LightSim：一种轻量级细胞传输模型模拟器用于交通信号控制研究
LightSim: A Lightweight Cell Transmission Model Simulator for Traffic Signal Control Research
AI总结：
LightSim是一种高效的交通信号控制模拟器，通过提升训练速度和降低实验门槛，促进强化学习在交通信号控制中的应用研究。
摘要：
强化学习用于交通信号控制受到模拟器的限制：在SUMO中训练需要数小时，重现结果往往需要数天的平台特定设置，而缓慢的迭代周期 discourages 需要严格评估的多种子实验。其中大部分成本是不必要的，因为对于信号定时优化相关的动态是队列形成和排放，这被细胞传输模型（CTM）作为宏观流模型捕捉。我们介绍了LightSim，一种纯Python、可pip安装的交通模拟器，具有Gymnasium和PettingZoo接口，能够在单个CPU上每秒运行超过20000步。在跨模拟器实验中，涵盖单个交叉口、网格网络、动脉走廊和六个真实城市网络的实验表明，LightSim在SUMO中对经典和强化学习策略的控制器排名得以保持，同时训练速度提高了3到7倍。LightSim作为开源基准发布，包含十九个内置场景、七个控制器和完整的强化学习流水线，将信号控制研究的门槛从数天降低到分钟。
英文摘要：
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.
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