  The aggravating effects of climate change and the growing population in
hurricane-prone areas escalate the challenges in large-scale hurricane
evacuations. While hurricane preparedness and response strategies vastly rely
on the accuracy and timeliness of the predicted households' evacuation
decisions, current studies featuring psychological-driven linear models leave
some significant limitations in practice. Hence, the present study proposes a
new methodology for predicting households' evacuation decisions constructed by
easily accessible demographic and resource-related predictors compared to
current models with a high reliance on psychological factors. Meanwhile, an
enhanced logistic regression (ELR) model that could automatically account for
nonlinearities (i.e., univariate and bivariate threshold effects) by an
interpretable machine learning approach is developed to secure the accuracy of
the results. Specifically, low-depth decision trees are selected for
nonlinearity detection to identify the critical thresholds, build a transparent
model structure, and solidify the robustness. Then, an empirical dataset
collected after Hurricanes Katrina and Rita is hired to examine the
practicability of the new methodology. The results indicate that the enhanced
logistic regression (ELR) model has the most convincing performance in
explaining the variation of the households' evacuation decision in model fit
and prediction capability compared to previous linear models. It suggests that
the proposed methodology could provide a new tool and framework for the
emergency management authorities to improve the estimation of evacuation
traffic demands in a timely and accurate manner.
