Shadow evacuation, defined as people evacuating from outside the official evacuation zone, can be extensive, can cause congestion, and can make it difficult for evacuees in high-risk areas to reach safety. For modeling and simulation, approaches typically involve selecting a distance from evacuation zones and a percentage of households evacuating within that distance. In previous studies, the distance and participation rate ranges were significant; this aspect created a need for better understanding and modeling of shadow evacuation. A model that can capture shadow evacuation could help reduce the subjectivity associated with previous approaches. This study accounts for stochastic responses to evacuation notices through a random-parameter binary logit model based on Hurricane Ivan survey data. Random coefficients are associated with variables representing the receipt of nonmandatory evacuation notices and the absence of evacuation notices. The statistical significance of the random coefficients explains the shadow evacuation phenomenon. Other sociodemographic, economic, and evacuation preparation factors, consistent with previous literature, also influence a household’s decision to evacuate. This study suggests that households that do not receive an evacuation notice are generally less likely to evacuate. In recognition of the fact that households’ perceived risk is related to their distance to the coast, the likelihood of evacuating for households that do not receive an evacuation notice decreases as their distances to the coast increase on average. A distance sensitivity factor is introduced to construct scenarios of the geographical extent of shadow evacuation, which can be used to understand the impact of shadow evacuation on clearance time.


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    Title :

    Modeling Shadow Evacuation for Hurricanes with Random-Parameter Logit Model


    Additional title:

    Transportation Research Record: Journal of the Transportation Research Board


    Contributors:


    Publication date :

    2016-01-01




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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