Abrupt changes in the environment, such as increasingly frequent and intense weather events due to climate change or the extreme disruption caused by the coronavirus pandemic, have triggered massive and precipitous human mobility changes. The ability to quickly predict traffic patterns in different scenarios has become more urgent to support short-term operations and long-term transportation planning, emergency management, and resource allocation. Urban traffic exhibits a high spatial correlation in which links adjacent to a congested link are likely to become congested due to spillback effects. The spillback behavior requires modeling the entire metropolitan area to recognize all of the upstream and downstream effects from intentional or unintentional perturbations to the network. However, there is a well-known trade-off between increasing the level of detail of a model and decreasing computational performance. This paper addresses these performance shortcomings by introducing a new platform MANTA for traffic microsimulation at the metropolitan-scale. MANTA employs a highly parallelized GPU implementation that is fast enough to run simulations on large-scale demand and networks within a few minutes. We test our platform to simulate the entire Bay Area metropolitan region over the course of the morning using half-second time steps. The runtime for the nine-county Bay Area simulation is just over four minutes, not including routing and initialization. This computational performance significantly improves state of the art in large-scale traffic microsimulation and offers new capacity for analyzing the detailed travel patterns and travel choices of individuals for infrastructure planning and emergency management.

中文翻译：

---

城市规模的网络流量分配 (MANTA) 微观仿真分析，用于敏捷交通规划

环境的突然变化，例如气候变化导致的天气事件越来越频繁和剧烈，或冠状病毒大流行造成的极端破坏，已经引发了大规模和急剧的人类流动性变化。快速预测不同场景下的交通模式的能力对于支持短期运营和长期交通规划、应急管理和资源分配变得更加迫切。城市交通表现出高度的空间相关性，其中与拥塞链路相邻的链路可能由于溢出效应而变得拥塞。回溢行为需要对整个大都市区进行建模，以识别有意或无意扰动对网络的所有上游和下游影响。然而，在增加模型的细节级别和降低计算性能之间存在众所周知的权衡。本文通过引入一个新的平台 MANTA 来进行大都市规模的交通微观仿真，从而解决了这些性能缺陷。MANTA 采用高度并行化的 GPU 实现，其速度足以在几分钟内针对大规模需求和网络运行模拟。我们测试我们的平台，以使用半秒的时间步长在整个上午的过程中模拟整个湾区大都市区。九县湾区模拟的运行时间只有四分钟多一点，不包括路由和初始化。