cityflow-project/CityFlow

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1011 stars · 205 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

CityFlow is an open-source microscopic traffic simulator designed as a multi-agent reinforcement learning environment for large-scale city traffic scenarios. It provides a fast, multithreaded C++ engine with a Python interface, significantly outperforming SUMO for city-wide traffic signal control research.

Use cases

• train reinforcement learning agents for traffic signal control
• simulate city-wide traffic with thousands of vehicles
• benchmark multi-agent RL algorithms on road networks
• compare traffic control policies against SUMO
• build custom road networks and traffic flows for transportation research
• run reproducible traffic simulations for ML experiments

When to choose

• you need a fast RL environment for large-scale traffic signal control
• SUMO is too slow for your city-scale simulation experiments
• you want a Python-friendly simulator for multi-agent reinforcement learning research
• reproducibility of traffic simulations matters for your work

When to avoid

• you need detailed driver behavior modeling or mobility features beyond traffic flow
• you require SUMO's ecosystem of tools, formats, and plugins
• you need a GUI-first traffic planning tool rather than a programmatic RL environment
• your scenario is small-scale where simulation speed is not a concern

Facets

library · maturity active

simulation machine-learning reinforcement-learning agent-framework simulation reinforcement-learning autonomous-vehicles machine-learning python cpp traffic-simulation traffic-signal-control multi-agent-reinforcement-learning gym-environment transportation linux macos docker

Member repositories

For agents

markdown · JSON · MCP: product_card(name="cityflow-project/CityFlow")