The objective of this project is to support research on a novel paradigm for estimating, predicting, and managing traffic in response to cascading failures. Cascading failures occur when disruptions in critical components (e.g., bridge collapses, highway failures, or major public transit disruptions) trigger widespread ripple effects across inter-connected infrastructure and service networks. Leveraging low-cost, ubiquitous, system-level data, this project seeks to understand how and why travelers alter their routes and modes during extreme events. The theories and models are further validated with case studies in Pittsburgh, Pennsylvania, and Baltimore, Maryland. This project has the potential to enhance resilience of the nation’s critical infrastructure and lifeline services, ultimately saving lives and reducing economic losses. It supports development of efficient strategies for emergency response, daily operations, and long-term planning. The project promotes interdisciplinary education by integrating findings into undergraduate and graduate curricula, offering an online short course, and mentoring researchers. Project outcomes are broadly disseminated through open-source software, tutorials, international conferences, and policy briefs - ensuring benefits for government agencies, industry stakeholders, and the public. Research funded by this project seeks to develop a theoretical and computational framework to model spatiotemporal vehicular and passenger flow under ca