Traffic simulators are important tools for tasks such as urban planning and transportation management. Microscopic simulators allow per-vehicle movement simulation, but require longer simulation time. The simulation overhead is exacerbated when there is traffic congestion and most vehicles move slowly. This in particular hurts the productivity of emerging urban computing studies based on reinforcement learning, where traffic simulations are heavily and repeatedly used for designing policies to optimize traffic related tasks. In this paper, we develop QarSUMO, a parallel, congestion-optimized version of the popular SUMO open-source traffic simulator. QarSUMO performs high-level parallelization on top of SUMO, to utilize powerful multi-core servers and enables future extension to multi-node parallel simulation if necessary. The proposed design, while partly sacrificing speedup, makes QarSUMO compatible with future SUMO improvements. We further contribute such an improvement by modifying the SUMO simulation engine for congestion scenarios where the update computation of consecutive and slow-moving vehicles can be simplified. We evaluate QarSUMO with both real-world and synthetic road network and traffic data, and examine its execution time as well as simulation accuracy relative to the original, sequential SUMO.
翻译:模拟交通模拟器是城市规划和运输管理等任务的重要工具。 微型模拟器允许每辆汽车进行模拟,但需要较长的模拟时间。 当交通堵塞和大多数车辆移动缓慢时,模拟间接费会加剧。 这尤其伤害基于强化学习的新兴城市计算研究的生产率,因为在这些学习中,交通模拟器被大量和反复地用于设计优化交通相关任务的政策。 在本文件中,我们开发了QarSUMO, 一种平行的、拥挤和优化的流行的SUMO开放源代码模拟器。 QarSUMO在SUMO顶部进行高水平的平行化工作, 以便利用强大的多核心服务器, 并在必要时使未来能够扩展到多点平行模拟。 拟议的设计在部分牺牲加速的同时,使QarSUMO与未来的SUMO改进工作相匹配。 我们还通过修改SUMO模拟引擎来改进这种改进, 从而可以简化对连续和缓慢移动车辆进行的最新计算。 我们用现实和合成公路网络和运输数据对QarSUSUMO进行高水平的平行时间进行模拟。