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policy brief

How California Cities Respond to State-level Parking Reform

Abstract

Minimum parking requirements—zoning regulations that require a certain number of parking spaces to be built with new developments—come with a long list of downsides. The requirements increase the costs of development, reduce housing densities, subsidize car ownership, reduce walkability, and make it difficult to adapt and reuse historic buildings. In response, cities as diverse as Anchorage, Buffalo, and San Diego have reduced or eliminated parking requirements in recent years.

In 2022, California became the first state to eliminate parking requirements in certain neighborhoods. Assembly Bill 2097 (AB 2097) prohibits, in most circumstances, local governments from imposing parking requirements within a half-mile of an existing or planned major transit stop such as a rail station, ferry terminal, or the intersection of frequent bus routes. The research team examined how cities are responding to this new statewide law and draw out lessons for parking policy as well as other types of state preemption of local land use regulations.

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other

2026 Chester Rapkin Award

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published journal article

Improving Transit System Resilience using Shared Autonomous Vehicles (SAVs)

Abstract

This study investigates the use of shared autonomous vehicles (SAVs) to enhance the resilience of fixedroute transit systems. This study focuses on the strategic deployment of SAVs during disruptive events to serve stranded transit passengers alongside regular SAV travelers. The study begins by detailing the Santa Clara Valley Transportation Authority (VTA) Light Rail Transit (LRT) system, highlighting its capacities, challenges, and the potential improvements offered by SAVs. The research team presents three disruptive scenarios. In the first scenario, a temporary transit disruption caused by an earthquake prompts the deployment of SAVs to transport stranded passengers to their homes or designated drop-off locations. The second scenario assumes a short-term transit operation disruption due to an earthquake in which SAVs transport stranded passengers to their homes or designated SAV drop-off locations. Third scenario examines a complete, long-term transit shutdown resulting from a safety/security issue, such as a mass shooting-induced multi-month system closure. The research team proposes and deploy an agent-based model to test various SAV deployment strategies in these three scenarios to understand the improvement of performance metrics such as traveler wait time and delay in the disrupted transit system. The results provide a concrete demonstration of the promising role of SAVs during transit system disruption. Through the disruption scenarios, the research provides insights into the performance under various fleet sizes and their availability of use. This study provides a valuable analysis for urban planners, transportation authorities, and policymakers interested in leveraging shared mobility and automation technologies for improved urban mobility resilience.

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published journal article

No Car, No Choice: Transportation Resources and School Choice in California

Abstract

School choice offers families an opportunity for children to attend schools other than those they are zoned to by residential location. Most families live beyond walking distance to choice schools, so vehicular transportation is needed to attend. Yet, most choice programs have not been designed with such access in mind, and most school bus programs are designed only for neighborhood schools. Such diminished access for children in households without cars presents an equity issue. This study examines the relationship between household transportation resources—automobiles, transit, and walkability—and the likelihood that children attend a choice school. The team matched data from the 2017 National Household Travel Survey California Add-On with school characteristics to identify the school each respondent ages 5–17 attends and the school to which school districts zoned them. A model was then fit that predicts choice of school type: neighborhood zone public, choice public, or private. Controlling for student, household, transportation, neighborhood, and assigned zone school characteristics, a student in a household with at least one vehicle had more than double the predicted probability of attending a choice public school compared with a student in a zero-vehicle household. The difference in predicted probabilities grew for students who were low-income, non-white, or zoned to low-performing schools. Living in neighborhoods with high-quality public transit access or high walkability did not affect the probabilities. These findings underscore the importance of transportation resources in enabling families to send their children to schools that best fit their needs.

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published journal article

Transportation and Quality-of-Life Effects of Los Angeles’ Mobility Wallet Program

Abstract

Transportation agencies have historically focused on public transit as the solution to the mobility barriers facing low-income travelers. Transit is an essential service for those who do not own automobiles, but studies show it often fails to meet the travel needs of the poor. The Los Angeles (CA) Mobility Wallet Pilot Program is a novel approach to addressing this issue. The program provided low-income recipients with resources to spend flexibly on a variety of shared transportation services. In this study, the research draws on interviews with 31 participants before, during, and after the 1-year pilot program to understand the effect of the mobility wallet on their travel behavior and quality of life. The findings are promising: increased travel frequency, decreased material hardship and stress, improved social connections, feelings of independence and autonomy, and improved wellbeing. The results suggest an important role for flexible, demand-side transportation subsidies in meeting the diverse transportation needs of low-income travelers.
When seeking to achieve transportation equity, planners should recognize the value in adopting policies and programs, such as a mobility wallet, that allow travelers to take advantage of a variety of modes to meet their individual and household needs.

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research report

Automated Vehicle Safety: Developing a Human-Autonomy Team Model – Findings from Driving Simulator Study

Publication Date

October 1, 2026

Author(s)

Camila Correa Jullian, Mollie Cohen D'Agostino, Ali Mosleh, Jiaqi Ma

Abstract

This study develops a conceptual driver-system performance model within the context of Human Reliability Analysis (HRA), using data from controlled driving simulator experiments involving takeover scenarios in vehicles equipped with Level 3 Automated Driving Systems (ADSs). Driving simulation-based experiments, collecting both objective and subjective data, informing the model’s development and quantification, with a focus on operation and takeover performance. These experiments examined how warning availability and traffic complexity affect driver takeover performance during automated driving failures. Fifty participants experienced ADS-initiated tailgating events across four warning/traffic conditions in a driving simulator. Warnings significantly reduced collision probability and improved driver reaction times and time-to-collision. Traffic complexity increased hard-braking, tailgating, and steering variability, and elevated workload and post-exposure distrust of the automation. A significant interaction between warning availability and traffic level was observed for collision probability. These initial findings indicate warnings provide measurable safety benefits, and will inform the calibration of the model, including warning design and timing, in future work.

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published journal article

California Transportation Planning Tool for Statewide Data-Driven Optimization

Abstract

Estimating travel behavior parameters is essential for understanding current travel patterns and for forecasting how travelers may respond to future changes in transportation systems. However, reliable estimation remains difficult when behavior must be inferred from sparse and aggregate observations, especially when jointly recovering several behavioral components. In this paper, the research team developed a bi-level estimation program for a combined destination, mode and route choice model with congestion. The inner level is a convex program at given scale parameters, and returns the taste coefficients as its dual variables together with the equilibrium travel pattern. The outer level selects the scale parameters by out-of-sample prediction, measured by how well the modeled travel pattern predicts observations withheld from estimation. To solve the bi-level problem at scale, the research team proposed a matrix-free Hierarchical Lagrangian Dual algorithm as the inner solver and an outer search by implicit differentiation, which uses the sensitivities of the inner solution to enable efficient search over the scale parameters and reaches the calibrated scales in a handful of iterations. The research team validated the approach on Los Angeles County using public origin-destination, mode-share and traffic-count data. The approach scales to a county-level problem with 2.7 million variables and substantially improves computational efficiency over a general-purpose conic solver. Compared with the fixed-scale benchmark, the calibrated model improves predictive performance at the destination and mode levels, with consistent gains across complementary goodness-of-fit measures.

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published journal article

Co-designing a Bicycle Lending Library for Lower-income Communities in California

Abstract

Commercial bikeshare systems have expanded urban mobility but continue to present structural barriers for low-income and minority communities. This study examines how design attributes, sociodemographic characteristics, and perceived community strength shapes residents’ likelihood of adopting a Bike Lending Library (BLL) in lower-income neighborhoods across California. One such community at the heart of this study is Santa Ana, a densely populated and culturally vibrant urban center with majority Latino/Hispanic community located in Orange County. The study found lock provision, bike weight, and cost are strong determinants of BLL rental choice among low-income Californians. The ICLV model reveals that ‘perceived community strength’, operationalized through five indicators of civic engagement and organizational capacity, has a statistically significant positive effect on BLL adoption. Male respondents showed significantly higher rental likelihood while low-income households showed significantly lower likelihood. Thematic analysis identified cross-cutting themes of safety and infrastructure, program design, affordability, community motivation, and program enthusiasm that converged across all data sources. This study provides one of the first applications of the ICLV framework to a community-based bike lending program, revealing that social and psychological dimensions of community life may be meaningful predictors of shared mobility adoption in disadvantaged urban contexts. Findings produce actionable recommendations for BLL program design, siting, pricing, and outreach, and offer a replicable mixed-methods framework for community-centered active transportation planning.

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research report

GHG Emissions Reductions from CRP Projects

Publication Date

September 1, 2026

Author(s)

Qiao Yu, Junmei Cheng, Taylor DeRouen, Jiaqi Ma, Yifang Zhu

Abstract

The Carbon Reduction Program (CRP) requires transportation projects to demonstrate measurable reductions in transportation-related greenhouse gas (GHG) emissions, but the impact of priced managed lanes and related roadway pricing projects on emissions can vary. Thus, there is a need for a transparent framework that can place evidence within a common statewide emissions metric. This report develops a screening framework to translate project-level GHG impact data from CRP-relevant projects in California into a wider statewide framework for emissions accounting. The preliminary framework separates the direct effects of a priced managed lane project on roadway travel demand from its indirect effects. In a case study analysis, the I-580 managed project lane is predicted to directly increase VMT and CO2 emissions through induced travel demand. However, reinvestment of the lane’s revenue in regional transit is projected to reduce VMT and CO2e
emissions. In conclusion, a CRP GHG accounting should evaluate direct roadway effects and reinvestment effects separately before integrating them within a common statewide emissions accounting framework.

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published journal article

Decarbonizing Medium- and Heavy-Duty Freight Networks: ALNS-VRP-Driven Charging Infrastructure Deployment for Zero-Emission Trucks in California

Abstract

California freight electrification depends as much on charging access and corridor planning as on vehicle adoption. Public agencies and fleets need evidence on whether depot and public charging networks can support truckload operations without increasing empty repositioning, modeled
charging-stop requirements, and route infeasibility. This study develops an operations-based freight charging assessment for mixed diesel and battery-electric truck operations and applies it to controlled California truckload scenarios. Rather than optimizing final station sites, the framework evaluates predefined depot-adjacent and public fast-charging sites through route feasibility, total and deadhead VMT, modeled charging stop rows, active charging locations, and cost-accounting indicators. The results are scenario-level route-record comparisons and do not reflect observed daily truck operations or session-level charging dwell measurements.