Traffic simulation provides interactive data for the optimization of traffic control policies. However, existing traffic simulators are limited by their lack of scalability and shortage in input data, which prevents them from generating interactive data from traffic simulation in the scenarios of real large-scale city road networks. In this paper, we present \textbf{C}ity \textbf{B}rain \textbf{Lab}, a toolkit for scalable traffic simulation. CBLab consists of three components: CBEngine, CBData, and CBScenario. CBEngine is a highly efficient simulator supporting large-scale traffic simulation. CBData includes a traffic dataset with road network data of 100 cities all around the world. We also develop a pipeline to conduct a one-click transformation from raw road networks to input data of our traffic simulation. Combining CBEngine and CBData allows researchers to run scalable traffic simulations in the road network of real large-scale cities. Based on that, CBScenario implements an interactive environment and a benchmark for two scenarios of traffic control policies respectively, with which traffic control policies adaptable for large-scale urban traffic can be trained and tuned. To the best of our knowledge, CBLab is the first infrastructure supporting traffic control policy optimization in large-scale urban scenarios. CBLab has supported the City Brain Challenge @ KDD CUP 2021. The project is available on GitHub:~\url{https://github.com/CityBrainLab/CityBrainLab.git}.

翻译：交通仿真为交通控制策略优化提供了交互式数据。然而，现有交通仿真器受限于可扩展性不足及输入数据匮乏，无法在真实大规模城市路网场景中生成交互式仿真数据。本文提出\textbf{C}ity \textbf{B}rain \textbf{Lab}（城市大脑实验室）这一可扩展交通仿真工具包。CBLab包含三个组件：CBEngine、CBData与CBScenario。CBEngine是一款支持大规模交通仿真的高性能仿真器；CBData包含覆盖全球100个城市路网数据的交通数据集。我们同时开发了一套流水线，可将原始路网数据一键转换为交通仿真输入数据。结合CBEngine与CBData，研究人员能够在真实大规模城市路网中运行可扩展交通仿真。基于此，CBScenario分别针对两种交通控制策略场景构建了交互式环境与基准测试，可训练和调优适用于大规模城市交通的控制策略。据我们所知，CBLab是首个支持大规模城市场景下交通控制策略优化的基础设施。CBLab已为KDD CUP 2021城市大脑挑战赛提供支撑。该项目已在GitHub开源：~\url{https://github.com/CityBrainLab/CityBrainLab.git}。