Towards the close of the first decade of the 21st Century, ride-hailing services began to enter the transportation market through smart phone applications that allowed consumers to hail and pay for a ride from drivers using their own vehicle. The information and communication technologies used by these platforms allow for more reliable service, to more locations, with shorter wait times, and at a lower cost than traditional taxi services and, perhaps, public transit. Today, an estimated 15% of adults across the U.S. and 21% in major cities have personally used these services. The successful entrance of ride-hailing services into the transportation market has raised questions about their effect on the overall transportation system, including congestion, total vehicle miles traveled (VMT), and greenhouse gas emissions (GHGs). Reliable answers are limited, in large part, because of their rapid expansion and the lack of publicly available data from these private ride-sharing companies. However, there is now a small body of research, most conducted in 2016 and 2017, that provides some initial evidence on the impacts of these services. This research includes population representative survey data, targeted ride-hailing user survey data, and measured ride-hailing driver and passenger activity data. In addition, the recent interest in automated vehicles has produced modeling studies that also provide insight into the potential effects of ride-hailing services. The following framework was developed to identify the range of possible travel effects, both positive and negative, on users of ride-hailing services. This includes the effects of ride-hailing on auto ownership, trip generation, destination choice, mode choice, network vehicle travel, and land use.

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To address roadway congestion problems, communities throughout the nation that are at risk for air quality problems are proposing major and costly beltway highway projects. In this study, an integrated land use and transportation model and an advanced travel demand model linked to a land allocation model are applied to evaluate different combinations of transit and highway investment alternatives, land use measures, and an auto pricing policy in the Sacramento region. Four policy and methodological questions are addressed in the simulation and evaluation of policy scenarios in this case study. First, what are the respective models' strengths and weaknesses, and what effect does this have of their evaluation of policies. Second, can transit investment, auto-pricing policies, and land use measures be just as, or more, effective in reducing congestion as highway alternatives and have the added benefit of improving air quality and protecting environmentally sensitive lands? Third, what is the relative significance of the results of the alternative scenarios simulated, given plausible errors in socio-economic projections? Fourth, can auto-pricing policies alone and/or in combination with other transit and land use policies significantly reduce vehicle emissions without imposing monetary losses on travelers? Finally, the implications of the answers to these questions are examined in the context of the transportation planning process.

Increasing transit use has many benefits, including reducing traffic congestion and greenhouse gas (GHG) emissions. However, riders need to be able to get to a station in order to use transit. Walking is an option only for those within a limited radius of a station. Driving to a station may be feasible for some, but providing sufficient parking can be expensive and land intensive. The rise of ride-hailing companies such as Uber and Lyft presents a new opportunity for bridging the “first-mile” gap to high quality transit. Transit agencies are beginning to launch pilot projects to test public-private partnerships with ride-hailing companies to increase access to transit.

This policy brief summarizes findings from researchers at UC Davis who used existing modeling tools and data to understand the potential market demand for a first-mile transit access service in the San Francisco Bay Area. They modeled the likelihood of commuters who drive alone to switch to using ride-hailing and the Bay Area Rapid Transit (BART) rail system to get to work based on travel time, cost, and distance to a BART station. They explored the magnitude of change in overall travel time and cost for travelers who switch from driving alone to using ride-hailing and BART, as well as potential changes to vehicle miles traveled (VMT) and GHG emissions at both the regional and station level.

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A considerable body of research on induced travel has emerged over the last several decades, and induced travel has been acknowledged by the U.S. Transportation Research Board and Environmental Protection Agency. This has brought renewed attention to the representation of induced travel in regional land use and travel demand models. A number of case studies (Sacramento, CA, Chittenden, VT, and Salt Lake City, UT) have assessed the ability of existing travel and land use models to represent the induced travel effects of new highway capacity (or elasticity of VMT with respect to lane miles and travel time). In addition, these studies have conducted sensitivity tests, by turning on and off model components, to isolate the relative contribution with respect to the models' representation of induced travel. The results indicate that when travel times are fed back to a land use model and/or the trip distribution step, then (1) models can represent induced travel within the range documented in the empirical literature and (2) the effect of new highway capacity on land use and trip distribution can significantly contribute to the model's representation of induced travel. If induced travel is not represented in travel and land use models, then the need for, and the benefit of, a highway project will tend to be overstated (e.g., 16% to 236% of vehicle hours traveled), and negative environmental effects will be understated (e.g., 72% to 192% of NOx vehicle emissions).

Automated vehicles (AVs) may significantly disrupt our transportation system, with potentially profound environmental effects. This policy brief outlines the mechanisms by which AVs may affect the environment through influencing travel demand, as well as the magnitude of these effects on vehicle miles travelled (VMT) and greenhouse gas (GHG) emissions. Personal AVs and AV taxis (or ride-hailing services) are likely to increase VMT and GHG, exacerbate traffic congestion in city centers, and potentially lead to suburban sprawl. Electrification and vehicle sharing may reduce some of these environmental effects, but targeted policies must be put in place to ensure that these solutions are effective.

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California led the nation by passing the first global warming legislation in the U.S. California is tasked with reducing green house gas (GHG) emissions to 1990 levels by 2020 and 80% below 1990 levels by 2050. The California Air Resources Board estimates that significant GHG reductions from passenger vehicles can be achieved through improvements in vehicle technology and the low carbon fuel standard; however, these reductions will not be enough to achieve 1990 levels if current trends in vehicle miles traveled (VMT) continue. Currently, most operational regional models in California have limited ability to represent the effects of transit, land use, and auto pricing strategies; efforts are now underway to develop more advanced modeling tools, including activity-based travel and land use models. In the interim, this report reviews the international modeling literature on land use, transit, and auto pricing policies to suggest a range of VMT and GHG reduction that regions might achieve if such policies were implemented. The synthesis of the literature categorizes studies, by geographic area, policy strength, and model type, to provide insight into order of magnitude estimates for 10-, 20-, 30-, and 40-years time horizons. The analysis also highlights the effects of modeling tools of differing quality, policy implementation timeframes, and variations in urban form on the relative effectiveness of policy scenarios.

Low-income rural residents, especially those who do not own a car, have limited transportation options for accessing jobs, health care, education, healthy food, and other basic services. Rural transit service is often expensive, infrequent, and hard to access because of long travel distances and low development densities. Transit providers face very high operating costs of fixed-route and dial-a-ride transit services because of low farebox recovery rates.

Shared-use mobility services such as ridehailing and carsharing largely serve major metropolitan areas. However, rural governments are beginning to consider whether these types of services may be able to augment existing transit services while providing cost-effective transportation access to rural residents.

This policy brief summarizes findings from a UC Davis study in which researchers compared the cost-effectiveness of existing inter-city transit service in rural disadvantaged communities in California’s San Joaquin Valley to hypothetical ridesharing and carsharing services. The researchers also reviewed existing shared-use mobility pilots and consulted with experts in shared mobility and local transportation planning to develop concepts for future shared mobility pilot programs in the San Joaquin Valley.

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Shared-use mobility services largely serve major metropolitan areas. However, increasingly officials, who represent rural communities, want to know whether these types of services may be able to provide more cost-effective access to rural residents than is currently possible by fixed-route and dial-a-ride transit services. Many of these officials must contend with low farebox recovery rates that threaten transit funding and subsequent cutbacks in transit services that are often strongly opposed by constituents. In this study, the cost-effectiveness of existing inter-city transit service in rural disadvantaged communities in the San Joaquin Valley (California) is compared to hypothetical ridesharing and carsharing services. The results show significant potential to reduce transit costs and reinvest those cost saving to expand shared mobility services. The cost-effectiveness analysis is supplemented with reviews of existing shared-use mobility pilots and consultations with experts in shared mobility and local transportation planning.

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The EasyConnect Low-Speed Modes Linked to Transit Planning Project (TO 5113) project represents the integration of innovative strategies to enhance transit use during the development and construction of a suburban transit oriented development at the Pleasant Hill Bay Area Rapid Transit (BART) District station in the East San Francisco Bay Area. This planning project brings together a unique partnership including small technology businesses, transportation agencies, city and county government, and academia. The project components include the introduction of shared-use low speed mode vehicles and electronic lockers at the proposed TOD. The evaluation of the EasyConnect field operational test (TO 6113-the next phase of this initiative) will provide insights into whether the introduction and integration of low-speed modes and elockers at the Pleasant Hill BART station can significantly increase transit access/use and cost effectively provide a last mile solution.

In this study, the authors use existing modeling tools and data from the San Francisco Bay Area (California) to understand the potential market demand for a “first” mile transit access service and possible reductions in vehicle miles traveled (VMT) (and thus GHGs) at both the regional and station level. The authors develop a project scenario that targets commuters who drive alone but could take rail (Bay Area Rapid Transit, or BART) to work. The authors use travel time, cost, and distance data from San Francisco Bay Area travel models, Google and BART APIs, and TNC fares to explore the magnitude of change in overall travel time and cost for travelers who switch from driving alone to using TNC and BART to travel to work. The analysis indicates that 31% of the identified drive-alone trips could reduce generalized costs (travel time and monetary costs) by switching to TNC and BART. If all travelers who could benefit from traveling by TNC and BART, did in fact switch from drive-alone travel, about 40 thousand new BART trips could be generated and over a half a million miles of VMT avoided during the morning commute period. Most of these trips experienced relatively high levels of cost and VMT savings, which may be more likely to motivate behavioral change. Examination of cost savings by income level and vehicle availability suggests that the new service is more likely to benefit lower income households with fewer vehicles. Sensitivity analyses indicate that even with increased TNC fares and waiting times, there is still a relatively large number of trips that could benefit from switching from driving to TNC and BART. Tests of a shared TNC BART access service suggest cost savings and VMT reductions even when travel time costs increase by 60%. Analysis of benefits by station indicate locations with relatively high market potential (i.e., trips and cost saving) and environmental benefits (VMT reduction) for early pilot implementation. These results can be used to estimate potential TNC fare subsidies to increase performance by station to achieve project objectives, such as equitable access, increased BART ridership, and reduced VMT.

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