Abrupt changes in the environment have triggered massive and precipitous changes in human mobility. This requires modeling entire metropolitan areas to recognize the broader effects on the network. However, there is a trade-off between increasing the level of detail of a model and decreasing computational performance. Current implementations compromise by simulating small spatial scales, and those that operate at larger scales often require access to expensive high performance computing systems or have computation times on the order of days or weeks that discourage productive research and planning. This paper introduces a new platform, MANTA (Microsimulation Analysis for Network Traffic Assignment), for traffic microsimulation at the metropolitan-scale, employing a highly efficient and parallelized GPU implementation. The runtime to simulate all morning trips, using half-second timesteps, for the nine-county San Francisco Bay Area is just over four minutes, significantly improving the state of the art in large-scale traffic microsimulation.

环境的突然变化引发了人类流动性的巨大而急剧的变化。这需要对整个大都市地区进行建模，以识别对网络的更广泛影响。但是，在提高模型的详细程度和降低计算性能之间存在权衡。当前的实现通过模拟小空间尺度来妥协，而那些在更大尺度上运行的实现通常需要访问昂贵的高性能计算系统或具有数天或数周的计算时间，这阻碍了生产性研究和规划。本文介绍了一个新平台，MANTA（网络流量分配微仿真分析），用于大都市规模的交通微仿真，采用高效和并行化的 GPU 实现。