UC Davis Institute of Transportation Studies

Nowadays, the effectiveness of any smart transportation management or control strategy would heavily depend on reliable traffic data collected by sensors. Two problems regarding sensor data quality have received attention: first, the problem of identifying malfunctioning sensors; second, reconstruction of traffic flow. Most existing studies concerned about identifying completely malfunctioning sensors whose data should be discarded. This project focuses on the problem of error detection and data recovery of partially malfunctioning sensors that could provide valuable information. By integrating a sensor measurement error model and a transportation network model, the authors propose a Generalized Method of Moments (GMM) based estimation approach to determine the parameters of systematic and random errors of traffic sensors in a road network. The proposed method allows flexible data aggregation that ameliorates identification and accuracy. The estimates regarding both systematic and random errors are utilized to conduct hypothesis test on sensor health and to estimate true traffic flows with observed counts. The results of three network examples with different scales demonstrate the applicability of the proposed method in a large variety of scenarios. This research improves fundamental knowledge on transportation data analytics as well as the effective management of data and information infrastructure in transportation practice.

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Understanding the potential benefits and unintended consequences of automation and electrification revolutions in freight is challenging for academics, practitioners, and decision-makers. On one hand, these revolutions could help mitigate the disproportionate impacts of freight transportation on externalities and improve efficiency; on the other hand, they could generate additional issues such as right-of-way conflicts, crashes, and traffic incidents. To shed light on these issues, this report conducts an extensive review of the state-of-the-practice of such innovations for both long-haul and last-mile freight distribution. The study concentrates on the potential barriers, challenges, and opportunities of the different innovations, and discusses the market readiness of some of the technologies. Finally, the authors discuss planning considerations for the advent and widespread use of these innovations, and provide research and policy considerations.

The potential for automated vehicles (AVs) to reduce parking in city centers has generated much excitement among urban planners. AVs could drop-off (DO) and pick-up (PU) passengers in areas where parking costs are high: personal AVs could return home or park in less expensive locations, and shared AVs could serve other passengers. Reduced on-street and off-street parking present numerous opportunities for redevelopment that could improve the livability of cities, for example, more street and sidewalk space for pedestrian and bicycle travel. However, reduced demand for parking would be accompanied by increased demand for curbside DO/PU space with related movements to enter and exit the flow of traffic. This change could be particularly challenging for traffic flows in downtown urban areas during peak hours, where high volumes of DOs and PUs are likely to occur. Only limited research examines the travel effects of a shift from parking to DO/PU travel and the impact of changes in parking supply. This study uses a microscopic road traffic model with local travel activity data to simulate personal AV parking scenarios in San Francisco's downtown central business district. These scenarios vary (1) the demand for DO and PU travel versus parking, (2) the supply of on-street and off-street parking, and (3) the total demand for parking and DO/PU travel due to an increase in the cost of travel to the central business district.

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This study was designed to identify lessons learned from experiences of multi-stakeholder collaboration in governance processes focused on adaptation to sea level rise for specific transportation corridors/assets across different areas of California. Four transportation assets in California were selected as case studies: State Route 37 in the Bay Area; the Cardiff Beach Living Shorelines Project and the LOSSAN railroad at Del Mar in San Diego County; and the Port of Long Beach in Los Angeles County. The study methods included attendance of policy meetings; document analysis; and interviews of staff at (local, regional, and state) government bodies, transportation agencies, climate collaboratives, etc. The study identified three major governance challenges shared among these cases: (1) stakeholder involvement or collaboration with ‘unusual’ partners; (2) jurisdictional fragmentation; and (3) lack of funding. The lessons learned to address these challenges were: (a) include a wide range of stakeholders early on in the project; (b) identify an intermediary or facilitator with relevant knowledge and social capital with the stakeholders; (c) establish a forum for negotiations and information exchange; (d) draft a memorandum of understanding with the rules of collaboration; (e) appoint a project manager to tie all the project parts and stakeholders together and sustain engagement; (f) structure the collaboration in tiers from technical/operational to executive/political; (g) explore options to make any given project a multi-benefit project; (h) advocate for a multi-year stream of funding rather than a lump sum; (i) leverage collaboration for funding and highlight, to potential funders, the collaborative element as a means to increase the efficiency of their investment. Issues to consider when deriving lessons from other jurisdictions were: differences in capacity, or available resources and staff; the numbers of actors involved; pre-existing positive collaborative relationships between the actors; exposure of transportation assets to sea-level rise; existing vulnerabilities of the corridor/asset; and the economic relevance of the corridor/asset.

Partially automated battery electric vehicles (BEVs) are being sold to and used by consumers. Estimates indicate that as of the end of 2019, there were over 700,000 Partially Automated Tesla Vehicles—the subject of this study—on the roads globally. Despite this, little research has been done to understand how they may be changing travel behavior. In this study, qualitative interviews with 36 users of Tesla BEVs with Autopilot were conducted. The goal of this was to understand how Autopilot is used, user experiences of the system, and whether the system has any impact on drivers’ travel behavior. The focus of the last of these aims was to determine whether Autopilot could cause or was causing an increase in vehicle miles traveled (VMT) among the study participants. Results from the interviews showed that partial automation leads to consumers travelling by car more and being more willing to drive in congested traffic. These changes are due to increased comfort, reduced stress, and increased relaxation due to the partial automation system, and because of the lower running costs of a BEV. The results also point to a need for further research of partially automated vehicles that are already on the market, as 11 of 17 reasons for increased VMT that have been identified in modeling studies of fully automated vehicles (not yet commercially available) applied to users of Autopilot.

California policy is incentivizing rapid adoption of zero emission electric vehicles for light-duty and freight applications. This project explored how locating charging facilities at California’s highway rest stops might impact electricity demand, grid operation, and integration of renewables like solar and wind into California’s energy mix. Assuming a growing population of electric vehicles to meet state goals, state-wide growth of electricity demand was estimated, and the most attractive rest stop locations for siting chargers identified. Using a California-specific electricity dispatch model developed at UC Davis, the project estimated how charging vehicles at these stations would impact renewable energy curtailment in California. It estimated the impacts of charging infrastructures on California’s electricity system and how they can be utilized to decrease the duck curve effect resulting from a large amount of solar energy penetration by 2050.

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This research synthesis consists of a set of white papers that jointly provide a review of research on the current practicefor setting speed limits and future opportunities to improve roadway safety. This synthesis was developed to inform thework of the Zero Traffic Fatalities Task Force, which was formed in 2019 by the California State Transportation Agencyin response to California Assembly Bill 2363 (Friedman). The statutory goal of the Task Force is to develop a structured,coordinated process for early engagement of all parties to develop policies to reduce traffic fatalities to zero. Thisreport addresses the following critical issues related to the work of the Task Force: (i) the relationship between trafficspeed and safety; (ii) lack of empirical justification for continuing to use the 85th percentile rule; (iii) why we need toreconsider current speed limit setting practices; (iv) promising alternatives to current methods of setting speed limits;and (v) improving road designs to increase road user safety.

Consumer education regarding the costs of electric vehicles (EVs), particularly in comparison with similar gasoline vehicles, is important for adoption. However, the complexity of comparing gasoline and electricity prices, and balancing long-term return-on-investment from fuel and maintenance savings with purchase premiums for EVs, makes it difficult for consumers to assess potential economic advantages. Online vehicle cost calculators (VCCs) may help consumers navigate this complexity by providing tailored estimates of different types of vehicles costs for users and enabling comparisons across multiple vehicles. However, VCCs range widely and there has been virtually no behavioral research to identify functionalities and features that determine their usefulness in engaging and educating consumers and promoting EV adoption. This research draws on a behavioral theory, systematic review of available VCCs, and user research with three VCCs to articulate design recommendations for effective VCCs.

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Local governments have steadily increased their initiative to address global climate change, and many present their proposed strategies through climate action plans (CAPs). This study conducts a literature review on current local approaches to greenhouse gas (GHG) reduction strategies by assessing CAPs in California and presents common strategies in the transportation sector along with useful tools. One identified limitation of many CAPs is the omission of quantitative economic cost and emissions data for decision-making on the basis of cost-effectiveness. Therefore, this study proposes a framework for comparing strategies based on their life cycle emissions mitigation potential and costs. The results data can be presented in a marginal abatement cost curve (MACC) to allow for side-by-side comparison of considered strategies. Researchers partnered with Yolo and Unincorporated Los Angeles Counties to analyze 7 strategies in the transportation and energy sectors (five and two, respectively). A MACC was subsequently developed for each county. Applying the life cycle approach revealed strategies that had net cost savings over their life cycle, indicating there are opportunities for reducing emissions and costs. The MACC also revealed that some emissions reduction strategies in fact increased emissions on a life cycle basis. Applying the MACC framework to two case study jurisdictions illustrated both the feasibility and challenges of including quantitative analysis in their decision-making process. An additional barrier to using the MACC framework in the context of CAPs, is the mismatch between a life cycle and annual accounting basis for GHG emissions. Future work could explore more efficient data collection, alternative scopes of emissions for reporting, and environmental justice concerns.

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Self-driving vehicles, as a revolution in mobility, are emerging and developing rapidly. However, public attitudes toward this new unproven technology are still uncertain. Given the significant influence of attitude toward a new technology on the intention to use it, the question arises as to why some people are in favor of this technology whereas others are not. Additionally, questions about the key attitudes influencing self-driving technology acceptance, where these attitudes come from, and how they interact with each other have not yet been addressed.

This study aims to explore these research questions based on data collected from people who live or work in the West Village area of the University of California, Davis, campus after a self-driving electric shuttle was piloted in this area. structural equation modeling was employed to explore interactions between attitude elements. The results show that affect—i.e., liking or enthusiasm for self-driving shuttles—strongly explains the acceptance of self-driving technology. A higher level of affect could be formed by strengthening an individual’s trust. Additionally, trust works as an important mediator between perceived risk, usefulness, and ease of use on both affect and intention to ride self-driving vehicles. Perceived risk captured more security and functional concerns, reflecting uncertainty around current self-driving technology. The model identified important bi-directional influences between trust and affect. Significant effects of mental and physical intangibility were also shown, but each works differently on cognitive beliefs. Individuals’ socio-demographic, lifestyle, and mobility characteristics also exert influences on attitude and self-driving technology acceptance.

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