National Center for Sustainable Transportation

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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The increasing diversity of vehicle type holdings and growing demand for BEVs and PHEVs have serious policy implications for travel demand and air pollution. Consequently, it is important to accurately predict or estimate the preference for vehicle holdings of households as well as the vehicle miles traveled by vehicle body- and fuel-type to project future VMT changes and mobile source emission levels. Leveraging the 2019 California Vehicle Survey data, this report presents the application of a utility-based model for multiple discreteness that combines multiple vehicle types with usage in an integrated model, specifically the MDCEV model. The model results suggest the important effects of household demographics, residence location, and built environment factors on vehicle body type and powertrain choice and usage. Further the predictions associated with changes inbuilt environment factors like population density can inform the design of land-use and transportation policies to influence household vehicle holdings and usage that can in turn impact travel demand and air quality issues in California.

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Traffic congestion is a major problem in large cities worldwide. This project uses high-frequency data from the Los Angeles metropolitan area combined with an instrument that varies spatially and temporally to estimate the causal impact of an additional vehicle mile traveled on travel times. Specifically, the research team exploits the network structure of the Los Angeles highway system and uses crashes on close alternative routes as exogenous shocks to traffic demand. To do so, the team relies on Google Maps to determine the ideal route and alternatives for over 19,000 real-world commutes. The researchers estimate that at peak times an additional trip reduces speed by, on average, 0.22%. They find the optimal toll at peak times is 33 cents per mile, with the toll being lower, even zero, off-peak. The researchers show how this toll varies over space and time, as well as report on its distributional effects. This toll would more than double highway speeds during peak times and only requires reducing vehicle miles traveled (VMT) at the peak by 10%. The resulting social welfare gains are over two billion dollars per year.

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This report considers the role of local option sales taxes (LOSTs) for transportation in influencing spending patterns in regional transportation plans adopted by Metropolitan Planning Organizations (MPOs) in California’s four largest metropolitan regions. Through quantitative analysis of information on LOST measures placed on the ballot from 1976 to 2022, and through case studies of recent LOSTs placed on the ballot in two counties, patterns in LOST expenditure by mode and purpose are compared over time and across regions, and with spending allocated by MPOs in their long-range regional transportation plans.

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This project is to investigate how accessibility of city blocks is quantified through the transport systems and real traffic flow datafrom the Los Angeles Metropolitan Area. The authors investigate the reachability problem and provide a solution with a functional system that is capable of visualizing the reachability map (isochrone). Unlike other studies, this approach is data-driven and does not depend on mathematical graph-theory to compute the isochrone which requires intensive computation. Instead, it focuses on directly processing the large amount of traffic flow data that the Integrated Media Systems Center at USC has collected from the Regional Integration of Intelligent Transportation Systems (RIITS) for more than 10 years under the Center’s existing Archived Traffic Data Management System (ADMS) project. The reachability map construction is based on vehicle trajectories so the researchers devised the Data-Driven Trajectory Generator (DDTG), a data-driven, model-free, and parameter-less algorithm for generating realistic vehicle trajectory datasets from ADMS data. Since real world traffic is incomplete with lots of temporal and spatial missing data, the researchers studied imputation and interpolation methods to complete the dataset. Their experiments with real-world trajectory and traffic data show that the datasets generated by DDTG follow distributions that are very close to the distributions of a real trajectory dataset. Furthermore, to demonstrate the resultsfrom the proposed research, a web application was developed in which users can select a location, travel time, and the time of year to see the evaluated accessibility info in the form of an isochrone map. The outcomes of this project—synthetic vehicle trajectory dataset and reachability map construction—will be helpful in evaluating accessibility of city blocks for transport systems over a large area, essential for policymakers for effective city planning as well as to improve the well-being of citizens.

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This study investigates how the COVID-19 pandemic has transformed people’s activity-travel patterns, using datasets collected through three waves of surveys in spring 2020, fall 2020, and summer 2021. With this dataset, it was possible to investigate evolving behavioral choices and preferences among respondents at different timepoints: fall 2019 (recollection of the past), spring 2020, fall 2020, summer 2021, and summer 2022 (future expectations). The study highlighted a large shift among California workers from physical commuting to working remotely in 2020, which was followed by a transition towards hybrid work by summer 2021. The shift to remote work and hybrid work varied considerably across population subgroups, and was most popular among higher-income, better-educated individuals, and urban residents. In terms of household vehicle ownership change, those tech-savvy and variety-seeking individuals were more likely to increase or replace household vehicles, while those who are pro-environment and pro-active are less likely to do so. COVID health concerns show concurrent effects of encouraging the adoption of a more pro-active lifestyle during the pandemic, but also leading to an increased desire to own vehicles in the future. Regarding shopping patterns, the number of respondents who shop online at least once per week increased nearly five-fold between fall 2019 and spring 2020, but such magnitude somewhat diminished by fall 2020. In general, the pandemic has generated a mix of short-lived temporary changes and potential longer-term impacts. The study provides various strategies to help increase transportation and social equity among various population groups as the communities recover from the pandemic.

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Every year the UC Davis Campus Travel Survey includes questions related to travel to campus during different days in the reference week. Respondents from all role groups indicate how often they travel to the campus during the reference week for school or work. They also indicate the reasons for not coming to work in-person during the same time period. Travel to campus was highly impacted by the COVID-19 pandemic in the 2020-2021 school year as campus switched to remote instruction. In 2021-22, the campus returned to in-person working and instruction which led to increased physical travel. Travel to campus further increased in 2022-23 as more students and staff returned to campus for in-person learning and working. 

This report discusses some of the key results from the survey on questions related to physical travel to campus and reasons for no travel to campus before and after COVID-19 pandemic. It presents a comparative analysis of physical travel to campus during the reference week across three years, 2019-20, 2021-22, and 2022-23. The report also provides details on how telecommuting patterns changed before and after the pandemic among both students and employees. 

This research developed EV Explorer 2.0, an online vehicle cost calculator (VCC) to meet the requirements of transportation network company (TNC) drivers considering acquiring an electric vehicle (EV). The tool was built to also support the needs of other users considering an EV, including other types of gig economy drivers as well as the general population of non-professional drivers. EV Explorer 2.0 includes several important features and functionalities to support the TNC driver use case that are not found in any other available tool: (1) It allows users to estimate TCO for used vehicles as well as new (others only estimate TCO for new vehicles); (2) Outputs include ridehail-driving income estimates, accounting for EV trip bonuses offered by Uber, net driving costs; (3) Estimates of total cost of driving (TCD) include charging network membership fees and charging session fees (in addition to electricity prices). It also includes key features found in other leading tools, such as presenting and tailoring EV purchase/lease incentive estimates (based on a database we developed), and innovative features to benefit all users, such asanimations conveying the social and environmental impacts of vehicle choice. Design features were informed and validated inuser testing with TNC drivers who had expressed interest in EV adoption.

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In the early stages of the COVID-19 pandemic, cities across the globe converted street space to non-automobile uses. This project studies four of these slow street programs in the U.S.: in Los Angeles, Portland, Oakland, and San Francisco. In each city, the slow streets (implemented in late spring to early fall 2020) are used as a treatment and compared to non-implemented control groups. The dependent variable is counts of dockless scooter trips passing a mid-block screenline for time periods both before and after slow street implementation. Those dockless scooter counts were obtained from historical data provided by Lime, a dockless scooter provider in each of the study cities. Two methodological approaches were used: differences-in differences (DID) and panel regression analysis with block fixed effects. For the DID analysis, the researchers used networks of candidate slow streets that were not implemented as the control group. Such control networks were available in Los Angeles, Oakland, and SanFrancisco. For the panel analysis, they used slow street segments implemented later in the study period as control segments for earlier implemented slow street segments, including fixed effects for blocks and for time periods in the panel regressions. The findings show statistically significant associations between increased dockless scooter trips and slow street implementation in each study city, using both DID and panel analyses. The associations are robust to different specifications. The authors calculate the magnitude of the slow street treatment effect by dividing the estimated treatment effect by a 2019 baseline of dockless trip counts. In the DID analysis, they find that slow street implementation increased dockless scooter trip counts from 22.16% to 74.5%, relative to a 2019 (before slow streets) baseline. In the panel analysis, the increase in dockless trip counts on slow streets ranged from 10.77% to 16.75%, relative to a 2019 baseline.

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This report presents the views expressed by UC Davis commuters related to various issues, challenges, and concerns that they have with the transportation facilities and services in the community in an open-ended question on the 2022-23 UC Davis Campus Travel Survey. The authors analyzed the contents of comments provided by survey respondents and identified common themes to group the comments. Based on this content analysis, they have grouped the views of students and employees across the themes of safety, parking facilities, transportation for people with disabilities, transit issues, biking, challenges with micro-mobility, and EV charging facilities. Only selected comments are included. They present the comments unedited and anonymously, indicating only whether the comment was provided by an Undergraduate Student (U), Graduate Student (G), Faculty (F), Post-Doc (P), or Staff (S).