24-28 September 2023
Bilbao, Bizkaia, Spain

Cross-simulator Datasets and Evaluations for Traffic Control Policies

26th IEEE International Conference on Intelligent Transportation Systems
(ITSC 2023)

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Aim of the Tutorial

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.

Agenda and Materials

09:00-09:15 am: Welcome, opening remarks

Movitations of this tutortial

Video, Slides

9:15 - 10:00 am: Hands-on Simulation with CBLab

Introduction to existing datasets and scenarios

Creating road networks from OpenStreetMap, Custom Templates and existing datasets

Running experiments on CBLab


10:00-10:30 am: Coffee break

10:30-11:15 am: RL for traffic signal control

Introduction of the principles of RL

Frequently used RL methods

Deep RL and formulating traffic control problem as a reinforcement learning problem

Video, Slides

11:15-12:00 pm Hands-on Implementation for Traffic Signal Control

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


12:00-12:30 pm Open problems in RL for traffic control

Simulating environment to real world, benchmarks, interpretability, safety issues, etc.