Group members: Sruangsaeng, Hunter, Ysabel, Alidu
This project investigates micromobility usage-specifically e-scooters and bike share in Seattle. Our primary focus is to understand where and when people ride, locate potential collision hotspots involving vulnerable road users (pedestrians, cyclists, e-scooter riders), and examine geographic factors (like slope, transit proximity, land use) that may influence both usage and collisions. We hope these insights will guide data-driven enhancements to infrastructure, user behavior, and environmental considerations- ultimately fostering safer and more sustainable micromobility adoption.
Seattle has rapidly expanded its micromobility offerings (e-scooters, bike shares) to provide low-emission, last-mile travel solutions. For example, Seattle's e-scooter pilot program recorded over 1.4 millions rides between 2020 and 2021 (Seattle Department of Transportation (SDOT), 2021). Meanwhile, a 2022 study by the National Association of City Transportation Officials (NACTO) found that micromobility devices can replace up to 30% of short car trips in dense urban areas, emphasizing their role in reducing vehicle miles traveled (NACTO, 2022). However, questions about safety and usage distribution persist. According to SDOT collision data, 21% of reported e-scooter crashes in 2021 led to serious injuries-indicating the need for improved infrastructure and rider education (SDOT, 2022). Additionally, steep slopes, uneven access to protected lanes, and transit deserts may further complicate safe micromobility usage for residents in certain neighborhoods (King County Mobility Coalition, 2020) By merging trip data (where and when people ride) with collision records and relevant geospatial layers (slope, transit stops, land-use types), we aim to
- Pinpoint high-usage corridors and collision hotspots
- Explore geographic influences (e.g., terrain, neighborhood zoning) on ridership and collisions
Problem Statement
- Where do e-scooter and bike share trips cluster, and do these align with collision-prone zones?
- How do factors like slope, proximity to transit, or land use affect micromobility usage and collision risk?
Objectives
- Spatiotemporal Patterns
- Identify ridership peaks (daily/weekly/seasonal) and produce usage hotspot maps.
- Collision Hotspots
- Detect VRU collision clusters using kernel density or Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to compare with usage patterns.
- Geographic Factors
- Integrate slope or land-use data to see how terrain/environment shapes micromobility usage and collisions.
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Micromobility Trip Data 🚲🛴
General Bikeshare Feed Specification (GBFS) – Standardized format for bike/scooter share data
🔗 GitHub Repository- Bird Feed
- Lime Feed
-
Seattle Micromobility & Collision Data 🏙️
Seattle Open GIS Portal
🔗 Seattle GIS Open Data- 🚦 Collision Data
- Seattle SDOT Collisions (All Years)
- 🚲 Infrastructure & Facilities
- SDOT Bike Facilities
- Bicycle Racks
- Sidewalks
- SDOT Channelization View
- Seattle Transportation Plan Bicycle Element
- Marked Crosswalks
- Beacon Assemblies
- 🚊 Transit & Traffic Features
- Frequent Transit Service Areas
- Streetcar Lines & Stations
- Areaways
- School Zones
- Railroad Crossings
- SDOT Intersections
- Transportation Table - Seattle Neighborhoods
- Current Land Use Zoning Detail
- Seattle Open Data Portal
🔗 Seattle Open Data - 🚲 Micromobility Counters
- Spokane St Bridge Bicycle Counter
- Thomas St Overpass Bike & Ped Counter
- 2nd Ave Cycle Track North of Marion St Bicycle Counter
- Elliott Bay Trail in Myrtle Edwards Park Bicycle & Pedestrian Counter
- Burke Gilman Trail North of NE 70th St Bicycle & Pedestrian Counter
- Fremont Bridge Bicycle Counter
- 🌦️ Environmental Data
- Road Weather Information Stations
- 🚦 Collision Data
-
Geographic & Slope Data 🌍
- 🔗 Washington State DNR LiDAR Portal – High-resolution LiDAR elevation data for slope analysis
- 🔗 Seattle ECA Steep Slope Dataset – Identifies areas with slopes ≥40%
- Tools/packages you’ll use (with links)
- We are going to need to learn how to ingest data from the General Bikeshare Feed Specification (GBFS).
- Geopandas: https://github.com/geopandas/geopandas
- Numpy: https://github.com/numpy/numpy
- Matplotlib: https://github.com/matplotlib/matplotlib
First visualise the data table to find the variables in each dataset. There is the need to determine if the variables are dependant/independant of each other, thus a correlation analysis of the variables would be conducted to determine those relations. The variables with positive correlations would be mapped/visualised to properly appreciate the relationships. Kernel density mapping would be used to show hospot areas of high-usage & high correaltion areas for micromobility. The usage patterns would be determined using geospatial correlations on slope and land use.
✔ Hotspot maps – High-usage & high-collision areas for micromobility
✔ Time-series insights – Micromobility demand across time periods
✔ Geospatial correlations – Influence of slope & land use on usage patterns
- Any other relevant information, images/tables, references, etc.
- Seattle Department of Transportation (SDOT).** (2021). *E-Scooter Pilot Program Evaluation Report. Seattle, WA.
- National Association of City Transportation Officials (NACTO). (2022). Shared Micromobility in the U.S.: 2022 Snapshot. https://nacto.org
- Seattle Department of Transportation (SDOT). (2022). Collision Data Dashboard. https://www.seattle.gov/transportation/
- King County Mobility Coalition. (2020). Mobility Needs Assessment for King County. King County, WA.