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

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...

Similarities and Differences of Mobility on Demand (MOD) and Mobility as a Service (MaaS)

Authors: Susan Shaheen and Adam Cohen Date: June 2020 Intro: In cities around the world, innovative and emerging shared modes are offering residents, businesses, travelers, and other users more options to access mobility, goods, and services. As these shared modes build a network of services in many cities, consumers are increasingly engaging in more complex multimodal decision-making processes. Rather than making decisions between modes, travelers are “modal chaining” to optimize route, travel time, and cost. Additionally, digital information and fare integration are contributing to new on-demand access models for mobility and goods delivery. On both sides of the Atlantic, two complementary approaches to multimodal access to public and private transportation services are evolving in parallel. In North America, consumers are assigning economic values to transportation services and making mobility decisions (including the decision not to travel and instead have a good or service delivered) based on cost, journey time, number 0f connections, convenience, and other attributes—a concept commonly referred to as mobility on demand (MOD). In Europe, services that allow travelers to sign up for mobility services in one bundled service are gaining popularity—a concept known as mobility as a service (MaaS). Practitioners are often faced with the questions: “What is MOD?” “What is MaaS?” and “How are MOD and MaaS similar and different?” This article aims to clarify these two concepts, explain their similarities and differences, and highlight a few public sector integrated mobility initiatives. Read the article...

Improving California’s Bay Area Rapid Transit District Connectivity and Access with Segway Human Transporter and Other Low-Speed Mobility Devices

Authors: Susan Shaheen, Caroline Rodier, and Amanda Eaken Date: 2005 Abstract:  To evaluate the potential for low-speed modes to improve transit access, the EasyConnect field test will offer shared-use Segway Human Transporters (HT), electric bicycles, and bicycles linked to a Bay Area Rapid Transit District station and surrounding employment centers in California. Because of safety concerns, research was conducted to understand the risks associated with these modes and potential risk factors. A review of the safety literature indicates that user error is the major cause of low speed mode crashes, and significant risk factors are poor surface conditions and obstructions to drivers’ vision. As a result, an extensive training program and carefully selected routes have been included in the field test. The regulatory and legislative history of the HT is chronicled to understand how concerns about its interaction with pedestrians have produced legislation that includes specific safety requirements. The low-speed modes used in this project will be equipped with safety devices, and participants will be required to wear helmets. The survey results of 13 HT implementation projects provide insight into potential advantages and challenges to the field test. Results of interviews and meetings with field test stakeholders are presented with a discussion of their influence on the field test design. Finally, conclusions and future project steps are discussed. View...

CarLink—A Smart Carsharing System

Authors: Susan Shaheen Date: September 1999 Editor’s Note: The author of this piece is today intensely involved in the second stage of her professional interest in carsharing. Starting several years ago, she began to look into as part of her doctoral research in transportation studies at an American university. Several years later, here she is as entrepreneur and manager behind an ambitious carsharing project. This is a report on the first months of their experience and goals for the future. View PDF....

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

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...