Highlights This study investigates individuals’ evacuation decision behavior in the context of no-notice emergency events. Two-step clustering is applied to capture potential heterogeneity in the evacuation decision. Cluster-specific multivariate ordered probit models are developed to analyze individuals’ evacuation decision.

    Abstract No-notice emergency events refer to unpredictable disasters such as earthquakes, chemical spills, or terrorist attacks, where it is impracticable to forewarn the public about their occurrence and design evacuation plans for them. This calls for an in-depth investigation of people’s evacuation behavior and identifying the most influential factors in their evacuation planning process to develop policy-sensitive pre-disaster plans for such events. As a response to this need, the current study investigates individuals’ evacuation decision behavior in the context of no-notice emergency events. Since it is highly expected that people will have heterogenous decision behavior in such situations, we first apply a clustering algorithm to classify them into three maximally homogeneous clusters. Then, cluster-specific multivariate ordered probit models are developed to estimate the likelihood of selecting any of the three options of ignoring the situation, seeking shelter at the place, and evacuating to a safe place. The model estimation results indicate that a wide spectrum of factors affect the evacuation decision including individuals’ socio-economic attributes, disaster characteristics, built-environment factors, and government evacuation order. Further, variations of estimated coefficients across the population clusters highlight the significant dissimilarities in decision behavior of their associated members.


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    Title :

    Evacuation decision behavior for no-notice emergency events


    Contributors:


    Publication date :

    2019-01-01


    Size :

    14 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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