Traffic simulation provides interactive data for the optimization of traffic 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 City Brain Lab, a toolkit for scalable traffic simulation. CBLab is consist of three components: CBEngine, CBData, and CBScenario. CBEngine is a highly efficient simulators 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 one-click transformation from raw road networks to input data of our traffic simulation. Combining CBEngine and CBData allows researchers to run scalable traffic simulation in the road network of real large-scale cities. Based on that, CBScenario implements an interactive environment and several baseline methods for two scenarios of traffic policies respectively, with which traffic 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 policy optimization on large-scale urban scenarios. The code is available on Github: https://github.com/CityBrainLab/CityBrainLab.git.
翻译:然而,现有的交通模拟器因其缺乏可缩放性和投入数据短缺而受到限制,因此无法在真正的大型城市公路网络的情景中从交通模拟中生成互动数据。在本论文中,我们介绍了城市脑实验室,这是可缩放交通模拟的工具包。CBLab由三个部分组成:CBENGine、CBATA和CBScenario。CBENGine是支持大规模交通模拟的高效模拟器。CBENGine包括一个具有全世界100个城市公路网络数据的交通数据集。我们还开发了一条管道,从原始公路网络进行一击转换,输入我们交通模拟的数据。CBENGine和CBData的结合使研究人员能够在实际大型城市的公路网络中进行可缩放交通模拟。CBSC环境是交互式环境,另外两种交通政策情景的基线方法,可据此对适应大规模城市交通的交通政策进行培训和调整。对于我们的知识而言,CBAB/MLA是支持大规模交通政策的最佳情景。