Citation

Wong SD, Yu M, Kuncheria A, Shaheen SA, Walker JL. Transportmetrica A: Transp. Sci. 2023; 19(2): e2017064.

Copyright

(Copyright © 2023, Informa - Taylor and Francis Group)

DOI

10.1080/23249935.2021.2017064

PMID

unavailable

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).

Language: en

Keywords

Hurricane evacuations; Joint Choice modeling; multi-choice latent class choice model; portfolio choice model; sharing economy