Given the destructive nature of hurricanes in tropical regions, pre-disaster evacuation has emerged as a critical approach for hurricane preparedness. Nevertheless, the compounding effects of natural hazards and the outbreak of infectious diseases, such as Covid-19, significantly challenge hurricane evacuation management. To investigate emergency responses under compound hazards, this study develops an activity-based model to measure the evacuation behaviors of individuals, using Hurricane Irma as a case study. Four scenarios are designed, including a single hurricane hazard, Hurricane Irma compounded with a pandemic like Covid-19, Hurricane Irma compounded with flood damage to the transportation network, and a combination of all these hazards. The metropolis-hasting algorithm is utilized to generate a population with socioeconomic attributes, which is then allocated to census block groups covering Palm Beach, Broward, Miami-Dade, and Monroe Counties in Florida. Datasets from multiple sources are used to measure evacuation decisions, which are subsequently simulated using MATSim. The results highlight the potential impacts of compound hazards on transportation systems, including increased congestions in scenarios involving compounded hurricanes and floods, especially between 10 a.m. and 7p.m. Moreover, a higher proportion of socially vulnerable populations is observed in scenarios involving compounded hurricanes and pandemics, particularly in the Key West area. The developed model could be further applied to measure the indirect impacts of natural hazards on transportation systems.

中文翻译：

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对复合灾害中的疏散活动进行建模：来自佛罗里达州东南部飓风艾尔玛的见解


鉴于飓风对热带地区的破坏性，灾前疏散已成为飓风准备的关键方法。然而，自然灾害和 Covid-19 等传染病爆发的复合效应对飓风疏散管理提出了重大挑战。为了调查复合灾害下的应急响应，本研究以飓风艾尔玛为案例研究，开发了一种基于活动的模型来衡量个人的疏散行为。设计了四种情景，包括单一的飓风灾害、飓风艾尔玛与 Covid-19 等大流行病相结合、飓风艾尔玛与洪水对交通网络的破坏相结合，以及所有这些灾害的组合。metropolis-hasting 算法用于生成具有社会经济属性的人口，然后将其分配给涵盖佛罗里达州棕榈滩县、布劳沃德县、迈阿密戴德县和门罗县的人口普查区块组。来自多个来源的数据集用于衡量疏散决策，然后使用 MATSim 进行模拟。结果强调了复合灾害对交通系统的潜在影响，包括在涉及复合飓风和洪水的情况下增加拥堵，尤其是在上午 10 点至晚上 7 点之间。此外，在涉及复合飓风和大流行病的情况下，观察到更高比例的社会弱势群体，尤其是在基韦斯特地区。开发的模型可以进一步应用于衡量自然灾害对交通系统的间接影响。