Institute of Transportation Studies at UC Berkeley

Recent technological improvements have greatly expanded the sharing economy (e.g., Airbnb, Lyft, and Uber), coinciding with growing need for transportation and sheltering resources in evacuations. To understand influencers on sharing willingness in evacuations, we employed a multi-modeling approach across four sharing scenarios using three model types: 1) four binary logit models that capture each scenario separately; 2) a multi-choice latent class choice model (LCCM) that jointly estimates multiple scenarios via latent classes; and 3) a portfolio choice model (PCM) that estimates dimensional dependency. We tested our approach by employing online survey data from 2017 Hurricane Irma evacuees (n=368).

The multi-model approach uncovered behavioral nuances undetectable with a single model. First, the multi-choice LCCM and PCM models uncovered scenario correlation, specifically willingness to share for both transportation scenarios and both sheltering scenarios. Second, the multi-choice LCCM found three classes – transportation sharers, adverse sharers, and interested sharers. Transportation sharers were more likely to be female, lower-income, and residents of Southwest Florida compared to adverse sharers. Interested sharers were more likely to be male, long-time residents, and higher-income compared to adverse sharers. Third, families with children were unwilling to share regardless of the model, while spare capacity (i.e., seatbelts, spare beds) had a positive but somewhat insignificant influence on sharing. Fourth, experienced home sharers were more willing to share shelter in the binary logit and PCM models. We suggest that local agencies consider holistic sharing mechanisms across resource types and time (i.e., before, during, and after a hurricane evacuation).