We ask how place-based transportation plans are being evaluated, and what insights from the broader policy and plan evaluation research literature might inform evaluation design. We complement a review of the evaluation literature with six expert interviews with 15 people. We find that California agencies and their community partners have high expectations for evaluations of place-based transportation plans. So far, however, those evaluations have been less successful in providing detailed information on outcomes and the causal impact of interventions. This does not reflect the shortcomings of the evaluation teams, but rather the inherent challenges in holistically assessing a diverse set of projects on different implementation timelines in a project area with porous boundaries. There is also a fundamental difficulty with the evaluation scale. California’s place-based transportation plans have often been evaluated individually. But in general, evaluations, particularly quantitative causal inference methods, are most effective with a larger number of projects or sites. We suggest a two-pronged approach to addressing the tensions that we identify between place-specific knowledge and generalizable conclusions. The first prong, at the site level, would emphasize process evaluations and assessment of outputs and outcomes. The second prong would focus on impacts across multiple sites and the extent to which place-based transportation programs have a causal role.
Bicycles and other forms of micromobility have been anecdotally used in past disasters to help save lives and improve community recovery. However, research and practice are scarce on this resilient transportation strategy, which limits its usefulness and possible benefits. To fill this gap, our paper investigates the potential role bicycles and micromobility in facilitating (or limiting) disaster response and recovery. Given the lack of exploration on the topic, we convened an online workshop where we conducted brainstorming and focus group discussions with disaster experts from various government agencies, not-for-profit organizations, academia, and policy groups. We present a synthesis of that discussion, along with a review of the existing literature. We conclude there is strong potential for bicycles and micromobility for different disaster phases, hazard types, and groups of people. However, multiple barriers exist related to implementation and safety, suggesting a need for future research and policy in the transportation and emergency management fields and practices.
In the event of a wildfire, government agencies need to make quick, well-informed decisions to safely evacuate people. Small communities, such as in Marin County, with a mix of residences and flammable vegetation in Wildland-Urban Interface zones tend to lack resources to conduct evacuation studies. Consequently, this study uses a framework of wildfire and traffic simulations to test the performance of potential evacuation strategies, including reducing the volume of evacuating vehicles through car-pooling, phasing evacuations by staggering evacuation times by zone, and prohibiting street parking in four representative areas of Marin County. Results show that reducing vehicle numbers lowers the average travel time by 20%-70% and average exposure time to wildfire by 27%-60% from the baseline. Phased evacuations with suitable time intervals lower the average travel time by 13.5%-70%, but may expose more vehicles to fire in some situations. Prohibiting street parking yields varying results due to different numbers of exits and evacuees. In some cases, prohibiting street parking reduces the average travel time by over 50%, while in other cases it only reduces the average travel time by 9%, contributing little to evacuation efficiency. Altogether, Marin County may want to consider developing a communication and parking plan to reduce the number of evacuating vehicles in wildfire situations. Phased evacuation is also highly recommended, but the suitable phasing interval depends on the speed of fire spread and number of evacuees. Further, whether to establish street parking prohibition policies for a certain area depends on the number of exits and the number of vehicles on the streets.
This study discusses the potential economic and development impacts that high-speed rail (HSR) may bring to California. The research reviews the reported impacts of HSR implementation in various countries, particularly in Europe, and case studies of selected HSR station-cities in France, Spain, and Italy. The analysis suggests that HSR could bring economic development to the state and stimulate population growth but might eventually lead to gentrification in certainlocations. Not all station-cities experience the same impacts, and certain conditions may foster greater economic development. Station location and connectivity to downtown areas would be particularly important in influencing these impacts, while peripheral stations would be less able to attract land use development and relocation of activities. The availability of rail service to larger cities (and connections to other major markets) and the coordination with urban planning and policy are key to determining the development of areas around HSR stations. The study indicates that for HSR to bring about desired economic development, the planning and design of stations and services must be integrated with the vision and urban plans of each station-city.
The primary metric for measuring electric vehicle (EV) adoption growth is new car sales. However, to enable mass market penetration, EV adoption in the used car market will play a crucial role. The used vehicle market is relatively under-studied or has been studied mostly for specific regions. This project analyzed US national consumer expenditure survey data that tracks households' expenditure on vehicle acquisition and operation. The study aim is to understand new versus used vehicle choice behavior and the consequent cost of vehicle ownership, with the larger aim of determining how much households who generally buy used vehicles can gain or lose if they transition from a used internal combustion engine vehicle (ICEV) to a used EV. A choice model and cluster analysis showed that ownership of used vehicles is influenced by family size, income, housing tenure, and age. For lower-income renters, current vehicle ownership and purchase costs tend to constitute a high fraction of their household income, raising concerns related to equity and suggesting that these households in particular should be considered in policies to encourage the EV transition. Moreover, while at present the average price paid for a used ICEV is approximately $18,000, the price of a comparable used EV can range between $14,000 (e.g., lower electric range Nissan Leaf) to $50,000 (high-range Tesla), suggesting the need for incentives to encourage the used EV market.
We interviewed eight subject-matter experts in California in 2023 tounderstand how travel behavior and priorities may change in response to direct experience with road collisions. Expertsrepresented a variety of perspectives, including medical doctors, advocates for active transportation safety, and advocates for people with disabilities. Their diverse specialties enabled us to capture a variety of concerns without triggering emotionally sensitive areas for people who have directly experienced road collisions. These experts identified common themes, including mental stress from the prospect of returning to driving—especially on freeways, lesser incidence of long-term changes in travel modes after experiencing a collision, dependence on others for rides in private vehicles, and changing routes or times of day of travel when traveling independently. These experts also explained how people’s mode choices are also affected by general concerns about collisions in the news more than by specific personal experiences with near misses. Interview subjects’ spoke of more specific concerns as well. These included but were not limited to, bicyclists using sidewalks instead of bike lanes when both are present, feeling stigmatized from using public transit or paratransit after experiencing a collision, and concerns with motorists treating bicyclists badly. These initial interviewsclarify areas of focus and methodology for future qualitative and quantitative studies on the intersection oftransportation safety and travel behavior change, particularly as they involve people who have directly experienced road collisions.
Autonomous vehicles (AVs) heavily rely on machine learning-based perception models to accurately interpret their surroundings. However, these crucial perception components are vulnerable to a range of malicious attacks. Even though individual attacks can be highly successful, the actual security risks such attacks can pose to our daily life are unclear. Various factors, such as lack of stealthiness, cost-effectiveness, and ease of deployment, can deter potential attackers from employing certain attacks, thereby reducing the actual risk. This research report presents the first quantitative risk assessment for physical adversarial attacks on AVs. The specific focus is on attacks on AV’s perception components due to their highly critical function and representation in existing research. The report defines the daily-life risk as the likelihood that a given type of attack will be employed in real life and the authors develop a problem-specific risk scoring system and accompanying metrics. They perform an initial evaluation of the proposed risk assessment method for all the reported attacks on AVs from 2017 to 2023. They quantitatively rank the daily-life risks posed by each of eight different categories of attacks s and find three attacks with the highest risks: 2D printed images, 2D patches, and coated camouflage stickers, which deserve more focused attention for potential future mitigation strategy development and policy making.
This report explores opportunities for employing autonomous driving technology to dampen stop-and-go waves on freeways. If successful, it could reduce fuel consumption and emissions. This technology was tested in an on-road experiment with 100 vehicles over one week. Public stakeholders were engaged to assess the planning effort and feasibility of taking the technology to the next level: a pilot involving 1000+ vehicles over several months. Considerations included the possible geographical boundaries, target fleets of vehicles, and suitable facilities such as bridges or managed lanes. Flow smoothing technology may improve the user experience and operations of managed lanes or bridges, however it may require external incentives such as reduced tolls to entice the traveling public to use it. This must be matched with other goals such as verifying vehicle occupancy. It might be possible for some hybrid solution that addresses both challenges to provide a way forward. A concept of operations needs to be developed specifically for a target road geometry and a California partner. This concept should benefit from lessons learned from previous pilot projects and will need to be defined so as to achieve both (1) a penetration rate sufficient to achieve measurable effects; and (2) sufficient quality and quantity of data to confirm benefits.
We explore how rail transit’s first- and last-mile issue might be addressed by partnering with transportation network companies (TNCs) like Uber and Lyft. The goal is to lure high-income commuters to shift from cars to TNCs and rail. We also explore how rail and TNC partnerships can improve travel for low-income commuters who currently rely on low-frequency bus service. We parametrically test subsidizing TNC fares for feeder services in the San Francisco Bay Area in
an idealized fashion. Inputs such as the residents’ value of time and vehicle ownership were taken from various local data sources. The communities that were selected for our study are served to different degrees by the BART rail system. We found that the optimal policy must be tailored to the characteristics of the community it serves. In dense, walkable communities with strong bus service near rail stations, TNC subsidies should be targeted to less-accessible neighborhoods and low-income commuters to not compete with bus transit and active modes like walking. For lower-density communities with limited dedicated bus feeder service, TNC subsidization can be applied more broadly, although disincentives, like increasing rail parking fees, must be considered carefully, because they can induce commuters to drive directly to work instead. We conclude with a discussion of how subsidies might be covered by reallocating existing resources in different ways.
A lack of reliable and affordable transportation exacerbates socioeconomic inequities for low-income individuals, especially people of color. Universal Basic Mobility (UBM) pilots or programs are a relatively new approach to addressing financial barriers to travel among the transport-disadvantaged. UBMs provide individuals with funds for various mobilityoptions, including transit and shared modes. This study reviews the UBM programs and pilots implemented in the United States. It also reviews international applications of Mobility as a Service (MaaS) platforms. These platforms may reduce the administrative cost of implementing UBMs and help users identify and compare available travel options. In addition, the review describes critical program design tradeoffs to consider when developing a UBM program or pilot. Finally, key UBM elements and lessons learned are summarized to assist other communities considering UBMs.
