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.

本研究提出了一种基于易获取的人口和资源预测因素的方法来预测家庭疏散决策，结合一个自动计算非线性阈值的可解释机器学习方法，其结果表明，在解释家庭疏散决策的变化和预测能力方面，增强的逻辑回归模型比以前的线性模型更具说服力，可为应急管理部门提供一种新的工具和框架来及时准确地评估疏散需求。