Prediction of time-dependent population behavior during hurricane evacuations
Date
2021
Authors
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Publisher
University of Delaware
Abstract
The large uncertainty in population behavior with regards to how many people leave, when, from where, and to where, is a key challenge in planning hurricane evacuations. Several empirical and theoretical studies have sought to understand evacuation behavior but few have focused on developing models for predicting population evacuation behavior for future applications. In this study, we improve hurricane evacuation behavior prediction through advances in modeling and data, and offer a comprehensive evaluation of predictive power that other researchers might adopt in evaluating their models. Specifically, we modify and apply a recently introduced dynamic discrete choice framework on survey data collected in a consistent format across four hurricanes. We also take advantage of the dynamic nature of the model and include more hurricane and forecast attributes. The final set of explanatory variables can be obtained at the regional scale; hence our model can be applied in the future for prediction. Through cross validation, out-of-sample predictive power of the model is evaluated across multiple metrics, including prediction of aggregate evacuation rates, individual behavior, and evacuation timing. Cross validation results are also compared with existing statistical models from literature. At the aggregate level across the four hurricanes, the total number of evacuations was predicted with only 1% error. Individual household level results suggest 70% and 72% accurate prediction of evacuees and non-evacuees, respectively. The mean absolute departure time error is approximately only 2 hours across the population. Results across all evaluation metrics imply our model performs better when compared to existing models in the literature.
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Keywords
Dynamic discrete choice, Evacuation, Hurricane, Modelling, Prediction, Timing