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Open Access Publications from the University of California
Cover page of Fostering the Use of Zero and Near Zero Emission Vehicles in Freight Operations

Fostering the Use of Zero and Near Zero Emission Vehicles in Freight Operations

(2020)

California is in the midst of improving its freight system. For example, the California Sustainable Freight Action Plan (CSFAP) established the goal of reaching a 25% increase in freight efficiency, the use of 100,000 zero emission vehicles and equipment (and maximize the number of near zero emission vehicles) in the system, and improving economic competitiveness. Although there are multiple strategies and approaches to help achieve these goals, this study focuses on analyzing the factors to foster the adoption of zero and near-zero emission vehicles. For example, the use of monetary and non-monetary incentives to elucidate behavioral changes (e.g., fleet purchase decisions). This study considered compressed (renewable) natural gas (CNG/RNG), hybrid electric (HE), battery electric (BE) and fuel-cell hydrogen (H2) vehicles. The research team collected information through a web-based stated preference survey sent (in two waves) to fleets and carrier companies to gather data about their economics, and their vehicle purchase preferences. However, the response rate was very small which limited the type of analyses conducted with the data. Alternatively, the study team developed a multi-criteria decision-making tool using a Spherical Fuzzy Analytical Hierarchy Process based on experts’ knowledge. The approach considered the variability in the technical and operational characteristics, market readiness, and other factors related to these technologies. The model helped provides insights about the most appropriate options for different uses (e.g., last mile, long-haul distribution). Specifically, the authors evaluate the alternatives using five criteria: economic; business, incentives & market-related; environmental & regulatory; infrastructure; and safety & vehicle performance factors. The analyses also consider twenty-one sub-criteria, e.g., total cost of ownership, payback period, brand image, financial & non-financial incentives, and public/private fueling/ charging infrastructure availability.

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Cover page of Electric Fleet Adoption Strategies – Addressing Storage and Infrastructure Needs

Electric Fleet Adoption Strategies – Addressing Storage and Infrastructure Needs

(2020)

Significant electrification of the transportation sector is necessary for the State to achieve several important greenhouse gas (GHG) reduction and renewable energy targets. The State’s electricity generation and transmission capabilities must increase in order to meet the demand generated by increasing levels of fleet electrification. The increased demand, combined with the Renewables Portfolio Standard (RPS) targets will require significantly increased energy storage capabilities that can accommodate demand while integrating renewable power sources into the grid. This project evaluated the mid to long-term energy storage needs of the electric grid for select fleet electrification scenarios. The analysis was conducted using Resolve, a power systems planning model, for RPS targets of 60% and 80% by 2030 and 2042 respectively. The results show that Electrical Energy Storage (EES) capacity requirements depend on a number of parameters, including Demand Response (DR), Electric Vehicle (EV) charging flexibility, and total EV population. The EES requirements for the 60% RPS scenarios range from 3.9 to 4.3 GW while for the 80% RPS scenarios, the range is from 18.5 to 20.4 GW.

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Cover page of Improving Transportation Information Resilience: Error Estimation for Networked Sensor Data

Improving Transportation Information Resilience: Error Estimation for Networked Sensor Data

(2020)

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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Cover page of Congestion Reduction Through Efficient Empty Container Movement Under Stochastic Demand

Congestion Reduction Through Efficient Empty Container Movement Under Stochastic Demand

(2020)

In today’s world, there is a significant amount of investigation regarding how to efficiently distribute loaded containers from the ports to the consignees. However, to fully maximize the process and become more environmentally friendly, one should also study how to allocate the empty containers created by these consignees. This is an essential part in the study of container movement since it balances out the load flow at each location. The problem of coordinating the container movement to reuse empty containers and lower truck miles is called the “Empty Container Problem”. In this work, the authors develop a scheduling assignment for loaded and empty containers that builds on earlier models but incorporates stochastic (random) future demand. Since this problem is meant to be solved daily and the solution implemented today affects tomorrow’s starting state, incorporating future demand is an important aspect. This report shows that the truck miles needed to satisfy the demand at all locations is reduced by about 4-7% when considering future stochastic demand as opposed to only considering today’s demand. Thus, leading to a cleaner and greener solution, creating less congestion and lowering the impact of freight movement on the environment.

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Cover page of The Impacts of Automated Vehicles on Center City Parking Demand

The Impacts of Automated Vehicles on Center City Parking Demand

(2020)

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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Cover page of Freight Load Balancing and Efficiencies in Alternative Fuel Freight Modes

Freight Load Balancing and Efficiencies in Alternative Fuel Freight Modes

(2020)

The current freight transportation network is highly unbalanced as routing decisions are made by individual users without coordination. Certain routes may become congested when chosen based on current traffic information without any anticipation that if other users do the same, these routes are no longer the best. This project developed a centrally coordinated load balancing system that considers all user demands and generates individual routes that balance freight loads across the network by minimizing cost. It is initially assumed that all vehicles are diesel and then gradually increases zero emissions vehicles such as electric trucks for a mixed fleet of trucks. The electric trucks add additional constraints due to limitation of range and charging time of batteries. As the number of electric trucks increases, the emissions reduce as expected; however, the cost of charging does not make their use less operational costly than the corresponding diesel trucks. The experiments show that for electric trucks to compete with diesel, charging should occur when drivers are off duty or in idle mode since the cost of charging is mainly due to the labor cost of the waiting driver. Several simulation experiments show the benefits of deploying electric trucks in a freight fleet with respect to environment and operational cost, provided charging is scheduled appropriately. It is shown that the proposed centrally coordinated load balancing system can easily incorporate different concepts such as the empty container re-use where the exchange of containers between users can be optimized to reduce empty trips. In order to better understand the implementation issues of a load balancing system, the report also includes results from interviews of individuals responsible for trucking operations in the Los Angeles region. All interviewed trucking companies are either drayage operations (hauling freight to and from ports or intermodal facilities) or short-haul operators that move goods between manufacturers, distribution center, and retail facilities. The answer for load balancing system varies between interviewees and it is recommended to follow an iterative fashion by first targeting trucking companies who already work collaboratively in associations and vertical markets. These clusters of firms have built working relationships, engage in communication, and have trust between members.

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Cover page of Utilizing Highway Rest Areas for Electric Vehicle Charging: Economics and Impacts on Renewable Energy Penetration in California

Utilizing Highway Rest Areas for Electric Vehicle Charging: Economics and Impacts on Renewable Energy Penetration in California

(2020)

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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Cover page of Facilitating Electric Vehicle Adoption with Vehicle Cost Calculators

Facilitating Electric Vehicle Adoption with Vehicle Cost Calculators

(2020)

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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Cover page of Greenhouse Gas Reduction Opportunities for Local Governments: A Quantification and Prioritization Framework

Greenhouse Gas Reduction Opportunities for Local Governments: A Quantification and Prioritization Framework

(2020)

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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Cover page of Exploring the Role of Attitude in the Acceptance of Self-driving Shuttles

Exploring the Role of Attitude in the Acceptance of Self-driving Shuttles

(2020)

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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