UCLA Institute of Transportation Studies

Most U.S. metropolitan areas developed alongside the automobile, producing neighborhoods of relatively low density. Consequently, access to opportunities in these neighborhoods is predicated on having an automobile, yet many households do not have the resources to purchase one outright, relying on automobile loans to spread out the purchase price. While automobile loans can enable automobile ownership, they also significantly increase the vehicle purchase price, particularly for non-white consumers subject to discriminatory lending practices.In this study, we rely on data from the University of California Consumer Credit Panel from Experian to examine the determinants and geography of automobile debt and its consequences in California, testing whether various automobile debt measures disproportionately affect non-white neighborhoods.We find that, controlling for other factors associated with automobile lending including income, Black and Latino/a neighborhoods have higher total automobile debt, debt burdens (debt relative to income), and automobile loan delinquency rates. In particular, Latino/a neighborhoods shoulder significant automobile debt, while borrowers in Black neighborhoods have the highest delinquency rates. Factors associated with lower total automobile debt and automobile debt burden include better credit ratings, higher residential densities, urban locations, and proximity to rail stations.The findings underscore the importance of policies to offset the costs of automobile ownership and access. As part of this, policymakers should adopt and enforce fair lending rules to combat discriminatory and predatory practices and facilitate access to high-quality financial institutions and products in communities of color.

Problem, research strategy, and findings

Covid-19 significantly altered work, out-of-home activity participation, and travel, with much activity time being moved into the home. If these patterns hold, they could imply significant long-term changes for homes, businesses, cities, and transportation. We examined data for 34,000 respondents to the American Time Use Survey from 2019 (the pre-pandemic period), 2021 (the pandemic period), and 2022 and 2023 (the post-pandemic period). We used ordinary least squares (OLS) regressions to study participation in 12 out-of-home activities, travel (by auto, transit, and walking), and 16 in-home activities. We observed sharp declines in overall out-of-home activity, travel by all modes, and 10 of the 12 specific out-of-home activities in 2021 compared with 2019, whereas time spent on 13 of the 16 in-home activities rose during that period. By 2023, most of these changes persisted: Time spent out-of-home, traveling by all modes, and on six out-of-home activities remained notably lower in 2023 than in 2019, whereas time spent on nine in-home activities remained higher. The trend away from out-of-home activities and travel appears to be persisting.

Takeaways for practice

First, given elevated remote work and shopping, planners should consider repurposing some office and retail land uses. Second, with fewer office workers, center cities may have to capitalize on other strengths such as recreational and residential desirability for some market segments, such as young people or others who prefer urban living. Third, more time at home may increase demand for more spacious and affordable housing, perhaps in lower-cost outlying suburbs of large metros and in smaller metropolitan areas. Finally, an end to ever-rising personal travel may lessen the need for costly interventions to increase the capacity of highway and transportation systems.

On October 13 – 15, 2024, nearly 170 representatives of government, private sector consulting frms and companies, non-proft and advocacy groups, and universities joined the 2024 UCLA Lake Arrowhead Symposium on Mega Events, Major Opportunities. This report summarizes the discussions, lessons learned, and action items from the convening.

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.

More than 500,000 people experience homelessness in the United States, and many turn to transit vehicles, stops, and stations for shelter. We present findings from a survey of 115 U.S. and Canadian transit operators that inquired about homelessness on transit systems. We find that homelessness is broadly present, though more concentrated on central hotspots, and worsened during the pandemic. In response, transit agencies often initiate a combination of punitive and outreach strategies. Based on our findings, we argue for better data collection, establishment of policies and protocols, engagement in outreach strategies, and partnering with service providers.

Many methods exist for engaging experts in interactive groups to explore, clarify, and/or decide on various issues. In an investigation of four possible future scenarios concerning transportation and land use in California, we developed a novel “hybrid policy Delphi” method for use with a panel of 18 experts. We applied it to explore the policies and practices that would likely lead to each of the four scenarios and the consequences that would result from them. Through our process, panel members discussed and reflected on the scenarios in multiple ways. The scenario they considered most desirable they also deemed least likely to occur, and they foresaw the likely trajectory of California transportation and land use leading to less desirable scenarios. Our mix of discussion and questionnaires traded the benefit of anonymity for the benefit of exploratory, interactive discussion. In addition, our use of surveys before and after meetings allowed us to track changes in panel opinion on a central question and discuss the survey results at meetings, at the cost of greater administrative effort. We discuss the results of this hybrid policy Delphi approach, reflect on how it worked, and conclude with a discussion of limitations and future directions.

U.S. freeways have come under scrutiny for their adverse impacts on low-income neighborhoods of color, primarily in urban centers. This article offers a comparative historical analysis of the impact of freeways on two communities in Southern California, which were ethnically diverse suburbs. Planning authorities in Pasadena and Pacoima chose freeway routes that displaced a greater share of households of color than the proposed alternatives. Meanwhile, neighboring white, wealthier communities successfully influenced routing decisions in consequential ways. Beyond the visible and immediate effects of the freeways, social inequity and environmental degradation persist in both neighborhoods today.

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.

Cities nationwide have adopted so-called road diets to improve traffic safety, though they are sometimes met with intense opposition from motorists who fear that road diets will increase traffic delays. Road diets typically convert four-lane roadways with no left-turn lanes into streets with a center left-turn lane, two through-traffic lanes, and (often) bicycle lanes and right turn pockets at intersections. The resulting safety improvements are often dramatic. The Federal Highway Administration currently recommends that road diets should be applied to roadways with fewer than 20,000 average daily trips, but that cities should carefully consider whether to apply road diets above 20,000 average daily traffic (ADT). However, study of higher-traffic-volume road diets to inform decisions about them has been limited. In particular, there is scant evidence that safety benefits erode and traffic delays increase meaningfully above this threshold, though this is implied by the 20,000-ADT threshold. To address this literature gap, we examined the safety and traffic outcomes of high-traffic-volume road diets in Los Angeles, CA. To do this, we compared collisions on five high-traffic-volume road diet corridors with 16 similar multilane, untreated street segments. We found that collisions, injuries, and deaths were lower by 31.2% to 100%, depending on the measure, whereas traffic speeds were lower by about 6.7% (peak) to 7.9% (off-peak). We concluded that in Los Angeles higher-traffic-volume road diets appeared to significantly increase safety with only minor effects on traffic speeds.

Headway, namely the distance between vehicles, is a key design factor for ensuring the safe operation of autonomous driving systems. There have been studies on headway optimization based on the speeds of leading and trailing vehicles, assuming perfect sensing capabilities. In practical scenarios, however, sensing errors are inevitable, calling for a more robust headway design to mitigate the risk of collision. Undoubtedly, augmenting the safety distance would reduce traffic throughput, highlighting the need for headway design to incorporate both sensing errors and risk tolerance models. In addition, prioritizing group safety over individual safety is often deemed unacceptable because no driver should sacrifice their safety for the safety of others. In this study, we propose a multi-objective optimization framework that examines the impact of sensing errors on both traffic throughput and the fairness of safety among vehicles. The proposed framework provides a solution to determine the Pareto frontier for traffic throughput and vehicle safety. ComDrive, a communication-based autonomous driving simulation platform, is developed to validate the proposed approach. Extensive experiments demonstrate that the proposed approach outperforms existing baselines.