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Abstract Evacuation destination choice modeling is an integral aspect of evacuation planning. Outputs from such models are required to estimate the clearance times on which evacuation orders are based. The number of evacuees arriving at each destination also informs allocation of resources and shelter planning. Despite its importance, evacuee destination modeling has not received as much attention as identifying who evacuates and when. In this study, we present a new approach to identify evacuees and determine where they go and when using privacy-enhanced smartphone location data. We demonstrate the method using data from four recent U.S. hurricanes affecting multiple geographies (Florence 2018, Michael 2018, Dorian 2019, and Ida 2021). We then build on those results to develop a new machine learning model that predicts the number of evacuees that move between pairs of metropolitan statistical areas. The machine learning model incorporates hurricane characteristics, which have not been thoroughly exploited by existing methods. The model’s predictive power is comprehensively evaluated through a tenfold cross validation, holdout validation using Hurricane Ida (2021), and comparison with the traditional gravity model. Results suggest that the new model substantially outperforms the traditional gravity model across all performance indicators. Analysis of feature importance in the machine learning model indicates that in addition to distance and population, hurricane characteristics are important in evacuee destination choices. 

Wind fragility curves for roof sheathing were developed for single-family building models to investigate the effects of roof shape and roof pitch on the wind performance of roof sheathing. For gable roofs, it was found that more complex roof shapes are more likely to suffer roof sheathing damage when subjected to high winds. The probability of no roof sheathing failure can be up to 36% higher for a simple gable roof than for a complex gable roof. For hip roofs with different configurations, variation in roof shape has minimal effect on roof sheathing fragility. Roof pitch effects were also evaluated for 10 pitch angles, ranging from 14° to 45°. Results suggest that for roof pitches smaller than 27°, the effects of this angle are more substantial on the performance of gable roofs than on hip roofs. For gable roofs, the probability of no roof sheathing failure can be up to 23% higher for a 23° roof pitch than that for an 18° roof pitch. Furthermore, the inclusion of complex roof shapes in a regional hurricane loss model for New Hanover County, North Carolina, accounted for a 44% increase in estimated annual expected losses from roof sheathing damages compared to a scenario in which all roofs are assumed to have rectangular roof shapes. Therefore, to avoid an underestimation of roof damages due to high-wind impact, the inclusion of complex roof geometries in hurricane loss modeling is strongly recommended.