@article{ref1,
title="Willingness of Hurricane Irma evacuees to share resources: a multi-modeling approach",
journal="Transportmetrica A: transport science",
year="2023",
author="Wong, Stephen D. and Yu, Mengqiao and Kuncheria, Anu and Shaheen, Susan A. and Walker, Joan L.",
volume="19",
number="2",
pages="e2017064-e2017064",
abstract="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).<p /> <p>Language: en</p>",
language="en",
issn="2324-9935",
doi="10.1080/23249935.2021.2017064",
url="http://dx.doi.org/10.1080/23249935.2021.2017064"
}