = count_df[(count_df['FCLASS'] == 7)] + fc8_df = count_df[(count_df['FCLASS'] == 8)] + fc9_df = count_df[(count_df['FCLASS'] == 9)] + fc10_df = count_df[(count_df['FCLASS'] == 10)] + fc11_df = count_df[(count_df['FCLASS'] == 11)] + fc12_df = count_df[(count_df['FCLASS'] == 12)] + fc30_df = count_df[(count_df['FCLASS'] == 30)] + fc32_df = count_df[(count_df['FCLASS'] == 32)] + # By Screenline + sl_1_df = count_df[(count_df['SCRNLINE1'] == 1) | (count_df['SCRNLINE2'] == 1)] + sl_2_df = count_df[(count_df['SCRNLINE1'] == 2) | (count_df['SCRNLINE2'] == 2)] + sl_3_df = count_df[(count_df['SCRNLINE1'] == 3) | (count_df['SCRNLINE2'] == 3)] + sl_4_df = count_df[(count_df['SCRNLINE1'] == 4) | (count_df['SCRNLINE2'] == 4)] + sl_5_df = count_df[(count_df['SCRNLINE1'] == 5) | (count_df['SCRNLINE2'] == 5)] + sl_6_df = count_df[(count_df['SCRNLINE1'] == 6) | (count_df['SCRNLINE2'] == 6)] + sl_7_df = count_df[(count_df['SCRNLINE1'] == 7) | (count_df['SCRNLINE2'] == 7)] + sl_8_df = count_df[(count_df['SCRNLINE1'] == 8) | (count_df['SCRNLINE2'] == 8)] + sl_9_df = count_df[(count_df['SCRNLINE1'] == 9) | (count_df['SCRNLINE2'] == 9)] + sl_10_df = count_df[(count_df['SCRNLINE1'] == 10) | (count_df['SCRNLINE2'] == 10)] + sl_11_df = count_df[(count_df['SCRNLINE1'] == 11) | (count_df['SCRNLINE2'] == 11)] + sl_12_df = count_df[(count_df['SCRNLINE1'] == 12) | (count_df['SCRNLINE2'] == 12)] + sl_13_df = count_df[(count_df['SCRNLINE1'] == 13) | (count_df['SCRNLINE2'] == 13)] + #sl_14_df = count_df[(count_df['SCRNLINE1'] == 14) | (count_df['SCRNLINE2'] == 14)] + #sl_15_df = count_df[(count_df['SCRNLINE1'] == 15) | (count_df['SCRNLINE2'] == 15)] + #sl_16_df = count_df[(count_df['SCRNLINE1'] == 16) | (count_df['SCRNLINE2'] == 16)] + #sl_17_df = count_df[(count_df['SCRNLINE1'] == 17) | (count_df['SCRNLINE2'] == 17)] + #sl_18_df = count_df[(count_df['SCRNLINE1'] == 18) | (count_df['SCRNLINE2'] == 18)] + #sl_19_df = count_df[(count_df['SCRNLINE1'] == 19) | (count_df['SCRNLINE2'] == 19)] + #sl_20_df = count_df[(count_df['SCRNLINE1'] == 20) | (count_df['SCRNLINE2'] == 20)] + #sl_21_df = count_df[(count_df['SCRNLINE1'] == 21) | (count_df['SCRNLINE2'] == 21)] + #sl_22_df = count_df[(count_df['SCRNLINE1'] == 22) | (count_df['SCRNLINE2'] == 22)] + #sl_23_df = count_df[(count_df['SCRNLINE1'] == 23) | (count_df['SCRNLINE2'] == 23)] + #sl_24_df = count_df[(count_df['SCRNLINE1'] == 24) | (count_df['SCRNLINE2'] == 24)] + #sl_25_df = count_df[(count_df['SCRNLINE1'] == 25) | (count_df['SCRNLINE2'] == 25)] + #sl_26_df = count_df[(count_df['SCRNLINE1'] == 26) | (count_df['SCRNLINE2'] == 26)] + + # Build list of dataframes to loop thru + sut_df_list = [count_df,#internal_df,external_df, + under_5k_df,btwn_5_10k_df,btwn_10_15k_df,btwn_15_20k_df,btwn_20_30k_df,btwn_30_40k_df,btwn_40_50k_df, + fc1_df,fc2_df,fc3_df,fc4_df,fc5_df,fc6_df,fc7_df,fc8_df,fc9_df,fc10_df,fc11_df,fc12_df,fc30_df,fc32_df, + sl_1_df,sl_2_df,sl_3_df,sl_4_df,sl_5_df,sl_6_df,sl_7_df,sl_8_df,sl_9_df,sl_10_df,sl_11_df,sl_12_df,sl_13_df] + + + # All Links with MUT Counts + count_df = df[df['MUT_Count'].notna()] + # By AADT Volume + under_5k_df = count_df[(count_df['AADT'] < 5000)] + btwn_5_10k_df = count_df[(count_df['AADT'] >= 5000) & (count_df['AADT'] < 10000)] + btwn_10_15k_df = count_df[(count_df['AADT'] >= 10000) & (count_df['AADT'] < 15000)] + btwn_15_20k_df = count_df[(count_df['AADT'] >= 15000) & (count_df['AADT'] < 20000)] + btwn_20_30k_df = count_df[(count_df['AADT'] >= 20000) & (count_df['AADT'] < 30000)] + btwn_30_40k_df = count_df[(count_df['AADT'] >= 30000) & (count_df['AADT'] < 40000)] + btwn_40_50k_df = count_df[(count_df['AADT'] >= 40000) & (count_df['AADT'] < 50000)] + #over_50k_df = count_df[(count_df['AADT'] >= 50000)] + # By Functional Class + fc1_df = count_df[(count_df['FCLASS'] == 1)] + fc2_df = count_df[(count_df['FCLASS'] == 2)] + fc3_df = count_df[(count_df['FCLASS'] == 3)] + fc4_df = count_df[(count_df['FCLASS'] == 4)] + fc5_df = count_df[(count_df['FCLASS'] == 5)] + fc6_df = count_df[(count_df['FCLASS'] == 6)] + fc7_df = count_df[(count_df['FCLASS'] == 7)] + fc8_df = count_df[(count_df['FCLASS'] == 8)] + fc9_df = count_df[(count_df['FCLASS'] == 9)] + fc10_df = count_df[(count_df['FCLASS'] == 10)] + fc11_df = count_df[(count_df['FCLASS'] == 11)] + fc12_df = count_df[(count_df['FCLASS'] == 12)] + fc30_df = count_df[(count_df['FCLASS'] == 30)] + fc32_df = count_df[(count_df['FCLASS'] == 32)] + # By Screenline + sl_1_df = count_df[(count_df['SCRNLINE1'] == 1) | (count_df['SCRNLINE2'] == 1)] + sl_2_df = count_df[(count_df['SCRNLINE1'] == 2) | (count_df['SCRNLINE2'] == 2)] + sl_3_df = count_df[(count_df['SCRNLINE1'] == 3) | (count_df['SCRNLINE2'] == 3)] + sl_4_df = count_df[(count_df['SCRNLINE1'] == 4) | (count_df['SCRNLINE2'] == 4)] + sl_5_df = count_df[(count_df['SCRNLINE1'] == 5) | (count_df['SCRNLINE2'] == 5)] + sl_6_df = count_df[(count_df['SCRNLINE1'] == 6) | (count_df['SCRNLINE2'] == 6)] + sl_7_df = count_df[(count_df['SCRNLINE1'] == 7) | (count_df['SCRNLINE2'] == 7)] + sl_8_df = count_df[(count_df['SCRNLINE1'] == 8) | (count_df['SCRNLINE2'] == 8)] + sl_9_df = count_df[(count_df['SCRNLINE1'] == 9) | (count_df['SCRNLINE2'] == 9)] + sl_10_df = count_df[(count_df['SCRNLINE1'] == 10) | (count_df['SCRNLINE2'] == 10)] + sl_11_df = count_df[(count_df['SCRNLINE1'] == 11) | (count_df['SCRNLINE2'] == 11)] + sl_12_df = count_df[(count_df['SCRNLINE1'] == 12) | (count_df['SCRNLINE2'] == 12)] + sl_13_df = count_df[(count_df['SCRNLINE1'] == 13) | (count_df['SCRNLINE2'] == 13)] + #sl_14_df = count_df[(count_df['SCRNLINE1'] == 14) | (count_df['SCRNLINE2'] == 14)] + #sl_15_df = count_df[(count_df['SCRNLINE1'] == 15) | (count_df['SCRNLINE2'] == 15)] + #sl_16_df = count_df[(count_df['SCRNLINE1'] == 16) | (count_df['SCRNLINE2'] == 16)] + #sl_17_df = count_df[(count_df['SCRNLINE1'] == 17) | (count_df['SCRNLINE2'] == 17)] + #sl_18_df = count_df[(count_df['SCRNLINE1'] == 18) | (count_df['SCRNLINE2'] == 18)] + #sl_19_df = count_df[(count_df['SCRNLINE1'] == 19) | (count_df['SCRNLINE2'] == 19)] + #sl_20_df = count_df[(count_df['SCRNLINE1'] == 20) | (count_df['SCRNLINE2'] == 20)] + #sl_21_df = count_df[(count_df['SCRNLINE1'] == 21) | (count_df['SCRNLINE2'] == 21)] + #sl_22_df = count_df[(count_df['SCRNLINE1'] == 22) | (count_df['SCRNLINE2'] == 22)] + #sl_23_df = count_df[(count_df['SCRNLINE1'] == 23) | (count_df['SCRNLINE2'] == 23)] + #sl_24_df = count_df[(count_df['SCRNLINE1'] == 24) | (count_df['SCRNLINE2'] == 24)] + #sl_25_df = count_df[(count_df['SCRNLINE1'] == 25) | (count_df['SCRNLINE2'] == 25)] + #sl_26_df = count_df[(count_df['SCRNLINE1'] == 26) | (count_df['SCRNLINE2'] == 26)] + + + # Build list of dataframes to loop thru + mut_df_list = [count_df,#internal_df,external_df, + under_5k_df,btwn_5_10k_df,btwn_10_15k_df,btwn_15_20k_df,btwn_20_30k_df,btwn_30_40k_df,btwn_40_50k_df, + fc1_df,fc2_df,fc3_df,fc4_df,fc5_df,fc6_df,fc7_df,fc8_df,fc9_df,fc10_df,fc11_df,fc12_df,fc30_df,fc32_df, + sl_1_df,sl_2_df,sl_3_df,sl_4_df,sl_5_df,sl_6_df,sl_7_df,sl_8_df,sl_9_df,sl_10_df,sl_11_df,sl_12_df,sl_13_df] + + # All Links with All Modes Counts + count_df = df[df['Tot_Count'].notna()] + # By AADT Volume + under_5k_df = count_df[(count_df['AADT'] < 5000)] + btwn_5_10k_df = count_df[(count_df['AADT'] >= 5000) & (count_df['AADT'] < 10000)] + btwn_10_15k_df = count_df[(count_df['AADT'] >= 10000) & (count_df['AADT'] < 15000)] + btwn_15_20k_df = count_df[(count_df['AADT'] >= 15000) & (count_df['AADT'] < 20000)] + btwn_20_30k_df = count_df[(count_df['AADT'] >= 20000) & (count_df['AADT'] < 30000)] + btwn_30_40k_df = count_df[(count_df['AADT'] >= 30000) & (count_df['AADT'] < 40000)] + btwn_40_50k_df = count_df[(count_df['AADT'] >= 40000) & (count_df['AADT'] < 50000)] + # By Functional Class + fc1_df = count_df[(count_df['FCLASS'] == 1)] + fc2_df = count_df[(count_df['FCLASS'] == 2)] + fc3_df = count_df[(count_df['FCLASS'] == 3)] + fc4_df = count_df[(count_df['FCLASS'] == 4)] + fc5_df = count_df[(count_df['FCLASS'] == 5)] + fc6_df = count_df[(count_df['FCLASS'] == 6)] + fc7_df = count_df[(count_df['FCLASS'] == 7)] + fc8_df = count_df[(count_df['FCLASS'] == 8)] + fc9_df = count_df[(count_df['FCLASS'] == 9)] + fc10_df = count_df[(count_df['FCLASS'] == 10)] + fc11_df = count_df[(count_df['FCLASS'] == 11)] + fc12_df = count_df[(count_df['FCLASS'] == 12)] + fc30_df = count_df[(count_df['FCLASS'] == 30)] + fc32_df = count_df[(count_df['FCLASS'] == 32)] + # By Screenline + sl_1_df = count_df[(count_df['SCRNLINE1'] == 1) | (count_df['SCRNLINE2'] == 1)] + sl_2_df = count_df[(count_df['SCRNLINE1'] == 2) | (count_df['SCRNLINE2'] == 2)] + sl_3_df = count_df[(count_df['SCRNLINE1'] == 3) | (count_df['SCRNLINE2'] == 3)] + sl_4_df = count_df[(count_df['SCRNLINE1'] == 4) | (count_df['SCRNLINE2'] == 4)] + sl_5_df = count_df[(count_df['SCRNLINE1'] == 5) | (count_df['SCRNLINE2'] == 5)] + sl_6_df = count_df[(count_df['SCRNLINE1'] == 6) | (count_df['SCRNLINE2'] == 6)] + sl_7_df = count_df[(count_df['SCRNLINE1'] == 7) | (count_df['SCRNLINE2'] == 7)] + sl_8_df = count_df[(count_df['SCRNLINE1'] == 8) | (count_df['SCRNLINE2'] == 8)] + sl_9_df = count_df[(count_df['SCRNLINE1'] == 9) | (count_df['SCRNLINE2'] == 9)] + sl_10_df = count_df[(count_df['SCRNLINE1'] == 10) | (count_df['SCRNLINE2'] == 10)] + sl_11_df = count_df[(count_df['SCRNLINE1'] == 11) | (count_df['SCRNLINE2'] == 11)] + sl_12_df = count_df[(count_df['SCRNLINE1'] == 12) | (count_df['SCRNLINE2'] == 12)] + sl_13_df = count_df[(count_df['SCRNLINE1'] == 13) | (count_df['SCRNLINE2'] == 13)] + #sl_14_df = count_df[(count_df['SCRNLINE1'] == 14) | (count_df['SCRNLINE2'] == 14)] + #sl_15_df = count_df[(count_df['SCRNLINE1'] == 15) | (count_df['SCRNLINE2'] == 15)] + #sl_16_df = count_df[(count_df['SCRNLINE1'] == 16) | (count_df['SCRNLINE2'] == 16)] + #sl_17_df = count_df[(count_df['SCRNLINE1'] == 17) | (count_df['SCRNLINE2'] == 17)] + #sl_18_df = count_df[(count_df['SCRNLINE1'] == 18) | (count_df['SCRNLINE2'] == 18)] + #sl_19_df = count_df[(count_df['SCRNLINE1'] == 19) | (count_df['SCRNLINE2'] == 19)] + #sl_20_df = count_df[(count_df['SCRNLINE1'] == 20) | (count_df['SCRNLINE2'] == 20)] + #sl_21_df = count_df[(count_df['SCRNLINE1'] == 21) | (count_df['SCRNLINE2'] == 21)] + #sl_22_df = count_df[(count_df['SCRNLINE1'] == 22) | (count_df['SCRNLINE2'] == 22)] + #sl_23_df = count_df[(count_df['SCRNLINE1'] == 23) | (count_df['SCRNLINE2'] == 23)] + #sl_24_df = count_df[(count_df['SCRNLINE1'] == 24) | (count_df['SCRNLINE2'] == 24)] + #sl_25_df = count_df[(count_df['SCRNLINE1'] == 25) | (count_df['SCRNLINE2'] == 25)] + #sl_26_df = count_df[(count_df['SCRNLINE1'] == 26) | (count_df['SCRNLINE2'] == 26)] + + + # Build list of dataframes to loop thru + allmodes_df_list = [count_df,#internal_df,external_df, + under_5k_df,btwn_5_10k_df,btwn_10_15k_df,btwn_15_20k_df,btwn_20_30k_df,btwn_30_40k_df,btwn_40_50k_df, + fc1_df,fc2_df,fc3_df,fc4_df,fc5_df,fc6_df,fc7_df,fc8_df,fc9_df,fc10_df,fc11_df,fc12_df,fc30_df,fc32_df, + sl_1_df,sl_2_df,sl_3_df,sl_4_df,sl_5_df,sl_6_df,sl_7_df,sl_8_df,sl_9_df,sl_10_df,sl_11_df,sl_12_df,sl_13_df] + + + # Add squared error column to each df + # Auto + for i in auto_df_list: + i['Auto_SqError'] = (i.Auto_Flow - i.Auto_Count)**2 + # SUT + for i in sut_df_list: + i['SUT_SqError'] = (i.SUT_Flow - i.SUT_Count)**2 + # MUT + for i in mut_df_list: + i['MUT_SqError'] = (i.MUT_Flow - i.MUT_Count)**2 + # All Modes + for i in allmodes_df_list: + i['Tot_SqError'] = (i.Tot_Flow - i.Tot_Count)**2 + + # Create attributes for 'y' in the next section + auto_loc = 0 + sut_loc = auto_loc + len(sut_df_list) + mut_loc = sut_loc + len(mut_df_list) + allmodes_loc = mut_loc + len(allmodes_df_list) + + + # Calculate Auto pct error and pct rmse from each dataframe and save in results dataframe + y = auto_loc + for i in auto_df_list: + if len(i) == 0: + results_df.at[y,"Number of Observations"] = 0 + y = y + 1 + #continue + else: + results_df.at[y,"Percent Error"] = pct_error(i.Auto_Count,i.Auto_Flow) + results_df.at[y,"Percent RMSE"] = pct_rmse(i.Auto_Count,i.Auto_SqError) + results_df.at[y,"Number of Observations"] = len(i) + results_df.at[y,"Sum of Counts"] = np.sum(i.Auto_Count) + results_df.at[y,"Mean of Counts"] = np.mean(i.Auto_Count) + results_df.at[y,"Median of Counts"] = np.median(i.Auto_Count) + results_df.at[y,"Count VMT, Links with Counts"] = vmt(i.Auto_Count,i.LENGTH) + results_df.at[y,'Modeled VMT, Links with Counts'] = vmt(i.Auto_Flow,i.LENGTH) + y = y + 1 + + # Calculate SUT pct error and pct rmse from each dataframe and save in results dataframe + y = sut_loc + for i in sut_df_list: + if len(i) == 0: + results_df.at[y,"Number of Observations"] = 0 + y = y + 1 + #continue + else: + results_df.at[y,"Percent Error"] = pct_error(i.SUT_Count,i.SUT_Flow) + results_df.at[y,"Percent RMSE"] = pct_rmse(i.SUT_Count, i.SUT_SqError) + results_df.at[y,"Number of Observations"] = len(i) + results_df.at[y,"Sum of Counts"] = np.sum(i.SUT_Count) + results_df.at[y,"Mean of Counts"] = np.mean(i.SUT_Count) + results_df.at[y,"Median of Counts"] = np.median(i.SUT_Count) + results_df.at[y,"Count VMT, Links with Counts"] = vmt(i.SUT_Count,i.LENGTH) + results_df.at[y,'Modeled VMT, Links with Counts'] = vmt(i.SUT_Flow,i.LENGTH) + y = y + 1 + + # Calculate MUT pct error and pct rmse from each dataframe and save in results dataframe + y = mut_loc + for i in mut_df_list: + if len(i) == 0: + results_df.at[y,"Number of Observations"] = 0 + y = y + 1 + #continue + else: + results_df.at[y,"Percent Error"] = pct_error(i.MUT_Count,i.MUT_Flow) + results_df.at[y,"Percent RMSE"] = pct_rmse(i.MUT_Count, i.MUT_SqError) + results_df.at[y,"Number of Observations"] = len(i) + results_df.at[y,"Sum of Counts"] = np.sum(i.MUT_Count) + results_df.at[y,"Mean of Counts"] = np.mean(i.MUT_Count) + results_df.at[y,"Median of Counts"] = np.median(i.MUT_Count) + results_df.at[y,"Count VMT, Links with Counts"] = vmt(i.MUT_Count,i.LENGTH) + results_df.at[y,'Modeled VMT, Links with Counts'] = vmt(i.MUT_Flow,i.LENGTH) + y = y + 1 + + # Calculate All Modes pct error and pct rmse from each dataframe and save in results dataframe + y = allmodes_loc + for i in allmodes_df_list: + if len(i) == 0: + results_df.at[y,"Number of Observations"] = 0 + y = y + 1 + #continue + else: + results_df.at[y,"Percent Error"] = pct_error(i.Tot_Count,i.Tot_Flow) + results_df.at[y,"Percent RMSE"] = pct_rmse(i.Tot_Count,i.Tot_SqError) + results_df.at[y,"Number of Observations"] = len(i) + results_df.at[y,"Sum of Counts"] = np.sum(i.Tot_Count) + results_df.at[y,"Mean of Counts"] = np.mean(i.Tot_Count) + results_df.at[y,"Median of Counts"] = np.median(i.Tot_Count) + results_df.at[y,"Count VMT, Links with Counts"] = vmt(i.Tot_Count,i.LENGTH) + results_df.at[y,'Modeled VMT, Links with Counts'] = vmt(i.Tot_Flow,i.LENGTH) + y = y + 1 + + + # Total VMT and Total VHT + + # Import ID fields and fields with Counts and Flows + # Link ID fields + NO = VisumPy.helpers.GetMulti(Visum.Net.Links,"No", activeOnly = True) + FCLASS = VisumPy.helpers.GetMulti(Visum.Net.Links,"TYPENO", activeOnly = True) + LENGTH = VisumPy.helpers.GetMulti(Visum.Net.Links,"Length", activeOnly = True) + SCRNLINE = VisumPy.helpers.GetMulti(Visum.Net.Links,r"CONCATENATE:SCREENLINES\CODE", activeOnly = True) + # Pull CONGTIME Auto by period, Length, and Flows by Period for Total VMT/Total VHT Calculations + CONGTIME_AM_C = VisumPy.helpers.GetMulti(Visum.Net.Links,"AMTCUR_A", activeOnly = True) + CONGTIME_PM_C = VisumPy.helpers.GetMulti(Visum.Net.Links,"PMTCUR_A", activeOnly = True) + CONGTIME_OP_C = VisumPy.helpers.GetMulti(Visum.Net.Links,"OPTCUR_A", activeOnly = True) + # Pull CONGTIME SUT by period, Length, and Flows by Period for Total VMT/Total VHT Calculations + CONGTIME_AM_S = VisumPy.helpers.GetMulti(Visum.Net.Links,"AMTCUR_MED", activeOnly = True) + CONGTIME_PM_S = VisumPy.helpers.GetMulti(Visum.Net.Links,"PMTCUR_MED", activeOnly = True) + CONGTIME_OP_S = VisumPy.helpers.GetMulti(Visum.Net.Links,"OPTCUR_MED", activeOnly = True) + # Pull CONGTIME SUT by period, Length, and Flows by Period for Total VMT/Total VHT Calculations + CONGTIME_AM_M = VisumPy.helpers.GetMulti(Visum.Net.Links,"AMTCUR_HVY", activeOnly = True) + CONGTIME_PM_M = VisumPy.helpers.GetMulti(Visum.Net.Links,"PMTCUR_HVY", activeOnly = True) + CONGTIME_OP_M = VisumPy.helpers.GetMulti(Visum.Net.Links,"OPTCUR_HVY", activeOnly = True) + + # Link Flows by Period + # AM + sov_flow = VisumPy.helpers.GetMulti(Visum.Net.Links,"AMSVOL", activeOnly = True) + hov_flow = VisumPy.helpers.GetMulti(Visum.Net.Links,"AMHVOL", activeOnly = True) + + AM_Auto_Flow = np.add(sov_flow,hov_flow) + AM_SUT_Flow = VisumPy.helpers.GetMulti(Visum.Net.Links,"AMMTVOL", activeOnly = True) + AM_MUT_Flow = VisumPy.helpers.GetMulti(Visum.Net.Links,"AMHTVOL", activeOnly = True) + AM_Tot_Flow = VisumPy.helpers.GetMulti(Visum.Net.Links,"AMVOLPCU_N", activeOnly = True) + # OP + sov_flow = VisumPy.helpers.GetMulti(Visum.Net.Links,"PMSVOL", activeOnly = True) + hov_flow = VisumPy.helpers.GetMulti(Visum.Net.Links,"PMHVOL", activeOnly = True) + + PM_Auto_Flow = np.add(sov_flow,hov_flow) + PM_SUT_Flow = VisumPy.helpers.GetMulti(Visum.Net.Links,"PMMTVOL", activeOnly = True) + PM_MUT_Flow = VisumPy.helpers.GetMulti(Visum.Net.Links,"PMHTVOL", activeOnly = True) + PM_Tot_Flow = VisumPy.helpers.GetMulti(Visum.Net.Links,"PMVOLPCU_N", activeOnly = True) + # OP + sov_flow = VisumPy.helpers.GetMulti(Visum.Net.Links,"OPSVOL", activeOnly = True) + hov_flow = VisumPy.helpers.GetMulti(Visum.Net.Links,"OPHVOL", activeOnly = True) + + OP_Auto_Flow = np.add(sov_flow,hov_flow) + OP_SUT_Flow = VisumPy.helpers.GetMulti(Visum.Net.Links,"OPMTVOL", activeOnly = True) + OP_MUT_Flow = VisumPy.helpers.GetMulti(Visum.Net.Links,"OPHTVOL", activeOnly = True) + OP_Tot_Flow = VisumPy.helpers.GetMulti(Visum.Net.Links,"OPVOLPCU_N", activeOnly = True) + + # Period for functions + Auto_Flow = VisumPy.helpers.GetMulti(Visum.Net.Links,auto_flow, activeOnly = True) + SUT_Flow = VisumPy.helpers.GetMulti(Visum.Net.Links,sut_flow, activeOnly = True) + MUT_Flow = VisumPy.helpers.GetMulti(Visum.Net.Links,mut_flow, activeOnly = True) + Tot_Flow = VisumPy.helpers.GetMulti(Visum.Net.Links,all_flow, activeOnly = True) + + + # Make Visum list with link data + summary_list = [NO, FCLASS, LENGTH, SCRNLINE, + CONGTIME_AM_C, CONGTIME_PM_C, CONGTIME_OP_C, + CONGTIME_AM_S, CONGTIME_PM_S, CONGTIME_OP_S, + CONGTIME_AM_M, CONGTIME_PM_M, CONGTIME_OP_M, + AM_Auto_Flow, AM_SUT_Flow, AM_MUT_Flow, AM_Tot_Flow, + PM_Auto_Flow, PM_SUT_Flow, PM_MUT_Flow, PM_Tot_Flow, + OP_Auto_Flow, OP_SUT_Flow, OP_MUT_Flow, OP_Tot_Flow, + Auto_Flow, SUT_Flow, MUT_Flow, Tot_Flow] + + # Put Visum link list into dataframe + df_all = pd.DataFrame(np.column_stack(summary_list), columns = ['NO', 'FCLASS', 'LENGTH', 'SCRNLINE', + 'CONGTIME_AM_C', 'CONGTIME_PM_C', 'CONGTIME_OP_C', + 'CONGTIME_AM_S', 'CONGTIME_PM_S', 'CONGTIME_OP_S', + 'CONGTIME_AM_M', 'CONGTIME_PM_M', 'CONGTIME_OP_M', + 'AM_Auto_Flow', 'AM_SUT_Flow', 'AM_MUT_Flow', 'AM_Tot_Flow', + 'PM_Auto_Flow', 'PM_SUT_Flow', 'PM_MUT_Flow', 'PM_Tot_Flow', + 'OP_Auto_Flow', 'OP_SUT_Flow', 'OP_MUT_Flow', 'OP_Tot_Flow', + 'Auto_Flow', 'SUT_Flow', 'MUT_Flow', 'Tot_Flow']) + + + # Break out SCRNLINE field to separate by commas into individual columns + df_all[['SCRNLINE']] = df_all[['SCRNLINE']].astype(str) + df_all = pd.concat([df_all,df_all['SCRNLINE'].str.split(',', expand = True)], axis = 1) + # Change Screenline field names + if 1 not in df_all: + df_all[1] = 0 + df_all = df_all.rename(columns = {0:'SCRNLINE1',1:'SCRNLINE2'}) + + ## Break out SCRNLINE field to separate by commas into individual columns + #df_all = pd.concat([df_all,df_all['SCRNLINE'].str.split(',', expand = True)], axis = 1) + ## Change Screenline field names + #df_all = df_all.rename(columns = {0:'SCRNLINE1',1:'SCRNLINE2'}) + + # Replace null values with 0 in the screenline fields + df_all['SCRNLINE1'] = df_all['SCRNLINE1'].replace('',np.nan).fillna(0) + df_all['SCRNLINE2'] = df_all['SCRNLINE2'].replace('',np.nan).fillna(0) + + # Convert all flow and time fields to float to make multiplication and other operations run smoothly. Read in as strings + df_all[['NO', 'FCLASS', 'LENGTH', 'SCRNLINE1','SCRNLINE2', + 'CONGTIME_AM_C', 'CONGTIME_PM_C', 'CONGTIME_OP_C', + 'CONGTIME_AM_S', 'CONGTIME_PM_S', 'CONGTIME_OP_S', + 'CONGTIME_AM_M', 'CONGTIME_PM_M', 'CONGTIME_OP_M', + 'AM_Auto_Flow', 'AM_SUT_Flow', 'AM_MUT_Flow', 'AM_Tot_Flow', + 'PM_Auto_Flow', 'PM_SUT_Flow', 'PM_MUT_Flow', 'PM_Tot_Flow', + 'OP_Auto_Flow', 'OP_SUT_Flow', 'OP_MUT_Flow', 'OP_Tot_Flow', + 'Auto_Flow', 'SUT_Flow', 'MUT_Flow', 'Tot_Flow']] = df_all[['NO', 'FCLASS', 'LENGTH', 'SCRNLINE1','SCRNLINE2', + 'CONGTIME_AM_C', 'CONGTIME_PM_C', 'CONGTIME_OP_C', + 'CONGTIME_AM_S', 'CONGTIME_PM_S', 'CONGTIME_OP_S', + 'CONGTIME_AM_M', 'CONGTIME_PM_M', 'CONGTIME_OP_M', + 'AM_Auto_Flow', 'AM_SUT_Flow', 'AM_MUT_Flow', 'AM_Tot_Flow', + 'PM_Auto_Flow', 'PM_SUT_Flow', 'PM_MUT_Flow', 'PM_Tot_Flow', + 'OP_Auto_Flow', 'OP_SUT_Flow', 'OP_MUT_Flow', 'OP_Tot_Flow', + 'Auto_Flow', 'SUT_Flow', 'MUT_Flow', 'Tot_Flow']].astype(float) + + # Convert ID fields to integer + df_all[['NO','FCLASS','SCRNLINE1','SCRNLINE2']] = df_all[['NO','FCLASS','SCRNLINE1','SCRNLINE2']].astype(int) + + + # For links with counts only, used for Pct. Error and Pct. RMSE + # Filter out links where count is null and by each condition + # All Links with Counts + count_df_all = df_all + # By AADT Volume + under_5k_df_all = df_all[df_all['NO'].isin(under_5k_df['NO'])] + btwn_5_10k_df_all = df_all[df_all['NO'].isin(btwn_5_10k_df['NO'])] + btwn_10_15k_df_all = df_all[df_all['NO'].isin(btwn_10_15k_df['NO'])] + btwn_15_20k_df_all = df_all[df_all['NO'].isin(btwn_15_20k_df['NO'])] + btwn_20_30k_df_all = df_all[df_all['NO'].isin(btwn_20_30k_df['NO'])] + btwn_30_40k_df_all = df_all[df_all['NO'].isin(btwn_30_40k_df['NO'])] + btwn_40_50k_df_all = df_all[df_all['NO'].isin(btwn_40_50k_df['NO'])] + # By Functional Class + fc1_df_all = df_all[(df_all['FCLASS'] == 1)] + fc2_df_all = df_all[(df_all['FCLASS'] == 2)] + fc3_df_all = df_all[(df_all['FCLASS'] == 3)] + fc4_df_all = df_all[(df_all['FCLASS'] == 4)] + fc5_df_all = df_all[(df_all['FCLASS'] == 5)] + fc6_df_all = df_all[(df_all['FCLASS'] == 6)] + fc7_df_all = df_all[(df_all['FCLASS'] == 7)] + fc8_df_all = df_all[(df_all['FCLASS'] == 8)] + fc9_df_all = df_all[(df_all['FCLASS'] == 9)] + fc10_df_all = df_all[(df_all['FCLASS'] == 10)] + fc11_df_all = df_all[(df_all['FCLASS'] == 11)] + fc12_df_all = df_all[(df_all['FCLASS'] == 12)] + fc30_df_all = df_all[(df_all['FCLASS'] == 30)] + fc32_df_all = df_all[(df_all['FCLASS'] == 32)] + # By Screenline + sl_1_df_all = df_all[(df_all['SCRNLINE1'] == 1) | (df_all['SCRNLINE2'] == 1)] + sl_2_df_all = df_all[(df_all['SCRNLINE1'] == 2) | (df_all['SCRNLINE2'] == 2)] + sl_3_df_all = df_all[(df_all['SCRNLINE1'] == 3) | (df_all['SCRNLINE2'] == 3)] + sl_4_df_all = df_all[(df_all['SCRNLINE1'] == 4) | (df_all['SCRNLINE2'] == 4)] + sl_5_df_all = df_all[(df_all['SCRNLINE1'] == 5) | (df_all['SCRNLINE2'] == 5)] + sl_6_df_all = df_all[(df_all['SCRNLINE1'] == 6) | (df_all['SCRNLINE2'] == 6)] + sl_7_df_all = df_all[(df_all['SCRNLINE1'] == 7) | (df_all['SCRNLINE2'] == 7)] + sl_8_df_all = df_all[(df_all['SCRNLINE1'] == 8) | (df_all['SCRNLINE2'] == 8)] + sl_9_df_all = df_all[(df_all['SCRNLINE1'] == 9) | (df_all['SCRNLINE2'] == 9)] + sl_10_df_all = df_all[(df_all['SCRNLINE1'] == 10) | (df_all['SCRNLINE2'] == 10)] + sl_11_df_all = df_all[(df_all['SCRNLINE1'] == 11) | (df_all['SCRNLINE2'] == 11)] + sl_12_df_all = df_all[(df_all['SCRNLINE1'] == 12) | (df_all['SCRNLINE2'] == 12)] + sl_13_df_all = df_all[(df_all['SCRNLINE1'] == 13) | (df_all['SCRNLINE2'] == 13)] + #sl_14_df_all = df_all[(df_all['SCRNLINE1'] == 14) | (df_all['SCRNLINE2'] == 14)] + #sl_15_df_all = df_all[(df_all['SCRNLINE1'] == 15) | (df_all['SCRNLINE2'] == 15)] + #sl_16_df_all = df_all[(df_all['SCRNLINE1'] == 16) | (df_all['SCRNLINE2'] == 16)] + #sl_17_df_all = df_all[(df_all['SCRNLINE1'] == 17) | (df_all['SCRNLINE2'] == 17)] + #sl_18_df_all = df_all[(df_all['SCRNLINE1'] == 18) | (df_all['SCRNLINE2'] == 18)] + #sl_19_df_all = df_all[(df_all['SCRNLINE1'] == 19) | (df_all['SCRNLINE2'] == 19)] + #sl_20_df_all = df_all[(df_all['SCRNLINE1'] == 20) | (df_all['SCRNLINE2'] == 20)] + #sl_21_df_all = df_all[(df_all['SCRNLINE1'] == 21) | (df_all['SCRNLINE2'] == 21)] + #sl_22_df_all = df_all[(df_all['SCRNLINE1'] == 22) | (df_all['SCRNLINE2'] == 22)] + #sl_23_df_all = df_all[(df_all['SCRNLINE1'] == 23) | (df_all['SCRNLINE2'] == 23)] + #sl_24_df_all = df_all[(df_all['SCRNLINE1'] == 24) | (df_all['SCRNLINE2'] == 24)] + #sl_25_df_all = df_all[(df_all['SCRNLINE1'] == 25) | (df_all['SCRNLINE2'] == 25)] + #sl_26_df_all = df_all[(df_all['SCRNLINE1'] == 26) | (df_all['SCRNLINE2'] == 26)] + + + # Build list of dataframes to loop thru + df_list_all = [count_df_all,#internal_df_all,external_df_all, + under_5k_df_all,btwn_5_10k_df_all,btwn_10_15k_df_all,btwn_15_20k_df_all,btwn_20_30k_df_all,btwn_30_40k_df_all,btwn_40_50k_df_all, + fc1_df_all,fc2_df_all,fc3_df_all,fc4_df_all,fc5_df_all,fc6_df_all,fc7_df_all,fc8_df_all,fc9_df_all,fc10_df_all,fc11_df_all,fc12_df_all, + fc30_df_all,fc32_df_all, + sl_1_df_all,sl_2_df_all,sl_3_df_all,sl_4_df_all,sl_5_df_all,sl_6_df_all,sl_7_df_all,sl_8_df_all,sl_9_df_all,sl_10_df_all,sl_11_df_all, + sl_12_df_all,sl_13_df_all] + + + # Calculate Auto Total VMT and Total VHT from each dataframe and save in results dataframe + y = auto_loc + for i in df_list_all: + if len(i) == 0: #sum(i.Auto_Flow) == 0.0: + y = y + 1 + continue + else: + results_df.at[y,"Total VMT"] = vmt(i.Auto_Flow,i.LENGTH) + results_df.at[y,"Total VHT"] = vht_dly(i.AM_Auto_Flow,i.CONGTIME_AM_C,i.PM_Auto_Flow,i.CONGTIME_PM_C,i.OP_Auto_Flow,i.CONGTIME_OP_C) + y = y + 1 + + # Calculate SUT Total VMT and Total VHT from each dataframe and save in results dataframe + y = sut_loc + for i in df_list_all: + if len(i) == 0: #sum(i.Auto_Flow) == 0.0: + y = y + 1 + continue + else: + results_df.at[y,"Total VMT"] = vmt(i.SUT_Flow,i.LENGTH) + results_df.at[y,"Total VHT"] = vht_dly(i.AM_SUT_Flow,i.CONGTIME_AM_S,i.PM_SUT_Flow,i.CONGTIME_PM_S,i.OP_SUT_Flow,i.CONGTIME_OP_S) + y = y + 1 + + # Calculate MUT Total VMT and Total VHT from each dataframe and save in results dataframe + y = mut_loc + for i in df_list_all: + if len(i) == 0: #sum(i.Auto_Flow) == 0.0: + y = y + 1 + continue + else: + results_df.at[y,"Total VMT"] = vmt(i.MUT_Flow,i.LENGTH) + results_df.at[y,"Total VHT"] = vht_dly(i.AM_MUT_Flow,i.CONGTIME_AM_M,i.PM_MUT_Flow,i.CONGTIME_PM_M,i.OP_MUT_Flow,i.CONGTIME_OP_M) + y = y + 1 + + # Calculate All Modes Total VMT and Total VHT from each dataframe and save in results dataframe + y = allmodes_loc + a = auto_loc + s = sut_loc + m = mut_loc + for i in df_list_all: + if len(i) == 0: #sum(i.Tot_Flow) == 0.0: + y = y + 1 + continue + else: + results_df.at[y,"Total VMT"] = results_df.at[a,"Total VMT"] + results_df.at[s,"Total VMT"] + results_df.at[m,"Total VMT"] # vmt(i.Tot_Flow,i.LENGTH) + results_df.at[y,"Total VHT"] = results_df.at[a,"Total VHT"] + results_df.at[s,"Total VHT"] + results_df.at[m,"Total VHT"] # vht(i.AM_Tot_Flow,i.CONGTIME_AM,i.MD_Tot_Flow,i.CONGTIME_MD,i.PM_Tot_Flow,i.CONGTIME_PM,i.NI_Tot_Flow,i.CONGTIME_NT) + y = y + 1 + a = a + 1 + s = s + 1 + m = m + 1 + + + # Apply the formatting function to specific columns + results_df['Percent Error'] = format_percent(results_df['Percent Error']) + results_df['Percent RMSE'] = format_percent(results_df['Percent RMSE']) + results_df['Total VMT'] = format_commas(results_df['Total VMT']) + results_df['Total VHT'] = format_commas(results_df['Total VHT']) + results_df['Number of Observations'] = format_commas(results_df['Number of Observations']) + results_df['Sum of Counts'] = format_commas(results_df['Sum of Counts']) + results_df['Mean of Counts'] = format_commas(results_df['Mean of Counts']) + results_df['Median of Counts'] = format_commas(results_df['Median of Counts']) + results_df['Count VMT, Links with Counts'] = format_commas(results_df['Count VMT, Links with Counts']) + results_df['Modeled VMT, Links with Counts'] = format_commas(results_df['Modeled VMT, Links with Counts']) + + + # Save Daily Summary file to new timestamped folder + results_df.to_csv(proj_dir+"outputs/reports/ModelRun_"+date+"/Assignment Results/AssignmentSummary.csv") + +# Transit Unlinked Trips Summary Table +def transit_report(): + + # Pull Lines table attributes + Name = VisumPy.helpers.GetMulti(Visum.Net.Lines,"Name", activeOnly = True) + Description = VisumPy.helpers.GetMulti(Visum.Net.Lines,"Emme_Description", activeOnly = True) + AM_Trips = VisumPy.helpers.GetMulti(Visum.Net.Lines,"AM_UL_TRIPS", activeOnly = True) + PM_Trips = VisumPy.helpers.GetMulti(Visum.Net.Lines,"PM_UL_TRIPS", activeOnly = True) + OP_Trips = VisumPy.helpers.GetMulti(Visum.Net.Lines,"OP_UL_TRIPS", activeOnly = True) + DLY_Trips = VisumPy.helpers.GetMulti(Visum.Net.Lines,"DLY_UL_TRIPS", activeOnly = True) + + # Make Visum list with link data + summary_list = [Name, Description, AM_Trips, PM_Trips, OP_Trips, DLY_Trips] + + # Put Visum link list into dataframe + df = pd.DataFrame(np.column_stack(summary_list), columns = ['Name', 'Description', 'AM_Trips', 'PM_Trips', 'OP_Trips', 'DLY_Trips']) + + # Save Transit results to folder + df.to_csv(proj_dir+"outputs/reports/ModelRun_"+date+"/Assignment Results/TransitSummary.csv") + + + +# Daily +assignment_summary('AUTO_COUNT_DLY', 'SUT_COUNT_DLY', 'MUT_COUNT_DLY', 'TOT_COUNT_DLY', 'AUTO_VOL_DLY', 'SUT_VOL_DLY', 'MUT_VOL_DLY', 'TOT_VOL_DLY') # 'DAILY_LCV_VOL', 'AM3_CTIME_C', 'AM3_CTIME_T', 'Daily') # Daily VHT Calculated from full day, so AM3_CTIME here is just a placeholder for the function + +# Transit +transit_report() + + diff --git a/skimming_and_assignment/visum/scripts/CostSkim_Setup.py b/skimming_and_assignment/visum/scripts/CostSkim_Setup.py new file mode 100644 index 0000000..0958dd1 --- /dev/null +++ b/skimming_and_assignment/visum/scripts/CostSkim_Setup.py @@ -0,0 +1,158 @@ +# Setting up for PrT cost skimming +# 6/24/2025 - Luke Gordon (RSG) +# Adapted from code from Chetan Joshi (PTV) + +import tables +import numpy as np +import pandas as pd +import os +import VisumPy.helpers as h +import openmatrix as omx + + +def costskim_setup(period, mode, vot): + + # Links + # Pull AddVals for skimming + addval1 = h.GetMulti(Visum.Net.Links,r"ADDVAL1" , activeOnly = True) # TEMPORARY, NEED TO REPLACE WITH ACTUAL TRAVEL TIME BY PERIOD USING VDF + addval2 = h.GetMulti(Visum.Net.Links,r"ADDVAL2" , activeOnly = True) + # Pull Toll Fields + EA_SOV_TOLL = h.GetMulti(Visum.Net.Links,r"EA_SOV_TOLL", activeOnly = True) + EA_SR2_TOLL = h.GetMulti(Visum.Net.Links,r"EA_SR2_TOLL", activeOnly = True) + EA_SR3_TOLL = h.GetMulti(Visum.Net.Links,r"EA_SR3_TOLL", activeOnly = True) + EA_MT_TOLL = h.GetMulti(Visum.Net.Links,r"EA_MT_TOLL" , activeOnly = True) + EA_HT_TOLL = h.GetMulti(Visum.Net.Links,r"EA_HT_TOLL" , activeOnly = True) + AM_SOV_TOLL = h.GetMulti(Visum.Net.Links,r"AM_SOV_TOLL", activeOnly = True) + AM_SR2_TOLL = h.GetMulti(Visum.Net.Links,r"AM_SR2_TOLL", activeOnly = True) + AM_SR3_TOLL = h.GetMulti(Visum.Net.Links,r"AM_SR3_TOLL", activeOnly = True) + AM_MT_TOLL = h.GetMulti(Visum.Net.Links,r"AM_MT_TOLL" , activeOnly = True) + AM_HT_TOLL = h.GetMulti(Visum.Net.Links,r"AM_HT_TOLL" , activeOnly = True) + MD_SOV_TOLL = h.GetMulti(Visum.Net.Links,r"MD_SOV_TOLL", activeOnly = True) + MD_SR2_TOLL = h.GetMulti(Visum.Net.Links,r"MD_SR2_TOLL", activeOnly = True) + MD_SR3_TOLL = h.GetMulti(Visum.Net.Links,r"MD_SR3_TOLL", activeOnly = True) + MD_MT_TOLL = h.GetMulti(Visum.Net.Links,r"MD_MT_TOLL" , activeOnly = True) + MD_HT_TOLL = h.GetMulti(Visum.Net.Links,r"MD_HT_TOLL" , activeOnly = True) + PM_SOV_TOLL = h.GetMulti(Visum.Net.Links,r"PM_SOV_TOLL", activeOnly = True) + PM_SR2_TOLL = h.GetMulti(Visum.Net.Links,r"PM_SR2_TOLL", activeOnly = True) + PM_SR3_TOLL = h.GetMulti(Visum.Net.Links,r"PM_SR3_TOLL", activeOnly = True) + PM_MT_TOLL = h.GetMulti(Visum.Net.Links,r"PM_MT_TOLL" , activeOnly = True) + PM_HT_TOLL = h.GetMulti(Visum.Net.Links,r"PM_HT_TOLL" , activeOnly = True) + EV_SOV_TOLL = h.GetMulti(Visum.Net.Links,r"EV_SOV_TOLL", activeOnly = True) + EV_SR2_TOLL = h.GetMulti(Visum.Net.Links,r"EV_SR2_TOLL", activeOnly = True) + EV_SR3_TOLL = h.GetMulti(Visum.Net.Links,r"EV_SR3_TOLL", activeOnly = True) + EV_MT_TOLL = h.GetMulti(Visum.Net.Links,r"EV_MT_TOLL" , activeOnly = True) + EV_HT_TOLL = h.GetMulti(Visum.Net.Links,r"EV_HT_TOLL" , activeOnly = True) + + + # Make Visum list with link data + att_list = [addval1,addval2, + EA_SOV_TOLL,EA_SR2_TOLL,EA_SR3_TOLL,EA_MT_TOLL,EA_HT_TOLL, + AM_SOV_TOLL,AM_SR2_TOLL,AM_SR3_TOLL,AM_MT_TOLL,AM_HT_TOLL, + MD_SOV_TOLL,MD_SR2_TOLL,MD_SR3_TOLL,MD_MT_TOLL,MD_HT_TOLL, + PM_SOV_TOLL,PM_SR2_TOLL,PM_SR3_TOLL,PM_MT_TOLL,PM_HT_TOLL, + EV_SOV_TOLL,EV_SR2_TOLL,EV_SR3_TOLL,EV_MT_TOLL,EV_HT_TOLL] + + # Put Visum link list into dataframe + df = pd.DataFrame(np.column_stack(att_list), columns = ['addval1','addval2', + 'EA_SOV_TOLL','EA_SR2_TOLL','EA_SR3_TOLL','EA_MT_TOLL','EA_HT_TOLL', + 'AM_SOV_TOLL','AM_SR2_TOLL','AM_SR3_TOLL','AM_MT_TOLL','AM_HT_TOLL', + 'MD_SOV_TOLL','MD_SR2_TOLL','MD_SR3_TOLL','MD_MT_TOLL','MD_HT_TOLL', + 'PM_SOV_TOLL','PM_SR2_TOLL','PM_SR3_TOLL','PM_MT_TOLL','PM_HT_TOLL', + 'EV_SOV_TOLL','EV_SR2_TOLL','EV_SR3_TOLL','EV_MT_TOLL','EV_HT_TOLL']) + + # TEMPORARY, NEED TO REPLACE WITH ACTUAL TRAVEL TIME BY PERIOD USING VDF AND VOLUME + df['time'] = df['addval1'] + + # Mode VOTs + sov_low = 3.39 + sov_med = 7.49 + sov_high = 20.51 + sr2_low = 5.11 + sr2_med = 10.92 + sr2_high = 27.30 + sr3_low = 7.76 + sr3_med = 16.82 + sr3_high = 44.04 + + # Set AddVal2 to Cost in Time + # SOV, Low VOT + if mode == "SOV" and vot == "Low": + df['addval2'] = df['time'] + (df[period+'_'+mode+'_TOLL'] / sov_low) * 3600 # Gen Cost (Time in Seconds) + # SR2, Low VOT + elif mode == "SR2" and vot == "Low": + df['addval2'] = df['time'] + (df[period+'_'+mode+'_TOLL'] / sr2_low) * 3600 # Gen Cost (Time in Seconds) + # SR3, Low VOT + elif mode == "SR3" and vot == "Low": + df['addval2'] = df['time'] + (df[period+'_'+mode+'_TOLL'] / sr3_low) * 3600 # Gen Cost (Time in Seconds) + # SOV, Medium VOT + elif mode == "SOV" and vot == "Medium": + df['addval2'] = df['time'] + (df[period+'_'+mode+'_TOLL'] / sov_med) * 3600 # Gen Cost (Time in Seconds) + # SR2, Medium VOT + elif mode == "SR2" and vot == "Medium": + df['addval2'] = df['time'] + (df[period+'_'+mode+'_TOLL'] / sr2_med) * 3600 # Gen Cost (Time in Seconds) + # SR3, Medium VOT + elif mode == "SR3" and vot == "Medium": + df['addval2'] = df['time'] + (df[period+'_'+mode+'_TOLL'] / sr3_med) * 3600 # Gen Cost (Time in Seconds) + # SOV, High VOT + elif mode == "SOV" and vot == "High": + df['addval2'] = df['time'] + (df[period+'_'+mode+'_TOLL'] / sov_high) * 3600 # Gen Cost (Time in Seconds) + # SR2, High VOT + elif mode == "SR2" and vot == "High": + df['addval2'] = df['time'] + (df[period+'_'+mode+'_TOLL'] / sr2_high) * 3600 # Gen Cost (Time in Seconds) + # SR3, High VOT + elif mode == "SR3" and vot == "High": + df['addval2'] = df['time'] + (df[period+'_'+mode+'_TOLL'] / sr3_high) * 3600 # Gen Cost (Time in Seconds) + + + # Set tolls by period and mode + df['toll'] = df[period+'_'+mode+'_TOLL'] # Money in Dollars + + # Set fields back in Visum + # Gen Cost + h.SetMulti(Visum.Net.Links ,r"ADDVAL2", df['addval2']) + # Toll + if mode == "SOV": + h.SetMulti(Visum.Net.Links ,r"TOLL_PRTSYS(S)", df['toll']) + elif mode == "SR2": + h.SetMulti(Visum.Net.Links ,r"TOLL_PRTSYS(SR2)", df['toll']) + elif mode == "SR3": + h.SetMulti(Visum.Net.Links ,r"TOLL_PRTSYS(SR3)", df['toll']) + + + + + + # REPEAT FOR CONNECTORS + # Pull AddVals for skimming + addval1 = h.GetMulti(Visum.Net.Connectors,r"ADDVAL1" , activeOnly = True) # TEMPORARY, NEED TO REPLACE WITH ACTUAL TRAVEL TIME BY PERIOD USING VDF + + # Make Visum list with link data + att_list = [addval1] + + # Put Visum link list into dataframe + df = pd.DataFrame(np.column_stack(att_list), columns = ['addval1']) + + # TEMPORARY, NEED TO REPLACE WITH ACTUAL TRAVEL TIME BY PERIOD USING VDF AND VOLUME + df['time'] = df['addval1'] + + # Set fields back in Visum + h.SetMulti(Visum.Net.Connectors ,r"ADDVAL1", df['time']) + h.SetMulti(Visum.Net.Connectors ,r"ADDVAL2", df['time']) + + + +# Read user inputs from Visum +proj_dir = Visum.GetPath(2) + +# Pull "Code" field from procedure sequence containing Code, DSegCode, and filename +procedure_code = Visum.Procedures.OperationExecutor.GetCurrentOperation().AttValue("CODE") # Example: outputs a string like -> '[["mfamsov","PuT","AM2_SOV.omx"],["mfmdMpe","PuT","MD1_MPE.omx"]]' +procedure_codes = eval(procedure_code) # Example: outputs a list of lists like -> [["mfamsov","PuT","AM2_SOV.omx"],["mfmdMpe","PuT","MD1_MPE.omx"]] + +# Loop thru each matrix set in the "Code" field and export +#for x in range(len(procedure_codes)): +per = procedure_codes[0] +m = procedure_codes[1] +tval = procedure_codes[2] + +costskim_setup(per, m, tval) + diff --git a/skimming_and_assignment/visum/scripts/Export_Feedback_OMXs.py b/skimming_and_assignment/visum/scripts/Export_Feedback_OMXs.py new file mode 100644 index 0000000..a4e95a8 --- /dev/null +++ b/skimming_and_assignment/visum/scripts/Export_Feedback_OMXs.py @@ -0,0 +1,42 @@ +# Export OMX files from LCOG model based on "Code" field in Procedure Sequence +# 4/29/2025 - Luke Gordon (RSG) +# Adapted from code from Chetan Joshi (PTV) + +import tables +import numpy as np +import pandas as pd +import os +import VisumPy.helpers as h +import openmatrix as omx + + + +def omx_export(mtx_code, mtx_dseg, omx_fn): #mtx_num, mtx_code): + omx_file = omx.open_file(omx_fn, 'w') + # Pull matrix out but close .omx file if there is an error + try: + mx = h.GetMatrixRaw(Visum, {"CODE": mtx_code , "DSegCode": mtx_dseg}) + except Exception as e: + print(f"Error getting matrix for CODE={mtx_code}, DSegCode={mtx_dseg}: {e}") + omx_file.close() + core_name = mtx_code + omx_file[core_name] = mx + omx_file.close() + + +# Read user inputs from Visum +proj_dir = Visum.GetPath(2) + +# Pull "Code" field from procedure sequence containing Code, DSegCode, and filename +procedure_code = Visum.Procedures.OperationExecutor.GetCurrentOperation().AttValue("CODE") # Example: outputs a string like -> '[["mfamsov","PuT","AM2_SOV.omx"],["mfmdMpe","PuT","MD1_MPE.omx"]]' +procedure_codes = eval(procedure_code) # Example: outputs a list of lists like -> [["mfamsov","PuT","AM2_SOV.omx"],["mfmdMpe","PuT","MD1_MPE.omx"]] + +# Loop thru each matrix set in the "Code" field and export +for x in range(len(procedure_codes)): + code = procedure_codes[x][0] + dsegcode = procedure_codes[x][1] + filename = procedure_codes[x][2] + + + omx_export(code, dsegcode, proj_dir + "outputs\\assignment\\feedback\\" + filename) + diff --git a/skimming_and_assignment/visum/scripts/Export_FinalAssign_OMXs.py b/skimming_and_assignment/visum/scripts/Export_FinalAssign_OMXs.py new file mode 100644 index 0000000..90b4448 --- /dev/null +++ b/skimming_and_assignment/visum/scripts/Export_FinalAssign_OMXs.py @@ -0,0 +1,42 @@ +# Export OMX files from LCOG model based on "Code" field in Procedure Sequence +# 4/29/2025 - Luke Gordon (RSG) +# Adapted from code from Chetan Joshi (PTV) + +import tables +import numpy as np +import pandas as pd +import os +import VisumPy.helpers as h +import openmatrix as omx + + + +def omx_export(mtx_code, mtx_dseg, omx_fn): #mtx_num, mtx_code): + omx_file = omx.open_file(omx_fn, 'w') + # Pull matrix out but close .omx file if there is an error + try: + mx = h.GetMatrixRaw(Visum, {"CODE": mtx_code , "DSegCode": mtx_dseg}) + except Exception as e: + print(f"Error getting matrix for CODE={mtx_code}, DSegCode={mtx_dseg}: {e}") + omx_file.close() + core_name = mtx_code + omx_file[core_name] = mx + omx_file.close() + + +# Read user inputs from Visum +proj_dir = Visum.GetPath(2) + +# Pull "Code" field from procedure sequence containing Code, DSegCode, and filename +procedure_code = Visum.Procedures.OperationExecutor.GetCurrentOperation().AttValue("CODE") # Example: outputs a string like -> '[["mfamsov","PuT","AM2_SOV.omx"],["mfmdMpe","PuT","MD1_MPE.omx"]]' +procedure_codes = eval(procedure_code) # Example: outputs a list of lists like -> [["mfamsov","PuT","AM2_SOV.omx"],["mfmdMpe","PuT","MD1_MPE.omx"]] + +# Loop thru each matrix set in the "Code" field and export +for x in range(len(procedure_codes)): + code = procedure_codes[x][0] + dsegcode = procedure_codes[x][1] + filename = procedure_codes[x][2] + + + omx_export(code, dsegcode, proj_dir + "outputs\\assignment\\final_assign\\" + filename) + diff --git a/skimming_and_assignment/visum/scripts/Export_Skim_OMXs.py b/skimming_and_assignment/visum/scripts/Export_Skim_OMXs.py new file mode 100644 index 0000000..dfcb335 --- /dev/null +++ b/skimming_and_assignment/visum/scripts/Export_Skim_OMXs.py @@ -0,0 +1,66 @@ +# Export OMX files from LCOG model based on "Code" field in Procedure Sequence +# 4/29/2025 - Luke Gordon (RSG) +# Adapted from code from Chetan Joshi (PTV) + +import tables +import numpy as np +import pandas as pd +import os +import VisumPy.helpers as h +import openmatrix as omx + + + +def omx_export(mtx_code, corename, mtx_dseg, omx_fn): + + omx_file = omx.open_file(omx_fn, 'a') + # Pull matrix out but close .omx file if there is an error + try: + mx = h.GetMatrixRaw(Visum, {"CODE": mtx_code , "DSegCode": mtx_dseg}) + except Exception as e: + print(f"Error getting matrix for CODE={mtx_code}, DSegCode={mtx_dseg}: {e}") + omx_file.close() + + core_name = corename + # Delete the core if it already exists + if core_name in omx_file: + del omx_file[core_name] + + + omx_file[core_name] = mx.astype(np.float32) + + # Create mapping to TAZ numbers + zones = np.array(h.GetMulti(Visum.Net.Zones,r"NO", activeOnly = True)) + taz_equivs = np.arange(1, len(zones) + 1) # 1-number of zones inclusive + + try: + omx_file.create_mapping('taz', taz_equivs) + omx_file.close() + except Exception as e: + omx_file.close() + + + +# Read user inputs from Visum +proj_dir = Visum.GetPath(2) + +# Pull "Code" field from procedure sequence containing Code, DSegCode, and filename +procedure_code = Visum.Procedures.OperationExecutor.GetCurrentOperation().AttValue("CODE") # Example: outputs a string like -> '[["mfamsov","PuT","AM2_SOV.omx"],["mfmdMpe","PuT","MD1_MPE.omx"]]' +procedure_codes = eval(procedure_code) # Example: outputs a list of lists like -> [["mfamsov","PuT","AM2_SOV.omx"],["mfmdMpe","PuT","MD1_MPE.omx"]] + +# Delete omx file if it exists +filename = procedure_codes[0][3] +if os.path.exists(filename): + os.remove(filename) + + +# Loop thru each matrix set in the "Code" field and export +for x in range(len(procedure_codes)): + code = procedure_codes[x][0] + core = procedure_codes[x][1] + dsegcode = procedure_codes[x][2] + filename = procedure_codes[x][3] + + + omx_export(code, core, dsegcode, proj_dir + "outputs\\skims\\" + filename) + diff --git a/skimming_and_assignment/visum/scripts/Import_Feedback_OMXs.py b/skimming_and_assignment/visum/scripts/Import_Feedback_OMXs.py new file mode 100644 index 0000000..dc32e6f --- /dev/null +++ b/skimming_and_assignment/visum/scripts/Import_Feedback_OMXs.py @@ -0,0 +1,41 @@ +# Export OMX files from LCOG model based on "Code" field in Procedure Sequence +# 4/30/2025 - Luke Gordon (RSG) +# Adapted from code from Chetan Joshi (PTV) + +import openmatrix as omx +import os +import shutil +import numpy as np +import pandas as pd +import VisumPy.matrices as vmx +import VisumPy.helpers as h + + +def omx_import(mtx_code, mtx_dseg, omx_fn): + omx_file = omx.open_file(omx_fn, 'r') + mx_name = omx_file.list_matrices()[0] # 1 matrix per .omx file + mx_array = omx_file[mx_name][:] + # Read matrix but close .omx file if there is an error + try: + h.SetMatrixRaw(Visum, {"CODE": mtx_code , "DSegCode": mtx_dseg}, mx_array) + except Exception as e: + print(f"Error getting matrix for CODE={mtx_code}, DSegCode={mtx_dseg}: {e}") + omx_file.close() + omx_file.close() + + +# Read user inputs from Visum +proj_dir = Visum.GetPath(2) + +# Pull "Code" field from procedure sequence containing Code, DSegCode, and filename +procedure_code = Visum.Procedures.OperationExecutor.GetCurrentOperation().AttValue("CODE") # Example: outputs a string like -> '[["mfamsov","PuT","AM2_SOV.omx"],["mfmdMpe","PuT","MD1_MPE.omx"]]' +procedure_codes = eval(procedure_code) # Example: outputs a list of lists like -> [["mfamsov","PuT","AM2_SOV.omx"],["mfmdMpe","PuT","MD1_MPE.omx"]] + +# Loop thru each matrix set in the "Code" field and export +for x in range(len(procedure_codes)): + code = procedure_codes[x][0] + dsegcode = procedure_codes[x][1] + filename = procedure_codes[x][2] + + + omx_import(code, dsegcode, proj_dir + "outputs\\assignment\\feedback\\" + filename) diff --git a/skimming_and_assignment/visum/scripts/Import_FinalAssign_OMXs.py b/skimming_and_assignment/visum/scripts/Import_FinalAssign_OMXs.py new file mode 100644 index 0000000..b3b845a --- /dev/null +++ b/skimming_and_assignment/visum/scripts/Import_FinalAssign_OMXs.py @@ -0,0 +1,41 @@ +# Export OMX files from LCOG model based on "Code" field in Procedure Sequence +# 4/30/2025 - Luke Gordon (RSG) +# Adapted from code from Chetan Joshi (PTV) + +import openmatrix as omx +import os +import shutil +import numpy as np +import pandas as pd +import VisumPy.matrices as vmx +import VisumPy.helpers as h + + +def omx_import(mtx_code, mtx_dseg, omx_fn): + omx_file = omx.open_file(omx_fn, 'r') + mx_name = omx_file.list_matrices()[0] # 1 matrix per .omx file + mx_array = omx_file[mx_name][:] + # Read matrix but close .omx file if there is an error + try: + h.SetMatrixRaw(Visum, {"CODE": mtx_code , "DSegCode": mtx_dseg}, mx_array) + except Exception as e: + print(f"Error getting matrix for CODE={mtx_code}, DSegCode={mtx_dseg}: {e}") + omx_file.close() + omx_file.close() + + +# Read user inputs from Visum +proj_dir = Visum.GetPath(2) + +# Pull "Code" field from procedure sequence containing Code, DSegCode, and filename +procedure_code = Visum.Procedures.OperationExecutor.GetCurrentOperation().AttValue("CODE") # Example: outputs a string like -> '[["mfamsov","PuT","AM2_SOV.omx"],["mfmdMpe","PuT","MD1_MPE.omx"]]' +procedure_codes = eval(procedure_code) # Example: outputs a list of lists like -> [["mfamsov","PuT","AM2_SOV.omx"],["mfmdMpe","PuT","MD1_MPE.omx"]] + +# Loop thru each matrix set in the "Code" field and export +for x in range(len(procedure_codes)): + code = procedure_codes[x][0] + dsegcode = procedure_codes[x][1] + filename = procedure_codes[x][2] + + + omx_import(code, dsegcode, proj_dir + "outputs\\assignment\\final_assign\\" + filename) diff --git a/skimming_and_assignment/visum/scripts/Import_WarmStart_OMXs.py b/skimming_and_assignment/visum/scripts/Import_WarmStart_OMXs.py new file mode 100644 index 0000000..55edd0c --- /dev/null +++ b/skimming_and_assignment/visum/scripts/Import_WarmStart_OMXs.py @@ -0,0 +1,41 @@ +# Export OMX files from LCOG model based on "Code" field in Procedure Sequence +# 4/30/2025 - Luke Gordon (RSG) +# Adapted from code from Chetan Joshi (PTV) + +import openmatrix as omx +import os +import shutil +import numpy as np +import pandas as pd +import VisumPy.matrices as vmx +import VisumPy.helpers as h + + +def omx_import(mtx_code, mtx_dseg, omx_fn): + omx_file = omx.open_file(omx_fn, 'r') + mx_name = omx_file.list_matrices()[0] # 1 matrix per .omx file + mx_array = omx_file[mx_name][:] + # Read matrix but close .omx file if there is an error + try: + h.SetMatrixRaw(Visum, {"CODE": mtx_code , "DSegCode": mtx_dseg}, mx_array) + except Exception as e: + print(f"Error getting matrix for CODE={mtx_code}, DSegCode={mtx_dseg}: {e}") + omx_file.close() + omx_file.close() + + +# Read user inputs from Visum +proj_dir = Visum.GetPath(2) + +# Pull "Code" field from procedure sequence containing Code, DSegCode, and filename +procedure_code = Visum.Procedures.OperationExecutor.GetCurrentOperation().AttValue("CODE") # Example: outputs a string like -> '[["mfamsov","PuT","AM2_SOV.omx"],["mfmdMpe","PuT","MD1_MPE.omx"]]' +procedure_codes = eval(procedure_code) # Example: outputs a list of lists like -> [["mfamsov","PuT","AM2_SOV.omx"],["mfmdMpe","PuT","MD1_MPE.omx"]] + +# Loop thru each matrix set in the "Code" field and export +for x in range(len(procedure_codes)): + code = procedure_codes[x][0] + dsegcode = procedure_codes[x][1] + filename = procedure_codes[x][2] + + + omx_import(code, dsegcode, proj_dir + "warmstart_omxs\\" + filename) diff --git a/skimming_and_assignment/visum/scripts/Initialize_Output_Bins.py b/skimming_and_assignment/visum/scripts/Initialize_Output_Bins.py new file mode 100644 index 0000000..6f0611c --- /dev/null +++ b/skimming_and_assignment/visum/scripts/Initialize_Output_Bins.py @@ -0,0 +1,37 @@ +# Set output_date field in Visum network attributes and create folders for reports + +""" +created 5/14/2025 + +@author: luke.gordon + +""" + +# Libraries +import VisumPy.helpers +import VisumPy.excel +import pandas as pd +import numpy as np +from datetime import datetime +import os.path + + + +# Calculate timestamp for folder name and save in Network attribute in Visum +date = datetime.now().strftime("(%Y-%m-%d)-%H_%M_%S") +Visum.Net.SetAttValue("output_date", date) + +# Read user inputs from Visum +proj_dir = Visum.GetPath(2) + +# Create timestamped folder for all results +os.mkdir(proj_dir+"outputs/reports/ModelRun_"+date) + +# Create folder for storing this runs assignment quality results +os.mkdir(proj_dir+"outputs/reports/ModelRun_"+date+"/PrT Assignment Quality Reports") + +# Create folder for storing this runs assignment results (Pct. Error, RMSE, etc.) +os.mkdir(proj_dir+"outputs/reports/ModelRun_"+date+"/Assignment Results") + +# Create folder for PuT assignment stats +os.mkdir(proj_dir+"outputs/reports/ModelRun_"+date+"/PuT Assignment Stats") \ No newline at end of file diff --git a/skimming_and_assignment/visum/scripts/KNRConnectors_Setup.py b/skimming_and_assignment/visum/scripts/KNRConnectors_Setup.py new file mode 100644 index 0000000..04a7458 --- /dev/null +++ b/skimming_and_assignment/visum/scripts/KNRConnectors_Setup.py @@ -0,0 +1,214 @@ + +#script to insert KNR connectors +# Chetan Joshi, PTV Portland OR 6/25/2025 + +import numpy as np +import scipy.spatial +import json +import csv +import os +import VisumPy.helpers as h +import pandas as pd +PRIO = 20480 + + +_TABLE = "KnRConstraints" +SCALEFACTOR = 5280 + +CONSPEED = 30 #mph +# FN = os.path.join(Visum.GetPath(2), "taz_connectors.json") +FN = os.path.join(Visum.GetPath(2), "connectors_knr.net") + +def vision_header(): + vision_head = [["$VISION"], + ["$VERSION:VERSNR", "FILETYPE", "LANGUAGE", "UNIT"], + [15,"Net","ENG","MI"]] + return vision_head + + +def create_knr_connectors(): + + # Check for type 10 connectors (knr connectors), if they exist, don't add any more connectors + checktype = np.array(h.GetMulti(Visum.Net.Connectors,r"TYPENO", activeOnly = True)) + if 10 in checktype: + Visum.Log(PRIO, 'KNR connectors already built!') + # Save original TSysSet in TSYS_HOLDING for later use + #tsysorig = h.GetMulti(Visum.Net.Connectors,r"TSYSSET", activeOnly = True) + #h.SetMulti(Visum.Net.Connectors, r"TSYS_HOLDING", tsysorig) + + return + else: + Visum.Log(PRIO, 'Building KNR Connectors') + # Save original TSysSet in TSYS_HOLDING for later use + #tsysorig = h.GetMulti(Visum.Net.Connectors,r"TSYSSET", activeOnly = True) + #h.SetMulti(Visum.Net.Connectors, r"TSYS_HOLDING", tsysorig) + + + + Visum.Log(PRIO, 'generate search list...') + tsys_constr = dict([tsys, [maxdist, maxstops]] for tsys, maxdist, maxstops in Visum.Net.TableDefinitions.ItemByKey(_TABLE).TableEntries.GetMultipleAttributes(["TSYS", "MAXDIST", "MAXSTOPS"])) + + cutoff = 5*SCALEFACTOR + tazs = Visum.Net.Zones.GetMultiAttValues("NO", False) + stp_no = np.array(Visum.Net.StopAreas.GetMultiAttValues("NO", False), dtype='int')[:, 1] + stop_tsys = dict([[sno, [nodeno, tsys]] for sno, nodeno, tsys in Visum.Net.StopAreas.GetMultipleAttributes(["NO", "NODENO", "DISTINCT:STOPPOINTS\\TSYSSET"])]) + taz_xy = np.array(Visum.Net.Zones.GetMultipleAttributes(["XCOORD", "YCOORD"], False)) + stp_xy = np.array(Visum.Net.StopAreas.GetMultipleAttributes(["XCOORD", "YCOORD"], False)) + + Visum.Log(PRIO, 'calculate nearest stop in catchment for each taz...') + + distance = scipy.spatial.distance.cdist(taz_xy, stp_xy, 'euclidean') + result = dict() + connector_table = [["$CONNECTOR:ZONENO","NODENO","DIRECTION","TYPENO","TSYSSET"]] + for ix, taz in tazs: + connector_nodes = set() + catchment_stops = stp_no.compress(distance[ix-1, :] <= cutoff) + catchment_distance = distance[ix-1, :].compress(distance[ix-1, :] <= cutoff) + + for tsys in tsys_constr: + max_dist, max_stops = tsys_constr[tsys] + max_dist = max_dist*SCALEFACTOR + stops_added = 0 + for dist, stopno in sorted(zip(catchment_distance, catchment_stops)): + snode, stsys = stop_tsys[stopno] + stsys = stsys.split(",") + if dist < max_dist and tsys in stsys: + connector_nodes.add(snode) + stops_added+=1 + if stops_added >= max_stops: + break + + result[taz] = list(connector_nodes) + + # THIS IS HOW CONNECTORS ARE ADDED USING COM - BUT THIS IS TOO SLOW: SO USE NET FILE BATCHING INSTEAD. + # for node in connector_nodes: + # if Visum.Net.Connectors.ExistsByKey(node, taz): + # Visum.Log(PRIO, "{} -> {} | already exists!".format(taz, node)) + # Visum.Net.Connectors.SourceItemByKey(taz, node).SetAttValue("TypeNo", 7) + # else: + # connector = Visum.Net.AddConnector(taz, node) + # connector.SetAttValue("TypeNo", 10) + + # GENERATE NET FILE BATCH TABLE FOR CONNECTORS. + # need to preserve original connectors esp: walk to destination(?) + for node in connector_nodes: + # --- ["$CONNECTOR:ZONENO","NODENO","DIRECTION","TYPENO"] + if Visum.Net.Connectors.ExistsByKey(node, taz): + # Visum.Log(PRIO, "{} -> {} | already exists!".format(taz, node)) + connector_table.append([taz, node, 'O', 7, 'i']) # origin/access connector + dtsys = Visum.Net.Connectors.DestItemByKey(node, taz).AttValue("TSYSSET") + connector_table.append([taz, node, 'D', 7, dtsys]) # destination/egress connector + else: + connector_table.append([taz, node, 'O', 10, 'i']) # origin/access connector + connector_table.append([taz, node, 'D', 10, '']) # destination/egress connector + + Visum.Log(PRIO, 'write results to file...') + # with open(FN, 'w') as out_file: + # json.dump(result, out_file, indent=2) + + with open(FN, 'w', newline='') as csvfile: + writer = csv.writer(csvfile, delimiter =';') + writer.writerows(vision_header()) + writer.writerows(connector_table) + + Visum.Log(PRIO, 'read knr connectors...') + + loadnetctrl = Visum.IO.CreateAddNetReadController() # read additionally controller + loadnetctrl.SetWhatToDo("CONNECTOR", 5) # conflict handling - overwrite attributes + Visum.IO.LoadNet(FN, ReadAdditive=True, RouteSearch=None, AddNetRead=loadnetctrl, NormalizePolygons=False, + MergeSameCoordPolygonPoints=False, DecimalsForMergeSameCoordPolygonPoints=-1) + + # Set Length for new connectors based on preexisting connector lengths (which come from Emme originally) + length = h.GetMulti(Visum.Net.Connectors,r"LENGTH" , activeOnly = True) + zoneno = h.GetMulti(Visum.Net.Connectors,r"ZONENO" , activeOnly = True) + typeno = h.GetMulti(Visum.Net.Connectors,r"TYPENO" , activeOnly = True) + att_list = [length,zoneno,typeno] + df = pd.DataFrame(np.column_stack(att_list), columns = ['length','zoneno','typeno']) + df_filtered = df[df['typeno'] < 10] # All preexisting connectors will be TypeNO = 0 or 7 + df_unique = df_filtered.groupby('zoneno').agg(length_max=('length', 'max')).reset_index() + df = pd.merge(df, df_unique, on='zoneno', how='left') + h.SetMulti(Visum.Net.Connectors,r"LENGTH" ,df['length_max']) + + ## Set Length for new connectors for direction not already set (using Visum calculated length) + #length = h.GetMulti(Visum.Net.Connectors,r"LENGTH" , activeOnly = True) + #zoneno = h.GetMulti(Visum.Net.Connectors,r"ZONENO" , activeOnly = True) + #nodeno = h.GetMulti(Visum.Net.Connectors,r"NODENO" , activeOnly = True) + #att_list = [length,zoneno,nodeno] + #df = pd.DataFrame(np.column_stack(att_list), columns = ['length','zoneno','nodeno']) + #df[['zoneno','nodeno']] = df[['zoneno','nodeno']].astype(str) + #df['concat'] = df['zoneno'] + df['nodeno'] + #df_unique = df.groupby('concat').agg(length_max=('length', 'max')).reset_index() + #df = pd.merge(df, df_unique, on='concat', how='left') + #h.SetMulti(Visum.Net.Connectors,r"LENGTH" ,df['length_max']) + + + + Visum.Log(PRIO, 'done!') + + +def set_connector_properties(knrdirection): + + # Save TSysSet in TSys_Holding if the original values are present in TSysSet + checktype = np.array(h.GetMulti(Visum.Net.Connectors,r"TSYSSET", activeOnly = True)) + if 'i' in checktype: + Visum.Log(PRIO, 'TSYS_HOLDING Overwriting Skipped') + else: + Visum.Log(PRIO, 'TSYS_HOLDING Overwritten') + tsys = h.GetMulti(Visum.Net.Connectors,r"TSYSSET", activeOnly = True) + h.SetMulti(Visum.Net.Connectors,r"TSYS_HOLDING" ,tsys) + + # Pull attributes + connector_type_dir = Visum.Net.Connectors.GetMultipleAttributes(["TypeNo", "Direction", "Length", "TSYS_HOLDING"]) + connector_tsys = [] + connector_time = [] + # we assume here that KNR is not open on any connector to start + for typeno, direction, distance, tsys_hold in connector_type_dir: + if knrdirection == "KTW": + if direction == 1: # Origin, leaving a zone + if typeno in [7, 10]: + connector_tsys.append("i") + connector_time.append([3600*distance/CONSPEED, 999999]) + else: + connector_tsys.append("") + connector_time.append([999999, 999999]) + + elif direction == 2: # Destination, entering a zone + if typeno == 10: + # a knr drive only connector- so no walk on this. + connector_tsys.append("") + connector_time.append([999999, 999999]) + else: + # could be a walk destination connector, + # we assume here that KNR is not open on any connector to start so keep Tsys the way it was (else: could also set to 'w') + connector_tsys.append(tsys_hold) + connector_time.append([999999, 3600*distance/2.5]) + elif knrdirection == "WTK": + if direction == 2: # Destination, entering a zone + if typeno in [7, 10]: + connector_tsys.append("i") + connector_time.append([3600*distance/CONSPEED, 999999]) + else: + connector_tsys.append("") + connector_time.append([999999, 999999]) + elif direction == 1: # Origin, leaving a zone + if typeno == 10: + # a knr drive only connector- so no walk on this. + connector_tsys.append("") + connector_time.append([999999, 999999]) + else: + # could be a walk destination connector, + # we assume here that KNR is not open on any connector to start so keep Tsys the way it was (else: could also set to 'w') + connector_tsys.append(tsys_hold) + connector_time.append([999999, 3600*distance/2.5]) + elif knrdirection == "WTW": + connector_tsys.append(tsys_hold) + connector_time.append([999999, 3600*distance/2.5]) + + h.SetMulti(Visum.Net.Connectors, "TSysSet", connector_tsys) + Visum.Net.Connectors.SetMultipleAttributes(["T0_TSYS(I)", "T0_TSYS(W)"], connector_time) + + + +code = Visum.Procedures.OperationExecutor.GetCurrentOperation().AttValue("CODE") +create_knr_connectors() +set_connector_properties(code) diff --git a/skimming_and_assignment/visum/scripts/OP_SetTransitRunTimes.py b/skimming_and_assignment/visum/scripts/OP_SetTransitRunTimes.py new file mode 100644 index 0000000..4b6f5c5 --- /dev/null +++ b/skimming_and_assignment/visum/scripts/OP_SetTransitRunTimes.py @@ -0,0 +1,76 @@ +# replicate Emme TTF in Visum +# chetan joshi, ptv portland or 2/11/2025 +# @luke.gordon, adapted further for LCOG model 2/11/2025 + + # a ft1 =timau * 1.15 + # a ft2 =timau * 1.2 + # a ft3 =timau + # a ft4 =timau * 1.09 + # a ft5 =60 * length / us1 + # a ft6 =60 * length / us1 + 60 * length / 180 + # a ft11 =(timau * 1.15) + # a ft12 =(timau * 1.3) + # a ft13 =(timau) + # a ft14 =(timau * 1.09) + # a ft15 =(60 * length / us1) + # a ft16 =(60 * length / us1 + 60 * length / 180) + # a ft21 =timau * 1.05 + # a ft22 =timau * 1.03 + +default_speed = 30 #mph -> default speed of transit + +def calc_ttf(ft, timau, length, us1): + if us1 <= 0: + us1 = default_speed + + if length: + transit_time = 3600*length / default_speed + + if timau < 999: + if ft == 1: + transit_time = timau * 1.15 + elif ft == 2: + transit_time = timau * 1.20 + elif ft == 3: + transit_time = timau + elif ft == 4: + transit_time = timau * 1.09 + elif ft == 5: + transit_time = 60* (60 * length / us1) + elif ft == 6: + transit_time = 60* (60 * length / us1 + 60 * length / 180) + elif ft ==11: + transit_time = timau * 1.15 + elif ft ==12: + transit_time = timau * 1.30 + elif ft ==13: + transit_time = timau + elif ft ==14: + transit_time =timau * 1.09 + elif ft == 15: + transit_time = 60* (60 * length / us1) + elif ft == 16: + transit_time = 60* (60 * length / us1 + 60 * length / 180) + elif ft == 21: + transit_time = timau * 1.05 + elif ft == 22: + transit_time = timau * 1.03 + else: + transit_time = 1 + + return transit_time + +def update_transit_time(): + tpitems = Visum.Net.TimeProfileItems.GetMultipleAttributes(["LINEROUTEITEM\\EMME_TTFINDEX", "SUM:USEDLINEROUTEITEMS\\OUTLINK\\AddVal2","SUM:USEDLINEROUTEITEMS\\POSTLENGTH", + "LINEROUTEITEM\\EMME_DATA1"]) + result = [] + for ft, timau, length, us1 in tpitems: + haul_time = calc_ttf(ft, timau, length, us1) + result.append([haul_time, ]) + + Visum.Net.TimeProfileItems.SetMultipleAttributes(["AddVal"], result) + + +update_transit_time() + + diff --git a/skimming_and_assignment/visum/scripts/PK_SetTransitRunTimes.py b/skimming_and_assignment/visum/scripts/PK_SetTransitRunTimes.py new file mode 100644 index 0000000..9eb0479 --- /dev/null +++ b/skimming_and_assignment/visum/scripts/PK_SetTransitRunTimes.py @@ -0,0 +1,76 @@ +# replicate Emme TTF in Visum +# chetan joshi, ptv portland or 2/11/2025 +# @luke.gordon, adapted further for LCOG model 2/11/2025 + + # a ft1 =timau * 1.15 + # a ft2 =timau * 1.2 + # a ft3 =timau + # a ft4 =timau * 1.09 + # a ft5 =60 * length / us1 + # a ft6 =60 * length / us1 + 60 * length / 180 + # a ft11 =(timau * 1.15) + # a ft12 =(timau * 1.3) + # a ft13 =(timau) + # a ft14 =(timau * 1.09) + # a ft15 =(60 * length / us1) + # a ft16 =(60 * length / us1 + 60 * length / 180) + # a ft21 =timau * 1.05 + # a ft22 =timau * 1.03 + +default_speed = 30 #mph -> default speed of transit + +def calc_ttf(ft, timau, length, us1): + if us1 <= 0: + us1 = default_speed + + if length: + transit_time = 3600*length / default_speed + + if timau < 999: + if ft == 1: + transit_time = timau * 1.15 + elif ft == 2: + transit_time = timau * 1.20 + elif ft == 3: + transit_time = timau + elif ft == 4: + transit_time = timau * 1.09 + elif ft == 5: + transit_time = 60* (60 * length / us1) + elif ft == 6: + transit_time = 60* (60 * length / us1 + 60 * length / 180) + elif ft ==11: + transit_time = timau * 1.15 + elif ft ==12: + transit_time = timau * 1.30 + elif ft ==13: + transit_time = timau + elif ft ==14: + transit_time =timau * 1.09 + elif ft == 15: + transit_time = 60* (60 * length / us1) + elif ft == 16: + transit_time = 60* (60 * length / us1 + 60 * length / 180) + elif ft == 21: + transit_time = timau * 1.05 + elif ft == 22: + transit_time = timau * 1.03 + else: + transit_time = 1 + + return transit_time + +def update_transit_time(): + tpitems = Visum.Net.TimeProfileItems.GetMultipleAttributes(["LINEROUTEITEM\\EMME_TTFINDEX", "SUM:USEDLINEROUTEITEMS\\OUTLINK\\AddVal1","SUM:USEDLINEROUTEITEMS\\POSTLENGTH", + "LINEROUTEITEM\\EMME_DATA1"]) + result = [] + for ft, timau, length, us1 in tpitems: + haul_time = calc_ttf(ft, timau, length, us1) + result.append([haul_time, ]) + + Visum.Net.TimeProfileItems.SetMultipleAttributes(["AddVal"], result) + + +update_transit_time() + + diff --git a/skimming_and_assignment/visum/scripts/PrTAssignmentQualityData_Export.py b/skimming_and_assignment/visum/scripts/PrTAssignmentQualityData_Export.py new file mode 100644 index 0000000..f859866 --- /dev/null +++ b/skimming_and_assignment/visum/scripts/PrTAssignmentQualityData_Export.py @@ -0,0 +1,59 @@ +# LCOG: Pull PrT Assignment Quality Report from Visum and save as csv file + +""" +created 5/14/2025 + +@author: luke.gordon + +""" + +# Libraries +import VisumPy.helpers +import VisumPy.excel +import pandas as pd +import numpy as np +import csv +#from datetime import datetime +import math +import os.path + + +# Pull timestamp for folder name and iteration number from Visum network attribute +date = Visum.Net.AttValue("output_date") +iter = Visum.Net.AttValue("iter") +iter = str(int(iter)) + +# Read user inputs from Visum +proj_dir = Visum.GetPath(2) + +# Pull "Code" field from procedure sequence to name files +code = Visum.Procedures.OperationExecutor.GetCurrentOperation().AttValue("CODE") + +# Pull PrT assignment quality report and format into a table for export +list = Visum.Workbench.Lists.CreatePrTAssQualityList + +list.AddKeyColumns() + +list.AddColumn(Attribut="MEANABSVOLDIFFTOTAL") +list.AddColumn(Attribut="MEANRELVOLDIFFTOTAL") +list.AddColumn(Attribut="ASSIGNEDDEMAND") +list.AddColumn(Attribut="VEHMITRAVPRT") +list.AddColumn(Attribut="VEHHOURTRAVT0") +list.AddColumn(Attribut="VEHHOURTRAVTCUR") +list.AddColumn(Attribut="VEHHOURIMP") +list.AddColumn(Attribut="TOTALEXCESSCOST") +list.AddColumn(Attribut="AVGEXCESSCOST") +list.AddColumn(Attribut="GAP") + +df = list.SaveToArray() + + +df = pd.DataFrame(df) + + +df = df.rename(columns={0 : 'Demand segment set code' , 1 : 'Iteration' , 2 : 'Mean absolute volume difference total' , 3 : 'Mean relative volume difference total' , + 4 : 'Assigned demand' , 5 : 'Vehicle miles traveled PrT' , 6 : 'Vehicle hours traveled t0' , 7 : 'Vehicle hours traveled tCur' , + 8 : 'Vehicle hour impedance' , 9 : 'Total excess cost' , 10: 'Mean excess cost', 11: 'Gap'}) + +df.to_csv(proj_dir+"outputs/reports/ModelRun_"+date+"/PrT Assignment Quality Reports/"+code+"_Iter"+iter+".csv") + diff --git a/skimming_and_assignment/visum/scripts/PrTSkim_PostProcessing.py b/skimming_and_assignment/visum/scripts/PrTSkim_PostProcessing.py new file mode 100644 index 0000000..2253a59 --- /dev/null +++ b/skimming_and_assignment/visum/scripts/PrTSkim_PostProcessing.py @@ -0,0 +1,64 @@ +# Post-processing PrT skims from Visum based on "Code" field in Procedure Sequence +# 6/17/2025 - Luke Gordon (RSG) +# Adapted from code from Chetan Joshi (PTV) + +import tables +import numpy as np +import pandas as pd +import os +import VisumPy.helpers as h +import openmatrix as omx + + +def prtskim_postprocessing(mtx_dseg): + + # Pull Intrazonal attributes out to set to diagonals + intrtime = np.array(h.GetMulti(Visum.Net.Zones,r"intrtime" , activeOnly = True)) + intrdist = np.array(h.GetMulti(Visum.Net.Zones,r"intrdist" , activeOnly = True)) + intrdist_wlk = np.array(h.GetMulti(Visum.Net.Zones,r"intrdist_wlk", activeOnly = True)) + + # Pull, Process, and Set matrices as needed by DSegCode + if mtx_dseg == 'wlk': # Run Walk distance processing + # Pull + dis_wlk = h.GetMatrixRaw(Visum, {"CODE": "DIS" , "DSegCode": mtx_dseg}) # Walk Distance + # Process + np.fill_diagonal(dis_wlk, intrdist_wlk) + # Set + h.SetMatrixRaw(Visum, {"CODE": "DIS" , "DSegCode": mtx_dseg}, dis_wlk) + + else: # Run Auto distance, time, and tolls processing + # Pull + dis = h.GetMatrixRaw(Visum, {"CODE": "DIS" , "DSegCode": mtx_dseg}) # Distance + time = h.GetMatrixRaw(Visum, {"CODE": "AD1" , "DSegCode": mtx_dseg}) # Time + toll = h.GetMatrixRaw(Visum, {"CODE": "TOL" , "DSegCode": mtx_dseg}) # Tolls + # Process + np.fill_diagonal(dis, intrdist) # Distance + np.fill_diagonal(time, intrtime) # Time + time = np.where(time == 9999.00, 9999.00, time / 60) # Seconds to Minutes + np.fill_diagonal(toll, 0) # Tolls + # Set + h.SetMatrixRaw(Visum, {"CODE": "DIS" , "DSegCode": mtx_dseg}, dis) + h.SetMatrixRaw(Visum, {"CODE": "AD1" , "DSegCode": mtx_dseg}, time) + h.SetMatrixRaw(Visum, {"CODE": "TOL" , "DSegCode": mtx_dseg}, toll) + + + + + +# Read user inputs from Visum +proj_dir = Visum.GetPath(2) + +dseg = Visum.Procedures.OperationExecutor.GetCurrentOperation().AttValue("CODE") # Example: outputs a string like 'AM' from AM in the code box +prtskim_postprocessing(dseg) + + +## Pull "Code" field from procedure sequence containing Code, DSegCode, and filename +#procedure_code = Visum.Procedures.OperationExecutor.GetCurrentOperation().AttValue("CODE") # Example: outputs a string like -> '[["mfamsov","PuT","AM2_SOV.omx"],["mfmdMpe","PuT","MD1_MPE.omx"]]' +#procedure_codes = eval(procedure_code) # Example: outputs a list of lists like -> [["mfamsov","PuT","AM2_SOV.omx"],["mfmdMpe","PuT","MD1_MPE.omx"]] +# +## Loop thru each matrix set in the "Code" field and export +#for x in range(len(procedure_codes)): +# dsegcode = procedure_codes[x][0] +# +# prtskim_postprocessing(dsegcode) + diff --git a/skimming_and_assignment/visum/scripts/PuTAssignmentStats_Export.py b/skimming_and_assignment/visum/scripts/PuTAssignmentStats_Export.py new file mode 100644 index 0000000..1b985f6 --- /dev/null +++ b/skimming_and_assignment/visum/scripts/PuTAssignmentStats_Export.py @@ -0,0 +1,66 @@ +# LCOG: Pull PrT Assignment Quality Report from Visum and save as csv file + +""" +created 5/21/2025 + +@author: edna.aguilar + +""" + +# Libraries +import VisumPy.helpers +import VisumPy.excel +import pandas as pd +import numpy as np +import csv +#from datetime import datetime +import math +import os.path + + +# Pull timestamp for folder name and iteration number from Visum network attribute +date = Visum.Net.AttValue("output_date") +iter = Visum.Net.AttValue("iter") +iter = str(int(iter)) + +# Read user inputs from Visum +proj_dir = Visum.GetPath(2) + +# Pull "Code" field from procedure sequence to name files +code = Visum.Procedures.OperationExecutor.GetCurrentOperation().AttValue("CODE") + +# Pull PrT assignment quality report and format into a table for export +list = Visum.Workbench.Lists.CreatePuTStatList + +list.AddKeyColumns() + +attributes = [ + "MeanJourneyTimePut", "MeanRideTimePuT", "MeanInVehTimePuT", + "MeanTransferWaitTimePut", "MeanOriginWaitTimePuT", "MeanWalkTimePuT", + "MeanPuTAuxTimePuT", "MeanSharingTravelTimePuT", "MeanAccessTimePuT", + "MeanEgressTimePuT", "MeanJourneyDistPuT", "MeanRideDistPuT", + "MeanNumTransfersPuT", + "TotalJourneyTimePuT", "TotalRideTimePuT", "TotalInVehTimePuT", + "TotalTransferWaitTimePuT", "TotalOriginWaitTimePuT", "TotalWalkTimePuT", + "TotalPuTAuxTimePut", "TotalSharingTravelTimePuT", "TotalAccessTimePuT", + "TotalEgressTimePuT", "TotalJourneyDistPuT", "TotalRideDistPuT", + "TotalNumTransfersPuT", + "PTripsUnlinkedPuT", "PTripsLinkedTot", "PTripsLinked0", "PTripsLinked1", + "PTripsLinked2", "PTripsLinkedGt2", "PTripsLinkedWRide", + "PTripsLinkedWoRide", "PTripsLinkedWoCon" +] + +for attr in attributes: + list.AddColumn(Attribut=attr) + +df = pd.DataFrame(list.SaveToArray()) + +# Convert to long format +data_only = df.iloc[0] +data_only.index = attributes +df_long = data_only.reset_index() +df_long.columns = ['Attribute', 'Value'] + +# Save +df_long.to_csv(proj_dir + "outputs/reports/ModelRun_" + date + "/PuT Assignment Stats/PuTAssignment_" + code + "_Iter" + iter + ".csv") + diff --git a/skimming_and_assignment/visum/scripts/PuTSkim_PostProcessing.py b/skimming_and_assignment/visum/scripts/PuTSkim_PostProcessing.py new file mode 100644 index 0000000..62549d0 --- /dev/null +++ b/skimming_and_assignment/visum/scripts/PuTSkim_PostProcessing.py @@ -0,0 +1,130 @@ +# Post-processing PuT skims from Visum based on "Code" field in Procedure Sequence +# 6/16/2025 - Luke Gordon (RSG) +# Adapted from code from Chetan Joshi (PTV) + +import tables +import numpy as np +import pandas as pd +import os +import VisumPy.helpers as h +import openmatrix as omx + + +# Create function to build skim matrices that weren't built during the skimming procedure itself if they don't yet exist +def creatematrices(code,mtx_dseg): + try: + mx = h.GetMatrixRaw(Visum, {"CODE": code , "DSegCode": mtx_dseg}) + except Exception as e: + mx = Visum.Net.AddMatrix(No=-1,ObjectTypeRef=2,MatrixType=4) + mx.SetAttValue("CODE",code) + mx.SetAttValue("DSegCode",mtx_dseg) + + +def putskim_postprocessing(mtx_dseg,knr_flag): + + # Build matrices if they don't yet exist + creatematrices("NBR" ,mtx_dseg) + creatematrices("IVTT",mtx_dseg) + creatematrices("STC" ,mtx_dseg) + #creatematrices("OVT" ,mtx_dseg) + creatematrices("VTC" ,mtx_dseg) + + # Pull matrices out of Visum as numpy arrays + wkt = h.GetMatrixRaw(Visum, {"CODE": "WKT" , "DSegCode": mtx_dseg}) # Walk time + act = h.GetMatrixRaw(Visum, {"CODE": "ACT" , "DSegCode": mtx_dseg}) # Access time + egt = h.GetMatrixRaw(Visum, {"CODE": "EGT" , "DSegCode": mtx_dseg}) # Egress time + ntr = h.GetMatrixRaw(Visum, {"CODE": "NTR" , "DSegCode": mtx_dseg}) # Number of transfers + nbr = h.GetMatrixRaw(Visum, {"CODE": "NBR" , "DSegCode": mtx_dseg}) # Number of boardings + wowt = h.GetMatrixRaw(Visum, {"CODE": "WOWT" , "DSegCode": mtx_dseg}) # Weighted origin wait time + wtwt = h.GetMatrixRaw(Visum, {"CODE": "WTWT" , "DSegCode": mtx_dseg}) # Weighted transfer wait time + ivtt = h.GetMatrixRaw(Visum, {"CODE": "IVTT" , "DSegCode": mtx_dseg}) # In-vehicle travel time + ivtt_a = h.GetMatrixRaw(Visum, {"CODE": "IVTT(a)" , "DSegCode": mtx_dseg}) # In-vehicle travel time (BRT) + ivtt_b = h.GetMatrixRaw(Visum, {"CODE": "IVTT(b)" , "DSegCode": mtx_dseg}) # In-vehicle travel time (Bus) + ivtt_e = h.GetMatrixRaw(Visum, {"CODE": "IVTT(e)" , "DSegCode": mtx_dseg}) # In-vehicle travel time (Streetcar) + ivtt_l = h.GetMatrixRaw(Visum, {"CODE": "IVTT(l)" , "DSegCode": mtx_dseg}) # In-vehicle travel time (LRT) + ivtt_r = h.GetMatrixRaw(Visum, {"CODE": "IVTT(r)" , "DSegCode": mtx_dseg}) # In-vehicle travel time (WES) + pla = h.GetMatrixRaw(Visum, {"CODE": "PLA" , "DSegCode": mtx_dseg}) # Stop type constant (raw sum) + stc = h.GetMatrixRaw(Visum, {"CODE": "STC" , "DSegCode": mtx_dseg}) # Stop type constant (final average) + #ovt = h.GetMatrixRaw(Visum, {"CODE": "OVT" , "DSegCode": mtx_dseg}) # Out of vehicle time + vtc = h.GetMatrixRaw(Visum, {"CODE": "VTC" , "DSegCode": mtx_dseg}) # Vehicle type constant + + # Process matrices + # Walk time + if knr_flag == 'wtw': + wkt = np.minimum(wkt + act + egt , 9999.00) + wkt = np.where((wkt < 0.01) | (wkt > 20.0) , 9999.00, wkt) + np.fill_diagonal(wkt, 9999.00) + elif knr_flag == 'ktw': + wkt = np.minimum(wkt + egt , 9999.00) + wkt = np.where((wkt < 0.01) | (wkt > 20.0) , 9999.00, wkt) + np.fill_diagonal(wkt, 9999.00) + elif knr_flag == 'wtk': + wkt = np.minimum(wkt + act , 9999.00) + wkt = np.where((wkt < 0.01) | (wkt > 20.0) , 9999.00, wkt) + np.fill_diagonal(wkt, 9999.00) + + + # Weighted origin wait time + wowt = np.where(wkt == 9999.00, 9999.00, wowt) + # Perceived In-vehicle time by Mode + if mtx_dseg == 'amPuT' or mtx_dseg == 'pmPuT': # Peak + ivtt_a = np.where(ivtt_a == 9999.00, 9999.00, ivtt_a * 0.95) + ivtt_l = np.where(ivtt_l == 9999.00, 9999.00, ivtt_l * 0.88) + ivtt_r = np.where(ivtt_r == 9999.00, 9999.00, ivtt_r * 0.88) + else: # Off-Peak + ivtt_a = np.where(ivtt_a == 9999.00, 9999.00, ivtt_a * 0.95) + ivtt_l = np.where(ivtt_l == 9999.00, 9999.00, ivtt_l * 0.86) + ivtt_r = np.where(ivtt_r == 9999.00, 9999.00, ivtt_r * 0.86) + # Number of boardings + nbr = np.where(ntr == 9999.00, 9999.00, ntr + 1) + # Stop type constant + stc = np.where((pla == 9999.00) | (nbr == 9999.00) , 9999.00, pla / nbr) + stc = np.where(stc == 9999.00, 0.00, stc) + # In-Vehicle time + ivtt = np.minimum(ivtt_a + ivtt_b + ivtt_e + ivtt_l + ivtt_r, 9999.00) + np.fill_diagonal(ivtt, 9999.00) + # Out of vehicle time + #ovt = np.minimum(wowt + wtwt + wkt, 9999.00) + # Vehicle type constant + if mtx_dseg == 'amPuT' or mtx_dseg == 'pmPuT': # Peak + vtc = np.where((ivtt == 9999.00) | (ivtt == 0.00), 9999.00, + ((0.0557 * ivtt_a) / ivtt) + ((0.000 * ivtt_e) / ivtt) + ((0.1858 * ivtt_l) / ivtt) + ((0.1858 * ivtt_r) / ivtt)) + else: # Off-Peak + vtc = np.where((ivtt == 9999.00) | (ivtt == 0.00), 9999.00, + ((0.0432 * ivtt_a) / ivtt) + ((0.0984 * ivtt_e) / ivtt) + ((0.1442 * ivtt_l) / ivtt) + ((0.1442 * ivtt_r) / ivtt)) + + # Set matrices in Visum + h.SetMatrixRaw(Visum, {"CODE": "WKT" , "DSegCode": mtx_dseg}, wkt ) # Walk time + h.SetMatrixRaw(Visum, {"CODE": "ACT" , "DSegCode": mtx_dseg}, act ) # Access time + h.SetMatrixRaw(Visum, {"CODE": "EGT" , "DSegCode": mtx_dseg}, egt ) # Egress time + h.SetMatrixRaw(Visum, {"CODE": "NTR" , "DSegCode": mtx_dseg}, ntr ) # Number of transfers + h.SetMatrixRaw(Visum, {"CODE": "NBR" , "DSegCode": mtx_dseg}, nbr ) # Number of boardings + h.SetMatrixRaw(Visum, {"CODE": "WOWT" , "DSegCode": mtx_dseg}, wowt ) # Weighted origin wait time + h.SetMatrixRaw(Visum, {"CODE": "WTWT" , "DSegCode": mtx_dseg}, wtwt ) # Weighted transfer wait time + h.SetMatrixRaw(Visum, {"CODE": "IVTT" , "DSegCode": mtx_dseg}, ivtt ) # In-vehicle travel time + h.SetMatrixRaw(Visum, {"CODE": "IVTT(a)" , "DSegCode": mtx_dseg}, ivtt_a) # In-vehicle travel time (BRT) + h.SetMatrixRaw(Visum, {"CODE": "IVTT(b)" , "DSegCode": mtx_dseg}, ivtt_b) # In-vehicle travel time (Bus) + h.SetMatrixRaw(Visum, {"CODE": "IVTT(e)" , "DSegCode": mtx_dseg}, ivtt_e) # In-vehicle travel time (Streetcar) + h.SetMatrixRaw(Visum, {"CODE": "IVTT(l)" , "DSegCode": mtx_dseg}, ivtt_l) # In-vehicle travel time (LRT) + h.SetMatrixRaw(Visum, {"CODE": "IVTT(r)" , "DSegCode": mtx_dseg}, ivtt_r) # In-vehicle travel time (WES) + h.SetMatrixRaw(Visum, {"CODE": "PLA" , "DSegCode": mtx_dseg}, pla ) # Stop type constant (raw sum) + h.SetMatrixRaw(Visum, {"CODE": "STC" , "DSegCode": mtx_dseg}, stc ) # Stop type constant (final average) + #h.SetMatrixRaw(Visum, {"CODE": "OVT" , "DSegCode": mtx_dseg}, ovt ) # Out of vehicle time + h.SetMatrixRaw(Visum, {"CODE": "VTC" , "DSegCode": mtx_dseg}, vtc ) # Vehicle type constant + + + +# Read user inputs from Visum +proj_dir = Visum.GetPath(2) + +# Pull "Code" field from procedure sequence containing Code, DSegCode, and filename +procedure_code = Visum.Procedures.OperationExecutor.GetCurrentOperation().AttValue("CODE") # Example: outputs a string like -> '[["mfamsov","PuT","AM2_SOV.omx"],["mfmdMpe","PuT","MD1_MPE.omx"]]' +procedure_codes = eval(procedure_code) # Example: outputs a list of lists like -> [["mfamsov","PuT","AM2_SOV.omx"],["mfmdMpe","PuT","MD1_MPE.omx"]] + +# Loop thru each matrix set in the "Code" field and export +for x in range(len(procedure_codes)): + dsegcode = procedure_codes[x][0] + knr_flag = procedure_codes[x][1] + +putskim_postprocessing(dsegcode,knr_flag) + diff --git a/skimming_and_assignment/visum/scripts/PuT_Skimming_Setup.py b/skimming_and_assignment/visum/scripts/PuT_Skimming_Setup.py new file mode 100644 index 0000000..8c46c08 --- /dev/null +++ b/skimming_and_assignment/visum/scripts/PuT_Skimming_Setup.py @@ -0,0 +1,396 @@ +# Prep for Skimming for Oregon Metro +# 6/12/2025 - Luke Gordon (RSG) +# Adapted from code from Chetan Joshi (PTV) + +import tables +import numpy as np +import pandas as pd +import os +import VisumPy.helpers as h +import openmatrix as omx + + +def skim_setup(period): + + def ismode(): # isbrt, isscr, islrt, & iswes + + # Pull attributes + sa_isbrt = h.GetMulti(Visum.Net.StopAreas,r"isbrt", activeOnly = True) + sa_isscr = h.GetMulti(Visum.Net.StopAreas,r"isscr", activeOnly = True) + sa_islrt = h.GetMulti(Visum.Net.StopAreas,r"islrt", activeOnly = True) + sa_iswes = h.GetMulti(Visum.Net.StopAreas,r"iswes", activeOnly = True) + sp_lrtsyscode = h.GetMulti(Visum.Net.StopAreas,r"FIRST:STOPPOINTS\DISTINCT:LINEROUTES\TSYSCODE", activeOnly = True) + + # Make Visum list with link data + att_list = [sa_isbrt,sa_isscr,sa_islrt,sa_iswes,sp_lrtsyscode] + + # Put Visum link list into dataframe + df = pd.DataFrame(np.column_stack(att_list), columns = ['sa_isbrt','sa_isscr','sa_islrt','sa_iswes','sp_lrtsyscode']) + + # Break out 'DISTINCT:LINEROUTES\TSYSCODE' field to separate by commas into individual columns + df[['sp_lrtsyscode']] = df[['sp_lrtsyscode']].astype(str) + df = pd.concat([df,df['sp_lrtsyscode'].str.split(',', expand = True)], axis = 1) + # Change Screenline field names + if 1 not in df: + df[1] = None + if 2 not in df: + df[2] = None + if 3 not in df: + df[3] = None + if 4 not in df: + df[4] = None + df = df.rename(columns = {0:'Mode1',1:'Mode2',2:'Mode3',3:'Mode4',4:'Mode5'}) + + # Calculate fields + df['isbrt'] = df.apply(lambda row: 1 if row['Mode1'] == 'a' or row['Mode2'] == 'a' or row['Mode3'] == 'a' or row['Mode4'] == 'a' or row['Mode5'] == 'a' else 0, axis=1) + df['isscr'] = df.apply(lambda row: 1 if row['Mode1'] == 'e' or row['Mode2'] == 'e' or row['Mode3'] == 'e' or row['Mode4'] == 'e' or row['Mode5'] == 'e' else 0, axis=1) + df['islrt'] = df.apply(lambda row: 1 if row['Mode1'] == 'l' or row['Mode2'] == 'l' or row['Mode3'] == 'l' or row['Mode4'] == 'l' or row['Mode5'] == 'l' else 0, axis=1) + df['iswes'] = df.apply(lambda row: 1 if row['Mode1'] == 'r' or row['Mode2'] == 'r' or row['Mode3'] == 'r' or row['Mode4'] == 'r' or row['Mode5'] == 'r' else 0, axis=1) + + # Set fields back in Visum + h.SetMulti(Visum.Net.StopAreas ,r"isbrt", df['isbrt']) + h.SetMulti(Visum.Net.StopAreas ,r"isscr", df['isscr']) + h.SetMulti(Visum.Net.StopAreas ,r"islrt", df['islrt']) + h.SetMulti(Visum.Net.StopAreas ,r"iswes", df['iswes']) + + def headway(period): # Headway and Headway_Halved + + # Pull attributes + tp_emmeheadway = h.GetMulti(Visum.Net.TimeProfiles,r"Emme_Headway", activeOnly = True) + tp_headwayhalved = h.GetMulti(Visum.Net.TimeProfiles,r"Headway_Halved", activeOnly = True) + + if period == 'AM' or period == 'PM': + tp_periodheadway = h.GetMulti(Visum.Net.TimeProfiles,r"LINEROUTE\EMME_DATA1", activeOnly = True) + else: + tp_periodheadway = h.GetMulti(Visum.Net.TimeProfiles,r"LINEROUTE\EMME_DATA2", activeOnly = True) + + # Make Visum list with link data + att_list = [tp_emmeheadway,tp_periodheadway,tp_headwayhalved] + + # Put Visum link list into dataframe + df = pd.DataFrame(np.column_stack(att_list), columns = ['tp_emmeheadway','tp_periodheadway','tp_headwayhalved']) + + # Calculate fields + df['tp_emmeheadway'] = df['tp_periodheadway'] * 60 + df['tp_headwayhalved'] = df['tp_emmeheadway'] / 2 + + # Set fields back in Visum + h.SetMulti(Visum.Net.TimeProfiles ,r"Emme_Headway", df['tp_emmeheadway']) + h.SetMulti(Visum.Net.TimeProfiles ,r"Headway_Halved", df['tp_headwayhalved']) + + + def op_bushr(): # op_bushr on lineroutes and stopareas + + # Line Routes + # Pull attributes + lr_opbushr = h.GetMulti(Visum.Net.LineRoutes,r"op_bushr", activeOnly = True) + lr_opheadway = h.GetMulti(Visum.Net.LineRoutes,r"EMME_DATA2", activeOnly = True) + + # Make Visum list with link data + att_list = [lr_opbushr,lr_opheadway] + + # Put Visum link list into dataframe + df = pd.DataFrame(np.column_stack(att_list), columns = ['lr_opbushr','lr_opheadway']) + + # Calculate fields + df['lr_opbushr'] = df.apply(lambda row: 1 / (row['lr_opheadway'] / 60) if row['lr_opheadway'] != 0 else 0,axis=1) + + # Set field back in Visum + h.SetMulti(Visum.Net.LineRoutes ,r"op_bushr", df['lr_opbushr']) + + + # Stop Areas + # Pull attributes + sa_lropbushr = h.GetMulti(Visum.Net.StopAreas,r"FIRST:STOPPOINTS\SUM:LINEROUTES\OP_BUSHR", activeOnly = True) + + # Set field back in Visum + h.SetMulti(Visum.Net.StopAreas ,r"op_bushr", sa_lropbushr) + + + def stoptype(period): # Stop type & stop type constant + + # Pull attributes + # Fields to set + sa_sttyp = h.GetMulti(Visum.Net.StopAreas,r"sttyp" , activeOnly = True) + sa_stcon = h.GetMulti(Visum.Net.StopAreas,r"stcon" , activeOnly = True) + # Condition Fields + sa_istc = h.GetMulti(Visum.Net.StopAreas,r"istc" , activeOnly = True) + sa_istm = h.GetMulti(Visum.Net.StopAreas,r"istm" , activeOnly = True) + sa_isbrt = h.GetMulti(Visum.Net.StopAreas,r"isbrt" , activeOnly = True) + sa_isscr = h.GetMulti(Visum.Net.StopAreas,r"isscr" , activeOnly = True) + sa_islrt = h.GetMulti(Visum.Net.StopAreas,r"islrt" , activeOnly = True) + sa_iswes = h.GetMulti(Visum.Net.StopAreas,r"iswes" , activeOnly = True) + sa_opbushr = h.GetMulti(Visum.Net.StopAreas,r"op_bushr", activeOnly = True) + + # Make Visum list with link data + att_list = [sa_sttyp,sa_stcon,sa_istc,sa_istm ,sa_isbrt,sa_isscr,sa_islrt,sa_iswes,sa_opbushr] + + # Put Visum link list into dataframe + df = pd.DataFrame(np.column_stack(att_list), columns = ['sa_sttyp','sa_stcon','sa_istc','sa_istm','sa_isbrt','sa_isscr','sa_islrt','sa_iswes','sa_opbushr']) + + # Convert condition fields & stop type constant to float + df[['sa_stcon','sa_istc','sa_istm','sa_isbrt','sa_isscr','sa_islrt','sa_iswes','sa_opbushr']] = df[[ + 'sa_stcon','sa_istc','sa_istm','sa_isbrt','sa_isscr','sa_islrt','sa_iswes','sa_opbushr']].astype(float) + + # Calculate Stop Type field + for x in range(len(df)): + if ((df.at[x,'sa_istc'] == 1) | (df.at[x,'sa_istm'] == 1) | (df.at[x,'sa_islrt'] == 1) | (df.at[x,'sa_iswes'] == 1)): + df.at[x,'sa_sttyp'] = 'A,B,C' # Is transit center, is transit mall, is LRT stop, is WES stop + elif ((df.at[x,'sa_isbrt'] == 1) | (df.at[x,'sa_isscr'] == 1) | (df.at[x,'sa_opbushr'] >= 4)): + df.at[x,'sa_sttyp'] = 'D' # Is BRT stop, is streetcar stop, has >= 4 transit vehicles per hour + else: + df.at[x,'sa_sttyp'] = 'E' # All stops with infrequent local bus service + + + # Calculate Stop Type Constant field + for y in range(len(df)): + if period == 'AM' or period == 'PM': # Peak + if df.at[y,'sa_sttyp'] == 'A,B,C': + df.at[y,'sa_stcon'] = 0.1582 + elif df.at[y,'sa_sttyp'] == 'D': + df.at[y,'sa_stcon'] = 0.0531 + elif df.at[y,'sa_sttyp'] == 'E': + df.at[y,'sa_stcon'] = 0.0000 + else: # Off-Peak + if df.at[y,'sa_sttyp'] == 'A,B,C': + df.at[y,'sa_stcon'] = 0.1075 + elif df.at[y,'sa_sttyp'] == 'D': + df.at[y,'sa_stcon'] = 0.0756 + elif df.at[y,'sa_sttyp'] == 'E': + df.at[y,'sa_stcon'] = 0.0000 + + # Set fields back in Visum + h.SetMulti(Visum.Net.StopAreas ,r"sttyp", df['sa_sttyp']) + h.SetMulti(Visum.Net.StopAreas ,r"stcon", df['sa_stcon']) + + + def waittime(period): # Wait time perception factor + + # Pull attributes + sa_sttyp = h.GetMulti(Visum.Net.StopAreas,r"sttyp" , activeOnly = True) + sa_wtpf = h.GetMulti(Visum.Net.StopAreas,r"wtpf" , activeOnly = True) + + # Make Visum list with link data + att_list = [sa_sttyp,sa_wtpf] + + # Put Visum link list into dataframe + df = pd.DataFrame(np.column_stack(att_list), columns = ['sa_sttyp','sa_wtpf']) + + # Calculate field + for y in range(len(df)): + if period == 'AM' or period == 'PM': # Peak + if df.at[y,'sa_sttyp'] == 'A,B,C': + df.at[y,'sa_wtpf'] = 0.88 + elif df.at[y,'sa_sttyp'] == 'D': + df.at[y,'sa_wtpf'] = 0.93 + elif df.at[y,'sa_sttyp'] == 'E': + df.at[y,'sa_wtpf'] = 1.00 + else: # Off-Peak + if df.at[y,'sa_sttyp'] == 'A,B,C': + df.at[y,'sa_wtpf'] = 0.86 + elif df.at[y,'sa_sttyp'] == 'D': + df.at[y,'sa_wtpf'] = 0.94 + elif df.at[y,'sa_sttyp'] == 'E': + df.at[y,'sa_wtpf'] = 1.00 + + # Set fields back in Visum + h.SetMulti(Visum.Net.StopAreas ,r"wtpf", df['sa_wtpf']) + + + def invehicleperceptionfactor(period): # In-vehicle perception factors + + # Pull attributes + tp_tsyscode = h.GetMulti(Visum.Net.TimeProfiles,r"TSYSCODE", activeOnly = True) + tp_ivpf = h.GetMulti(Visum.Net.TimeProfiles,r"ivpf" , activeOnly = True) + + # Make Visum list with link data + att_list = [tp_tsyscode,tp_ivpf] + + # Put Visum link list into dataframe + df = pd.DataFrame(np.column_stack(att_list), columns = ['tp_tsyscode','tp_ivpf']) + + # Calculate field + for y in range(len(df)): + if period == 'AM' or period == 'PM': # Peak + if df.at[y,'tp_tsyscode'] == 'a': + df.at[y,'tp_ivpf'] = 0.95 + elif ((df.at[y,'tp_tsyscode'] == 'l') or (df.at[y,'tp_tsyscode'] == 'r')): + df.at[y,'tp_ivpf'] = 0.88 + else: + df.at[y,'tp_ivpf'] = 1.00 + else: # Off-Peak + if df.at[y,'tp_tsyscode'] == 'a': + df.at[y,'tp_ivpf'] = 0.95 + elif ((df.at[y,'tp_tsyscode'] == 'l') or (df.at[y,'tp_tsyscode'] == 'r')): + df.at[y,'tp_ivpf'] = 0.86 + else: + df.at[y,'tp_ivpf'] = 1.00 + + # Set fields back in Visum + h.SetMulti(Visum.Net.TimeProfiles ,r"ivpf", df['tp_ivpf']) + + + def boardingpenalty(period): # Boarding penalty (seconds) + + # Pull attributes + tp_tsyscode = h.GetMulti(Visum.Net.TimeProfiles,r"TSYSCODE", activeOnly = True) + tp_brdpen = h.GetMulti(Visum.Net.TimeProfiles,r"brdpen" , activeOnly = True) + + # Make Visum list with link data + att_list = [tp_tsyscode,tp_brdpen] + + # Put Visum link list into dataframe + df = pd.DataFrame(np.column_stack(att_list), columns = ['tp_tsyscode','tp_brdpen']) + + # Calculate field + for y in range(len(df)): + if period == 'AM' or period == 'PM': # Peak + if ((df.at[y,'tp_tsyscode'] == 'l') or (df.at[y,'tp_tsyscode'] == 'r')): + df.at[y,'tp_brdpen'] = 000.0 + elif ((df.at[y,'tp_tsyscode'] == 'a') or (df.at[y,'tp_tsyscode'] == 'e')): + df.at[y,'tp_brdpen'] = 372.0 + else: + df.at[y,'tp_brdpen'] = 439.8 + else: # Off-Peak + if ((df.at[y,'tp_tsyscode'] == 'l') or (df.at[y,'tp_tsyscode'] == 'r')): + df.at[y,'tp_brdpen'] = 000.0 + elif ((df.at[y,'tp_tsyscode'] == 'a') or (df.at[y,'tp_tsyscode'] == 'e')): + df.at[y,'tp_brdpen'] = 166.8 + else: + df.at[y,'tp_brdpen'] = 540.6 + + # Add global boarding penalty + for y in range(len(df)): + df.at[y,'tp_brdpen'] = df.at[y,'tp_brdpen'] + 231 + + # Set fields back in Visum + h.SetMulti(Visum.Net.TimeProfiles ,r"brdpen", df['tp_brdpen']) + + + def dwelltime(): + + # Pull attributes + tpi_tsyscode = h.GetMulti(Visum.Net.TimeProfileItems,r"TIMEPROFILE\TSYSCODE" , activeOnly = True) + tpi_emmedwt = h.GetMulti(Visum.Net.TimeProfileItems,r"LINEROUTEITEM\EMME_DWT", activeOnly = True) + tpi_prelength = h.GetMulti(Visum.Net.TimeProfileItems,r"PreLength" , activeOnly = True) + tpi_dwelltime = h.GetMulti(Visum.Net.TimeProfileItems,r"DWELL_TIME" , activeOnly = True) + + + # Make Visum list with link data + att_list = [tpi_tsyscode,tpi_emmedwt,tpi_prelength,tpi_dwelltime] + + # Put Visum link list into dataframe + df = pd.DataFrame(np.column_stack(att_list), columns = ['tpi_tsyscode','tpi_emmedwt','tpi_prelength','tpi_dwelltime']) + + # Convert fields to float + df[['tpi_emmedwt','tpi_prelength','tpi_dwelltime']] = df[['tpi_emmedwt','tpi_prelength','tpi_dwelltime']].astype(float) + + # Calculate field + for y in range(len(df)): + if df.at[y,'tpi_tsyscode'] == 'b': # Local Bus dwell time based on prelength and Emme_DWT + df.at[y,'tpi_dwelltime'] = df.at[y,'tpi_emmedwt'] * 60 * df.at[y,'tpi_prelength'] + else: # Non-Local Bus dwell time based on Emme_DWT only + df.at[y,'tpi_dwelltime'] = df.at[y,'tpi_emmedwt'] * 60 + + # Set fields back in Visum + h.SetMulti(Visum.Net.TimeProfileItems ,r"DWELL_TIME", df['tpi_dwelltime']) + + + def calc_ttf(ft, timau, length, us1, default_speed): # INPUT TO RUNTIME FUNCTION + if us1 <= 0: + us1 = default_speed + + if length: + transit_time = 3600*length / default_speed + + if timau < 999: + if ft == 1: + transit_time = timau * 1.15 + elif ft == 2: + transit_time = timau * 1.20 + elif ft == 3: + transit_time = timau + elif ft == 4: + transit_time = timau * 1.09 + elif ft == 5: + transit_time = 60* (60 * length / us1) + elif ft == 6: + transit_time = 60* (60 * length / us1 + 60 * length / 180) + elif ft ==11: + transit_time = timau * 1.15 + elif ft ==12: + transit_time = timau * 1.30 + elif ft ==13: + transit_time = timau + elif ft ==14: + transit_time =timau * 1.09 + elif ft == 15: + transit_time = 60* (60 * length / us1) + elif ft == 16: + transit_time = 60* (60 * length / us1 + 60 * length / 180) + elif ft == 21: + transit_time = timau * 1.05 + elif ft == 22: + transit_time = timau * 1.03 + else: + transit_time = 1 + + return transit_time + + def runtime(period): + tpitems = Visum.Net.TimeProfileItems.GetMultipleAttributes(["LINEROUTEITEM\\EMME_TTFINDEX", "SUM:USEDLINEROUTEITEMS\\OUTLINK\\"+period+"_TTC","SUM:USEDLINEROUTEITEMS\\POSTLENGTH", + "LINEROUTEITEM\\EMME_DATA1"]) + + #else: + # tpitems = Visum.Net.TimeProfileItems.GetMultipleAttributes(["LINEROUTEITEM\\EMME_TTFINDEX", "SUM:USEDLINEROUTEITEMS\\OUTLINK\\AddVal3","SUM:USEDLINEROUTEITEMS\\POSTLENGTH", + # "LINEROUTEITEM\\EMME_DATA1"]) + result = [] + + default_speed = 30 + for ft, timau, length, us1 in tpitems: + haul_time = calc_ttf(ft, timau, length, us1, default_speed) + result.append([haul_time, ]) + + Visum.Net.TimeProfileItems.SetMultipleAttributes(["AddVal"], result) + + def combinerundwelltimes(): + + # Pull attributes + tpi_addval = h.GetMulti(Visum.Net.TimeProfileItems,r"ADDVAL" , activeOnly = True) + tpi_dwelltime = h.GetMulti(Visum.Net.TimeProfileItems,r"DWELL_TIME" , activeOnly = True) + + # Make Visum list with link data + att_list = [tpi_addval,tpi_dwelltime] + + # Put Visum link list into dataframe + df = pd.DataFrame(np.column_stack(att_list), columns = ['tpi_addval','tpi_dwelltime']) + + # Convert fields to float + df[['tpi_addval','tpi_dwelltime']] = df[['tpi_addval','tpi_dwelltime']].astype(float) + + # Calculate field + df['tpi_addval'] = df['tpi_addval'] + df['tpi_dwelltime'] + + # Set fields back in Visum + h.SetMulti(Visum.Net.TimeProfileItems ,r"ADDVAL", df['tpi_addval']) + + + + # RUN FUNCTIONS + # Setting Skim Attributes + ismode() + headway(period) + op_bushr() + stoptype(period) + waittime(period) + invehicleperceptionfactor(period) + boardingpenalty(period) + # Setting Dwell and Run times + dwelltime() + runtime(period) + combinerundwelltimes() + + +per = Visum.Procedures.OperationExecutor.GetCurrentOperation().AttValue("CODE") # Example: outputs a string like 'AM' from AM in the code box +skim_setup(per) + diff --git a/skimming_and_assignment/visum/scripts/Skimming_Setup.py b/skimming_and_assignment/visum/scripts/Skimming_Setup.py new file mode 100644 index 0000000..54a1923 --- /dev/null +++ b/skimming_and_assignment/visum/scripts/Skimming_Setup.py @@ -0,0 +1,396 @@ +# Prep for Skimming for Oregon Metro +# 6/12/2025 - Luke Gordon (RSG) +# Adapted from code from Chetan Joshi (PTV) + +import tables +import numpy as np +import pandas as pd +import os +import VisumPy.helpers as h +import openmatrix as omx + + +def put_skim_setup(period): + + def ismode(): # isbrt, isscr, islrt, & iswes + + # Pull attributes + sa_isbrt = h.GetMulti(Visum.Net.StopAreas,r"isbrt", activeOnly = True) + sa_isscr = h.GetMulti(Visum.Net.StopAreas,r"isscr", activeOnly = True) + sa_islrt = h.GetMulti(Visum.Net.StopAreas,r"islrt", activeOnly = True) + sa_iswes = h.GetMulti(Visum.Net.StopAreas,r"iswes", activeOnly = True) + sp_lrtsyscode = h.GetMulti(Visum.Net.StopAreas,r"FIRST:STOPPOINTS\DISTINCT:LINEROUTES\TSYSCODE", activeOnly = True) + + # Make Visum list with link data + att_list = [sa_isbrt,sa_isscr,sa_islrt,sa_iswes,sp_lrtsyscode] + + # Put Visum link list into dataframe + df = pd.DataFrame(np.column_stack(att_list), columns = ['sa_isbrt','sa_isscr','sa_islrt','sa_iswes','sp_lrtsyscode']) + + # Break out 'DISTINCT:LINEROUTES\TSYSCODE' field to separate by commas into individual columns + df[['sp_lrtsyscode']] = df[['sp_lrtsyscode']].astype(str) + df = pd.concat([df,df['sp_lrtsyscode'].str.split(',', expand = True)], axis = 1) + # Change Screenline field names + if 1 not in df: + df[1] = None + if 2 not in df: + df[2] = None + if 3 not in df: + df[3] = None + if 4 not in df: + df[4] = None + df = df.rename(columns = {0:'Mode1',1:'Mode2',2:'Mode3',3:'Mode4',4:'Mode5'}) + + # Calculate fields + df['isbrt'] = df.apply(lambda row: 1 if row['Mode1'] == 'a' or row['Mode2'] == 'a' or row['Mode3'] == 'a' or row['Mode4'] == 'a' or row['Mode5'] == 'a' else 0, axis=1) + df['isscr'] = df.apply(lambda row: 1 if row['Mode1'] == 'e' or row['Mode2'] == 'e' or row['Mode3'] == 'e' or row['Mode4'] == 'e' or row['Mode5'] == 'e' else 0, axis=1) + df['islrt'] = df.apply(lambda row: 1 if row['Mode1'] == 'l' or row['Mode2'] == 'l' or row['Mode3'] == 'l' or row['Mode4'] == 'l' or row['Mode5'] == 'l' else 0, axis=1) + df['iswes'] = df.apply(lambda row: 1 if row['Mode1'] == 'r' or row['Mode2'] == 'r' or row['Mode3'] == 'r' or row['Mode4'] == 'r' or row['Mode5'] == 'r' else 0, axis=1) + + # Set fields back in Visum + h.SetMulti(Visum.Net.StopAreas ,r"isbrt", df['isbrt']) + h.SetMulti(Visum.Net.StopAreas ,r"isscr", df['isscr']) + h.SetMulti(Visum.Net.StopAreas ,r"islrt", df['islrt']) + h.SetMulti(Visum.Net.StopAreas ,r"iswes", df['iswes']) + + def headway(period): # Headway and Headway_Halved + + # Pull attributes + tp_emmeheadway = h.GetMulti(Visum.Net.TimeProfiles,r"Emme_Headway", activeOnly = True) + tp_headwayhalved = h.GetMulti(Visum.Net.TimeProfiles,r"Headway_Halved", activeOnly = True) + + if period == 'AM' or period == 'PM': + tp_periodheadway = h.GetMulti(Visum.Net.TimeProfiles,r"LINEROUTE\EMME_DATA1", activeOnly = True) + else: + tp_periodheadway = h.GetMulti(Visum.Net.TimeProfiles,r"LINEROUTE\EMME_DATA2", activeOnly = True) + + # Make Visum list with link data + att_list = [tp_emmeheadway,tp_periodheadway,tp_headwayhalved] + + # Put Visum link list into dataframe + df = pd.DataFrame(np.column_stack(att_list), columns = ['tp_emmeheadway','tp_periodheadway','tp_headwayhalved']) + + # Calculate fields + df['tp_emmeheadway'] = df['tp_periodheadway'] * 60 + df['tp_headwayhalved'] = df['tp_emmeheadway'] / 2 + + # Set fields back in Visum + h.SetMulti(Visum.Net.TimeProfiles ,r"Emme_Headway", df['tp_emmeheadway']) + h.SetMulti(Visum.Net.TimeProfiles ,r"Headway_Halved", df['tp_headwayhalved']) + + + def op_bushr(): # op_bushr on lineroutes and stopareas + + # Line Routes + # Pull attributes + lr_opbushr = h.GetMulti(Visum.Net.LineRoutes,r"op_bushr", activeOnly = True) + lr_opheadway = h.GetMulti(Visum.Net.LineRoutes,r"EMME_DATA2", activeOnly = True) + + # Make Visum list with link data + att_list = [lr_opbushr,lr_opheadway] + + # Put Visum link list into dataframe + df = pd.DataFrame(np.column_stack(att_list), columns = ['lr_opbushr','lr_opheadway']) + + # Calculate fields + df['lr_opbushr'] = df.apply(lambda row: 1 / (row['lr_opheadway'] / 60) if row['lr_opheadway'] != 0 else 0,axis=1) + + # Set field back in Visum + h.SetMulti(Visum.Net.LineRoutes ,r"op_bushr", df['lr_opbushr']) + + + # Stop Areas + # Pull attributes + sa_lropbushr = h.GetMulti(Visum.Net.StopAreas,r"FIRST:STOPPOINTS\SUM:LINEROUTES\OP_BUSHR", activeOnly = True) + + # Set field back in Visum + h.SetMulti(Visum.Net.StopAreas ,r"op_bushr", sa_lropbushr) + + + def stoptype(period): # Stop type & stop type constant + + # Pull attributes + # Fields to set + sa_sttyp = h.GetMulti(Visum.Net.StopAreas,r"sttyp" , activeOnly = True) + sa_stcon = h.GetMulti(Visum.Net.StopAreas,r"stcon" , activeOnly = True) + # Condition Fields + sa_istc = h.GetMulti(Visum.Net.StopAreas,r"istc" , activeOnly = True) + sa_istm = h.GetMulti(Visum.Net.StopAreas,r"istm" , activeOnly = True) + sa_isbrt = h.GetMulti(Visum.Net.StopAreas,r"isbrt" , activeOnly = True) + sa_isscr = h.GetMulti(Visum.Net.StopAreas,r"isscr" , activeOnly = True) + sa_islrt = h.GetMulti(Visum.Net.StopAreas,r"islrt" , activeOnly = True) + sa_iswes = h.GetMulti(Visum.Net.StopAreas,r"iswes" , activeOnly = True) + sa_opbushr = h.GetMulti(Visum.Net.StopAreas,r"op_bushr", activeOnly = True) + + # Make Visum list with link data + att_list = [sa_sttyp,sa_stcon,sa_istc,sa_istm ,sa_isbrt,sa_isscr,sa_islrt,sa_iswes,sa_opbushr] + + # Put Visum link list into dataframe + df = pd.DataFrame(np.column_stack(att_list), columns = ['sa_sttyp','sa_stcon','sa_istc','sa_istm','sa_isbrt','sa_isscr','sa_islrt','sa_iswes','sa_opbushr']) + + # Convert condition fields & stop type constant to float + df[['sa_stcon','sa_istc','sa_istm','sa_isbrt','sa_isscr','sa_islrt','sa_iswes','sa_opbushr']] = df[[ + 'sa_stcon','sa_istc','sa_istm','sa_isbrt','sa_isscr','sa_islrt','sa_iswes','sa_opbushr']].astype(float) + + # Calculate Stop Type field + for x in range(len(df)): + if ((df.at[x,'sa_istc'] == 1) | (df.at[x,'sa_istm'] == 1) | (df.at[x,'sa_islrt'] == 1) | (df.at[x,'sa_iswes'] == 1)): + df.at[x,'sa_sttyp'] = 'A,B,C' # Is transit center, is transit mall, is LRT stop, is WES stop + elif ((df.at[x,'sa_isbrt'] == 1) | (df.at[x,'sa_isscr'] == 1) | (df.at[x,'sa_opbushr'] >= 4)): + df.at[x,'sa_sttyp'] = 'D' # Is BRT stop, is streetcar stop, has >= 4 transit vehicles per hour + else: + df.at[x,'sa_sttyp'] = 'E' # All stops with infrequent local bus service + + + # Calculate Stop Type Constant field + for y in range(len(df)): + if period == 'AM' or period == 'PM': # Peak + if df.at[y,'sa_sttyp'] == 'A,B,C': + df.at[y,'sa_stcon'] = 0.1582 + elif df.at[y,'sa_sttyp'] == 'D': + df.at[y,'sa_stcon'] = 0.0531 + elif df.at[y,'sa_sttyp'] == 'E': + df.at[y,'sa_stcon'] = 0.0000 + else: # Off-Peak + if df.at[y,'sa_sttyp'] == 'A,B,C': + df.at[y,'sa_stcon'] = 0.1075 + elif df.at[y,'sa_sttyp'] == 'D': + df.at[y,'sa_stcon'] = 0.0756 + elif df.at[y,'sa_sttyp'] == 'E': + df.at[y,'sa_stcon'] = 0.0000 + + # Set fields back in Visum + h.SetMulti(Visum.Net.StopAreas ,r"sttyp", df['sa_sttyp']) + h.SetMulti(Visum.Net.StopAreas ,r"stcon", df['sa_stcon']) + + + def waittime(period): # Wait time perception factor + + # Pull attributes + sa_sttyp = h.GetMulti(Visum.Net.StopAreas,r"sttyp" , activeOnly = True) + sa_wtpf = h.GetMulti(Visum.Net.StopAreas,r"wtpf" , activeOnly = True) + + # Make Visum list with link data + att_list = [sa_sttyp,sa_wtpf] + + # Put Visum link list into dataframe + df = pd.DataFrame(np.column_stack(att_list), columns = ['sa_sttyp','sa_wtpf']) + + # Calculate field + for y in range(len(df)): + if period == 'AM' or period == 'PM': # Peak + if df.at[y,'sa_sttyp'] == 'A,B,C': + df.at[y,'sa_wtpf'] = 0.88 + elif df.at[y,'sa_sttyp'] == 'D': + df.at[y,'sa_wtpf'] = 0.93 + elif df.at[y,'sa_sttyp'] == 'E': + df.at[y,'sa_wtpf'] = 1.00 + else: # Off-Peak + if df.at[y,'sa_sttyp'] == 'A,B,C': + df.at[y,'sa_wtpf'] = 0.86 + elif df.at[y,'sa_sttyp'] == 'D': + df.at[y,'sa_wtpf'] = 0.94 + elif df.at[y,'sa_sttyp'] == 'E': + df.at[y,'sa_wtpf'] = 1.00 + + # Set fields back in Visum + h.SetMulti(Visum.Net.StopAreas ,r"wtpf", df['sa_wtpf']) + + + def invehicleperceptionfactor(period): # In-vehicle perception factors + + # Pull attributes + tp_tsyscode = h.GetMulti(Visum.Net.TimeProfiles,r"TSYSCODE", activeOnly = True) + tp_ivpf = h.GetMulti(Visum.Net.TimeProfiles,r"ivpf" , activeOnly = True) + + # Make Visum list with link data + att_list = [tp_tsyscode,tp_ivpf] + + # Put Visum link list into dataframe + df = pd.DataFrame(np.column_stack(att_list), columns = ['tp_tsyscode','tp_ivpf']) + + # Calculate field + for y in range(len(df)): + if period == 'AM' or period == 'PM': # Peak + if df.at[y,'tp_tsyscode'] == 'a': + df.at[y,'tp_ivpf'] = 0.95 + elif ((df.at[y,'tp_tsyscode'] == 'l') or (df.at[y,'tp_tsyscode'] == 'r')): + df.at[y,'tp_ivpf'] = 0.88 + else: + df.at[y,'tp_ivpf'] = 1.00 + else: # Off-Peak + if df.at[y,'tp_tsyscode'] == 'a': + df.at[y,'tp_ivpf'] = 0.95 + elif ((df.at[y,'tp_tsyscode'] == 'l') or (df.at[y,'tp_tsyscode'] == 'r')): + df.at[y,'tp_ivpf'] = 0.86 + else: + df.at[y,'tp_ivpf'] = 1.00 + + # Set fields back in Visum + h.SetMulti(Visum.Net.TimeProfiles ,r"ivpf", df['tp_ivpf']) + + + def boardingpenalty(period): # Boarding penalty (seconds) + + # Pull attributes + tp_tsyscode = h.GetMulti(Visum.Net.TimeProfiles,r"TSYSCODE", activeOnly = True) + tp_brdpen = h.GetMulti(Visum.Net.TimeProfiles,r"brdpen" , activeOnly = True) + + # Make Visum list with link data + att_list = [tp_tsyscode,tp_brdpen] + + # Put Visum link list into dataframe + df = pd.DataFrame(np.column_stack(att_list), columns = ['tp_tsyscode','tp_brdpen']) + + # Calculate field + for y in range(len(df)): + if period == 'AM' or period == 'PM': # Peak + if ((df.at[y,'tp_tsyscode'] == 'l') or (df.at[y,'tp_tsyscode'] == 'r')): + df.at[y,'tp_brdpen'] = 000.0 + elif ((df.at[y,'tp_tsyscode'] == 'a') or (df.at[y,'tp_tsyscode'] == 'e')): + df.at[y,'tp_brdpen'] = 372.0 + else: + df.at[y,'tp_brdpen'] = 439.8 + else: # Off-Peak + if ((df.at[y,'tp_tsyscode'] == 'l') or (df.at[y,'tp_tsyscode'] == 'r')): + df.at[y,'tp_brdpen'] = 000.0 + elif ((df.at[y,'tp_tsyscode'] == 'a') or (df.at[y,'tp_tsyscode'] == 'e')): + df.at[y,'tp_brdpen'] = 166.8 + else: + df.at[y,'tp_brdpen'] = 540.6 + + # Add global boarding penalty + for y in range(len(df)): + df.at[y,'tp_brdpen'] = df.at[y,'tp_brdpen'] + 231 + + # Set fields back in Visum + h.SetMulti(Visum.Net.TimeProfiles ,r"brdpen", df['tp_brdpen']) + + + def dwelltime(): + + # Pull attributes + tpi_tsyscode = h.GetMulti(Visum.Net.TimeProfileItems,r"TIMEPROFILE\TSYSCODE" , activeOnly = True) + tpi_emmedwt = h.GetMulti(Visum.Net.TimeProfileItems,r"LINEROUTEITEM\EMME_DWT", activeOnly = True) + tpi_prelength = h.GetMulti(Visum.Net.TimeProfileItems,r"PreLength" , activeOnly = True) + tpi_dwelltime = h.GetMulti(Visum.Net.TimeProfileItems,r"DWELL_TIME" , activeOnly = True) + + + # Make Visum list with link data + att_list = [tpi_tsyscode,tpi_emmedwt,tpi_prelength,tpi_dwelltime] + + # Put Visum link list into dataframe + df = pd.DataFrame(np.column_stack(att_list), columns = ['tpi_tsyscode','tpi_emmedwt','tpi_prelength','tpi_dwelltime']) + + # Convert fields to float + df[['tpi_emmedwt','tpi_prelength','tpi_dwelltime']] = df[['tpi_emmedwt','tpi_prelength','tpi_dwelltime']].astype(float) + + # Calculate field + for y in range(len(df)): + if df.at[y,'tpi_tsyscode'] == 'b': # Local Bus dwell time based on prelength and Emme_DWT + df.at[y,'tpi_dwelltime'] = df.at[y,'tpi_emmedwt'] * 60 * df.at[y,'tpi_prelength'] + else: # Non-Local Bus dwell time based on Emme_DWT only + df.at[y,'tpi_dwelltime'] = df.at[y,'tpi_emmedwt'] * 60 + + # Set fields back in Visum + h.SetMulti(Visum.Net.TimeProfileItems ,r"DWELL_TIME", df['tpi_dwelltime']) + + + def calc_ttf(ft, timau, length, us1, default_speed): # INPUT TO RUNTIME FUNCTION + if us1 <= 0: + us1 = default_speed + + if length: + transit_time = 3600*length / default_speed + + if timau < 999: + if ft == 1: + transit_time = timau * 1.15 + elif ft == 2: + transit_time = timau * 1.20 + elif ft == 3: + transit_time = timau + elif ft == 4: + transit_time = timau * 1.09 + elif ft == 5: + transit_time = 60* (60 * length / us1) + elif ft == 6: + transit_time = 60* (60 * length / us1 + 60 * length / 180) + elif ft ==11: + transit_time = timau * 1.15 + elif ft ==12: + transit_time = timau * 1.30 + elif ft ==13: + transit_time = timau + elif ft ==14: + transit_time =timau * 1.09 + elif ft == 15: + transit_time = 60* (60 * length / us1) + elif ft == 16: + transit_time = 60* (60 * length / us1 + 60 * length / 180) + elif ft == 21: + transit_time = timau * 1.05 + elif ft == 22: + transit_time = timau * 1.03 + else: + transit_time = 1 + + return transit_time + + def runtime(period): + if period == 'AM' or period == 'PM': # Peak + tpitems = Visum.Net.TimeProfileItems.GetMultipleAttributes(["LINEROUTEITEM\\EMME_TTFINDEX", "SUM:USEDLINEROUTEITEMS\\OUTLINK\\AddVal1","SUM:USEDLINEROUTEITEMS\\POSTLENGTH", + "LINEROUTEITEM\\EMME_DATA1"]) + else: # Off-Peak + tpitems = Visum.Net.TimeProfileItems.GetMultipleAttributes(["LINEROUTEITEM\\EMME_TTFINDEX", "SUM:USEDLINEROUTEITEMS\\OUTLINK\\AddVal2","SUM:USEDLINEROUTEITEMS\\POSTLENGTH", + "LINEROUTEITEM\\EMME_DATA1"]) + result = [] + + default_speed = 30 + for ft, timau, length, us1 in tpitems: + haul_time = calc_ttf(ft, timau, length, us1, default_speed) + result.append([haul_time, ]) + + Visum.Net.TimeProfileItems.SetMultipleAttributes(["AddVal"], result) + + def combinerundwelltimes(): + + # Pull attributes + tpi_addval = h.GetMulti(Visum.Net.TimeProfileItems,r"ADDVAL" , activeOnly = True) + tpi_dwelltime = h.GetMulti(Visum.Net.TimeProfileItems,r"DWELL_TIME" , activeOnly = True) + + # Make Visum list with link data + att_list = [tpi_addval,tpi_dwelltime] + + # Put Visum link list into dataframe + df = pd.DataFrame(np.column_stack(att_list), columns = ['tpi_addval','tpi_dwelltime']) + + # Convert fields to float + df[['tpi_addval','tpi_dwelltime']] = df[['tpi_addval','tpi_dwelltime']].astype(float) + + # Calculate field + df['tpi_addval'] = df['tpi_addval'] + df['tpi_dwelltime'] + + # Set fields back in Visum + h.SetMulti(Visum.Net.TimeProfileItems ,r"ADDVAL", df['tpi_addval']) + + + + # RUN FUNCTIONS + # Setting Skim Attributes + ismode() + headway(period) + op_bushr() + stoptype(period) + waittime(period) + invehicleperceptionfactor(period) + boardingpenalty(period) + # Setting Dwell and Run times + dwelltime() + runtime(period) + combinerundwelltimes() + + +procedure_code = Visum.Procedures.OperationExecutor.GetCurrentOperation().AttValue("CODE") # Example: outputs a string like 'AM' from AM in the code box +put_skim_setup(procedure_code) +