Data-Driven Modeling of Hurricane Evacuee's Individual Decision-Making for Enhanced Hurricane Evacuation Planning: Florida Case Study in the COVID-19 Pandemic

Individual evacuation decision making has been studied for multiple decades mainly using theory-based approaches, such as random utility theory. This study aims to bridge the research gap that no studies have adopted data-driven approaches in modeling the compliance of hurricane evacuees with government-issued evacuation orders using survey data. To achieve this, we conducted a survey in two coastal metropolitan regions of Florida (Jacksonville and Tampa) during the 2020 Atlantic hurricane season. After preprocessing survey data, we employed three supervised learning algorithms with different complexities, namely, multinomial logistic regression, random forest, and support vector classifier, to predict evacuation decisions under various hypothetical hurricane threats. We found that the evacuation decision is mainly determined by people's perception of hurricane risk regardless of whether the government issued an order; COVID-19 risk is not a major factor in evacuation decisions but influences the destination type choice if an evacuation decision is made. Additionally, past and future evacuation destination types were found to be highly correlated. After comparing the algorithms for predicting evacuation decisions, we found that random forest can achieve satisfactory classification performance, especially for certain categories or when some categories are merged. Finally, we presented a conceptual optimization model to incorporate the data-driven modeling approach for evacuation behavior into a government-led evacuation planning framework to improve the compliance rate.