Traffic control policies are essential for managing traffic flow on roadways and highways. By implementing traffic signal control and dynamic pricing, cities can reduce congestion, increase safety, reduce environmental impact, better use resources, and improve the quality of life for residents. With traffic control policies commonly tested and evaluated using simulations since simulations provide a safe and controlled environment. however, existing traffic simulators are limited by their shortage in input data and possible inconsistency between different simulators, which prevents them from generating interactive data from traffic simulation in the scenarios of real road networks. Different simulations are designed with different assumptions or objectives, which can lead to bias and inconsistency in the results.
To solve this issue, this tutorial will provide the hands-on usage on CBLab and LibSignal, to compare different control policies across different simulation environments with different datasets. Our libraries have the following features, which make them a high-quality benchmark for cross-simulator comparison: (1) Unified: our library builds a systematic pipeline to implement, use and evaluate traffic signal control models in a unified platform. The data configuration, model instantiation, and standardized evaluation procedure are shared across simulators. (2) Comprehensive: we provide over ten models covering two widely-used traffic simulators reproduced to form a comprehensive model warehouse and multiple datasets commonly used from different resources.
Introduction to existing datasets and scenarios
Creating road networks from OpenStreetMap, Custom Templates and existing datasets
Running experiments on CBLab
Introduction of the principles of RL
Frequently used RL methods
Deep RL and formulating traffic control problem as a reinforcement learning problem
Design of reward, state, action
Different models in RL (e.g., Policy-based, value-based) and their pros and cons will be discussed
Step-by-step instructions on setting up the Python environment with LibSignal
Simulating environment to real world, benchmarks, interpretability, safety issues, etc.