Facing the escalating effects of climate change, it is critical to improve the prediction and understanding of the hurricane evacuation decisions made by households in order to enhance emergency management. Current studies in this area often have relied on psychology-driven linear models, which frequently exhibited limitations in practice. The present study proposed a novel interpretable machine learning approach to predict household-level evacuation decisions by leveraging easily accessible demographic and resource-related predictors, compared to existing models that mainly rely on psychological factors. An enhanced logistic regression model (that is, an interpretable machine learning approach) was developed for accurate predictions by automatically accounting for nonlinearities and interactions (that is, univariate and bivariate threshold effects). Specifically, nonlinearity and interaction detection were enabled by low-depth decision trees, which offer transparent model structure and robustness. A survey dataset collected in the aftermath of Hurricanes Katrina and Rita, two of the most intense tropical storms of the last two decades, was employed to test the new methodology. The findings show that, when predicting the households’ evacuation decisions, the enhanced logistic regression model outperformed previous linear models in terms of both model fit and predictive capability. This outcome suggests that our proposed methodology could provide a new tool and framework for emergency management authorities to improve the prediction of evacuation traffic demands in a timely and accurate manner.

面对气候变化日益严重的影响，提高对家庭飓风疏散决策的预测和理解对于加强应急管理至关重要。目前该领域的研究通常依赖于心理学驱动的线性模型，但在实践中经常表现出局限性。与主要依赖心理因素的现有模型相比，本研究提出了一种新颖的可解释机器学习方法，通过利用易于获取的人口统计和资源相关预测因素来预测家庭层面的疏散决策。开发了增强型逻辑回归模型（即可解释的机器学习方法），通过自动考虑非线性和相互作用（即单变量和双变量阈值效应）来进行准确预测。具体来说，非线性和交互检测是通过低深度决策树实现的，它提供了透明的模型结构和鲁棒性。在卡特里娜飓风和丽塔飓风（过去二十年中最强烈的两场热带风暴）过后收集的调查数据集被用来测试新方法。研究结果表明，在预测家庭的疏散决策时，增强型逻辑回归模型在模型拟合度和预测能力方面均优于之前的线性模型。这一结果表明，我们提出的方法可以为应急管理当局提供新的工具和框架，以改进及时、准确的疏散交通需求预测。