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 .
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.
Featured Research and Projects Using CityFlow
- 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) 
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