CityFlow is a multi-agent reinforcement learning environment for large-scale city traffic scenario.

Checkout these features!

• A microscopic traffic simulator which simulates the behavior of each vehicle, providing highest level detail of traffic evolution.
• Supports flexible definitions for road network and traffic flow
• Provides friendly python interface for reinforcement learning
• Fast! Elaborately designed data structure and simulation algorithm with multithreading. Capable of simulating city-wide traffic. See the performance comparison with SUMO [1].

Performance comparison between CityFlow with different number of threads (1, 2, 4, 8) and SUMO. From small 1x1 grid roadnet to city-level 30x30 roadnet. Even faster when you need to interact with the simulator through python API.

• PressLight: Learning Max Pressure Control to Coordinate Traffic Signals in Arterial Network (KDD 2019)
• CoLight: Learning Network-level Cooperation for Traffic Signal Control
• Traffic Signal Control Benchmark
• TSCC2050: A Traffic Signal Control Game by Tianrang Intelligence (in Chinese) [2]

• WWW 2019 Demo Paper
• Home Page
• Documentation and Quick Start
• Docker