TY  - JOUR
PY  - 2023//
TI  - Willingness of Hurricane Irma evacuees to share resources: a multi-modeling approach
JO  - Transportmetrica A: transport science
A1  - Wong, Stephen D.
A1  - Yu, Mengqiao
A1  - Kuncheria, Anu
A1  - Shaheen, Susan A.
A1  - Walker, Joan L.
SP  - e2017064
EP  - e2017064
VL  - 19
IS  - 2
N2  - 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>
LA  - en
SN  - 2324-9935
UR  - http://dx.doi.org/10.1080/23249935.2021.2017064
ID  - ref1
ER  -