Read “Mobility on Demand (MOD) and Mobility as a Service (MaaS): Similarities, Differences, and Potential Implications for Transportation in the Developing World ” by Susan Shaheen and Adam Cohen
Innovative and emerging transportation services, such as shared mobility, MOD, and MaaS, are expanding across the developing world. MOD emphasizes the commodification of passenger mobility and goods delivery and transportation systems management, whereas MaaS primarily focuses on passenger mobility aggregation and subscription services. The public
sector can support and leverage MOD and MaaS through a variety of service, information, fare integration, and data sharing partnerships. In particular, the growth of “super” apps in Africa and Asia are offering consumers all-in-one mobile platforms for a variety of transportation and shopping options, mobile wallets, and other services that, in some cases, offer deeper levels of integration and are more advanced than comparable platforms in Europe and North America. While research on “super” apps is
limited, anecdotal evidence suggests that by bundling a variety of consumer services together, these apps have the potential to enhance traveler convenience, multimodal trip planning, and access to goods and services.
Read “Strategies to Overcome Transportation Barriers for Rent Burdened Oakland Residents” by Alexandra Pan and Susan Shaheen
Shared mobility is gaining traction in the transportation community as a potentially more environmentally friendly alternative to automobile travel and complement to public transit. However, adoption and use of shared mobility by low-income individuals lags behind other demographic groups. Additional research is needed to better understand the transportation needs of low-income travelers and how public agencies, community-based organizations, and shared mobility operators can work together to best serve those needs.
This research fills gaps in understanding the potential policy strategies that could be effective at increasing the access, awareness, and use of shared mobility by low-income individuals. We employ Oakland, California as our primary study site (see Figure 1 and Table 1 for more detail). In this report, we present our findings on barriers to shared mobility from a review of existing shared mobility social equity initiatives, expert interviews (n=13) and focus groups with rent burdened residents of East Oakland (n=24). We further investigate barriers and implications for transportation use in an online survey (n=177), as well as longitudinal panel of phone and video interviews (n=31) with rent burdened Oakland residents. Rent burden refers to the percentage of income spent on rent and can more widely capture the population of Oakland residents who are struggling to keep up with rising housing costs.
Read “Bridging the Income and Digital Divide with Shared Automated Electric Vehicles” by Jessica Lazarus, Gordon Bauer, Jeffery Greenblatt, and Susan Sheen
This research investigates strategies to improve the mobility of low-income travelers by incentivizing the use of electric SAVs (SAEVs) and public transit. We employ two agent-based simulation engines, an activity-based travel demand model of the San Francisco Bay Area, and vehicle movement data from the San Francisco Bay Area and the Los Angeles Basin to model emergent travel behavior of commute trips in response to subsidies for TNCs and public transit. Sensitivity analysis was conducted to assess the impacts of different subsidy scenarios on mode choices, TNC pooling and match rates, vehicle occupancies, vehicle miles traveled (VMT), and TNC revenues. The scenarios varied in the determination of which travel modes and income levels were eligible to receive a subsidy of $1.25, $2.50, or $5.00 per ride. Four different mode-specific subsidies were investigated, including subsidies for 1) all TNC rides, 2) pooled TNC rides only, 3) all public transit rides, and 4) TNC rides to/from public transit only. Each of the four modespecific subsidies were applied in scenarios which subsidized travelers of all income levels, as well as scenarios that only subsidized low-income travelers (earning less than $50,000 annual household income). Simulations estimating wait times for TNC trips in both the San Francisco Bay Area and Los Angeles regions also revealed that wait times are distributed approximately equally across low- and high-income trip requests.
Read “To Pool or Not to Pool? Understanding the Time and Price Tradeoffs of OnDemand Ride Users – Opportunities, Challenges, and Social Equity Considerations for Policies to Promote Shared-Ride Services” by Susan Shaheen, Jessica Lazarus, Juan Caicedo, and Alexandre Bayen
On-demand mobility services including transportation network companies (also known as ridesourcing and ridehailing) like Lyft and Uber are changing the way that people travel by providing dynamic mobility that can supplement public transit and personal-vehicle use. However, TNC services have been found to contribute to increasing vehicle mileage, traffic congestion, and greenhouse gas emissions. Pooling rides ⎯ sharing a vehicle by multiple passengers to complete journeys of similar origin and destination ⎯ can increase the average vehicle occupancy of TNC trips and thus mitigate some of the negative impacts. Several mobility companies have launched app-based pooling services in recent years including app-based carpooling services (e.g., Waze Carpool, Scoop) that match drivers with riders; pooled on-demand ride services (e.g., Uber Pool and Lyft Shared rides) that match multiple TNC users; and microtransit services (e.g., Bridj, Chariot, Via) that offer on-demand, flexibly routed service, typically in larger vehicles such as vans or shuttles. However, information on the potential impacts of these options is so far limited. This research employs a general population stated preference survey of four California metropolitan regions (Los Angeles, Sacramento, San Diego, and the San Francisco Bay Area) in Fall 2018 to examine the opportunities and challenges for drastically expanding the market for pooling, accounting for differences in emergent travel behavior and preferences across the four metropolitan regions surveyed. The travel profiles, TNC use patterns, and attitudes and perceptions of TNCs and pooling are analyzed across key socio-demographic attributes to enrich behavioral understanding of marginalized and price sensitive users of on-demand ride services. This research further develops a discrete choice model to identify significant factors influencing a TNC user’s choice to pool or not to pool, as well as estimating a traveler’s value of time (VOT) across different portions of a TNC trip. This research provides key insights and social equity considerations for policies that could be employed to reduce vehicle miles traveled and emissions from passenger road transportation by incentivizing the use of pooled on-demand ride services and public transit.
Read “Public Transit and Shared Mobility COVID-19 Recovery: Policy Recommendations and Research Needs” by Susan Shaheen and Stephen Wong.
While the COVID-19 crisis has devastated many public transit and shared mobility services, it has also exposed underlying issues in how these services are provided to society. As ridership drops and revenues decline, many public and private providers may respond by cutting service or reducing vehicle maintenance to save costs. As a result, those who depend on public transit and shared mobility services, particularly those without access to private automobiles, will experience further loss of their mobility. These transportation shifts will be further influenced by changing work-from-home policies (e.g., telework). While uncertainty remains, work-from-home will likely alter public transit and shared mobility needs and patterns, necessitating different services, operation plans, and business structures.
Read “Synthesis of State-Level Planning and Strategic Actions on Automated Vehicles: Lessons and Policy Guidance for California” by Stephen Wong and Susan Shaheen.
This synthesis provides a summary and comparative analysis of actions states across the United States are taking inresponse to automated vehicles (AVs). The research focuses on state-level stakeholder forums (e.g., task forces, committees) and state-level strategic actions (e.g., studies, initiatives, programs) initiated by a state legislature, agovernor, or a state agency. The analysis found that AV stakeholder forums and strategic actions address a diverse set offocus areas, but they pay minimal attention to the implications of AVs on the environment, public health, social equity, land use, public transit, goods movement, and emergency response. Also, forums and strategic actions commonly include members from state transportation departments, the legislature, and academia; however, representatives from industry and non-governmental organizations (NGOs) are included less often. Academia and researchers participate in themajority of AV forums and actions, either in an advisory capacity (i.e., sharing expertise and experience) and/or through conducting research. Based on this analysis, the synthesis concludes with a recommendation for California to form a state-level working group representing leaders from the public sector, industry, NGOs, and academia to advise the Governor and the Legislature on AV policy across a range of focus areas.
Read “Mobility on Demand Planning and Implementation: Current Practices, Innovations, and Emerging Mobility Futures” by Susan Shaneen, Adam Cohen, Jacquelyn Broader, Richard Davis, Les Brown, Radha Neelakantan, and Deepak Gopalakrishna.
This report provides Mobility on Demand (MOD) planning and implementation practices and tools to support communities. The report discusses different stakeholders in the MOD ecosystem and the role of partnerships in filling spatial, temporal, and other service gaps. Additionally, the report discusses how MOD can be integrated into transportation planning and modeling. The report also discusses shared mobility implementation considerations, such as rights-of-way management, multimodal integration, data sharing, equity, labor impacts, and the role of pilot evaluations. Finally, the report discusses technology developments with implications for the MOD ecosystem, such as shared automated vehicles (SAVs), urban air mobility (UAM), and last-mile delivery innovations. This report is a practical resource with: 1) current practices for planning and implementing MOD; 2) case studies and lessons learned; 3) considerations to help public agencies advance MOD in their communities; and 4) resources and recommended reading.
Read “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” by Stephen Wong, Jacquelyn Broader, and 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. 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.
Read “Similarities and Differences of Mobility on Demand (MOD) and Mobility as a Service (MaaS)” by Susan Shaheen and Adam Cohen.
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 “Shared ride services in North America: definitions, impacts, and the future of pooling” by Susan Shaheen and Adam Cohen.
Shared ride services allow riders to share a ride to a common destination. They include ridesharing (carpooling and vanpooling); ridesplitting (a pooled version of ridesourcing/transportation network companies); taxi sharing; and microtransit. In recent years, growth of Internet-enabled wireless technologies, global satellite systems, and cloud computing – coupled with data sharing – are causing people to increase their use of mobile applications to share a ride. Some shared ride services, such as carpooling and vanpooling, can provide transportation, infrastructure, environmental, and social benefits. This paper reviews common shared ride service models, definitions, and summarises existing North American impact studies. Additionally, we explore the convergence of shared mobility; electrification; and automation, including the potential impacts of shared automated vehicle (SAV) systems. While SAV impacts remain uncertain, many practitioners and academic research predict higher efficiency, affordability, and lower greenhouse gas emissions. The impacts of SAVs will likely depend on the number of personally owned automated vehicles; types of sharing (concurrent or sequential); and the future modal split among public transit, shared fleets, and pooled rides. We conclude the paper with recommendations for local governments and public agencies to help in managing the transition to highly automated vehicles and encouraging higher occupancy modes.
Read “A Revealed Preference Methodology to Evaluate Regret Minimization with Challenging Choice Sets: A Wildfire Evacuation Case Study” by 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. 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.
Read ‘“Three Ps in a MOD:” Role for mobility on demand (MOD) public-private partnerships in public transit provision‘ by Emma Lucken, Karen Trapenberg Frick, and Susan Shaheen.
The growing number of public transportation agencies partnering with Mobility on Demand (MOD) or Mobility as a Service (MaaS) companies raises the question of what role MOD companies can, should, and currently play in the provision of public transport. In this article, we develop a typology reflecting 62 MOD public-private partnerships (MOD PPPs) in the United States and present lessons learned. We conducted 34 interviews with representatives from four MOD companies and 27 public agencies. The interviews spanned October 2017 to April 2018. The resulting MOD PPP typology consists of four service models: 1) First-Mile/Last-Mile (FMLM), 2) Low Density, 3) Off-Peak, and 4) Paratransit. The typology also includes two MOD asset contribution models: 1) Agency-Operated MOD and 2) Agency-Subsidized Private MOD. Lessons learned for limiting competition with fixed-route public transit include: a) if agencies have sufficient resources, they can generally maintain greater data access and control over the service with Agency-Operated MOD than Agency-Subsidized Private MOD; b) public agencies can supplement the Agency-Operated MOD model with Agency-Subsidized Private MOD during peak demand; c) public agencies sometimes encourage FMLM transfers to fixed-route public transit by creating service zones that divide trip generators and attractors and assigning one or two designated transfer stops to each zone; and d) one approach to protecting fixed-route public transit is to restrict Low-Density MOD services to trips that start and end outside a geofenced fixed-route service area.
Read “Bridging the Gap Between Evacuations and the Sharing Economy” by Stephen Wong, Joan Walker, PhD, and Susan Shaheen, PhD.
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.
Read “Review of California Wildfire Evacuations from 2017 to 2019” by Stephen Wong, Jacquelyn Broader, and Susan Shaheen, PhD.
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.
Read “Fleeing from Hurricane Irma: Empirical Analysis of Evacuation Behavior Using Discrete Choice Theory” by 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.
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.
Read “Shared Mobility Policy Playbook” by Susan Shaheen, PhD, Adam Cohen, Michael Randolph, Emily Farrar, Richard Davis, and Aqshems Nichols.
The Shared Mobility Policy Playbook provides an introduction and definitions of shared mobility services, mode-specific resources for agencies looking to develop policies in their community, and policy-focused tools demonstrating case studies and best practices for shared mobility.
This playbook has been designed for individuals and practitioners who want to know more about shared mobility and to communities interested in incorporating shared mobility into their transportation ecosystem. It is a practical guide with resources, information, and tools for local governments, public agencies, and non-governmental organizations seeking to incorporate and manage innovative and emerging shared mobility services. The following are suggested uses of this playbook:
- Access shared mobility resources including: opportunities, lessons learned, and best practices for deploying shared mobility across the United States.
- Use this playbook as a guide for strategic transportation planning and incorporating shared mobility into transportation plans and models.
- Reference best practices, lessons learned, and case studies to aid public policy development.
Read “Social Equity Impacts of Congestion Management Strategies” by Susan Shaheen, PhD, Adam Stocker, and Ruth Meza.
This white paper examines the social equity impacts of various congestion management strategies. The paper includes a comprehensive list of 30 congestion management strategies and a discussion of equity implications related to each strategy. The authors analyze existing literature and incorporate findings from 12 expert interviews from academic, non-governmental organization (NGO), public, and private sector respondents to strengthen results and fill gaps in understanding. The literature review applies the Spatial – Temporal – Economic – Physiological – Social (STEPS) Equity Framework (Shaheen et al., 2017) to identify impacts and classify whether social equity barriers are reduced, exacerbated, or both by a particular congestion mitigation measure. The congestion management strategies discussed are grouped into six main categories, including: 1) pricing, 2) parking and curb policies, 3) operational strategies, 4) infrastructure changes, 5) transportation services and strategies, and 6) conventional taxation. The findings show that the social equity impacts of certain congestion management strategies are not well understood, at present, and further empirical research is needed. Congestion mitigation measures have the potential to affect travel costs, commute times, housing, and accessibility in ways that are distinctly positive or negative for different populations. For these reasons, social equity implications of congestion management strategies should be understood and mitigated for in planning and implementation of these strategies.
Read “Current State of the Sharing Economy and Evacuations: Lessons from California” by Stephen Wong and Susan Shaheen, PhD.
In many evacuations including wildfire evacuations, public agencies often do not have enough resources to evacuate and shelter all citizens. Consequently, we propose that the sharing economy, through private companies and/or private citizens, could be leveraged in disasters for transportation and sheltering resources. To assess this feasibility, we distributed surveys to individuals impacted by three major wildfires in California: 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). Using these data, we find that private citizens are moderately to highly likely to share transportation and sheltering resources in future disasters, but numerous reservations persist about sharing. We also find significant spare capacity in evacuating vehicles and potential homes. To supplement this work, we also conducted four focus groups (n=37) of vulnerable populations to determine the benefits and limitations of a sharing economy strategy in terms of equity. Groups included low-income (2017 December Southern California Wildfires), older adult (2017 October Northern California Wildfires), individuals with disabilities (2017 October Northern California Wildfires), and Spanish-speaking (2018 Mendocino Complex Wildfire). We find that while severe equity limitations exist, groups were able to develop several recommendations for successfully leveraging sharing economy resources for the general population and their specific vulnerable group. We conclude with several local agency and statewide recommendations for building a sharing economy framework for California to prepare for future evacuations.
Read “Shared Micromobility Policy Toolkit: Docked and Dockless Bike and Scooter Sharing” by Susan Shaheen, PhD and Adam Cohen.
This toolkit outlines policies and practices for cities integrating shared micromobility into the built environment. The toolkit is divided into four sections that: 1) define shared micromobility and its impacts, 2) describe users of shared micromobility and market potential, 3) review best practices and case studies for curb space management and related policies, and 4) provide a summary of key findings from the toolkit.
Read “One-Way Electric Vehicle Carsharing in San Diego: An Exploration of the Behavioral Impacts of Pricing Incentives on Operational Efficiency” by Susan Shaheen, Ph.D., Elliot Martin, Ph.D., and Apsaar Bansal.
This project is a two-year evaluation of pricing/incentives applied to the one-way, all electric carsharing system operated by car2go in San Diego, CA. This system is the only electric vehicle-based, one-way carsharing system with instant access (i.e., accessible without reservation) operating in the U.S. The goal of this project is to work with car2go and the San Diego region to develop and evaluate pricing/incentive structures for their members, which improve system operational efficiency (vehicle redistribution, state-of-charge management, use of vehicles placed at public transit stations) and encourage shared-vehicle use.
Read “Understanding Evacuee Behavior: A Case Study of Hurricane Irma” by Stephen Wong; Susan Shaheen, PhD; and Joan Walker, PhD.
In September 2017, Hurricane Irma prompted one of the largest evacuations in U.S. history of over six million people. This mass movement of people, particularly in Florida, required considerable amounts of public resources and infrastructure to ensure the safety of all evacuees in both transportation and sheltering. Given the extent of the disaster and the evacuation, Hurricane Irma is an opportunity to add to the growing knowledge of evacuee behavior and the factors that influence a number of complex choices that individuals make before, during, and after a disaster. At the same time, emergency management agencies in Florida stand to gain considerable insight into their response strategies through a consolidation of effective practices and lessons learned. To explore these opportunities, we distributed an online survey (n = 645) across Florida with the help of local agencies through social media platforms, websites, and alert services. Areas impacted by Hurricane Irma were targeted for survey distribution. The survey also makes notable contributions by including questions related to reentry, a highly under-studied aspect of evacuations. To determine both evacuee and non-evacuee behavior, we analyze the survey data using descriptive statistics and discrete choice models. We conduct this analysis across a variety of critical evacuation choices including decisions related to evacuating or staying, departure timing, destination, evacuation shelter, transportation mode, route, and reentry timing.
- The density of urban centers is likely to increase.
- Suburban and exurban areas are likely to expand.
- A reduction in parking is likely.
This operational concept report provides an overview of the Mobility on Demand (MOD) concept and its evolution, description of the MOD ecosystem in a supply and demand framework, and its stakeholders and enablers. Leveraging the MOD ecosystem framework, this report reviews the key enablers of the system including business models and partnerships, land use and different urbanization scenarios, social equity and environmental justice, policies and standards, and enabling technologies. This review is mostly focused on the more recent forms of MOD (e.g., shared mobility).
The results support that exposure to PHVs or EVs through carsharing has influenced customer ZEV perceptions to be more positive and has commensurately increased the propensity for an individual to buy a ZEV. Furthermore, the data suggest that certain socio-demographic groups, such as younger people and women, are more interested in purchasing these vehicles after using them in carsharing.
Shared Mobility – the shared use of a vehicle, bicycle, or other low-speed mode – is an innovativetransportation solution that enables users to have short-term access to transportation modes on an “as-needed” basis. Shared mobility includes carsharing, personal vehicle sharing (or peer-to-peer (P2P) carsharing), bikesharing, scooter sharing, shuttle services, ridesharing, and on-demand ride services. It can also include commercial delivery vehicles providing flexible goods movement. Shared mobility has had a transformative impact on many global cities by enhancing transportation accessibility while simultaneously reducing ownership of personal automobiles. In the context of carsharing and bikesharing, vehicles and bicycles are typically unattended, concentrated in a network of locations where the transaction of checking out a vehicle or bicycles is facilitated through information technology (IT) and other technological innovations. Usually, carsharing and bikesharing operators are responsible for the cost of maintenance, storage, parking, and insurance/fuel (if applicable). In the context of classic ridesharing (carpooling and vanpooling) and on-demand ride services, such as transportation network companies (TNCs), many of these providers employ IT to facilitate the matching of riders and drivers for trip making.