This research presents a risk-informed framework to derive metrics characterizing and tracking the safety performance of heavy-duty automated vehicles (HD-AVs) operations. A combination of traditional and novel hazard identification methods are leveraged to study potential human-system interactions in HD-AVs operations, ranging from safety drivers to remote operators.
In the San Francisco Bay Area, earthquake damage to a small number of critical transportation facilities can produce regionwide mobility impacts. This report evaluates the Berkeley Hills Tunnel, a key Bay Area Rapid Transit (BART) cross-hills connection, using an integrated model that links tunnel seismic performance to transportation consequences during disruption and recovery. The model couples scenario-based seismic assessment of the tunnel with screening-level fragility for substitute corridors and propagates the resulting time-dependent capacity states through a transportation simulation. At a representative recovery-stage demand of 25 percent of normal, mean cross-hills travel time rises from about 9 minutes in the mildest scenario (327-year return period) to about 41 minutes in the most severe (654-year). In that severe case, a fully subscribed bus bridge can lower the mean cross-hills travel time by up to 30 percent. Uncertainty quantification identifies recovery-stage demand as the dominant source of travel-time variance and shows that unfavorable capacity combinations can nearly double travel time relative to the central estimate. Disruption falls disproportionately on transit-dependent travelers, who either endure longer travel times or pay an extra travel fee. Agencies can use the workflow to assess disruption impacts and identify likely bottlenecks, compare recovery-stage operational strategies, and target equitable service for affected populations.
Airports play a critical role in emergency response and disaster recovery, yet are highly vulnerable to earthquakes. Past earthquakes in California, including the 1989 Loma Prieta, 1994 Northridge, and 2014 South Napa events, caused infrastructure damage, power outages, and operational delays at airports, affecting both safety and mobility. As seismic risk remains high across the state, improving airport resilience is critical to maintaining transportation access and supporting emergency operations.Earthquake Early Warning (EEW) systems offer a way to reduce these risks by providing seconds to tens of seconds of advance notice before strong shaking begins. While this technology has been around for several decades across the world, the development of the ShakeAlert system has brought it to the West Coast recently, giving airports in the region the chance to be early adopters of the technology in the United States. To better understand how EEW could be used in airport settings, we examined its operational applications, benefits, and implementation challenges across different types of airports.
The safe deployment of autonomous vehicles (AVs) depends on the ability of automated driving systems (ADS) to handle rare, complex, and safety-critical “edge cases.” This study develops a novel framework for identifying and analyzing such scenarios using the National Highway Traffic Safety Administration (NHTSA) ADS crash dataset. Two complementary approaches are applied. First, large language models (LLMs) are used to directly analyze crash narratives, identifying edge cases through high-risk keyword and phrase detection and anomaly-based rarity analysis. Second, LLMs are employed to extract structured variables from narrative fields, which are then analyzed using hierarchical clustering to systematically isolate unusual crash groups. Edge cases are characterized by a higher prevalence of unusual crash partner behaviors, non-motorist involvement, roadway anomalies, and disengagement of AV systems, highlighting their distinct and atypical nature. The findings underscore the importance of focusing AV evaluation on rare, high-risk scenarios that challenge ADS performance. The study advances AV safety research and can provide a foundation for refining testing protocols, safety standards, and regulatory frameworks to better capture the operational limits of AVs.
This study examines the relationship between levels of access to zero-emission vehicles (ZEVs) at car dealerships in California and rates of ZEV adoption in surrounding areas. ZEV accessibility scores for census tracts—based on available ZEV inventory and proximity to dealerships—correlated significantly with ZEV adoption rates, even after controlling for income, demographics, charging infrastructure, and consumer incentives. Our results suggest that a 1% increase in ZEV accessibility correlated with an increase in ZEV adoption by 0.73% in disadvantaged communities (DACs) versus 0.125% in non-DACs. This indicates a latent demand in DACs constrained by supply-side barriers. Dealership-based incentives positively correlated with local ZEV adoption. Policies that support equitable inventory distribution among dealerships, expanded dealership-based incentive programs, and coordination with charging infrastructure planning may increase ZEV adoption in DACs.
The COVID-19 pandemic fundamentally reshaped global mobility, forcing a departure from previous travel norms. In California, initial declines in public transit and ride-hailing were accompanied by a surge in private vehicle interest and shifts to active travel. To assess whether spatial and temporal travel patterns shifted between 2019 and 2023, this study analyzed Performance Measurement System freeway data, regional transit boarding records, and spatial econometric modeling of Streetlight Insight data. The findings reveal a near-recovery of weekday freeway volumes and stable weekend patterns, yet an uneven transit recovery that favors buses over rail and weekends over weekdays. There was significant spatial dependence in active transportation; while the primary factors influencing walking and cycling remain consistent, their relative magnitudes have shifted. Ultimately, Californians’ choices of transportation modes are different after the pandemic than they were before it. Integrating these altered patterns into current planning frameworks is essential for developing resilient, equitable, and sustainable transportation policies.
This research evaluates LA Metro's GoPass program—a fare-free transit initiative for K-12 and community college students—through asurvey of Los Angeles Community College District (LACCD) students. We investigated students' travel preferences through a discretechoice experiment, assessed GoPass awareness and participation impacts on transit use, and analyzed spatial and seasonal ridership patterns from TAP card records. GoPass participation increased transit use by 26 percentage points for school trips and 19 percentage points for discretionary trips, with greater benefits for socioeconomically disadvantaged students. Even students aware of GoPass but not participating showed a 5.8 percentage point increase in school-related transit use. However, 39% of eligible students were unaware of the program, and car access reduced transit use probability by 37 percentage points. The discrete choice experiment revealed students valued travel time savings at $27–$54 per hour and Wi-Fi availability at $6–$7 per trip. Modest travel time reductions generated larger welfaregains than free fares alone, suggesting service improvements may offer better returns than fare subsidies. Analysis of 1.1 million TAP cardrecords showed substantial seasonal variation: summer ridership declined 37.5% for buses and 32.2% for rail. Single-vehicle householdsmaintained more consistent usage (+11.9%), while three-vehicle households showed the largest declines (-40.9%). Areas with high transit stop density and high violent crime maintained more consistent usage, reflecting transit-dependent populations. These findingsdemonstrate that increasing student transit ridership requires addressing service quality, coverage, and program awareness beyond farereductions alone.
Cargo e-bikes are emerging as a promising solution for sustainable urban freight delivery, offering advantages over conventional van-based systems in emissions reduction, congestion mitigation, and operational flexibility. This white paper examines the current state of cargo e-bike deployment in the United States, identifies key barriers to adoption, and provides evidence-based recommendations for policymakers and urban planners. Despite a rapidly growing global market—with projected annual growth of 11–15% through 2034—cargo e-bike deployment faces three interconnected challenges: infrastructure gaps (microhub locations, bicycle network coverage, and parking); regulatory inconsistenciesacross jurisdictions regarding vehicle specifications and permitted infrastructure use; and operational challenges including higher upfront costs and battery-related concerns. To overcome these barriers, this report recommends that cities establish microhub networks, expand bicycle infrastructure, ensure adequate parking with charging capabilities,develop consistent regulatory frameworks, and implement financial incentives and pilot programs. Coordinated action across these domains can enable cargo e-bikes to realize their potential as transformative elements of sustainable urban logistics.
This study investigates free and reduced fare pass programs (FRFPs) for K-12, post-secondary, and college students across California since the COVID-19 pandemic and their role in ridership recovery. A survey of 67 transit agencies, including 34 with established FRFPs, indicates that most of them maintained their FRFPs between fiscal years 2018-2019 and 2022-2023, showcasing agencies' resilience despite financial uncertainty. LA Metro's GoPass enrolled over 241,000 students and generated 1.2 million monthly boardings, inspiring similar initiatives. Post-pandemic, K-12 FRFPs saw significant expansion, improved funding, and a surge in ridership across participating agencies. These findings underscore the positive influence of fare-based incentives on student mobility and attendance.
This study highlights that Inductive Loop Detectors (ILDs), sensors embedded into the pavement for traffic control, are concerningly vulnerable to novel cyber and physical attacks.
