LightSim: A Lightweight Cell Transmission Model Simulator for Traffic Signal Control Research

TL;DR

LightSim is a fast, open-source traffic simulator that accurately models queue dynamics for traffic signal control, enabling rapid reinforcement learning experiments and reducing simulation time from days to minutes.

Contribution

We introduce LightSim, a lightweight Python-based traffic simulator that maintains controller rankings while significantly accelerating training and evaluation processes.

Findings

LightSim runs over 20,000 steps per second on a single CPU.

It preserves controller rankings compared to SUMO across various network types.

Training time is reduced by a factor of 3 to 7.

Abstract

Reinforcement learning for traffic signal control is bottlenecked by simulators: training in SUMO takes hours, reproducing results often requires days of platform-specific setup, and the slow iteration cycle discourages the multi-seed experiments that rigorous evaluation demands. Much of this cost is unnecessary, since for signal timing optimization the relevant dynamics are queue formation and discharge, which the Cell Transmission Model (CTM) captures as a macroscopic flow model. We introduce LightSim, a pure Python, pip-installable traffic simulator with Gymnasium and PettingZoo interfaces that runs over 20000 steps per second on a single CPU. Across cross-simulator experiments spanning single intersections, grid networks, arterial corridors, and six real-world city networks, LightSim preserves controller rankings from SUMO for both classical and reinforcement learning strategies while training 3 to 7 times faster. LightSim is released as an open-source benchmark with nineteen built-in scenarios, seven controllers, and full reinforcement learning pipelines, lowering the barrier to signal control research from days to minutes.