Compliance, Congestion, and Social Equity: Tackling Critical Evacuation Challenges through the Sharing Economy, Joint Choice Modeling, and Regret Austin Brown, Executive Director, Policy Institute for Energy, Environment, and the Economy, University of Minimization Stephen D. Wong, Transportation Sustainability Research Center, Institute of Transportation Studies, University of California, Berkeley A Dissertation for the Doctor of Philosophy in Civil and Environmental Engineering, Emphasis in Transportation Engineering Fall 2020 Compliance, Congestion, and Social Equity: Tackling Critical Evacuation Challenges through the Sharing Economy, Joint Choice Modeling, and Regret Minimization By Stephen David Wong A dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Engineering-Civil and Environmental Engineering and the Designated Emphasis in Transportation Engineering in the Graduate Division of the University of California, Berkeley Committee in charge: Professor Susan Shaheen (Co-Chair) Professor Joan Walker (Co-Chair) Professor Karen Trapenberg Frick Professor Mark Hansen Fall 2020 Compliance, Congestion, and Social Equity: Tackling Critical Evacuation Challenges through the Sharing Economy, Joint Choice Modeling, and Regret Minimization Copyright 2020 by Stephen David Wong Abstract Compliance, Congestion, and Social Equity: Tackling Critical Evacuation Challenges through the Sharing Economy, Joint Choice Modeling, and Regret Minimization By Stephen David Wong Doctor of Philosophy in Civil and Environmental Engineering Emphasis in Transportation Engineering University of California, Berkeley Professor Susan Shaheen, Co-Chair Professor Joan Walker, Co-Chair Evacuations are a primary transportation strategy to protect populations from natural and human- made disasters. Recent evacuations, particularly from hurricanes and wildfires, have exposed three critical evacuation challenges: 1) persistent evacuation non-compliance to mandatory evacuation orders; 2) poor transportation response, leading to heavy congestion, slow evacuation clearance times, and high evacuee risk; and 3) minimal attention in ensuring all populations, especially those most vulnerable, have transportation and shelter. With ongoing climate change and increasing land development and population growth in high-risk areas, these evacuation challenges will only grow in size, frequency, and complexity, further straining transportation response in disaster situations. Research Objectives and Theoretical and Methodological Contributions: To tackle these three challenges and improve evacuation outcomes, I explored three research areas: the sharing economy, (joint) choice modeling, and regret minimization. 1) Sharing Economy: The sharing economy has grown rapidly in the past two decades, opening new mechanisms to share, sell, and buy goods and services via technology. Similar to other economic forms, the sharing economy must contend with and respond to external shocks, including disasters. Within this response, an opportunity arises: the sharing economy through private companies or residents could theoretically be a mechanism to increase available assets in evacuations and disasters. Due to the recent development of the sharing economy, research has yet to explore and assess this strategy fully. With limited evacuation literature in this area, an initial question arises: To date, what has been the role of the sharing economy in disasters? In addition, what are the benefits and limitations, particularly for vulnerable groups? On the private resident side, are people willing to share mobility and sheltering resources, and what influences this willingness? To address these questions and explore this new strategy, I tested the feasibility of the sharing economy by assessing the: 1 • Current state of the sharing economy in evacuations, benefits and limitations of the sharing economy in disasters, and the willingness of individuals to provide shared resources through archival research, expert interviews, and post-disaster surveys; • Effect of different factors, including trust and compassion, on willingness to share transportation and sheltering through simple discrete choice models; • Extent to which sharing economy platforms and shared resources can benefit or limit social equity for vulnerable populations through focus groups and application of the STEPS (spatial, temporal, economic, physiological, social) equity framework; and • Behavioral nuances of different models – binary logit models, multi-choice latent class choice model, and portfolio choice model – for the willingness of individuals to share resources in multiple evacuation scenarios for transportation and sheltering. 2) (Joint) Choice Modeling: Disasters are stressful and complex events in which individuals must make rare choices related to evacuations and their safety. First, individuals must decide to evacuate or stay, after which evacuees must navigate through multiple complicated choices including departure day, departure time of day, transportation mode, destination, shelter type, route, and reentry time. Current evacuation behavior literature, while reflecting significant strides in recent years, contains several severe gaps. Much literature is focused on whether to evacuate or stay, with limited research on the complex decisions that must follow this initial choice. In addition, research has only minimally explored the different behavioral responses of unobserved classes of people or the influence of attributes of alternatives on choice. Choice modeling has also focused primarily on hurricanes, leaving a wide gap in the evacuation literature on wildfire behavior. What influences choice making in evacuations, particularly choices beyond the decision to evacuate or stay and especially for wildfire evacuations? Do attributes of alternatives or unobserved classes add behavioral understanding? Most importantly, literature has not considered the theoretical possibility that evacuation choices are inherently joint and multi-dimensional. What choices are correlated and dimensionally dependent, and how should this be modeled? I addressed these research gaps by applying a series of discrete choice models that conduct: • An attribute-based assessment of wildfire evacuation choices beyond the decision to evacuate or stay through simple multinomial logit models; • A latent classification of individuals for the decision to evacuate or stay via a latent class choice model for hurricanes; and • An assessment of decision-dimensional dependency of hurricane choices and wildfire choices (departure day, departure time of day, destination, shelter type, transportation mode, and route) using a portfolio choice model. 3) Regret Minimization: Due to the risky and rare context of evacuations, people likely make decisions differently than under normal circumstances. Regret has been found to influence choices that are difficult and when individuals receive rapid feedback on whether their choices had positive or negative outcomes. Given the unique characteristics of disasters and evacuations, regret minimization (i.e., choice making by minimizing future anticipated regret) could theoretically present a more valid decision rule in evacuations than utility maximization, which has been assumed for most evacuation choice models. Literature in this area is limited, with few studies testing regret minimization in evacuations and only in a stated preference setting. Does random regret minimization (RRM) better describe evacuation behavior than traditional random utility maximization (RUM) in choice models? With no empirical testing of this theory in the literature 2 using post-disaster data, what methodology should be used in a revealed preference setting to reconstruct complex evacuation choice sets and test regret minimization? To answer these research questions and test the theory of regret in evacuations, I analyzed: • Regret minimizing behavior of wildfire evacuees by developing a revealed preference (RP) methodology for challenging choice sets. Empirical Contributions: One primary challenge in the evacuation field is the collection of post- disaster data, which can be difficult for a variety of reasons related to finding participants, securing funding, not interfering with recovery efforts, and deploying data-gathering instruments quickly. Finding enough participants for data collection is especially difficult for wildfire evacuations (compared to hurricane evacuations), due to their smaller size. To meet these challenges and contribute data to the broader evacuation field, I distributed online surveys, collecting responses from individuals impacted by three disasters: • 2017 Hurricane Irma in Florida: n=645 (collected Oct. - Dec. 2017); • 2017 December Southern California Wildfires: n=226 (collected Apr. - June 2019); and • 2018 Carr Wildfire: n= 284 (collected Feb. - Apr. 2019). One critical limitation of online (and disaster) surveys is the failure to represent vulnerable populations. Consequently, I supplemented the wildfire surveys with a series of four focus groups composed of individuals from four vulnerable groups – low-income individuals, older adult, individuals with disabilities, Spanish-speaking individuals – each impacted by a California wildfire between 2017 and 2018 (collected Aug. 2018 - Apr. 2019). To establish a foundation for my research on the sharing economy, I also interviewed 24 high-ranking experts on the benefits and limitations of this strategy