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    Data-Driven Modeling of Hurricane Evacuee’s Individual Decision-Making for Enhanced Hurricane Evacuation Planning: Florida Case Study in the COVID-19 Pandemic

    Source: Natural Hazards Review:;2024:;Volume ( 025 ):;issue: 004::page 04024032-1
    Author:
    Shijie Chen
    ,
    Yanshuo Sun
    ,
    Tingting Zhao
    ,
    Minna Jia
    ,
    Tian Tang
    DOI: 10.1061/NHREFO.NHENG-1976
    Publisher: American Society of Civil Engineers
    Abstract: Individual evacuation decision making has been studied for multiple decades mainly using theory-based approaches, such as random utility theory. This study aims to bridge the research gap that no studies have adopted data-driven approaches in modeling the compliance of hurricane evacuees with government-issued evacuation orders using survey data. To achieve this, we conducted a survey in two coastal metropolitan regions of Florida (Jacksonville and Tampa) during the 2020 Atlantic hurricane season. After preprocessing survey data, we employed three supervised learning algorithms with different complexities, namely, multinomial logistic regression, random forest, and support vector classifier, to predict evacuation decisions under various hypothetical hurricane threats. We found that the evacuation decision is mainly determined by people’s perception of hurricane risk regardless of whether the government issued an order; COVID-19 risk is not a major factor in evacuation decisions but influences the destination type choice if an evacuation decision is made. Additionally, past and future evacuation destination types were found to be highly correlated. After comparing the algorithms for predicting evacuation decisions, we found that random forest can achieve satisfactory classification performance, especially for certain categories or when some categories are merged. Finally, we presented a conceptual optimization model to incorporate the data-driven modeling approach for evacuation behavior into a government-led evacuation planning framework to improve the compliance rate.
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      Data-Driven Modeling of Hurricane Evacuee’s Individual Decision-Making for Enhanced Hurricane Evacuation Planning: Florida Case Study in the COVID-19 Pandemic

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4298423
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    contributor authorShijie Chen
    contributor authorYanshuo Sun
    contributor authorTingting Zhao
    contributor authorMinna Jia
    contributor authorTian Tang
    date accessioned2024-12-24T10:10:12Z
    date available2024-12-24T10:10:12Z
    date copyright11/1/2024 12:00:00 AM
    date issued2024
    identifier otherNHREFO.NHENG-1976.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4298423
    description abstractIndividual evacuation decision making has been studied for multiple decades mainly using theory-based approaches, such as random utility theory. This study aims to bridge the research gap that no studies have adopted data-driven approaches in modeling the compliance of hurricane evacuees with government-issued evacuation orders using survey data. To achieve this, we conducted a survey in two coastal metropolitan regions of Florida (Jacksonville and Tampa) during the 2020 Atlantic hurricane season. After preprocessing survey data, we employed three supervised learning algorithms with different complexities, namely, multinomial logistic regression, random forest, and support vector classifier, to predict evacuation decisions under various hypothetical hurricane threats. We found that the evacuation decision is mainly determined by people’s perception of hurricane risk regardless of whether the government issued an order; COVID-19 risk is not a major factor in evacuation decisions but influences the destination type choice if an evacuation decision is made. Additionally, past and future evacuation destination types were found to be highly correlated. After comparing the algorithms for predicting evacuation decisions, we found that random forest can achieve satisfactory classification performance, especially for certain categories or when some categories are merged. Finally, we presented a conceptual optimization model to incorporate the data-driven modeling approach for evacuation behavior into a government-led evacuation planning framework to improve the compliance rate.
    publisherAmerican Society of Civil Engineers
    titleData-Driven Modeling of Hurricane Evacuee’s Individual Decision-Making for Enhanced Hurricane Evacuation Planning: Florida Case Study in the COVID-19 Pandemic
    typeJournal Article
    journal volume25
    journal issue4
    journal titleNatural Hazards Review
    identifier doi10.1061/NHREFO.NHENG-1976
    journal fristpage04024032-1
    journal lastpage04024032-17
    page17
    treeNatural Hazards Review:;2024:;Volume ( 025 ):;issue: 004
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
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