Understanding the Impacts of COVID-19 Pandemic on Human Mobility, Transportation Network Redesign and System Resilience

This NSF grant will study the impacts of the coronavirus disease of 2019 (COVID-19) pandemic on existing transportation and logistics systems and operations, the needs for transportation-system redesign, and their resilience under future disruptions.

The research will integrate models and techniques in mathematical optimization, data analytics and epidemiology, to study how mobility interacts with virus spread in different transportation networks.

The project will deliver an interdisciplinary framework for analyzing transportation networks and their resilience with high modeling flexibility and policy adaptability.

The results will be built into open-source tools and online computation/simulation platforms, so that policymakers can conveniently deploy models developed in this project to redesign specific transportation systems, with guaranteed resilience.

The research activities are integrated with educational and outreach activities, to extend the knowledge obtained from this research to a broader audience, including developing new interdisciplinary courses, organizing workshops, sharing automated data analysis tools for studying real-world mobility and infection data, and engaging undergraduate and graduate students, particularly female and underrepresented minority groups, in the research and education.

This research will draw from a broad range of methodologies and approaches, including network optimization, graph theories, statistical learning, stochastic/robust optimization and simulation.

The development, validation, and calibration of the research will push the frontiers of transportation and critical infrastructure analysis in three key thrusts: (1) linking and visualizing supply-demand and mobility needs in different transit systems with infection status and lockdown policies; (2) developing short-term and long-term solutions of transportation network redesign to limit virus spread while satisfying travel needs; (3) analyzing the resilience, operational efficiency and reliability of the new systems.

For the first thrust, the research will focus on predictive models for learning new supply-demand patterns and their spatiotemporal distributions.

For the second thrust, we will use integer programing, stochastic and robust optimization models to improve solutions by also taking into account social-distancing and other disease-mitigation strategies.

For the third thrust, we will use agent-based simulation and uncertainty quantification methods to study system resilience as well as cost and operational efficiency under various disruptions (e.g., demand surge, supply shortage, or link breakdown).