In 2017, California passed Senate Bill 1 (SB1) to bolster transportation infrastructure funding. Using data primarily from the California Department of Transportation (Caltrans)’s official SB1 progress reports, we analyze the severity of cost overruns, delays, and cancellations across SB1 Transportation Projects. Although events such as the COVID-19 pandemic likely caused some of these negative outcomes, our statistical models show consistent patterns of overruns associated with fiscal periods, programs, and geographic locations. Our results indicate that the common 20% contingency is generally insufficient, indicating the need for better risk estimation in project planning. We also suggest amplifying data transparency on project performance and re-evaluating project selection criteria to avoid rewarding underestimation of project costs and duration and penalizing accurate estimation.
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.
To understand the extent to which micromobility services such as bike-share and scooter-share are enabling car-light lifestyles by replacing driving, we explore the trip-chaining patterns of micromobility users. We use travel diary data collected from micromobility users in 48 cities across the US. Our analysis incorporated 15,985 trip chains from 1,157 survey participants who provided at least seven days of travel diary data, and an imputed dataset of 35,623 trip chains from 1,838 participants from the same survey. Our analysis of both datasets shows that a considerable portion of car owners are leaving their cars at home when using micromobility. This suggests that, for a subset of users, micromobility can form part of a car-free or car-light day of travel, despite having a car available. Trip chains with less frequent car use are composed of a variety of different modes in combination with micromobility. Micromobility services are supportive of complex trip chains that include both work and non-work trips with reduced reliance on cars. The use of micromobility services tends to entirely replace shorter car trips on shorter-length trip chains. Our findings show the importance of considering the chain of trips rather than individual trips to understand the sustainability potential of micromobility services. The policy implications of these findings are improving methods of travel behavior analysis of shared mobility services.
While transportation infrastructure and efficiency should inform where to build more housing, little is known about how housing allocation and development processes can be coordinated more systematically with transportation. To date, transportation-housing coordination has often relied on the densification of areas near rail transit stations, putting heavy burdens on these locations and their residents. Much less attention has been paid to how densification can be achieved in a more equitable manner by encompassing other sites. This report directs attention to non-rail locations, specifically low vehicle miles traveled (VMT) areas and bus corridors, and examines the challenges that can arise in promoting densification more broadly. It shows that data uncertainties can make it challenging to identify low VMT locations and that prioritizing only low VMT locations for residential development may have limited effectiveness in expanding housing opportunities in high opportunity areas. The report further explores ways to achieve more inclusive densification of non-rail transit areas and highlights the importance of anti-displacement strategies.
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.
The State of California has increasingly urged construction of affordable housing development in transit-rich areas (California Department of Housing and Community Development 2024) but so far transit-oriented development has generally not reduced vehicle miles traveled for low-income renters (Chatman et al. 2019; Lund, Cervero, and Willson 2004). This report quantifies the cost of daily travel needs for affordable housing residents in San Diego, California, especially seniors aged 62 and older, in two ways. First, it analyzes their trip travel time for the entire San Diego region using activity-based model (ABM) data. Second, it summarizes results from surveys of residents in six affordable housing buildings, three of which provide supportive housing to seniors. Overall, it finds that affordable housing residents use public transit more often than those who have access to a car. But traveling by public transit takes much longer on public transit than traveling by personal vehicle. Survey respondents under age 62 expressed greater dissatisfaction with the costs of public transit ridership, compared to seniors, and were also more likely to express dissatisfaction if they were working. Seniors were more likely to express dissatisfaction with the conditions of public transit stops.
The COVID-19 pandemic has had a significant impact on public transit ridership in the United States, especially for rail transit. Land use, development density, and the pedestrian environment are strongly associated with station-level transit ridership. This study examines how these characteristics affect transit ridership pre- and post-COVID and how they differ across station types based on longitudinal data for 242 rail stations belonging to Bay Area Rapid Transit, San Diego Metropolitan Transit System, Sacramento Regional Transit, and LA Metro between 2019 and 2021. We found overall a 72% decrease in station-level ridership, but changes were not uniform. Station areas with a higher number of low-income workers and more retail or entertainment jobs tend to have lower ridership declines, while areas with a large number of high-income workers, high-wage jobs, and higher job accessibility by transit had more ridership losses. When comparing station area ridership and activity changes based on mobile phone user data, ridership declined more drastically than activity across all four rail systems, which implies that rail transit riders switched to other modes of transportation when accessing the station areas. Given these findings, it is likely that rail transit services oriented toward commute travel, especially core station areas with jobs for higher income workers, will continue to have an uneven recovery, posing critical implications for transit resilience planning and equity in the post-pandemic era. Considering sources of funding other than passenger fares to sustain rail transit, strategizing to reinvent and reinforce downtowns as destinations, and shifting rail transit services to appeal to non-commute travel can be promising strategies to support rail transit.
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.
There is growing interest in the use of hydrogen as a transportation fuel but the environmental benefits of using hydrogen depend critically on how it is produced and distributed. Leading alternatives to using fossil natural gas to make hydrogen through the conventional method of steam methane reforming include using electrolyzers to split water into hydrogen and oxygen, and the use of biogas as an alternative feedstock to fossil natural gas. This report examines the latest carbon intensity (CI) estimates for these and various other hydrogen production processes, adding important nuances to the general “colors of hydrogen” scheme that has been used in recent years. CI values for hydrogen production can vary widely both within and across hydrogen production pathways. The lowest CI pathways use biomass or biogas as a feedstock, and solar or wind power. The report also analyses jobs creation from new hydrogen production facilities and shows that these benefits can be significant for large-scale facilities based on either future biomass/biogas-to-hydrogen or solar-hydrogen production technologies. Recommendations include setting stricter goals for the state’s Low Carbon Fuel Standard (LCFS) program to continue to reduce the carbon footprint of California’s transportation fuels.
