面向敏捷交通规划的大城市网络交通分配微观仿真分析（MANTA）
Microsimulation Analysis for Network Traffic Assignment (MANTA) at Metropolitan-Scale for Agile Transportation Planning
摘要：
环境的突然变化，例如气候变化导致的不可预见事件，引发了人类流动的大规模急剧变化。快速预测不同场景下的交通模式的能力变得更加迫切，以支持短期运营和长期交通规划。这需要对整个大都市区域进行建模，以识别对网络的上游和下游影响。然而，众所周知，在提高模型的细节水平和降低计算性能之间存在权衡。为了实现交通微观仿真所需的细节水平，当前的实现通常通过模拟小的空间尺度来折衷，并且那些在较大尺度上操作的实现通常需要访问昂贵的高性能计算系统，或者具有数天或数周的计算时间，这阻碍了生产性研究和实时规划。本文通过介绍一个新的大城市规模交通微观仿真平台MANTA（网络交通分配微观仿真分析），解决了性能上的不足。MANTA采用高度并行化的GPU实现，能够在几分钟内运行大都市规模的模拟。对于旧金山湾区的九个县，使用半秒的时间步长模拟所有早晨的行程的运行时间刚刚超过四分钟，不包括路由和初始化。这种计算性能显着提高了大规模交通微观仿真的艺术状态。MANTA扩展了分析详细的旅行模式和个人旅行选择的能力，以进行基础设施规划。
英文摘要：
Abrupt changes in the environment, such as unforeseen events due to climate change, have triggered massive and precipitous changes in human mobility. The ability to quickly predict traffic patterns in different scenarios has become more urgent to support short-term operations and long-term transportation planning. This requires modeling entire metropolitan areas to recognize the upstream and downstream effects on the network. However, there is a well-known trade-off between increasing the level of detail of a model and decreasing computational performance. To achieve the level of detail required for traffic microsimulation, current implementations often 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 real-time planning. This paper addresses the performance shortcomings by introducing a new platform, MANTA (Microsimulation Analysis for Network Traffic Assignment), for traffic microsimulation at the metropolitan-scale. MANTA employs a highly parallelized GPU implementation that is capable of running metropolitan-scale simulations within a few minutes. The runtime to simulate all morning trips, using half-second timesteps, for the nine-county San Francisco Bay Area is just over four minutes, not including routing and initialization. This computational performance significantly improves the state of the art in large-scale traffic microsimulation. MANTA expands the capacity to analyze detailed travel patterns and travel choices of individuals for infrastructure planning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
