This project develops new methods to forecast real-time population behavior during natural disasters, potentially transforming the current state of emergency response in a cost-effective way. To understand how individuals, infrastructure systems, and emergency services should prepare and respond during such disasters, this project utilizes data available from multiple sources including from transportation systems and online social media. Using innovative data science approaches to integrate data from multiple sources increases the quality of the data available for emergency response prediction and improved evacuation traffic management. Research outputs will be shared with the practitioner community to facilitate improved decision making for emergency agencies in hurricane evacuation and disaster management. This scientific research contribution thus supports NSF's mission to promote the progress of science and to advance our national welfare. In this case, the benefits will be insights to improve emergency response, which will save lives, economic losses, and reduce panic, anger and confusion during a future event.
The project combines heterogeneous data sources from transportation systems and social media, in a unified framework-providing better information for modeling dynamic population behavior during hurricanes. To accurately predict evacuation demand, this project leverages large-scale real-time data, rarely used by existing emergency decision support tools. It advances the data science of disaster management by developing novel information fusion techniques to represent population and its behavior while employing government survey and social media data, text-mining approaches to extract evacuation intent from social media data, and evacuation traffic prediction models to optimize transportation resources. Through its innovative data gathering and modeling approaches, this project will enhance our ability to deal with future hurricanes. The project engages a broader participation of graduate and undergraduate students including from under-represented groups and plans a broader dissemination of results to traffic engineers and emergency management officials from local counties and cities.
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.
