With growing urbanization worldwide, efficient management of traffic infrastructure is critical for transportation agencies and city planners. It is essential to have tools that help analyze large volumes of stored traffic data and make effective interventions. To address this need, we present ``BigSUMO", an end-to-end, scalable, open-source framework for analytics, interruption detection, and parallel traffic simulation. Our system ingests high-resolution loop detector and signal state data, along with sparse probe trajectory data. It first performs descriptive analytics and detects potential interruptions. It then uses the SUMO microsimulator for prescriptive analytics, testing hundreds of what-if scenarios to optimize traffic performance. The modular design allows integration of different algorithms for data processing and outlier detection. Built using open-source software and libraries, the pipeline is cost-effective, scalable, and easy to deploy. We hope BigSUMO will be a valuable aid in developing smart city mobility solutions.

翻译：随着全球城市化进程的加速，交通基础设施的高效管理对交通部门和城市规划者至关重要。开发能够帮助分析海量存储交通数据并实施有效干预的工具显得尤为必要。为满足这一需求，本文提出“BigSUMO”——一个端到端、可扩展、开源的交通分析、中断检测与并行仿真框架。本系统接收高分辨率环形检测器与信号状态数据，以及稀疏的探测车轨迹数据。系统首先执行描述性分析并检测潜在的中断事件，随后利用SUMO微观仿真器进行规范性分析，通过测试数百种假设场景以优化交通运行性能。其模块化设计支持集成不同的数据处理与异常检测算法。该流水线基于开源软件与库构建，具有成本效益高、可扩展性强、易于部署的特点。我们期望BigSUMO能为智慧城市出行解决方案的开发提供有力支持。