Collaborative Research: ATD: Geospatial Modeling and Risk Mitigation for Human Movement Dynamics under Hurricane Threats

University Of Florida, Gainesville FL

Investigators

Abstract

Hurricanes pose a significant threat to coastal states. In recent years, the high frequency of hurricane threats has severely impacted the livelihood of people and the economic activities of impacted regions. As a hurricane approaches diverse stakeholders need to make timely decisions. For example, should a business shut down for a few days, accounting for both safety risks and revenue losses? Or should a community evacuate its residents when there is still large uncertainty about the hurricane's trajectory? Where should public resources such as policing and commodity supplies be allocated, in case of a need for mass evacuation? This research leverages the information in the human dynamics data, collected from mobile phone geopositioning and vehicle traffic monitoring, and develops state-of-the-art statistical and geospatial models to enable data science-driven decision-making. The output of this research will provide useful data science toolboxes for improving the effectiveness of disaster prevention and relief efforts. On the methodologies, this project aims to harness the information in multiple flow datasets, and use statistical and geospatial modeling to address the following component tasks. The first component focuses on demand prediction and surge detection in the traffic flow network during a hurricane evacuation. This task will develop a temporal flow network model, quantify the traffic change rate, and identify the important spatiotemporal covariates that influence the demand. The second component focuses on the dynamic optimization of evacuation flows and demand dispersion. This task will combine the power of statistical prediction for the evacuation demand, combinatorial optimization on flow maximization, and optimal transport to disperse excess demand to significantly improve the efficacy of directing traffics away from hurricane-impacted regions. The third component focuses on quantifying the impacts on economic activities using cellphone tracking data during the hurricane preparation and post-hurricane restoration periods. This task will quantify the economic dependency graphs and their changes under hurricane threats, and find out the sub-populations at large economic risk due to shutdown. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.