Skip to content

Scenario repo for the paper “Analyzing the Robustness of Adaptive Traffic Control System Using Reinforcement Learning for Urban Traffic Flow"

License

Notifications You must be signed in to change notification settings

Red-Pheonix/Grid-4x4-Scenario

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

14 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Grid 4x4 Scenario Files

This is the repo for the Grid 4x4 scenario for analyzing robustness of ATCS-RL systems. Let me provide a brief description of the contents.

Network File

The grid4x4.net.xml network file represents the Grid 4x4 scenario in SUMO for running the tests. It is a synthetic network containing 16 traffic lights.

Case Files

Using the Grid 4x4 network as a base, there are numerous config files representing different traffic conditions. The cases are described in the paper in detail. The configuration files used for cases are described below:

Cases Configuration File Used in the Case
Training Case morning_6.sumocfg
Case 1 and Case 8 morning_1.sumocfg
Case 2 morning_2.sumocfg
Case 3 morning_3.sumocfg
Case 4 morning_4.sumocfg
Case 5 morning_5.sumocfg
Case 6 evening_1.sumocfg
Case 7 pse_1.sumocfg
Case 9 and Case 10 combined.sumocfg

Processing statistics file

The process_stat_file.py takes the output from a SUMO simulation run with the --statistic-output argument and processes the file. The goal of the processing was to make sure that the calculated average travel time is close to the one calculated by LibSignal.

Note that this is only used in the Ingolstadt scenario for the FixedTime model from LibSignal library.

Notes

If you need furthur details about the experiments, refer to the paper first. If there are still questions left, feel free to ask. I will try my best to answer them.

About

Scenario repo for the paper “Analyzing the Robustness of Adaptive Traffic Control System Using Reinforcement Learning for Urban Traffic Flow"

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages