Merge branch 'master' of https://github.com/tgbnhy/k-paths

README.md

@@ -1,12 +1,11 @@
 # k-paths
 ## Introduction
-This repo holds the source code and experiments of k-paths clustering.
+This repo holds the source code and scripts for reproduce the experiments of k-paths clustering.
 
 ## Usage
-1. mvn clean package
-A .jar file will be generated under folder "target".
 
-2. Run the experiments.
+1. If you run in Eclipse, just go to "au.edu.rmit.trajectory.expriments.kpathEfficiency", and click the "run configuration", creat a new java application, and fill the following parameters:
+
 E:\dataset\new_porto\reassign\porto_mm_edge.dat 10 1000000 E:\dataset\new_porto\reassign\new_edge_street.txt E:\dataset\new_porto\reassign\new_graph.txt
 
 Then, all the result will be recorded into the log file under the "logs" folder.
@@ -16,8 +15,11 @@ arg[1] is the number of clusters
 arg[2] is the number of trajectories in the datafile which will be clustered
 arg[3] is the edge info file which contains the street name
 arg[4] is the graph file
-arg[5] is the city name. 
+arg[5] is the city name.
 
+2. If you want to run from commands (recommended):
+A. mvn clean package
+A .jar file will be generated under folder "target".
  java -Xmx16192M -cp ./torch-clus-0.0.1-SNAPSHOT.jar au.edu.rmit.trajectory.expriments.EBD ./data_porto/porto_mm_edge.dat 10 100000 ./data_porto/new_edge_street.txt ./data_porto/new_graph.txt porto
 
  #run the tdrive clustering
@@ -34,17 +36,22 @@ arg[5] is the city name.
  java -Xmx16192M -cp ./torch-clus-0.0.1-SNAPSHOT.jar au.edu.rmit.trajectory.expriments.EBD ./data_tdrive/beijing_mm_edge.txt.reassign 10 1000 ./data_tdrive/new_id_edge_raw_beijing.txt ./data_tdrive/beijing_graph_new.txt tdrive
  #run the porto clustering
 
- java -Xmx16192M -cp ./torch-clus-0.0.1-SNAPSHOT.jar au.edu.rmit.trajectory.expriments.EBD ./data_nantong/trajectory_edge_all.txt 10 1000 ./data_nantong/id_edge_raw.txt ./data_nantong/id_edge.txt nantong
-
- #streaming
- java -Xmx16192M -cp ./torch-clus-0.0.1-SNAPSHOT.jar au.edu.rmit.trajectory.clustering.StreamEvaluation		./data_nantong/trajectory_edge_all.txt 10 1000 ./data_nantong/id_edge_raw.txt ./data_nantong/id_edge.txt nantong ./data_nantong/time_car_edge.txt
 
 ### Datasets
 We use the map-matched dataset, it covers the road network, and trajectory data composed of integer ids.
 https://sites.google.com/site/shengwangcs/torch
 
+Download the dataset from the above link, and put the dataset into "data_porto" or "data_tdrive".
+
 ## Paper
 
 
+## Visualization
+We use mapv to visulized the cluster result using different color.
+
+A visualization using dynamic flow can be found in http://115.146.93.77:8080/TTorchServer/.
+
+
 ## Citation
 
+

data_porto/README.md

@@ -0,0 +1,2 @@
+# Dataset of mapped T-drive
+Download from https://drive.google.com/drive/u/2/folders/16FJOg7Sve8ub6SNCbc_TBkiTTg2Js4Kr