Hurricanes cause significant economic and human costs, requiring individuals to make critical evacuation decisions under uncertainty and stress. To enhance the understanding of this decision-making process, we propose using Bayesian Networks (BNs) to model evacuation decisions during hurricanes. We collected questionnaire data from two significant hurricane events: Hurricane Harvey and Hurricane Irma. We employed a data-driven approach by first conducting variable selection using mutual information, followed by BN structure learning with two constraint-based algorithms. The robustness of the learned structures was enhanced by model averaging based on bootstrap resampling. We examined and compared the learned structures of both hurricanes, revealing potential causal relationships among key predictors of evacuation, including risk perception, information received from media, suggestions from family and friends, and neighbors evacuating. Our findings highlight the significant role of social influence, providing valuable insights into the process of evacuation decision-making. Our results demonstrate the applicability and effectiveness of data-driven BN modeling in evacuation decision making.

翻译：飓风造成重大的经济与人员损失，迫使个体在不确定性和压力下做出关键的疏散决策。为深化对此决策过程的理解，我们提出使用贝叶斯网络（BNs）对飓风期间的疏散决策进行建模。我们收集了两次重大飓风事件（飓风哈维与飓风艾尔玛）的问卷数据。采用数据驱动方法，首先通过互信息进行变量选择，随后运用两种基于约束的算法进行BN结构学习。通过基于自助重采样的模型平均方法，增强了所学结构的稳健性。我们检验并比较了两次飓风的学习结构，揭示了疏散关键预测因子（包括风险感知、媒体接收信息、亲友建议及邻居疏散行为）之间潜在的因果关系。研究结果突显了社会影响的重要作用，为理解疏散决策过程提供了有价值的见解。我们的成果证明了数据驱动的BN建模在疏散决策研究中的适用性与有效性。