UC Davis Institute of Transportation Studies

This report identifies and summarizes the empirical evidence on potential mitigation measures for State Highway System (SHS) projects. For each of the measures on the list, the research team completed a systematic search of the academic literature to identify studies meeting specified search criteria, focusing on studies from the last decade but drawing on older studies when helpful for selected topics. In each of the sections of the report, the criteria for and results of those searches are summarized, including, where possible, the estimated size of the effect of the measure on vehicle miles traveled (VMT). The quantity and quality of the evidence varies widely across the measures: some measures have strong evidence in support of their use for VMT mitigation, while others have limited evidence as to the impact or good evidence of a limited impact; for some measures, no direct empirical evidence is available. A companion report assesses the available methods for estimating the effectiveness of the potential VMT mitigation measures.

This report investigates how local governments (cities and counties) are implementing California’s Senate Bill 743, adopted in 2013 to eliminate traffic delay, measured using level-of-service (LOS) standards, as a basis for analyzing and mitigating transportation-related impacts of development projects and plans as called for under the California Environmental Quality Act (CEQA). Based on a survey of local planning directors in California, administered in Spring, 2024, the report finds that more thanfour-fifths of localities are continuing to apply LOS standards on an “off-CEQA” basis in the permitting process for individual development projects, as well as in community-level plans and policies. Most respondent localities reported that using both VMT and LOS at both the project- and plan-level has not created conflicts, indicating that they are able to align VMT and LOS. Mitigation strategies reported as effective in reducing VMT and also improving LOS include improving active travel facilities,supporting mixed-use development, and relaxing parking requirements; these strategies can be deemed “best practices” foraligning VMT and LOS objectives. 

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Pilot programs in California and beyond are exploring universal basic mobility (UBM), which calls upon government actors to ensure that everyone can access transportation services for basic needs. UBM addresses the problem of transport poverty, which is defined in various ways but is generally when transportation spending puts one below the poverty line or transportation is exceedingly time-consuming, unsafe, or unavailable. This research evaluated UBM-inspired pilot programs in Oakland and Bakersfield, via pre- and post-pilot surveys and interviews during the programs.

Both pilots provided free-fare transportation services (shared micromobility in both cities and public transit services in Oakland) to populations vulnerable to transport poverty (residents of a low-income, minority-majority community in East Oakland and current and former foster youth in Bakersfield). Participants replaced car trips and/or walking with shared mobility and/or public transportation and reported improved access to jobs, food, health care, and social and recreational opportunities. They were able to go more places, more efficiently, and perhaps even enjoy the trip. The services helped participants carry out activities with more comfort and dignity and yielded social and cultural benefits. Lessons learned for program design and administration include the need for: providing some car-based services; clear communications throughout the program; training/support components for new mobility options; troubleshooting operations; and planning for turnover in program staff and fast changes in micromobility services.

This report summarizes the findings from ten sets of analyses that investigated ways the COVID-19 pandemic transformed people's activity-travel patterns. Data were collected through three waves of surveys in Spring 2020, Fall 2020, and Summer 2021 in California and the rest of the US. We found that there was a substantial shift among California workers from physical commuting to exclusive remote work in 2020, followed by a transition to hybrid working schedules by Summer 2021. The adoption of remote work and hybrid work varied significantly among population subgroups, with higher income, more educated individuals, and urban residents showing the greatest shift to these arrangements. In terms of mode use and vehicle ownership, increased concerns about the use of shared modes of travel correlated with an increasing desire to own a car. We observed a major decrease in walking for commuting purposes and a significant increase in walking and biking for non-work trips. The study also found a reduction in the demand for, and/or an elevated aversion to, ridehailing because of the shared nature of the service. Regarding shopping patterns, the study found a nearly five-fold increase in the number of respondents who shopped online at least once per week between Fall 2019 and Spring 2020. However, part of this increase vanished by Fall 2020. Overall, the pandemic brought both temporary changes and longer-term impacts. The study proposes strategies to promote sustainable transportation and social equity among different population groups as communities strive to recover from the pandemic.

The UC Davis Campus Travel Survey is an annual survey led by Transportation Services (TS)—formerly known as Transportation and Parking Services (TAPS)—and the National Center for Sustainable Transportation (NCST), part of the Institute of Transportation Studies (ITS) at UC Davis. It collects a rich set of data about travel to the UC Davis campus, demographics, and attitudes toward travel.

The 2023-24 survey collected data from 4,774 people affiliated with UC Davis about their travel to campus during a single week in October and November 2023. It used a stratified random sampling method with the intent to gather a representative sample of the campus population. About 14 percent of those invited responded to this year’s survey. For the statistics presented throughout this report, we weight the responses by campus role (freshman, sophomore, junior, senior, Master’s, PhD, faculty, and staff) and gender so that the proportion of respondents in each group reflects their proportion in the campus population.

Transportation-disadvantaged populations often face significant challenges in meeting their basic travel needs. Microtransit, a technology-enabled transit mobility solution, has the potential to address these issues by providing on-demand, affordable, and flexible services with multi-passenger vehicles. The ways in which microtransit supports underserved populations and the factors influencing its adoption are not well-studied, however. This research examines SmaRT Ride, a microtransit pilot program in the Sacramento, California, area operated by Sacramento Regional Transit. The project evaluates a broad range of factors influencing microtransit adoption and travel behavior among underserved populations using original revealed choice survey data collected from February – May 2024 with online and intercept surveys. A descriptive analysis revealed that SmaRT Ride has improved transportation access for these communities, complements the transit system by connecting fixed-route transit, and offers a cost-effective alternative to other transportation modes. A binary logistic regression was employed to explore differences between microtransit users and non-users with microtransit awareness. The results indicate that homeownership, employment status, frequency of public transit service use, and attitude towards transit significantly affect microtransit use. Homeowners are more likely to use microtransit, while households without employed members are less likely. In contrast, part-time employees show a higher inclination to use microtransit. Regular public transit users are also more likely to incorporate microtransit into their routines, with a positive attitude toward public transit further increasing the likelihood of its use. The nuanced understanding of microtransit adoption presented here can inform targeted strategies to promote its use among transportation-disadvantaged groups. The results suggest that integrating microtransit with existing transit, outreach programs, discounted or free access, extended service hours, and supporting homeownership and affordable housing in transit-rich areas can encourage microtransit adoption by low-income and/or underserved individuals.

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The goal of this project was to assess the capabilities of the travel demand forecasting models (TDMs) used by California’s metropolitan planning organizations (MPOs) with respect to forecasting the increase in vehicle miles of travel induced by highway capacity expansion. An expert panel assisted with the development of review questions to be used in assessing the models. These questions were used to assess each of the models currently used by the eighteen MPOs in California based on information found in readily available documents. The assessment found that seven MPOs are using activity-basedmodels, nine are using four-step, trip-based models, and two are using hybrid models. In general, the activity-based models do a better job of capturing possible induced travel effects. Only one model includes explicit feedback between the transportation system and land use patterns. The readily-available documentation of travel demand forecasting models in California is insufficient for fully understanding the variables included in each model component and the structure of feedbacks between components of the models.

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The historical impacts of transportation planning and investment have left lasting scars on communities of color and low-income communities. This research evaluates online equity tools that exist as spatial dashboards —i.e., interactive maps in which the parameters of interaction are controlled. Twelve tools ranging from the national to the local level were identified and qualitatively assessed for their ability to address conditions related to transportation equity. The evaluation focused on how each tool defines disadvantaged communities, the outcomes they measure (benefits, burdens, or other), their ease of use, and their ability to guide decisions about equity. The findings show a diversity of methods and metrics in defining disadvantage, with most relying on composite demographic indexes and comparative population thresholds. Tools most commonly provided accessibility metrics to assess transportation benefits, while incorporating a range of environmental and health indicators as burden measures. A minority of tools had integrated features to support planning or project implementation. This study provides examples of promising practices in transportation equity support tools.

Crash modification factor (CMF) is an effectiveness measure of safety countermeasures. It is widely used by state agencies to evaluate and prioritize various safety improvement projects. The Federal Highway Administration (FHWA) CMF Clearinghouse provides CMFs for a broad range of countermeasures, but still, the existing CMFs often cannot meet the needs for characterizing the safety impacts of countermeasures in new scenarios. Developing CMFs, meanwhile, is costly, time-consuming, and requires extensive data collection. A more cost-effective way to provide preliminary CMF estimations is needed. To address this need, this study develops a low-cost and easily extendable data-driven framework for CMF predictions. This framework performs data mining on existing CMF records in the FHWA CMF Clearinghouse. To tackle the heterogeneity of data, interdisciplinary techniques to maintain model compatibility were created and used. The project also integrates multiple machine-learning models to learn the complex hidden relationships between different safety countermeasure scenarios. Finally, the proposed framework is trained against the CMF Clearinghouse data and performs comprehensive evaluations. The results show that the proposed framework can provide CMF predictions for new countermeasure scenarios with reasonable accuracy, with overall mean absolute errors less than 0.2. We also discuss an enhanced approach that leverages structured information in certain CMF descriptions, which can boost the CMF prediction accuracy, showing a mean absolute error less than 0.1 in a case study.