research report

Creating an Inclusive Bicycle Level of Service: Virtual Bicycle Simulator Study

Publication Date

February 1, 2025

Author(s)

Julia Griswold, Edna Aguilar, Han Wang, Md Mintu Miah

Abstract

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. The research team 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, the team conducted in-person experiments with a VR bike simulator using the same pairs of videos. The 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.

research report

Capturing Transit Rider Perspectives on Safety and Harassment: Lessons from San Francisco

Abstract

Personal safety concerns continue to be one of the most critical issues among transit riders and women and gender minorities in particular. These safety concerns stem from the experience of sexual harassment that people who identify as women face frequently. While harassment can be a common occurrence, the vast majority of these experiences go unreported to transit agencies, leaving agencies without information about the magnitude of this problem on their system. This report details work with the San Francisco Municipal Transportation Agency (SFMTA) in their efforts to understand and address this problem. The SFMTA, working with two UCLA graduate students, designed a survey that drew from previous survey efforts and was tailored to address their interests and needs. This report documents the process of developing and deploying the questionnaire, in an effort to help other agencies take the first steps to better understanding rider safety and harassment. Through breaking down SFMTA’s approach, this report intends to inspire and inform similar efforts at other agencies.

policy brief

Battery Electric Trucks Are Well-Suited for Regional Haul Operations and Offer Significant Environmental and Health Benefits to Communities

Abstract

Heavy-duty diesel trucks contribute significantly to air pollution and greenhouse gas emissions, particularly in regions with high freight activity, such as Southern California. In communities near freight hubs, this has resulted in severe public health challenges. California is leading efforts to address these emissions, with regulations requiring zero-emission trucks by 2045. Battery electric trucks (BETs) are a promising solution, but they have primarily been deployed in limited use cases like drayage operations. As BET technology improves, understanding their real-world performance in regional haul applications is critical to expanding their adoption. Regional haul applications differ from other trucking operations in that they typically involve medium-distance routes, often under 150 miles, with trucks returning to a home base daily for charging. While regional haul does not account for the majority of truck miles in California, it represents a significant and growing segment of freight operations, particularly in densely populated areas where emission reductions can have the greatest impact on air quality and public health. To better understand BET performance, this study examined the real-world activity of 15 BETs operating in eight regional distribution fleets across Southern California. It analyzed the trucks’ travel and charging patterns, as well as how much of their operations occurred in disadvantaged communities.

published journal article

A Deep-Learning Approach to Detect and Classify Heavy-Duty Trucks in Satellite Images

Abstract

Heavy-duty trucks serve as the backbone of the supply chain and have a tremendous effect on the economy. However, they severely impact the environment and public health. This study presents a novel truck detection framework by combining satellite imagery with Geographic Information System (GIS)-based OpenStreetMap data to capture the distribution of heavy-duty trucks and shipping containers in both on-road and off-road locations with extensive spatial coverage. The framework involves modifying the CenterNet detection algorithm to detect randomly oriented trucks in satellite images and enhancing the model through ensembling with Mask RCNN, a segmentation-based algorithm. GIS information refines and improves the model’s prediction results. Applied to part of Southern California, including the Port of Los Angeles and Long Beach, the framework helps assess the environmental impact of heavy-duty trucks in port-adjacent communities and understand truck density patterns along major freight corridors. This research has implications for policy, practice, and future research.

research report

Road Capacity as a Fundamental Determinant of Vehicle Travel

Abstract

Reducing vehicle miles traveled (VMT) is a central plank of climate policy in California. VMT, however, has proved stubbornly resistant to policies to reduce it. While urban growth has become more compact and public transit service levels have been maintained or increased, these positive trends have not translated into less driving. This report argues that substantial reductions in vehicle travel in congested urban regions can only be achieved through reducing road capacity. It may be difficult to achieve substantial reductions in vehicle travel by relying solely on public transit, walking and cycling, and land use planning for compact, mixed-use development without an equal emphasis on limiting road capacity expansions, and even reducing current capacity.

policy brief

Automobile Debt Increased Substantially during the Pandemic

Abstract

Most car buyers use some form of financing to purchase a vehicle, and almost half of all California borrowers carry some amount of automobile debt. While automobile loans enable lower-income households—who might otherwise be priced out of vehicle ownership—to make payments over time, this debt can significantly strain household budgets. The COVID-19 pandemic elevated the importance of owning a private vehicle as concerns over viral person-to-person transmission made traveling by car an even more attractive compared to communal transportation (e.g., public transit). Moreover, a host of pandemic-related services, including testing and vaccination, were either only or best accessible by car.

To better understand how COVID-19 impacted car ownership, this research project explored whether automobile loans (and in turn debt) in California—particularly in communities of color where workers were more likely to work outside of the home—increased during the pandemic. It drew on a one-percent sample of the University of California Consumer Credit Panel, a dataset from Experian of every loan and borrower in California.

policy brief

Road Expansion is the Fundamental Cause of Growth in Vehicle Travel

Abstract

California is unlikely to meet its climate goals if it doesn’t reduce vehicle travel. So far, however, state and local efforts to reduce vehicle miles traveled (VMT) have fallen short of expectations, even as cities grow more compact and public transit funding has increased. To better understand the role of highway expansion in meeting California’s climate goals, this study analyzed whether a simple model that only considers road capacity and population growth can predict VMT as well as traditional transportation models. It also looked at the share of recent VMT growth that has been caused by expanded road capacity, and the reductions in VMT from transit and other projects funded by California’s climate investments.

research report

Changes in Activity-Travel Patterns and Vehicle Ownership During the COVID-19 Pandemic in California

Publication Date

October 1, 2024

Author(s)

Giovanni Circella, Xiatian Iogansen, Grant Matson, Keita Makino, Yongsung Lee

Abstract

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. 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. There was a major decrease in walking for commuting purposes observed 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.

published journal article

Impact of Debris Removal Post-Wildfires on Pavement Fatigue and Rutting Lives: Case Studies of California’s Camp and Carr Fires

Abstract

Between 2017 and 2018, California experienced a series of four devastating fires, including the Camp and Carr Fires, which ranked among the most destructive fires in U.S. history. During these fires, roads were critical in the evacuation, rescue operations, goods transportation, and access to critical services. Additionally, postfire, road infrastructure became crucial for removing hazardous and nonhazardous waste from fire-affected areas to major landfills and recycling facilities. Despite the significance of pavements in this process, previous studies have not quantitatively assessed the potential damage caused to pavements by the additional trucks used in debris removal operations. This research aimed to address this knowledge gap by collecting precise traffic data for the routes taken to waste management facilities, including data on the number of trips involved in debris transportation. The traffic information was then utilized to calculate changes in equivalent single axle loads and traffic index values for pavement design. Pavement structures were obtained from the available core database. Pavement simulation results showed that of the nine studied highways, only one exhibited a reduction in cracking life of about 2 years. However, Skyway, the main artery in the town of Paradise, demonstrated a significantly accelerated fatigue cracking failure by 14.3 years. A sensitivity analysis of fire intensity showed other highway sections that were structurally adequate could be affected by larger fires. The presented methodology could be used in traffic planning as part of debris management operations to avoid vulnerable pavement sections.

policy brief

How Cooperation Between Connected Automated Vehicles and Smart Infrastructure Can Improve Situational Awareness for Traffic Safety

Publication Date

February 1, 2025

Author(s)

Xin Xia, Jiaqi Ma, Zhaoliang Zheng, Yunpeng Luo, Fayzah Alshammari, Letian Gao, Hao Xiang, Alfred Chen

Abstract

Escalating trends in pedestrian and cyclist fatalities points to a pressing need to improve traffic safety, especially for vulnerable road users such as pedestrians, cyclists, and scooters. A key challenge in enhancing intersection safety is the lack of accurate, detailed, and real-time data that captures the complexities of these dynamic and uncertain environment. If intersections themselves could “see” the diverse array of vehicles, pedestrians, cyclists, and scooters, each with unique movement patterns and safety needs, this could vastly improve safety. Making intersections “smart” by equipping them with Light Detection and Ranging (LiDAR) technology that can capture a detailed and real-time 3D environment could facilitate the accurate detection of vehicles and other road users, to better control signal timing and assist future connected vehicles (CVs) and/or connected automated vehicles (CAVs) in driving safely.