Recent technological improvements have expanded the sharing economy (e.g. Airbnb, Lyft, and Uber), coinciding with a growing need for evacuation resources. To understand factors that influence sharing willingness in evacuations, we employed a multi-modeling approach using three model types: (1) four binary logit models that capture sharing scenario separately; (2) a portfolio choice model (PCM) that estimates dimensional dependency, and (3) a multi-choice latent class choice model (LCCM) that jointly estimates multiple scenarios via latent classes. We tested our approach by employing online survey data from Hurricane Irma (2017) evacuees (n=368). The multi-model approach uncovered behavioral nuances undetectable with one model. For example, the multi-choice LCCM and PCM models uncovered scenario correlation and the multi-choice LCCM found three classes – transportation sharers, adverse sharers, and interested sharers – with different memberships. We suggest that local agencies consider broader sharing mechanisms across resource types and time (i.e. before, during, and after evacuations).


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    Titel :

    Willingness of Hurricane Irma evacuees to share resources: a multi-modeling approach


    Beteiligte:
    Wong, Stephen D. (Autor:in) / Yu, Mengqiao (Autor:in) / Kuncheria, Anu (Autor:in) / Shaheen, Susan A. (Autor:in) / Walker, Joan L. (Autor:in)


    Erscheinungsdatum :

    15.03.2023


    Format / Umfang :

    36 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt




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