National Center for Sustainable Transportation

The UC Davis Campus Travel Survey is an annual survey led by Transportation Services (TS)—formerly known as Transportation and Parking Services (TAPS)—and the National Center for Sustainable Transportation (NCST), part of the Institute of Transportation Studies (ITS) at UC Davis. It collects a rich set of data about travel to the UC Davis campus, demographics, and attitudes toward travel.

The 2023-24 survey collected data from 4,774 people affiliated with UC Davis about their travel to campus during a single week in October and November 2023. It used a stratified random sampling method with the intent to gather a representative sample of the campus population. About 14 percent of those invited responded to this year’s survey. For the statistics presented throughout this report, we weight the responses by campus role (freshman, sophomore, junior, senior, Master’s, PhD, faculty, and staff) and gender so that the proportion of respondents in each group reflects their proportion in the campus population.

Transportation-disadvantaged populations often face significant challenges in meeting their basic travel needs. Microtransit, a technology-enabled transit mobility solution, has the potential to address these issues by providing on-demand, affordable, and flexible services with multi-passenger vehicles. The ways in which microtransit supports underserved populations and the factors influencing its adoption are not well-studied, however. This research examines SmaRT Ride, a microtransit pilot program in the Sacramento, California, area operated by Sacramento Regional Transit. The project evaluates a broad range of factors influencing microtransit adoption and travel behavior among underserved populations using original revealed choice survey data collected from February – May 2024 with online and intercept surveys. A descriptive analysis revealed that SmaRT Ride has improved transportation access for these communities, complements the transit system by connecting fixed-route transit, and offers a cost-effective alternative to other transportation modes. A binary logistic regression was employed to explore differences between microtransit users and non-users with microtransit awareness. The results indicate that homeownership, employment status, frequency of public transit service use, and attitude towards transit significantly affect microtransit use. Homeowners are more likely to use microtransit, while households without employed members are less likely. In contrast, part-time employees show a higher inclination to use microtransit. Regular public transit users are also more likely to incorporate microtransit into their routines, with a positive attitude toward public transit further increasing the likelihood of its use. The nuanced understanding of microtransit adoption presented here can inform targeted strategies to promote its use among transportation-disadvantaged groups. The results suggest that integrating microtransit with existing transit, outreach programs, discounted or free access, extended service hours, and supporting homeownership and affordable housing in transit-rich areas can encourage microtransit adoption by low-income and/or underserved individuals.

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High charging loads associated with fleets of commercial electric vehicles (EVs) are expected to significantly stress electric power distribution networks, leading to high costs seen by fleet operators. To address these challenges, this report presents a highly flexible smart charging (SC) algorithm for managing EV fleets that arrive and depart from a common depot on a schedule. The algorithm features (i) primary consideration of multiple fleet operator preferences (e.g. minimizing cost, using carbon-free energy), (ii) secondary consideration of grid impact that leverages the existence of multiple optimal (or near-optimal) ways to satisfy fleet operator preferences, and (iii) automatic detection and handling of infeasibility due to large energy demands (characteristic of fleet charging). Provided in this document are two numerical impact assessment studies in which the SC algorithm is shown to be superior to conventional rapid charging, and conventional ‘smart’ charging solutions on the market. These studies utilize a set of synthetic, but realistic fleet charging requirements, a physics-based model of a real feeder and one year of real, hourly load data for that feeder. The first numerical study shows that the proposed SC algorithm can lead to significant (up to 44%, but scenario-dependent) reductions in a fleet operator’s annual electricity bill. The second numerical study shows that significant transformer overloading and voltage drop issues can be associated with conventional fleet charging methods, and that the proposed SC algorithm eliminates these issues, thereby enabling higher EV penetration levels and offsetting infrastructure upgrades.

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In this study, a modeling framework was developed to generate high-resolution synthetic fleets, for use with synthetic household modeling in activity-based travel models, by integrating various data sources. The synthetic households were generated by pairing household locations and demographic attributes, and synthetic fleets were assigned to the households so that travel demand model outputs would have vehicles associated with each model-predicted tour for energy and emissions analysis. The CO emissions were modeled for each vehicle and each link traversed by vehicles as predicted by the travel demand model, and the results of the synthetic fleet (by employing Monte Carlo simulations and Bootstrap techniques) were compared with those from standard regional and sub-regional fleet configurations. The results demonstrated that using a traditional sub-regional fleet scenario produced 30% higher predicted emissions than when the synthetic fleet was employed with predicted vehicle trips, and that using a regional average fleet (applied throughout the region) produced emissions that were more than 50% higher than synthetic fleet emissions. Lowest household emissions were associated with low-income and non-working households, and highest emissions were associated with moderate-income households and one-person high income household groups. The results presented in the research are not necessarily conclusive, because the licensed vehicle data procured for Atlanta appear to be biased toward older vehicles. Model year penetration rates are accounted for in these analyses, but the authors believe that the variability in the registration mix for newer vehicles is likely underestimated in the data procured for these analyses. The authors conclude that access to statewide registration data will be required to remove potential biases that exist in licensed private data sets. Nevertheless, the study does demonstrate that properly pairing vehicle model years with the most active households (and their daily trips) significantly impacts energy and emissions analysis.

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This study delves into the energy and emissions impacts of Shared Autonomous and Electric Vehicles (SAEVs) on disadvantaged communities in California. It explores the intersection of evolving transportation technologies—electric, autonomous, and shared mobility—and their implications for equity, energy consumption, and emissions. Through high-resolution spatial and temporalanalyses, this research evaluates the distribution of benefits and costs of SAEVs across diverse populations, incorporatingenvironmental justice principles. Our quantitative findings reveal that electrification of the vehicle fleet leads to a 63% to 71% decrease in CO2 emissions even with the current grid mix, and up to 84%-87% under a decarbonized grid with regular charging. The introduction of smart charging further enhances these benefits, resulting in a 93.5% - 95% reduction in CO2 emissions. However, the distribution of these air quality benefits is uneven, with disadvantaged communities experiencing approximately 15% less benefits compared to more advantaged areas. The study emphasizes the critical role of vehicle electrification and grid decarbonization in emissions reduction, and highlights the need for policies ensuring equitable distribution of SAEV benefits to promote sustainable and inclusive mobility.

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Recent California regulatory efforts, United States goals, and industry roadmaps all target net-zero greenhouse gas (GHG) emissions from the cement and concrete industries within a few decades. While changes in production of cement and concrete, including varying constituents, can greatly reduce GHG emissions, carbon dioxide removal (CDR) will be needed to meet this net-zero goal. Hydrated cement in concrete can carbonate (i.e., form carbon-based minerals with atmospheric CO2) and thus act as a CDR mechanism. This process occurs faster with a large surface area, such as crushed concrete at its end-of-life (EoL), which can be uniquely leveraged by transportation infrastructure projects. In this work, a literature review of key parameters that can facilitate desired CO2 uptake for transportation projects at their end of life is conducted and an initial meta-analyses of data from the literature to inform CO2 uptake for individual projects is performed. Initial considerations for what concomitant impacts may arise from this process are presented. Finally, experiments to fill a key gap in understanding how thin crushed concrete must be spread to maximize uptake reactions are conducted. Cumulatively, findings will inform whether carbonation can be implementedin a way that would support policies that include carbonation as a route for reducing emissions from cement-based materials in transportation applications

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The path to transportation decarbonization will rely heavily on electric vehicles (EVs) in the United States. EV diffusion forecasting tools are necessary to predict the impacts of EVs on local energy demand and environmental quality. Few EV adoption models operate at a fine spatial scale and those that do still rely on aggregated demographic information. This adoption model is one of the first attempts to employ a synthetic population to examine EV distribution at a fine spatial and demographic scale. Using a synthetic population at the Census-Tract-level, enriched with household fleet body types and home-charging access, the researchers consider the effect of vehicle body type on EV spatial distribution and home-charging access in California. The project examines two EV body type mixes in a high electrification scenario where 8 million EVs are distributed across 6 million households in California: a “Small Vehicles” scenario where 6 million EVs are passenger cars and 2 million EVs are trucks, sport utility vehicles (SUVs), or vans and a “Large Vehicles” scenario with 4 million of each category. The authors find that an electrification scenario with more electric trucks and SUVs serves to distribute electrified households more evenly throughout the state, shifting them from urban to rural counties, while there is little impact on home-charging access.

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As urban areas grow and city populations expand, traffic congestion has become a significant problem, particularly in regions with substantial truck traffic. This study presents a coordinated freight routing system designed to optimize network utility and reduce congestion through personalized routing guidance and incentive mechanisms. The system customizes incentives and payments for individual drivers based on current traffic conditions and their specific routing preferences. Using a mixed logit model with a linear utility specification, the system captures drivers' route choice behaviors and decisions accurately. Participation is voluntary, ensuring most drivers receive a combined expected utility, including incentives, exceeding their anticipated utility under User Equilibrium (UE). This structure encourages drivers to follow suggested routes. Data collection on drivers' routing choices allows the system to update utility parameter estimates using a hierarchical Bayes estimator, ensuring routing suggestions remain relevant and effective. The system operates over defined intervals, where truck drivers submit their intended Origin-Destination (OD) pairs to a central coordinator. The coordinator assigns routes and payments, optimizing overall system costs and offering tailored incentives to maximize compliance. Experimental results on the Sioux Falls network validate the system's effectiveness, showing significant improvements in the objective function. This study highlights the potential of a coordinated routing system to enhance urban traffic efficiency by dynamically adjusting incentives based on drivers’ choice data and driver behavior. 

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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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This study applies bootstrap analysis to historic transportation project item cost data to develop improved estimates of item costconfidence bounds for use in transportation project cost uncertainty analysis (a component of lifecycle analysis). Bootstrap regression results of confidence bounds will then be integrated into AASHTOWare Project Cost Estimator so that Monte Carlo procedures can estimate project-level confidence intervals for use in lifecycle project cost analysis and transportation capital planning. Data and functions contained within AASHTOWare (a cost estimation software licensed to the Departments of Transportation for over 40 states and the District of Columbia) are employed in the analyses. Coordinating with the Georgia Department of Transportation to obtain a research license for AASHTOWare took longer than expected, resulting in projectdelays. This report summarizes the work completed to date and describes the remaining steps required to finish the study and for the primary author to publish a final dissertation.

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