Institute of Transportation Studies at UC Berkeley

Recent studies suggest that there could be significant value to electric vehicle (EV) drivers and power companies from incorporating EVs into the state’s electrical power grids, known as Vehicle-Grid Integration (VGI). However, the benefits could be highly variable depending on the location of the utility territory, vehicle type and battery capacity, the relevant timeframe, and whether the connection involves only managed charging or includes bidirectional charging permitting vehicle to grid (V2G) power transfer, and other factors. Various studies conducted to date generally conclude that the opportunities for V2G could have two to three times the value of managed (or “smart”) charging. However, there are considerable additional complications for grid integration, including variable and site-specific implementation costs. Some savings such as deferring distribution system upgrades can be very significant but are also site-specific and depend on the level of curren and projected demands for electric power on the individual distribution feeder lines, and are therefore difficult to predict.

Bicycle level of service (BLOS) is an essential performance measure for transportation agencies to monitor and prioritize improvements to infrastructure, but existing measures do not capture the nuance of facility differences on the state highway system. However, with the advancements in virtual reality (VR) technology, a VR bicycle simulator is an ideal tool to safely gather user feedback on a variety of bicycling environments and conditions. This research explored the benefits and limitations of using a VR environment to assess individuals’ bike infrastructure preferences. We conducted a bicyclist user experience survey in person on SafeTREC’s VR bicycle simulator and online and compared the results. The online survey consisted of showing participants pairs of VR videos of biking scenarios and asking them to choose the one that they preferred. To validate the online survey responses, we conducted in-person experiments with a VR bike simulator using the same pairs of videos. Our analysis indicates that 63 percent of the responses were consistent while a smaller percentage of responses (37 percent) changed after the simulator ride due to better perception provided by the simulator virtual environment. The outcome of this study helped to validate the online survey responses of the study.

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.

The widespread adoption of electric vehicles (EV) is a centerpiece of California’s strategy to reach net-zero carbon emissions, but it is not fully known how and where EVs are being used, and how and where they are being charged. This report provides the first at-scale estimate of EV home charging. Previous estimates were based on conflicting surveys or extrapolated from a small, unrepresentative sample of households with dedicated EV meters. We combined billions of hourly electricity meter measurements with address-level EV registration records from California households, including roughly 40,000 EV owners. The average EV increases overall household load by 2.9 kilowatt-hours per day, well under half the amount assumed by state regulators. Results imply that EVs travel less than expected on electric power, raising questions about transportation electrification for climate policy.

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.

The escalating number of injuries and fatalities among cyclists is a pressing safety concern. In the United States, communities are actively seeking strategies to boost cyclist safety, with some states implementing bike-specific policies, such as stop-as-yield laws, to support cyclists. Stop-as-yield laws allow cyclists to treat stop signs as yield signs. The laws are not yet widely implemented, and their potential safety impact is a subject of debate among transportation experts and advocates. This study investigates how stop-as-yield laws can positively or negatively affect safety and provides insights and guidelines for California policymakers and safety practitioners if the law passes in California. We collected cyclist data from five states that have enacted stop-as-yield laws—Idaho, Arkansas, Oregon, Washington and Delaware—and data from some of their contiguous states without such legislation. Using an observational before-after study with comparison groups at the state level, the research examined changes in cyclist crash frequencies after the laws were implemented. Additionally, a random-effects negative binomial regression model with panel data was employed to estimate a law’s overall impact. The results did not indicate a significant change in cyclist crashes among the states with stop-as-yield laws.

In the United States, public transit agencies are increasingly growing interested in deploying open-loop payment systems for public transit fare payments. This interest is based on the benefits these systems can offer, from faster boarding times to the potential of attracting more riders.  Open-loop fare payment systems’ popularity is evidenced by the growing number of American public transit agencies who have deployed them; most of whom (63%) are located in California. The overlap between public transit riders who are both transit-dependent and financially excluded (i.e., have no or limited access to financial services) creates the opportunity for public transit agencies deploying open-loop payment systems to leverage these systems to increase financial service access for transit dependent, financially excluded riders. Individuals who are both transit-dependent and financially excluded are typically low-income, identify as part of a racial or ethnic minority group, immigrants, and/or women. As a result of these demographic characteristics, this work focuses on these populations. Additionally, financial inclusion, especially for these populations, is a critical step for economic and social mobility in the United States. This research focuses on California and explores how to leverage public transit agency deployment of open-loop payment systems to increase riders' financial service access. This research is comprised of a literature review, expert interviews (n=11), population needs mapping, and partnership proposals. In general, public transit agencies can strategically work with financial sector-based partners who focus on serving the transit agencies' priority rider groups. 

Suburban areas have lower density development than urban areas, which may make them less accessible for the growingpopulation of low- and moderate-income suburban residents, particularly those without a personal vehicle. This research examines factors that lead these households to move to suburban areas and identifies accessibility barriers they face. We use a mixed-methods approach with Public Use Microdata Sample (PUMS) data from the U.S. Census, online/in-person surveys (n=208), and interviews conducted in English and Spanish (n=25) with households in Contra Costa County with an income of less than $75,000. To understand key differences in housing and transportation choices between urban and suburban residents, these data were compared to survey and interview data from low-income Oakland residents from 2020-2021. We found that low- and moderate-income households choose to live in suburbs due to rising rents and otherrequirements (e.g., credit score, rental history) in urban areas, and a desire for home ownership and safer environment for children. Yet lack of tenant protections is leaving them vulnerable to rising rents in suburban areas. Transportation costs are higher in suburbs due to longer commutes and higher reliance on personal vehicles. Despite higher levels of carownership in the suburbs, households often go without a car due to maintenance issues or inability to make car payments. When faced with the lack of an automobile, suburban households have few quality transportation alternatives.

Located within the seismically active Pacific Ring of Fire, California's transportation infrastructure, especially in the Bay Area, is susceptible to earthquakes. A review of current research and stakeholder interviews revealed a growing awareness of emergency preparedness among local jurisdictions and transit agencies in recent years. However, many have yet to formalize and publish their recovery plans. This study introduces an agent-based multimodal transportation simulation tool to enhance post-earthquake transportation resilience. Integrating a road network simulator with a metro system simulator, the tool employs an optimized Dijkstra-based algorithm to calculate optimal routes, travel times, and fares. A case study is conducted for the East Bay, using the simulator to gauge the impact of a compromised Bay Area Rapid Transit (BART) system. The results suggested that original BART passengers could face either longer commute times or higher costs during the recovery phase of a major earthquake without appropriate policies. Such outcomes could disproportionately burden low-income riders, affecting their mobility and overall travel time.

As jurisdictions update their High Injury Networks, discrepancies between the initial and updated HINs are to be expected. However, this lack of stability and consistency can negatively impact the prioritization of limited resources. In order to mitigate known issues with crash data underreporting and statistical biases, I examined strategies for utilizing data on underlying roadway characteristics to augment traditional collision analysis.

Using the City of Oakland as a case study city, I assessed the stability of the pedestrian High Injury Network across two consecutive five-year periods (2012-2016 and 2017-2021), created with the same methodology. I found that the two HINs identified similar segments, particularly along arterials, but were less consistent in identifying the segments’ start and end points due to variation in crash data. I propose a methodology for finalizing High Injury Network extents based on segment characteristics (number of lanes, posted speed limit, and functional classification), and intersection characteristics (traffic signal presence and estimated pedestrian volumes). Applied to the Oakland case study, this approach results in a High Injury Network that is more stable over time, more focused (fewer street miles), and captures a higher percentage of fatal and severe crashes. This approach has the potential to smooth over inconsistencies in crash reporting, reduce the frequency of network updates needed, and shift High Injury Networks from being reactive to more proactive.