Can Sharing Economy Platforms Increase Social Equity for Vulnerable Populations in Disaster Response and Relief? A Case Study of the 2017 and 2018 California Wildfires

Stephen Wong, Jacquelyn Broader, Susan Shaheen

Ensuring social equity in evacuations and disasters remains a critical challenge for many emergency management and transportation agencies. Recent sharing economy advances – including transportation network companies (TNCs, also known as ridehailing and ridesourcing), carsharing, and homesharing – may supplement public resources and ensure more equitable evacuations. To explore the social equity implications of the sharing economy in disasters, we conducted four focus groups (n=37) of vulnerable populations impacted by California wildfires in 2017 or 2018.

A Revealed Preference Methodology to Evaluate Regret Minimization with Challenging Choice Sets: A Wildfire Evacuation Case Study

Stephen Wong, Caspar Chorus, Susan Shaheen, and Joan Walker

Regret is often experienced for difficult, important, and accountable choices. Consequently, we hypothesize that random regret minimization (RRM) may better describe evacuation behavior than traditional random utility maximization (RUM). However, in many travel related contexts, such as evacuation departure timing, specifying choice sets can be challenging due to unknown attribute levels and near-endless alternatives, for example.

Bridging the Gap Between Evacuations and the Sharing Economy

Stephen Wong, Joan Walker, and Susan Shaheen

This paper examines the opportunities for addressing evacuations by leveraging the sharing economy. To support this research, we use a mixed-method approach employing archival research of sharing economy actions, 24 high-ranking expert interviews, and a survey of individuals impacted by Hurricane Irma in 2017 (n=645). Using these data, we contribute to the literature in four key ways. First, we summarize sharing economy company actions in 30 U.S. disasters.

Fleeing from Hurricane Irma: Empirical Analysis of Evacuation Behavior Using Discrete Choice Theory

Stephen Wong, Adam Pel, Susan Shaheen, and Caspar Chorus

This paper analyzes the observed decision-making behavior of a sample of individuals impacted by Hurricane Irma in 2017 (n = 645) by applying advanced methods based in discrete choice theory. Our first contribution is identifying population segments with distinct behavior by constructing a latent class choice model for the choice whether to evacuate or not. We find two latent segments distinguished by demographics and risk perception that tend to be either evacuation-keen or evacuation-reluctant and respond differently to mandatory evacuation orders.

UC Berkeley releases report on disaster evacuations

July 12, 2019

BERKELEY — A U.C. Berkeley team that conducted wildfire evacuation focus group research in Lake County released a report this week that recommends more use by government agencies of the sharing economy during disaster evacuations.