Abstract Hurricanes are one of the most dangerous catastrophes faced by the USA. The associated life losses can be reduced by proper planning and estimation of evacuation demand by emergency planners. Traditional evacuation demand estimation involves a sequential process of estimating various decisions such as whether to evacuate or stay, evacuation destination, and accommodation type. The understanding of this sequence is not complete nor restricted to strict sequential ordering. For instance, it is not clear whether the evacuation destination decision is made before the accommodation type decision, or the accommodation type decision is made first or both are simultaneously made. In this paper, we develop a nested logit model to predict the relative ordering of evacuation destination and accommodation type that considers both sequential and simultaneous decision making. Household survey data from Hurricane Matthew is used for computing empirical results. Empirical results underscore the importance of developing a nested structure among various outcomes. In addition to variables related to risk perception and household characteristics, it is found that social networks also affect this decision-making process.

中文翻译：

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估计飓风中疏散目的地和住宿类型的顺序

摘要飓风是美国面临的最危险的灾难之一。紧急计划人员可以通过适当的计划和估计疏散需求来减少相关的生命损失。传统的撤离需求估算涉及估算各种决策（例如是否撤离或停留，撤离目的地和住宿类型）的顺序过程。对此序列的理解不完整，也不限于严格的顺序排序。例如，不清楚是在住宿类型决定之前做出避难目的地决定，还是先做出住宿类型决定，还是同时做出两者。在本文中，我们开发了一个嵌套的logit模型来预测疏散目的地和住宿类型的相对顺序，同时考虑了顺序决策和同步决策。来自马修飓风的住户调查数据用于计算经验结果。实证结果强调了在各种结果之间建立嵌套结构的重要性。除了与风险感知和家庭特征相关的变量外，还发现社交网络也影响这一决策过程。