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Open Access Publications from the University of California
Cover page of Education as a Key Factor in Policy Support: An Evaluation of National Mileage Fee Support as it Varies with Information and Attitudes

Education as a Key Factor in Policy Support: An Evaluation of National Mileage Fee Support as it Varies with Information and Attitudes

(2024)

As governing bodies continue to explore mileage fees as an alternative to the gas tax, the uncertainty surrounding public support remains a critical barrier to policy uptake. This study examines the extent to which public perceptions of mileage fees are guided by misinformation or lack of information using a national, internet-based survey. Hypothetical voting opportunities were used to gather respondent support for mileage fees, coupled with educational treatments that address mileage fee fairness, privacy, and costs. The findings indicate that respondents are largely misinformed or lack information about mileage fees and the gas tax. Pre-education, only 32% of respondents supported the policy, but post-education, 46% of respondents supported the policy. Through binomial, multinomial, and fixed effect modeling, we examined the factors associated with policy support, changes in policy support, and the educational treatments. Ultimately, our findings indicate that education can play a key role in increasing support for a mileage fee policy as an alternative to the gas tax.

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Cover page of End of Life EV Battery Policy Simulator: A dynamic systems, mixed-methods approach

End of Life EV Battery Policy Simulator: A dynamic systems, mixed-methods approach

(2024)

Lithium-ion batteries (LIBs) are the enabling technology for modern electric vehicles (EVs), allowing them to reach driving ranges and costs comparable to internal combustion engine vehicles, an important development with EVs being integral to greenhouse gas mitigation efforts. However, LIB advancements include the use of rapidly evolving and chemically diverse batteries as well as larger battery packs, raising concerns about battery production sustainability as well as battery end-of-life (EoL). This study seeks to respond to these concerns by analyzing potential pathways for EoL EV batteries, quantifies flows of retiring EV battery materials, proposes economically and environmentally preferable LIB EoL strategies, and recommends pertinent policies with an emphasis on environmental justice. The researchers used a loosely coupled dynamic systems model that utilized life cycle assessment and material flow analysis and a mixed methods research approach. They find that the U.S. can make significant gains in securing supply chains for critical materials and decrease life cycle environmental impacts through the adoption of Recycled Content Standard policies similar to those found in the European Union. In addition, they examine the currently understood waste hierarchy in the context of LIB technology. Comparing immediate recycling to repurposing and reusing, they find that repurposing and reusing reduces life cycle environmental impacts relative to recycling. This project also includes an investigation of EoL battery collection and transportation and the vehicle afterlife ecosystem, as well as general stakeholders in the LiB life cycle, informed by expert interviews and a case study of a developing lithium industry in Imperial, California.

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Cover page of Developing an Efficient Dispatching Strategy to Support Commercial Fleet Electrification

Developing an Efficient Dispatching Strategy to Support Commercial Fleet Electrification

(2024)

The adoption of battery electric trucks (BETs) as a replacement for diesel trucks has potential to significantly reduce greenhouse gas (GHG) emissions from the freight transportation sector. However, BETs have shorter driving range and lower payload capacity, which need to be taken into account when dispatching them. This paper addresses the energy-efficient dispatching of BET fleets, considering backhauls and time windows. To optimize vehicle utilization, customers are categorized into two groups: linehaul customers requiring deliveries and backhaul customers requiring pickups, where the deliveries need to be made following the last-in-first-out principle. The objective is to determine a set of energy-efficient routes that integrate both linehaul and backhaul customers, while considering factors such as limited driving range, payload capacity of BETs and the possibility of en route recharging. The problem is formulated as a mixed-integer linear programming (MILP) model and propose an adaptive large neighborhood search (ALNS) metaheuristic algorithm to solve it. The effectiveness of the proposed strategy is demonstrated through extensive experiments using a real-world case study from a logistics company in Southern California. The results indicate that the proposed strategy leads to a significant reduction in total energy consumption compared to the baseline strategy, ranging from 7% to 40%, while maintaining reasonable computational time. This research contributes to the development of sustainable transportation solutions in the freight sector by providing a practical and more efficient approach for dispatching BET fleets. The findings emphasize the potential of BETs in achieving energy savings and advancing the goal of green logistics. 

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Cover page of Stochastic Ridesharing System with Flexible Pickup and Drop-off

Stochastic Ridesharing System with Flexible Pickup and Drop-off

(2024)

Ridesharing can help reduce traffic congestion, greenhouse gas emissions and increase accessibility to transportation in major metropolitan areas across the United States. A robust rideshare system needs to take uncertainties such as traffic congestion and passenger cancellations into account. In this report, the authors propose a data-driven stochastic rideshare system that integrates those sources of uncertainties. Instead of assuming a probability distribution, the approach learns the underlying distribution in travel times and passenger cancellations from historical data. The authors first provide a mathematical model of the problem. Later they propose a stochastic average approximation approach for solving the routing and flexible pickup and drop-off selection problem. They also propose a Branch-and-Price heuristic and Adaptive Large Neighborhood Search-basedmetaheuristic to solve the underlying rideshare routing problem. To validate the approach, the authors construct test cases based on the New York City taxicab dataset. Numerical results show that the proposed branch and price-based solution approach can efficiently solve small instances while being close to the true optimum. On the other hand, the ALNS-based approach can solve medium to large instances with a small computational time budget while being robust to uncertainties. The proposed approach can help transportation officials and rideshare planners design more robust rideshare systems to alleviate traffic congestion in California.

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Cover page of Integrated Modeling of Electric Vehicle Energy Demand and Regional Electricity Generation

Integrated Modeling of Electric Vehicle Energy Demand and Regional Electricity Generation

(2024)

This paper describes a model for developing highly resolved, time-of-day specific electric vehicle charging demand profiles from travel survey data. Since timing of vehicle charging is dependent on electric vehicle supply equipment (EVSE) availability, four EVSE scenarios are considered: 1) home only, 2) home and workplace only, 3) universal EVSE, and 4) a probabilistic scenario where EVSE availability varies by location. To illustrate the implications of differing demand profiles on power grid operation with high renewable generating capacity, the profiles are in a typical regional economic dispatch model. The results provide a valuable approach for understanding the interactions between vehicle electrification and renewable energy deployment while exploring an updated range of assumptions about EVSE availability and charging behaviors for New York and the six New England states. All scenarios result in increased peak demand and increased generation by non-renewable generating sources. This indicates that incentive mechanisms that influence charging decisions are necessary to attain lower emissions outcomes.

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Cover page of If Pooling with a Discount were Available for the Last Solo-Ridehailing Trip, How Much Additional Travel Time Would Users Have Accepted and for Which Types of Trips?

If Pooling with a Discount were Available for the Last Solo-Ridehailing Trip, How Much Additional Travel Time Would Users Have Accepted and for Which Types of Trips?

(2024)

Pooled trips in private vehicles, or pooling, can lead to smaller environmental impacts and more efficient use of the limited roadway capacity, especially during peak hours. However, pooling has not been well adopted in part because of difficulties in coordinating schedules among various travelers and the lack of flexibility to changes in schedules and locations. In the meantime, ridehailing (RH) provides pooled services at a discounted fare (compared to the single-travel-party option) via advanced information and communication technology. This study examines individuals’ preferences for/against pooled RH services using information collected among travelers answering a set of questions related to their last RH trip. In doing so, both trip attributes and rider characteristics are considered. Taste heterogeneity is modeled in a way that assumes the presence of unobserved groups (i.e., latent classes), each with unique preferences, in a given sample of RH riders (N=1,190) recruited in four metropolitan regions in Southern U.S. cities from June 2019 to March 2020. The researchers find two latent classes with qualitatively different preferences, choosy poolers and non-selective poolers, regarding their choice in favor of/against pooling based on wait time, travel costs, purpose, and travel party size of the last RH trip. Personal characteristics are also identified, specifically age and three attitudes (travel satisfaction, environmentalism, and travel multitasking), which account for individuals’ class membership. This research contributes to the literature by explicitly modeling taste heterogeneity towards pooled ridehailing. In addition, unlike existing studies either at the person level or employing stated-preference data, a trip-level analysis is performed in connection with revealed preferences, which generates more realistic and relevant implications to policy and practice.

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Cover page of Emissions and Health Impact of Electric Vehicle Adoption on Disadvantaged Communities

Emissions and Health Impact of Electric Vehicle Adoption on Disadvantaged Communities

(2023)

Vehicle electrification has attracted strong policy support in California due to its air quality and climate benefits from adoption. However, it is unclear whether these benefits are equitable across the state’s sensitive populations and socioeconomic groups and whether disadvantaged communities are able to take advantage of the emission savings and associated health benefits of electric vehicle (EV) adoption. In this study, we analyze the statewide health impacts from the reduction of on-road emissions reduction (from reducing gasoline powered cars) and the increase in power plant emissions (from EV charging) across disadvantaged communities (DACs) detected by using the environmental justice screening tool CalEnviroScreen. The results indicate that EV adoption will reduce statewide primary PM2.5 emissions by 24.02-25.05 kilotonnes and CO2 emissions by 1,223-1,255 megatonnes through 2045, and the overall monetized emission-related health benefits from decreased mortality and morbidity can be 2.52-2.76 billion dollars overall. However, the average per capita per year air pollution benefit in DACs is about $1.60 lower than that in the least 10% vulnerable communities in 2020, and this disparity expands to over $31 per capita per year in 2045, indicating that the benefits overlook some of the state's most vulnerable population, and suggesting clear distributive and equity impacts of existing EV support policies. This study contributes to our growing understanding of environmental justice rising from vehicle electrification, underscoring the need for policy frameworks that create a more equitable transportation system.

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Cover page of Coordinated Traffic Flow Control in a Connected Environment

Coordinated Traffic Flow Control in a Connected Environment

(2023)

Freeway and arterial transportation networks in most districts are managed separately without any coordination. This lack of coordination increases the severity of traffic congestion when one or both systems reach their capacities. Some field studies have observed reductions in travel time by coordinating freeway ramps with adjacent arterial signals. To advance the investigation, we propose an integrated traffic management strategy that involves variable speed limit, lane change, ramp metering for freeway traffic flow control, and a traffic-responsive signal control scheme for adjacent traffic light intersections.The variable speed limit and lane change control are designed to alleviate congestion at a lane-drop bottleneck in an arbitrary section, and reject potential uncertainties from measurements or model parameters. The ramp metering algorithm takes advantage of the signal plan of neighboring arterial intersection when estimating on-ramp demands. The signal plan for each arterial intersection is determined by a simulation-based cycle length model and estimated demands of all directions, part ofwhich depend on off-ramp flow measurements. The above data sharing mechanism strengthens the connection between thefreeway and arterial networks and enhances the control performance. We demonstrate the effectiveness and quantify the benefits provided by the proposed system in terms of traffic mobility, safety and emission using microscopic traffic simulations.

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Cover page of Southern California Transit Training Consortium Online Training in Electrical Systems and Battery Electric Safety Training

Southern California Transit Training Consortium Online Training in Electrical Systems and Battery Electric Safety Training

(2023)

In partnership with the Southern California Regional Transit Training Consortium (SCRTTC), the California State University at Long Beach (CSULB) expanded potential audiences and offered program support for the Electric Vehicle Transit Bus High Voltage Safety Awareness class, which was previously developed under the National Center for Sustainable Transportation (NCST). CSULB expanded both the number of online offerings and the geographic reach by opening the class to transit agencies and campus fleet operators within the NCST network. The course was designed to enhance a technician's basic electrical skills and 2-circuit diagnosis, while teaching students how to work with a Digital Volt-Ohm Meter (DVOM). The effort supports the broader goal of building the workforce needed to support the transition to alternative energy and zero emission bus fleets.

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Cover page of All Aboard! Easier Transit Travel with Standardized Payments

All Aboard! Easier Transit Travel with Standardized Payments

(2023)

This study explores interest in, and the challenges faced by transit agencies and operators in the adoption of open-loop payment systems. The research team focuses on the ways that agencies view passenger needs in the context of adopting open payments. Challenges with cash payments, an increasingly cashless society, and the expanding offerings of digital payment options have spurred increased interest in open-loop payments among transit operators. Paying for transit with cash can require additional time at boarding, add extra steps for passengers who must pay with exact fare, and result in service inefficiencies. It presents security concerns for drivers, and administrative burdens for agencies. While the full costs of cash handling vary per agency, the cost of handling and moving cash may be considerable. Pioneering transit agencies are adopting open payment systems that accept credit cards, debit cards, and smartphone/watch-based transactions. However, there is a huge diversity among transit agencies and as such, agencies face different challenges and to different degrees when considering the adoption of open payment systems. Challenges can include financial barriers, capacity limitations, technological challenges, the duration of existing contracts, competing needs, and a number of passenger challenges such as lack of credit cards or smartphones, or lack of familiarity with the technology. This study uses data collected from California transit agencies in the fall of 2022 that gathered information about agency perceptions of open-loop payments and the challenges with adopting open fare collection systems, and whether assistance programs would benefit transit agencies interested in adopting open-loop payments. Results of the present study indicate that the majority of agencies are considering or have considered implementing open payment systems, but agencies are not fully aware of the assistance available from the California Integrated Travel Program to help in the transition to digital and open payment systems. This study sheds light on the challenges facing small to medium transit agencies in the transition of California’s transit systems to open-loop payment systems.

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