by Innovative Mobility Research | Jun 15, 2020 |
Authors: Stephen Wong, Jacquelyn Broader, and Susan Shaheen Date: June 2020 Abstract: 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. To structure these data, we employed the Spatial Temporal Economic Physiological Social (STEPS) equity framework in an evacuation context. We contribute to the literature by: 1) summarizing the focus groups and their opinions on the sharing economy in evacuations; 2) capturing wildfire evacuation obstacles through the STEPS transportation equity framework; and 3) linking STEPS and focus group results to explore the future potential of shared resources. Using STEPS, we also expand our shared resource exploration to 18 vulnerable groups. We found that all focus groups were highly concerned with driver availability and reliability and the ability of vehicles to reach evacuation zones, not necessarily safety and security. Each group also expressed specific limitations related to their vulnerability. For example, individuals with disabilities were most concerned with inaccessible vehicles and homes. Using the STEPS framework, we found that while multiple vulnerable groups could gain considerable benefits from shared resources, 10 of the 18 groups experience three or more key challenges to implementation. We offer several policy recommendations to address equity-driven planning and shared resource limitations. View...
by Innovative Mobility Research | May 18, 2020 |
Authors: Stephen Wong, Caspar Chorus, Susan Shaheen, and Joan Walker Date: May 2020 Abstract: 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. This has implications especially for estimating RRM models, which calculates attribute-level regret via pairwise comparison of attributes across all alternatives in the set. While stated preference (SP) surveys solve such choice set problems, revealed preference (RP) surveys collect actual behavior and incorporate situational and personal constraints, which impact rare choice contexts (e.g., evacuations). Consequently, we designed an RP survey for RRM (and RUM) in an evacuation context, which we distributed from March to July 2018 to individuals impacted by the 2017 December Southern California Wildfires (n=226). While we hypothesized that RRM would outperform RUM for evacuation choices, this hypothesis was not supported by our data. We explain how this is partly the result of insufficient attribute-level variation across alternatives, which leads to difficulties in distinguishing non-linear regret from linear utility. We found weak regret aversion for some attributes, and we identified weak class-specific regret for route and mode choice through a mixed-decision rule latent class choice model, suggesting that RRM for evacuations may yet prove fruitful. We derive methodological implications beyond the present context toward other RP studies involving challenging choice sets and/or limited attribute variability. View...
by Innovative Mobility Research | Apr 14, 2020 |
Authors: Stephen Wong, Joan Walker, and Susan Shaheen Date: April 2020 Abstract: 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. Second, we discuss results from 24 expert interviews on 11 sharing economy benefits (ranging from resource redundancy to positive company press coverage) and 13 limitations (ranging from driver reliability to the digital divide). Experts included six directors/executives of emergency/transportation agencies, two executives of sharing economy companies, and eight senior-level agency leaders. Third, we use these interviews, specifically negative opinions of the sharing economy, to inform our Hurricane Irma survey, which contributes empirical evidence of the feasibility of shared resources. Despite just 1.1% and 5.4% of respondents using transportation network companies (TNCs, also known as ridesourcing and ridehailing) and homesharing respectively during the Irma evacuation, some respondents were extremely willing to offer their own resources including transportation before evacuating (29.1%), transportation while evacuating (23.6%), and shelter for free (19.2%) in a future disaster. We also find spare capacity of private assets exists for future evacuations with just 11.1% and 16% of respondents without spare seatbelts and beds/mattresses, respectively. Finally, we conclude with practice-ready policy recommendations for public agencies to leverage shared resources including: communication partnerships, surge flagging (i.e., identifying and reducing unfair price increases), and community-based sharing systems. View...
by Innovative Mobility Research | Apr 14, 2020 |
Authors: Stephen Wong, Adam Pel, Susan Shaheen, and Caspar Chorus Date: February 2020 Abstract: 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. Evacuees subsequently face a multi-dimensional choice composed of concurrent decisions of their departure day, departure time of day, destination, shelter type, transportation mode, and route. While these concurrent decisions are often analyzed in isolation, our second contribution is the development of a portfolio choice model (PCM), which captures decision-dimensional dependency (if present) without requiring choices to be correlated or sequential. A PCM reframes the choice set as a bundle of concurrent decision dimensions, allowing for flexible and simple parameter estimation. Estimated models reveal subtle yet intuitive relations, creating new policy implications based on dimensional variables, secondary interactions, demographics, and risk-perception variables. For example, we find joint preferences for early-nighttime evacuations (i.e., evacuations more than three days before landfall and between 6:00 pm and 5:59 am) and early-highway evacuations (i.e., evacuations more than three days before landfall and on a route composed of at least 50% highways). These results indicate that transportation agencies should have the capabilities and resources to manage significant nighttime traffic along highways well before hurricane landfall. View...
by Innovative Mobility Research | Mar 25, 2020 |
Authors: Stephen D. Wong, Jacquelyn C. Broader, Susan A. Shaheen, PhD Date: March 2020 Abstract: Between 2017 and 2019, California experienced a series of devastating wildfires that together led over one million people to be ordered to evacuate. Due to the speed of many of these wildfires, residents across California found themselves in challenging evacuation situations, often at night and with little time to escape. These evacuations placed considerable stress on public resources and infrastructure for both transportation and sheltering. In the face of these clear challenges, transportation and emergency management agencies across California have widely varying levels of preparedness for major disasters, and nearly all agencies do not have the public resources to adequately and swiftly evacuate all populations in danger. To holistically address these challenges and bolster current disaster and evacuation planning, preparedness, and response in California, we summarize the evacuations of eleven major wildfires in California between 2017 and 2019 and offer a cross-comparison to highlight key similarities and differences. We present results of new empirical data we collected via an online survey of individuals impacted by: 1) the 2017 October Northern California Wildfires (n=79), 2) the 2017 December Southern California Wildfires (n=226), and 3) the 2018 Carr Wildfire (n=284). These data reveal the decision-making of individuals in these wildfires including choices related to evacuating or staying, departure timing, route, sheltering, destination, transportation mode, and reentry timing. We also present results related to communication and messaging, non-evacuee behavior, and opinion of government response. Using the summarized case studies and empirical evidence, we present a series of recommendations for agencies to prepare for, respond to, and recover from wildfires....
