Signatures and Barcodes: Data-driven Understanding of Transportation System Performance during Extreme Events

该项目的重点是了解自然灾害等极端事件对应急响应和恢复服务所需的城市交通系统的影响。 受持续城市化和极端天气事件频率的推动，该项目将研究新方法，以量化城市级规模的基础设施性能和弹性。项目工作的成果将为负责极端事件缓解和应对的当局提供数据驱动的见解。 将为州和地方交通官员举办关于这项工作成果的研讨会。 该项目的一部分将通过与伊利诺伊州几何实验室的合作伙伴关系进行，以促进工程和数学学生的跨学科本科研究经验。该项目的中心是创建数据驱动的方法，通过解释全市和多年的交通数据集来调查极端事件对交通基础设施的影响。从多线性代数和计算拓扑学的概念将进行调查，严格量化极端事件对交通系统的影响。开发的方法将被用来构建“签名”，这是一个城市范围内的道路网络的拥堵程度的可解释的模式，和“条形码”，它总结了网络的连通性。签名和条形码将被设计为更好地量化极端事件的时空影响，作为对典型拥堵模式和连通性结构的偏离。 所开发的方法将应用于美国社区的大型移动数据集。

This project focuses on understanding the effects of extreme events such as natural disasters on urban transportation systems necessary for emergency response and recovery services. Motivated both by continued urbanization and the frequency of extreme weather events, this project will investigate novel methods to quantify infrastructure performance and resilience at city-level scales. Outcomes of the project work will provide data-driven insights relevant to authorities responsible for extreme event mitigation and response. Seminars will be given to state and local transportation officials on the results of this work. Parts of the project will be carried out via partnerships with the Illinois Geometry Laboratory to facilitate interdisciplinary undergraduate research experiences for engineering and mathematics students.This project centers on the creation of data-driven methods to investigate the effects of extreme events on transportation infrastructure by interpreting citywide and multiyear traffic datasets. Concepts from multilinear algebra and computational topology will be investigated to rigorously quantify the effects of extreme events on the transportation system. The developed methods will be used to construct "signatures," which are interpretable patterns in the congestion level of a citywide road network, and "barcodes," which summarize the network connectivity. The signatures and barcodes will be designed to better quantify the spatiotemporal effects of extreme events as deviations from typical congestion patterns and connectivity structures. The developed methods will be applied on large publically available mobility datasets in US communities.