University of California Institute of Transportation Studies

Traffic signals, while serving an important function to coordinate vehicle movements through intersections, also cause frequent stops and delays, particularly when they are not properly timed. Such stops and delays contribute to significant amount of fuel consumption and greenhouse gas emissions. The recent development of connected and automated vehicle (CAV) technology provides new opportunities to enable better control of vehicles and intersections, that in turn reduces fuel consumption and emissions. In this paper, we propose platoon-trajectory-optimization (PTO) to minimize the total fuel consumption of a CAV platoon through a signalized intersection. In this approach, all CAVs in one platoon are considered as a whole, that is, all other CAVs follow the trajectory of the leading one with a time delay and minimum safety gap, which is enabled by vehicle to vehicle communication. Moreover, the leading CAV in the platoon learns of the signal timing plan just after it enters the approach segment through vehicle to infrastructure communication. We compare our PTO control with the other two controls, in which the leading vehicle adopts the optimal trajectory (LTO) or drive with maximum speed (AT), respectively, and the other vehicles follow the leading vehicle with a simplified Gipps’ car-following model. Furthermore, we extend the controls into multiple platoons by considering the interactions between the two platoons. The numerical results demonstrate that PTO has better performance than LTO and AT, particularly when CAVs have enough space and travel time to smooth their trajectories. The reduction of travel time and fuel consumption can be as high as 40% and 30% on average, respectively, in the studied cases, which shows the great potential of CAV technology in reducing congestion and negative environmental impact of automobile transportation.

Little is known about demand for EVs in the mass market. In this paper, we exploit a natural experiment that provides variation in large EV subsidies targeted at low- and middle-income households in California. Using transaction-level data, we estimate two important policy parameters using triple differences: the subsidy elasticity of demand for EVs and the rate of subsidy pass-through. Estimates show that demand for EVs amongst low- and middle-income households is price-elastic and pass-through is complete. We use these estimates to calculate the expected subsidy bill required for California to reach its goal of 1.5 million EVsby 2025.

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.

COVID-19 altered travel patterns in the U.S. Studies have analyzed the effect of the pandemic on travel mode, including working from home, but few have focused on automobile ownership—a relationship with potentially long-term consequences for accessibility, household budgets and debt, and policy efforts to meet climate goals.To understand the association between the pandemic and automobile ownership, we rely on a unique credit panel dataset from Experian and examine three different automobile loan-related outcome measures: annualized growth rate of new automobile loan balances, average new loan size, and the number of new loans. We focus specifically on changes across loans in neighborhoods by race/ethnicity, hypothesizing larger increases in automobile debt in Black and Latino/a neighborhoods, where workers are less likely to be able to telework. The annualized growth rate of new automobile loans increased during the pandemic across all neighborhoods by race/ethnicity, increasing most rapidly in Latino/a neighborhoods. Controlling for other factors, loan size increased similarly across neighborhoods by race/ethnicity. The increase in automobile lending in Latino/a neighborhoods, therefore, likely was explained by a significant uptick in the number of new loans.The growth in automobile lending during the pandemic was potentially prompted by pandemic-induced changes in the need for automobiles and facilitated by an expanded social safety net. As the pandemic and its various forms of public financial assistance recede, the findings underscore the importance of ongoing assistance in enabling automobile ownership or shared access among households with limited means whose livelihoods depend on the access that vehicles provide.

Daily vehicle travel collapsed with the onset of the COVID-19 pandemic in early 2020 but largely bounced back by late 2021. The pandemic caused dramatic changes to working, schooling, shopping, and leisure activities, and to the travel associated with them. Several of these changes have so far proven enduring. So, while overall vehicle travel had largely returned to pre-pandemic levels by late 2021, the underlying drivers of this travel have likely changed.

To examine one element of this issue, we analyzed whether patterns of daily trip-making shifted temporally between the fall of 2019 and 2021 in the Greater Los Angeles megaregion. We used location-based service data to examine vehicle trip originations for each hour of the day at the U.S. census block group level in October 2019 and October 2021. We observed notable shifts in the timing of post-pandemic PM peak travel, so we examined changes in the ratio of mid-week trips originating in the early afternoon (12–3:59 PM) and the late afternoon/early evening (4–7:59 PM).

We found a clear shift in the temporal distribution of PM trip-making, with relatively more late PM peak period trip-making prior to the pandemic, and more early PM peak trip-making in 2021. The peak afternoon/evening trip-making hour shifted from 5–5:59 PM to 3–3:59 PM. We also found that afternoon/evening trip-making in each year is largely explained by three workplace-area/school-area factors: (1) the number of schoolchildren in a block group (earlier); (2) block groups with large shares of potential remote workers (earlier), and (3) block groups with large shares of low-wage jobs and workers of color (later, except for Black workers in 2021). We found the earlier shift in PM peak travel between pre- and late-pandemic periods to be explained most by (1) higher shares of potential remote workers and (2) higher shares of low-wage jobs and workers of color. These findings suggest that the rise of working from home has likely led to a shift in PM peak travel earlier in the afternoon when school chauffeuring trips are most common. This is especially true for low-income workers and workers of color.

Throughout the ongoing COVID-19 pandemic, changes in daily activity-travel routines and time-use behavior, including the widespread adoption of telecommuting, have been manifold. This study considers how telecommuters have responded to the changes in activity-travel scheduling and time allocation. In particular, we consider how workers utilized time during the pandemic by comparing workers who telecommuted with workers who continued to commute. Commuters were segmented into those who worked in telecommutable jobs (potential telecommuters) and those who did not (commuters). Our empirical analysis suggested that telecommuters exhibited distinct activity participation and time use patterns from the commuter groups. It also supported the basic hypothesis that telecommuters were more engaged with in-home versus out-of-home activity compared to potential telecommuters and commuters. In terms of activity time-use, telecommuters spent less time on work activity but more time on caring for household members, household chores, eating, socializing and recreation activities than their counterparts. During weekdays, a majority of telecommuters did not travel and in general this group made fewer trips per day compared to the other two groups. Compared to telecommuters, potential telecommuters made more trips on both weekdays and weekends while non-telecommutable workers made more trips only on weekdays. The findings of this study provide initial insights on time-use and the associated activity-travel behavior of both telecommuter and commuter groups during the pandemic.

Vehicle automation represents a new safety frontier that may necessitate a repositioning of our safety oversight systems. This white paper serves as a primer on the technical and legal landscape of automated driving system (ADS) safety. It introduces the latest AI and machine learning techniques that enable ADS functionality. The paper also explores the definitions of safety from the perspectives of standards-setting organizations, federal and state regulations, and legal disciplines. The paper identifies key policy options building on topics raised in the White House’s Blueprint for an AI Bill of Rights, outlining a Blueprint for ADS safety. The analysis concludes that potential ADS safety reforms might include either reform of the Federal Motor Vehicle Safety Standards (FMVSS), or a more holistic risk analysis “safety case” approach. The analysis also looks at caselaw on liability in robotics, as well as judicial activity on consumer and commercial privacy, recognizing that the era of AI will reshape liability frameworks, and data collection must carefully consider how to build in accountability and protect the privacy of consumers and organizations. Lastly, this analysis highlights the need for policies addressing human-machine interaction issues, focusing on guidelines for safety drivers and remote operators. In conclusion, this paper reflects on the need for collaboration among engineers, policy experts, and legal scholars to develop a comprehensive Blueprint for ADS safety and highlights opportunities for future research.