UC Davis Institute of Transportation Studies

The widespread adoption of electric vehicles (EV) is a centerpiece of California’s strategy to reach net-zero carbon emissions, but it is not fully known how and where EVs are being used, and how and where they are being charged. This report provides the first at-scale estimate of EV home charging. Previous estimates were based on conflicting surveys or extrapolated from a small, unrepresentative sample of households with dedicated EV meters. We combined billions of hourly electricity meter measurements with address-level EV registration records from California households, including roughly 40,000 EV owners. The average EV increases overall household load by 2.9 kilowatt-hours per day, well under half the amount assumed by state regulators. Results imply that EVs travel less than expected on electric power, raising questions about transportation electrification for climate policy.

There is growing interest in the use of hydrogen as a transportation fuel but the environmental benefits of using hydrogen depend critically on how it is produced and distributed. Leading alternatives to using fossil natural gas to make hydrogen through the conventional method of steam methane reforming include using electrolyzers to split water into hydrogen and oxygen, and the use of biogas as an alternative feedstock to fossil natural gas. This report examines the latest carbon intensity (CI) estimates for these and various other hydrogen production processes, adding important nuances to the general “colors of hydrogen” scheme that has been used in recent years. CI values for hydrogen production can vary widely both within and across hydrogen production pathways. The lowest CI pathways use biomass or biogas as a feedstock, and solar or wind power. The report also analyses jobs creation from new hydrogen production facilities and shows that these benefits can be significant for large-scale facilities based on either future biomass/biogas-to-hydrogen or solar-hydrogen production technologies. Recommendations include setting stricter goals for the state’s Low Carbon Fuel Standard (LCFS) program to continue to reduce the carbon footprint of California’s transportation fuels.

This study examined the sustainability of shared micromobility services using data from 48 cities in the US using a 21-day smartphone travel diary and survey data. Population-weighted analysis indicated a much smaller share of transit connection than in prior reported studies, with more reliable data. However methodological decisions could be a cause for such discrepancies suggesting a sensitivity analysis of this same data may be a good next research step. Results also indicated median VMT reduced per micromobility trip to be roughly 0.15 miles for e-scooter share trips and 0.25 miles for bike share (including e-bike) trips. Models of mode substitution confirm prior evidence of factors affecting car substitution including trip distance as the strongest factor. This study also proposed two frameworks for building a sketch planning tool for examining VMT reduction from future micromobility services. This tool could help cities and regions better plan for the micromobility services to achieve real VMT and GHG reduction goals. While more research is needed to employ this framework, it helps motivate a series of additional research topics to inform a decision support tool for shared micromobility planning. 

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To achieve California’s ambitious climate goals, a shift to hydrogen fuel for some transportation sectors may be essential.In this report, we explore the build-out of a hydrogen fuel distribution system including uptake of light-, medium-, and heavy-duty fuel cell electric vehicles. Our analysis of Base and High Case scenarios includes costs of building and operating a hydrogen vehicle and fuel system and estimates workforce impacts. We consider scenarios with about 125,000 vehicles by 2030 in the Base Case and 250,000 in the high case. This increases by an order of magnitude to 2045. Vehicle and station investment costs associated with the Base Case reach anywhere from $4 to 12 billion USD by 2030 and increase by a factor of eight by 2045. Costs per kg of hydrogen, including fuel transmission to stations and station costs delivered to vehicles, could be in the range of $4 to 8 per kg. This becomes $6 to 10/kg as a final delivered cost, if production of hydrogen were to cost $2/kg. Workforce impacts in the Base Case include 600 to 2,200 jobs created by 2030, rising rapidly thereafter. This report was prepared by the ITS-UC Davis Energy Futures Hydrogen Program in partnership with the UCLA Luskin Center for Innovation.

In 2018, California voters rejected Proposition 6, a ballot initiative that sought to repeal state gasoline taxes and vehicle fees enacted as part of the 2017 Road Repair and Accountability Act. This paper examines the relationship between support for the proposition, political ideology and the economic burdens imposed by the Act. For every hundred dollars of annual per-household costs imposed by the Road Repair and Accountability Act, support for proposition rose by 3–5 percentage points, roughly comparable to a commensurate increase in the share of ”liberal” voters. Notably, the relationship between voting and the economic burden of the policy is seven times strong in the most conservative tracts relative to the most liberal tracts. This heterogeneity has important implications for the popular support for environmental taxes, as conservative areas in California and elsewhere tend to bear a higher burden from transportation and energy taxes than liberal areas.

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Under the Advanced Clean Cars II (ACC II) rule, California must move to 100% zero emission vehicle (ZEV) sales by 2035. Tomake this transition equitable, it is important to understand how we can support ZEV adoption in all communities–including rural communities. The aim of this study is to explore the experiences and perceptions of current rural plug-in electric vehicle (PEV) owners, identify barriers to charging and ownership, and suggest factors to guide the development of infrastructure in rural areas. (PEVs include battery-electric vehicles [BEVs] and plug-in hybrid vehicles.) Semi-structured interviews were conducted with rural PEV owners and included questions related to travel behavior, at-home and public charging experiences, and motivation for household vehicle purchase. Major themes were extracted from the interviews including that PHEV owners tend to have minimal at-home and public charging requirements, while BEV owners require access to Level 2 charging at home and reliable fast charging in public spaces. Additionally, the magnitude of public charging reliability and availability issues appear to be greater in rural than non-rural areas. Grid reliability issues and specific vehicle requirements were also points of discussion among rural PEV owners. The findings of this report could inform policy makers, car manufacturers, and PEV charging companies to better serve rural communities in the transition to 100% PEV sales.

Managing traffic flow in high-occupancy toll (HOT) lanes is a tough balancing act and current tolling schemes often lead to either under- or over-utilization of HOT lane capacity. The inherent linear/nonlinear relationship between flow and tolls in HOT lanes suggest that recent advances in machine learning and the use of a data-driven model may help set toll rates for optimal flow and lane use. In this research project, a data-driven model was developed, using long short-term memory (LSTM) neural networks to capture the underlying flow-toll pattern on both HOT and general-purpose lanes. Then, a dynamic control strategy, using linear quadratic regulator (LQR) feedback controller was implemented to fully utilize the HOT lane capacity while maintaining congestion-free conditions. A case study of the I-580 freeway in Alameda County, California was carried out. The control system was evaluated in terms of vehicle hours traveled and person hours traveled for solo drivers and carpoolers. Results show that the tolling strategy helps to mitigate congestion in HOT and general-purpose lanes, benefiting every traveler on I-580.

To maximize the greenhouse gas (GHG) emission reduction potential of Battery Electric Vehicles (BEVs), it is critical to develop EV dynamic charging management strategies. These strategies leverage the temporal variability in emissions associated with generated electricity to align EV charging with periods of low-carbon power generation. This study introduces a deep neural network tool to enable BEV drivers to make charging sessions align with the availability of cleaner energy resources. This study leverages a Long Short-Term Memory network to forecast individual BEV vehicle miles traveled (VMT) up to two days ahead, using a year-long dataset of driving and charging patterns from 66 California-based BEVs. Based on the predicted VMT, the model then estimates the vehicle's energy needs and the necessity of a charging session. This allows drivers to charge theirvehicles strategically, prioritizing low-carbon electricity periods without risking incomplete journeys. This framework empowers drivers to actively contribute to cleaner electricity consumption with minimal disruption to their daily routines. The tool developed in this project outperforms benchmark models such as recurrent neural networks and autoregressive integrated moving averages, demonstrating its predictive capabilities. To enhance the reliability of predictions, confidence intervals are integrated into the model, ensuring that the model does not disrupt drivers' daily routine trips when skipping non-critical charging events. The potential benefits of the tool are demonstrated by applying it to real-world EV data, finding that if drivers follow the tool’s predictive suggestion, they can reduce overall GHG emissions by 41% without changing their driving patterns. This study also found that even charging in regions with higher carbon-intensity electricity than California can achieve Californian emission levels for EV charging in the short term through strategic management of non-critical charging events. This findingreveals new possibilities for further emissions reduction from EV charging, even before the full transition to a carbon-neutral grid. 

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This project convened a series of meetings and workshops to prioritize listening to multi-sector stakeholders from local government, advocacy, and industry in US cities where autonomous vehicles are operating. The objective was to listen and learn from all stakeholders, raise issues surrounding accessibility and equity, and to solicit responses. Key findings from the workshops include a consensus across the three sectors on the need for good channels of multi-stakeholder communication, and voices across all sectors agreed on the importance of disability access and serving diverse populations. Many parties, representing voices from all sectors, recognized that federal regulatory activities appear to be moving too slowly. Preventing any roadway incidents is a priority for many stakeholders, and some suggest a playbook for handling day-to-day roadway issues and common standards for first-responder interactions. Disability access is a high priority across all sectors, and there many see nearer term to accommodations for blind, hearing-impaired riders, but the timeline for providing service to people with non-folding wheelchairs is less clear. There is also ongoing debate surrounding the limits of regulatory purview, the role for cities, and how to actualize equitable expansion into rural areas. There is more work to do to advance a multi-sector dialogue around the role for local governments and community-based organizations in shepherding a safe, equitable and sustainable expansion of autonomous vehicles.

During the early months of the pandemic, stay-at-home orders and concerns about infection catalyzed a shift toward online activities, such as remote work and e-shopping, resulting in a significant decrease in conventional travel. However, as the effects of the pandemic diminished, the pandemic-induced online activities began to subside, and conventional travel started to rebound. The challenge among transportation planners and policymakers is to determine the lasting effects of the pandemic and adjust the policies accordingly. In the same efforts to understand the evolving travel-related activities and inform policymaking, the 3 Revolutions Future Mobility Program at the University of California, Davis, conducted four waves of mobility surveys between Spring 2020 and Fall 2023. Key findings from the analysis of these data reveal that remote work and a combination of remote work and physical commuting (i.e., hybrid work) emerge as an enduring outcome of the pandemic. The pandemic accelerated the rise of e-shopping, both for grocery and non-grocery purchases, with findings demonstrating the critical influence of socio-demographic factors, including age, gender, and income, on e-shopping adoption and frequency. The findings show that socio-demographic factors such as work status, income level, and work arrangements are associated with household vehicle ownership changes and individual vehicle miles traveled (VMT). In particular, an increase in commute frequency reduces the likelihood of vehicle shedding, while amplifying the likelihood of vehicle acquisition. In the meantime, remote workers exhibit lower commuting VMT but higher non-commuting VMT compared to hybrid workers. The findings demonstrate a similarity between the percentage of respondents who used public transit, bikes, e-bikes, and e-scooters for commuting and non-commuting trips to some degree between 2019 and 2023. These insights underscore that adapting to shifting activity and transportation patterns is crucial for policymakers and planners to build a sustainable and inclusive post-pandemic future.