3 Revolutions Future Mobility Program

Pooled trips in private vehicles, or pooling, can lead to smaller environmental impacts and more efficient use of the limited roadway capacity, especially during peak hours. However, pooling has not been well adopted in part because of difficulties in coordinating schedules among various travelers and the lack of flexibility to changes in schedules and locations. In the meantime, ridehailing (RH) provides pooled services at a discounted fare (compared to the single-travel-party option) via advanced information and communication technology. This study examines individuals’ preferences for/against pooled RH services using information collected among travelers answering a set of questions related to their last RH trip. In doing so, both trip attributes and rider characteristics are considered. Taste heterogeneity is modeled in a way that assumes the presence of unobserved groups (i.e., latent classes), each with unique preferences, in a given sample of RH riders (N=1,190) recruited in four metropolitan regions in Southern U.S. cities from June 2019 to March 2020. The researchers find two latent classes with qualitatively different preferences, choosy poolers and non-selective poolers, regarding their choice in favor of/against pooling based on wait time, travel costs, purpose, and travel party size of the last RH trip. Personal characteristics are also identified, specifically age and three attitudes (travel satisfaction, environmentalism, and travel multitasking), which account for individuals’ class membership. This research contributes to the literature by explicitly modeling taste heterogeneity towards pooled ridehailing. In addition, unlike existing studies either at the person level or employing stated-preference data, a trip-level analysis is performed in connection with revealed preferences, which generates more realistic and relevant implications to policy and practice.

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This study investigates how the COVID-19 pandemic has transformed people’s activity-travel patterns, using datasets collected through three waves of surveys in spring 2020, fall 2020, and summer 2021. With this dataset, it was possible to investigate evolving behavioral choices and preferences among respondents at different timepoints: fall 2019 (recollection of the past), spring 2020, fall 2020, summer 2021, and summer 2022 (future expectations). The study highlighted a large shift among California workers from physical commuting to working remotely in 2020, which was followed by a transition towards hybrid work by summer 2021. The shift to remote work and hybrid work varied considerably across population subgroups, and was most popular among higher-income, better-educated individuals, and urban residents. In terms of household vehicle ownership change, those tech-savvy and variety-seeking individuals were more likely to increase or replace household vehicles, while those who are pro-environment and pro-active are less likely to do so. COVID health concerns show concurrent effects of encouraging the adoption of a more pro-active lifestyle during the pandemic, but also leading to an increased desire to own vehicles in the future. Regarding shopping patterns, the number of respondents who shop online at least once per week increased nearly five-fold between fall 2019 and spring 2020, but such magnitude somewhat diminished by fall 2020. In general, the pandemic has generated a mix of short-lived temporary changes and potential longer-term impacts. The study provides various strategies to help increase transportation and social equity among various population groups as the communities recover from the pandemic.

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The majority of people with disabilities in the United States of America (US) are licensed drivers or use transportation modes based on private vehicles. Despite this, people with disabilities, including licensed drivers, still often encounter difficulties that limit their overall mobility and quality of life. Research about the problems with private vehicular modes facing people with disabilities remains sparse. Existing research suggests that some disabilities make driving impossible, while poverty often associated with disability makes owning and modifying vehicles to fit users’ needs unaffordable. People with disabilities who cannot drive or cannot afford to own a vehicle may use rental cars or carsharing services, get rides from friends or family, or use ridehailing services or taxis, but car-oriented land use patterns and the higher costs of modified vehicles together may compromise the availability of these modes for people with disabilities. Better understanding of the challenges that people with disabilities face with these modes and of associated land use issues is critical for new modes & policies to sustainably improve the mobility of people with disabilities.

A significant fraction of people with disabilities in the United States of America (US) do not drive, and these people disproportionately use public transit and paratransit services compared to drivers with disabilities. Substantial research exists regarding not only the ease for people with disabilities to use public transit and paratransit services but also the availability of such services and the availability of nearby pedestrian infrastructure. However, much less research exists regarding the effects of shared micromobility services, car-free areas, and consolidation of public transit services on the mobility of people with disabilities. This systems-level thinking about not only first-order effects but also second- and higher-order effects is critical for the development of policies that more effectively address the mobility needs of people with disabilities.

This study investigates the disruptive changes brought to transportation by emerging technologies and the COVID-19 pandemic through the analysis of repeated cross-sectional datasets that were collected with multiple survey waves administered in various regions of the United States and Canada. The first data collection was administrated in 2019 through the recruitment of respondents with an online opinion company. As the COVID-19 pandemic started to disrupt the world starting in 2020, two additional rounds of data collection were carried out in Spring 2020 and Fall 2020, to study the disruptions in activity and travel patterns that were caused by the pandemic. Starting in 2020, the data collection was extended to 15 U.S. regions: Los Angeles, Sacramento, San Diego and San Francisco in California; Atlanta, Boston, Chicago, Denver, Detroit, Kansas City, New York, Salt Lake City, Seattle, Tampa and Washington D.C. in other U.S. regions. In addition, the study covered also Toronto and Vancouver in Canada. Several thousands of respondents participated in the various waves of surveys. Some of these respondents were part of the longitudinal component of the dataset, built through inviting previous survey respondents to participate in the new waves of data collection. Additional respondents were recruited using online opinion panels and convenience sampling. The study enabled by the analysis of the data collected with this series of surveys helps understand how mobility patterns are evolving in the country as new technologies disrupt the transportation sector and they evolve from the pre-pandemic to the post-pandemic era. It helps make planning decisions and guide policymaking through an annual data collection that allows us to collect critically-needed information on the evolution of travel patterns and the adoption of new transportation technologies and trends in the selected regions, every year. In this report, the researchers briefly describe the series of data collection and present some summary findings from the analysis of the data collected before and during the COVID-19 pandemic.

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This project aims to provide recommendations on the methodology and design specifications for the travel demand model to be built for the Link21 program in the Northern California megaregion. The Link21 program is a major rail investment program that will considerably improve and upgrade the passenger rail services in the Northern California megaregion, centered around the Transbay Corridor between Oakland and San Francisco in the San Francisco Bay Area. To support this effort, we reviewed the current and potential travel markets for the Link21 program, assessed the available travel demand models that could be used to support the modeling efforts for the Link21 program, and conducted interviews with experts from academic institutions, metropolitan planning organizations, state and federal agencies, and US DOE national labs. Considering the goals and objectives of the Link21 program, a list of 20 critical, important, and optional modeling features were identified, which should be considered for the Link21 program. We reviewed 11 existing travel demand models based on the evaluation of their modeling features, and present four proposed modeling approaches which could be considered to support the Link21 program. For each modeling approach, we summarize pros and cons in terms of fulfilling the requirements of the Link21 program. The four modeling approaches include: 1) building on the Metropolitan Transportation Commission (MTC) TM 2.1 regional travel demand model without a dedicated long-distance travel model component; 2) building on the MTC TM 2.1 regional travel demand model with a dedicated long-distance travel model component; 3) building on the San Francisco County Transportation Authority (SFCTA) regional travel demand model with or without a dedicated long-distance travel model component; and 4) building on the California High Speed Rail (CHSR) or the new statewide rail model that is currently under development. The study also discusses some sources of uncertainties that might affect future travel demand and the modeling practice in the Link21 regions. These include the impacts of the COVID-19 pandemic on work patterns and activity/travel choices, the introduction of shared mobility services, micromobility, the potential deployment of Mobility as a Service (MaaS) solutions, and the forthcoming deployment of connected and automated vehicles (CAVs). Given the complexity of the Link21 program and the requested 18-month timeline for developing a new travel demand model to support the program, we recommend that the model development for the Link21 program build on an existing modeling framework and adopt a modular system, which can be updated over time. An initial model release would become available in the proposed timeline of 18 months, while future updates and improvements in the model components could be added in future model updates. This process also would be well-suited to address eventual modeling issues that could arise with the initial model release, and it would benefit from the development and updates of other models in the Northern California megaregion that are being carried out in parallel.

Emerging transportation services, whose development and adoption have been enabled by information and communication technology, are largely transforming people’s travel and activity patterns. This study investigates the emerging transportation trends and how they transform travel-related decision-making in the population at large through the application of a unique longitudinal approach. As part of this project, a second wave of data collection in 2018 was built with a rotating panel structure as a continuation of the research efforts that started with the collection of the 2015 California Millennials Dataset. This report focuses on the analyses of the data collected in this project, in particular on the differences in attitudes towards transportation and the environment among different generational groups, the adoption and use of shared mobility services, and their relationship with vehicle ownership, the interest in the adoption of alternative fuel vehicles, and the interest in the future adoption of connected and automated vehicles. Due to the small number of respondents who participated in both surveys, for the purposes of the analyses contained in this report, we treated the data as repeated cross-sectional and analyzed the data from each survey separately. The study helps researchers evaluate the complex relationship between observed/latent characteristics and individual travel-related choices and decision-making. The study highlights attitudinal and mode-choice differences across generations. It explores the factors impacting current adoption of and future interest in new transportation technology including alternative fuel vehicles, automated vehicles and shared mobility. Divergent consumer segments are witnessed within each of these markets, with distinctive socio-demographics, latent attitudes, built environment, and level of familiarity with new technologies, which shape the uniqueness of their vehicle ownership, residential location, travel behavior, activity patterns, and lifestyle.

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This study helps understand how the anticipated emergence of autonomous vehicles will affect various aspects of society and transportation, including travel demand, vehicle miles traveled, energy consumption, and emissions of greenhouse gases and other pollutants. The study begins with a literature review on connected and automated vehicle (CAV) technology for light-duty vehicles, the factors likely to affect CAV adoption, expected impacts of CAVs, and approaches to modeling these impacts. The study then uses a set of modifications in the California Statewide Travel Demand Model (CSTDM) to simulate the following scenarios for the deployment of passenger light-duty CAVs in California by 2050: (0) Baseline (no automation); (1) Private CAV; (2) Private CAV + Pricing; (3) Private CAV + Zero emission vehicles (ZEV); (4) Shared CAV; (5) Shared CAV + Pricing; (6) Shared CAV + ZEV. The modified CSTDM is used to forecast travel demand and mode share for each scenario, and this output is used in combination with the emission factors from the EMission FACtor model (EMFAC) and Vision model to calculate energy consumption and criteria pollutant emissions. The modeling results indicate that the mode shares of public transit and in-state air travel will likely sharply decrease, while total vehicle miles traveled and emissions will likely increase, due to the relative convenience of CAVs. The study also reveals limitations in models like the CSTDM that primarily use sociodemographic factors and job/residence location as inputs for the simulation of activity participation and tour patterns, without accounting for some of the disruptive effects of CAVs. The study results also show that total vehicle miles traveled and vehicle hours traveled could be substantially impacted by a modification in future auto travel costs. This means that the eventual implementation of pricing strategies and congestion pricing policies, together with policies that support the deployment of shared and electric CAVs, could help curb tailpipe pollutant emissions in future scenarios, though they may not be able to completely offset the increases in travel demand and road congestion that might result from CAV deployment. Such policies should be considered to counteract and mitigate some of the undesirable impacts of CAVs on society and on the environment.

Robo-taxis (automated vehicles operating in a ride-hailing model) have the potential to improve mobility while reducing traffic, emissions, and energy use. However, such outcomes depend largely on increasing riders per vehicle. Public policy that incentivizes industry to design robo-taxis to support ride-pooling may be critical to achieving positive outcomes. This research reviews current shared automated vehicle designs and literature related to potential consumer risks and benefits of ride-pooling in robo-taxis in order to articulate potential design solutions to promote pooling.