National Center for Sustainable Transportation

As technology advances and threats intensify, rail and public transit systems must adapt to take advantage of emerging opportunities and provide reliable service regardless of external changes. These changes reflect the pressing need for infrastructure agencies to augment their adaptive capacity. However, very few studies have examined effective adaptations and the influential factors that support or inhibit adaptation implementation in the context of these often physically inflexible and resource-limited systems. As a result, the implementation of adaptations remains uneven across agencies and system types in the United States. To address this gap, this study synthesizes findings from semi-structured interviews with 44 experts to characterize factors and options influencing adaptive capacity for rail and public transit systems to support reliable, resilient, and safe rail and transit service in the US. This study presents successfully implemented adaptations for several hazards (storms, temperature fluctuations, wildfires, and others) within an open-access adaptation dashboard for practitioners to refer to as challenges arise. This research further provides specific recommendations to improve rail and public transit service reliability, system resilience, and organizational flexibility.

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Anthropogenic noise and light have been shown to impact wildlife behavior, distribution, movement, and population fitness and survival. Traffic noise and light can inhibit wildlife use of areas adjacent to roads, impair wildlife perception of traffic risks, and cause a barrier effect to wildlife occurrence and movement well beyond road edges. A critical action being taken by states to repair wildlife movement across roadways is construction of wildlife crossings, theoretically providing a safe passage across roads. Planning the location of theses crossings and to some degree their design currently does not take into account traffic noise and light impacts on wildlife approaching the structures. The authors developed statewide models of traffic noise and light intrusion into areas adjacent to state highways to aid understanding of wildlife responses to noise and light and to aid locating and designing crossings to maximize wildlife approach and use. These models have the added benefit of estimating traffic impacts as part of environmental analysis associated with delivering transportation projects. The authors used the NoiseModeling software in QGIS and novel light-modeling tools developed in ArcGIS to model the “noise-scape” and “light-scape” at high-resolution around California state highways. They used different putative sources of traffic noise and light and model propagation (depending on traffic volumes) of both across adjacent landscapes out to at least 1 kilometer. The authors tested the models in the field using transects of noise and light measurements. The outputs were digital (raster) maps that showed the high and low impact areas.

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This project employs the outputs from MOVES-Matrix 4.0 to generate a data set that can be employed by users of the 2018 Fuel and Emissions Calculator (Version 3.0) to update energy use and emission rates to reflect the latest outputs from the U.S. Environmental Protection Agency’s (USEPA’s) MOVES 4.0 energy use and emission rate model for the Atlanta, Georgia summer scenario. The data employed in this project were generated as part of the National Center for Sustainable Transportation project entitled MOVES-Matrix 4.0 for High-Performance On-road Energy Use and Emission Rate Modeling Applications (Lu, et al., 2025). As described in this report, the team queried more than 90 billion cells within full MOVES-Matrix 4.0 data set to generate MOVES 4.0 data that can be substituted for the older MOVES 2014 data in Fuel and Emissions Calculator (FEC) Version 3.0 for Georgia. The query output data are contained in an Excel spreadsheet, allowing users to update their personal copies of the FEC. Should technology transfer funds become available in 2025 from another source, the research team will update and release the next full version of the FEC model, which will be accompanied by an updated user manual. 

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Since the beginning of the twenty-first century, young people have become a considerable share of public transit users. However, younger representatives of this age group still face many barriers to access transit infrastructure and benefit from its service. This study examines a Youth Cruz Free program launched by the Santa Cruz METRO in March 2023, allowing kids and teenagers of school age to use the service for free. The researchers were interested in understanding how the program has changed the travel behavior of teenagers, whether it encourages adolescents to use the service after they turn 18, and what factors influence their overall ridership of Santa Cruz METRO. To reach both current high school students and recent graduates, the researchers completed two rounds of surveys distributed among high schools in Santa Cruz County. The surveys asked about adolescents’ sociodemographics, the availability of different modes of transportation, school attendance, and other variables that might change patterns in their mobility and encourage/discourage transit adoption. After completing multiple statistical tests, the researchers identified the factors impacting teenagers’ travel behavior. Hispanic/Latinx students were found to ride the METRO more frequently than White students. Students with a driver’s license and students with higher numbers of household vehicles are less likely to use public transportation. Perceptions of safety also correlate significantly with youth ridership. Surprisingly, variables such as gender, job frequency, and the walking distance from the bus stop were not significantly related to bus use frequency. While additional research could aim to better target all riders, including communities of color and those in rural areas, the findings support the growing literature on youth ridership and fare free programs. The results suggest perceptions of safety are worth addressing at a systemic level, and that public transit becomes less desirable once individuals have access to personal vehicles and licenses. 

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Meeting and sustaining a requirement that 100 percent of new passenger vehicle and light-duty truck sales be zero emission vehicles (ZEVs) requires everyone who acquires a new vehicle to only acquire ZEVs. This puts an onus on understanding resistance to ZEVs: who is resistant and why. These questions are addressed using survey data from repeated cross-sectional samples of all-car buying households in California in the years 2017, 2019, and 2021. Concepts of resistance are introduced and provisionally mapped onto Consideration, a multidimensional assessment of what consumers have already done vis-à-vis two types of ZEVs: battery and fuel cell electric vehicles (BEVs and FCEVs). Results indicate that active consumer resistance did not abate for BEVs over the study period, and that while it did abate slightly for FCEVs the probability of active resistance became less dependent on assessments of FCEV performance, fuel availability, or comparisons to conventional gasoline-fueled vehicles. Resistance based in political beliefs is extended from ZEVs to thepolicy requiring ZEVs using data from an additional survey of car-owning households in California from late 2023 to early 2024. The attitude that cost and convenience matter more in daily decisions than do environmental effects has a strong influence on the likeliness of disagreeing with the ZEV sales requirement. Conceptual shortcomings are noted in the mapping of resistance onto Consideration which limit the usefulness of Consideration as proxy for resistances going forward as is the lack direct measures of political affiliation in the extension to resistance to policy. A comprehensive set of suggestions to improve the direct measurement of different forms of resistance is provided. 

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California sets ambitious climate goals that demand a sharp reduction in the per-capita vehicle miles traveled (VMT). Traditionally, researchers have relied on three types of VMT data collection methods – travel surveys, passively collected data, and simulated data – to estimate VMT or understand factors affecting VMT. However, each of these methods has disadvantages for obtaining a reliable VMT dataset. Although travel surveys are an inexpensive way to collect VMT data with rich traveler attributes, they often suffer from bias and errors in reporting or recalling. Passively collected VMT data, such as traffic count data, can provide precise VMT data, yet they often lack information about “who” and “why” of travel. Lastly, simulated VMT data is useful when making counterfactual testing, but they are after all not real data. In this study, to fulfill the gap between current data collection strategies, the authors introduce a WhatsApp-based VMT data collection framework. The framework deployed on the Azure cloud platform automatically lets study participants submit photos of their odometer, communicates with them for administrative events, manages submitted images and other information of the participants, and displays that data on the web UI for the study administrator. With a pilot study with 77 participants, the authors successfully collected 173 photos of the odometer. Although only 3 photos out of 173 were unusable, demonstrating the capability of the framework, a couple of potential improvements, such as an interface that aligns the photo timestamps over the days or better recruitment approaches, should be addressed in future work.

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The transition to zero-emission freight transportation is a critical component of California’s climate strategy, yet the adoption of battery electric trucks (BETs) in both long-haul and short-haul operations faces significant challenges. Limited charging infrastructure, long charging durations, grid reliability concerns, and regulatory constraints—such as Hours of Service (HOS) requirements—pose operational hurdles for fleet operators. This study develops a comprehensive optimization framework for electric truck fleet management, addressing the interplay between infrastructure limitations, operational uncertainties, and energy cost fluctuations. This research will also provide scalable insights for policymakers and industry leaders, supporting the broader transition to sustainable freight transportation. Investments in ultra-fast charging infrastructure, extended-range BETs, and microgrid-based energy management are key to accelerating the electrification of freight operations while ensuring cost efficiency and operational resilience. For long-haul electric trucking, this study leverages real-world data to optimize charging stops, minimize delays, and ensure regulatory compliance. Using a combination of mixed-integer programming and linearization techniques, the proposed model dynamically identifies cost-effective charging strategies, balancing factors such as driver wages, energy costs, and charging delays. For short-haul trucking, this study introduces a novel dispatching model—the electric vehicle routing problem with backhauls and time windows under uncertainty.

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This study introduces MOVES-Matrix 4.0, an innovative high-performance implementation of MOVES 4.0.1 that generates exactly same energy and emission rate results as the Environmental Protection Agency’s latest version of MOVES 4.0.1, but allows users to deploy the MOVES model in complex and dynamic analyses. The team utilized the same conceptual design used in MOVES-Matrix 2014 and MOVES-Matrix 3.0, and updated the configurations on PACE supercomputing clusters to account for the programming changes with respect to MOVES databases (e.g., migration to MariaDB) and MOVES’ algorithm updates since MOVES2014b (e.g., extended VSP/STP parameters). The MOVES-Matrix 4.0 system develops sub-matrices of energy and emission rates by executing 181,818 MOVES runs to generate more than 5.8 trillion energy and emission rates in thepopulated matrix for a single modeling region (represented by a unique combination of fuel specification regime and inspection and maintenance program). Performance tests demonstrate that MOVES-Matrix 4.0 produces the exact same results as MOVES4 (insignificant internal rounding errors that are less than 0.0005%). In modeling applications, generating emission rates from MOVES-Matrix is 200 times faster than running a MOVES instance. MOVES-Matrix 4.0 is ready to be used for large-scale, dynamic transportation network analyses and emissions modeling, given its open-source nature, and its compatibility with various scripting languages.

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This report develops an equitable and sustainable freight-oriented land use (LU) methodology to support future planning activities, enabling the integration of freight activity across urban, suburban, and rural areas and facilitating the transition of heavy- and medium-duty vehicles toward zero-emission. The methods include a literature review to identify freight sustainable strategies, policy analysis at different scales, characterization of local context, and demand/supply patterns. The latter examines the spatial distribution and land use characteristics of freight facilities and retail/service sectors in the Sacramento region to inform sustainable and equitable planning strategies. This analysis identifies co-location patterns, accessibility gaps, and sectoral interactions using a multi-dimensional approach integrating spatial clustering, distance analysis, population-employment dynamics, and environmental burdens. Data sources include Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics (LODES), American Community Survey (ACS), CalEnviroScreen, and OpenStreetMap, alongside geospatial tools in R. The findings suggest the need for targeted interventions to address potential conflicts, service deserts, and environmental justice concerns. The study proposes actionable strategies for planners to support balanced economic development and improve access to essential services. 

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In 2018, pursuant to Senate Bill (SB) 743 (2013), the Governor’s Office of Planning and Research (OPR) and the California Natural Resources Agency promulgated regulations and technical guidance that eliminated automobile level of service (LOS) as a transportation impact metric for land development projects under the California Environmental Quality Act (CEQA), and replaced it with Vehicle Miles Traveled (VMT). The authors investigated the equity effects of VMT mitigation measures and developed a framework for evaluating those effects at the project level. The authors then applied the framework to two highway expansion case studies in California. They found that most VMT mitigation would be implemented at least partially within the project impact areas, as well as some disadvantaged communities, but would generally benefit communities outside of the project area, too. Most of the proposed mitigation measures would not displace existing residences or businesses or pose a significant risk of gentrification. Many of the measures showed substantial potential to improve accessibility to jobs, though less potential to improve accessibility to grocery stores. Community engagement and empowerment was harder to gauge. Overall, the five-part framework can provide a first-cut assessment of the equity effects of VMT mitigation measures during the environmental review phase of VMT-generating projects, like roadway expansions.

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