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research report

Quantifying Major Travel Delay Reduction Benefits from Shifting Air Passenger Traffic to Rail

Abstract

This study provides a method to quantify the benefits of reducing the costs from flight delays by shifting air passenger traffic to high-speed rail (HSR). The first estimate was the number of flight reductions by each quarter hour for airport origin and destination pairs based on HSR ridership forecasts in the California High-Speed Rail 2020 Business Plan. Lasso models are then applied to estimate the impact of the reduced queuing delay at SFO, LAX and SAN airports on arrival delays at national Core 29 airports. Finally, these delay reductions are monetized using aircraft operating costs per hour and the value of passenger time per hour. The research team applied several different variations of this approach, for example, considering delay at all 29 Core airports or just major California airports, different scenarios for future airport capacity and flight schedules, and different forecasts for future HSR ridership. The estimated mid-range delay cost savings are $51-88 million (2018 dollars) in 2029 and $235-392 million (2018 dollars) in 2033. The estimated savings are similar to, but slightly lower than, those based on cost estimates to upgrade airport capacity to handle passenger traffic that could be diverted to HSR.

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published journal article

What factors influence the adoption and use of dockless electric bike-share? A case study from the Sacramento region

Abstract

Now that dockless electric bike-share systems have become a fixture in major cities in the U.S., it is important to understand why someone chooses to use the service. Beyond socio-demographics, factors such as mode-related attitudes, the social environment, and the availability of the service may influence both its adoption and frequency of use. This study modeled dockless electric bike-share adoption and use frequency using data collected from a household survey and a bike-share user survey from the Sacramento region. The study used integrated choice and latent variable models to understand the influence of attitudes on electric bike-share adoption and use frequency. Three latent variables − bike affinity, car necessity, and bike social environment − were developed using responses to eleven statements. The models show that apart from socio-demographics, attitudes related to bike affinity and bike social environments significantly and positively influence bike-share adoption with a large effect size, whereas the car necessity attitude significantly and negatively influences the use frequency with a large effect size. Individuals with low incomes are less likely to adopt the bike-share service. The availability of electric bike-share in key locations (home and/or work and/or school) where an individual frequently goes significantly and positively influences adoption with a large effect size but does not influence use frequency. Findings from this study can inform the dockless electric bike-share policies of cities as well as the rebalancing strategies of service providers.

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policy brief

Truck Parking and Idling is Having an Impact on Disadvantaged Communities in California

Abstract

Under California Assembly Bill 617 (Garcia, 2017), local and state agencies are working to reduce air pollution exposure in low income communities. These communities—often referred to as AB 617 communities—are disproportionately impacted by air pollution due to their proximity to transportation corridors, industrial installations, and logistics centers. A research team at the University of California, Davis investigated the impact of truck parking related activities on air quality in California’s AB 617 communities in Kern County, including truck idling, time spent searching for parking, and parking locations in communities. Searching for parking involves trucks driving extra miles to find available parking spaces, which leads to additional fuel consumption and increased emissions of pollutants such as nitrogen oxides (NOx) and particulate matter (PM 2.5 and PM 10). Once parked, prolonged or illegal parking can exacerbate congestion, noise, and localized pollution. These combined activities heighten exposure to harmful emissions in EJ communities, potentially leading to health issues (e.g., asthma and cardiovascular diseases). A comprehensive policy framework addressing truck parking facilities, management, and air pollution control is crucial for improving air quality and living conditions in AB 617 communities. There are a number of initiatives that could contribute to improving the conditions on these communities.

An Analysis of Travel Characteristics of Carless Households in California

Status

Complete

Project Timeline

August 1, 2016 - July 31, 2017

Principal Investigator

Project Team

Suman Mitra

Campus(es)

UC Irvine

Project Summary

In spite of their substantial number in the U.S., the research team’s understanding of the travel behavior of households who do not own motor vehicles (labeled “carless” herein) is sketchy. The goal of this paper is to start filling this gap for California. The research team performs parametric and non-parametric tests to analyze trip data from the 2012 California Household Travel Survey (CHTS) after classifying carless households as voluntarily carless, involuntarily carless, or unclassifiable based on a California Household Travel Survey question that inquires why a carless household does not own any motor vehicle. The research finds substantial differences between the different categories of carless households. Compared to their voluntarily carless peers, involuntarily carless households travel less frequently, their trips are longer and they take more time, partly because their environment is not as well adapted to their needs. They also walk/bike less, depend more on transit, and when they travel by motor vehicle, occupancy is typically higher. Their median travel time is longer, but remarkably, it is similar for voluntarily carless and motorized households. Overall, involuntarily carless households are less mobile, which may contribute to a more isolated lifestyle with a lower degree of well-being. Compared to motorized households, carless households rely a lot less on motor vehicles and much more on transit, walking, and biking. They also take less than half as many trips and their median trip distance is less than half as short. This study is a first step toward better understanding the transportation patterns of carless households.

State-Level Strategies for Reducing Vehicle Miles of Travel

Status

Complete

Project Timeline

August 1, 2016 - July 31, 2017

Principal Investigator

Campus(es)

UC Davis

Project Summary

California has set ambitious goals for reducing its greenhouse gas emissions to 40% below 1990 levels by 2030 and 80% below 1990 levels by 2050. To meet these goals, the state must achieve a 15 percent reduction in total travel by light-duty vehicles by 2050 compared to expected levels.1,2 Under current state policies, reductions of this magnitude are unlikely. Strong evidence exists that strategies across four categories – pricing, infill development, transportation investments, and travel demand management programs – can reduce vehicle miles of travel (VMT).3 The state can directly implement some of these strategies, particularly pricing strategies, through state-level policies. Others depend on actions by regional and local governments, though state-level policies can encourage their implementation through incentives, requirements, or other mechanisms.

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policy brief

Examining Both Trip Level Mode Replacements and Daily Activity Patterns of Users is Required to Understand the Sustainability Potential of Micromobility

Abstract

Micromobility options such as electric bike-share and scooter-share services are a fundamental part of the existing shared mobility landscape. Research has shown that micromobility use can reduce car dependence. This is accomplished through trip-level mode replacement and adjustments in mode-use configurations in daily travel. Understanding the full potential of micromobility services as a car replacement can help cities better plan for the services to meet environmental sustainability goals. Researchers at the University of California, Davis collected GPS-based travel diary data from individual micromobility users from 48 cities in the US and examined their travel behavior and micromobility use patterns. They found that micromobility services can displace car use. To achieve environmental sustainability goals, cities must pursue options that will deliver benefits, such as micromobility services. This policy brief summarizes the findings from that research and provides policy implications.

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policy brief

Traffic Collisions Change How Victims Think About Safety

Publication Date

September 1, 2025

Author(s)

Md. Musfiqur Rahman Bhuiya, Jesus M. Barajas, Prashanth Venkataram

Abstract

Traffic safety remains a pressing concern in California. Over the past five years, the state has averaged more than 3,751 reported traffic fatalities annually, with likely more unreported. While policies and research often focus on crash prevention and severity reduction, less is known about how collisions affect individuals’ travel behavior and perceptions of road safety. To better understand these effects, the research team conducted interviews and focus groups with people who had direct or indirect experience with traffic collisions and near misses. The researchers also spoke with professionals who support collision victims, such as physicians, therapists, faith leaders, and advocacy groups representatives. Discussions focused on perceptions of road safety, transportation mode choices, and travel behavior of someone involved in a traffic collision or near miss before and after the incident.

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published journal article

Investigating Travel Demand Heterogeneity During and After the Pandemic in the Northern California Megaregion: A Data-Driven Analysis of Origin-Destination Structural Patterns

Abstract

The study delves into the complexities of travel disruption and recovery during and after the COVID-19 pandemic. Using a data-driven methodology, we explore spatial-temporal patterns across regions by times of the day, weekdays/weekends, and trip purposes. Using passively collected location-based data from January 2019 to October 2021 in the Northern California Megaregion, our analysis compares travel patterns through the structural similarity of origin-destination (OD) matrices. Introducing the concept of a “local sliding geographical window” based on natural trip flow, the study identifies various impacts of the pandemic on travel demand including but not limited to (a) trip volume and recovery (e.g., weekday trips dropped by 47% in April 2020, gradually recovering already by October 2021); (b) impact on home-based work and other trips which were significantly disrupted on weekdays compared with non-home-based; (c) OD pattern changes (e.g., all sub-regions experienced significant changes, but the San Francisco Bay area faced the maximum disruptions); (d) gradual recovery with regional variations (e.g., San Francisco lagged in its travel activity recovery but this improved after April 2021, whereas the Northern San Joaquin Valley recovered fastest); (e) disruption and recovery linked to socioeconomic factors (e.g., parts of San Francisco, characterized by higher income, white-collar jobs, faced maximum disruption, whereas the Northern San Joaquin Valley, with a higher proportion of blue-collar workers, experienced the least disruption); and (f) differential recovery rates across and within regions, with areas rich in white-collar jobs showing slower recovery for work trips compared with areas with a higher proportion of blue-collar jobs.

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published journal article

Teleworkers and Physical Commuters During the COVID-19 Pandemic: the Change in Mobility Related Attitudes and the Intention to Telecommute in the Future

Abstract

The COVID-19 pandemic has disrupted commuting habits, with many individuals shifting to telecommuting. This study examines the impact of disrupted commuting habits on psychological constructs, such as attitudes or active lifestyle. Using longitudinal survey data from the California panel study of emerging transportation, the study compares two groups (those who started telecommuting, N = 458, and those who continued physically commuting, N = 523) at two points (early pandemic 2020 and later pandemic 2021). Exploratory factor analysis was used to extract the latent psychological constructs and structural equation modeling was used to model the intention to telecommute in the future for each year. Results show that some psychological constructs (such as attitude toward sustainable modes) remain stable across groups and time, while others (such as concern about pathogens) depend on both group and stage of the pandemic. The intention to telecommute in the future remains high and is mainly dependent on individuals’ attitude toward it and their tech-savviness, rather than on a concern about pathogens or demographics. The findings may inform policies that promote sustainable and flexible mobility options, like telecommuting, that have the potential to enhance work-life balance in a post-pandemic world.

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research report

Drivers’ Responses to Eco-driving Applications: Effects on Fuel Consumption and Driving Safety

Abstract

Onboard eco-driving systems provide drivers with real-time information about their driving behavior and road conditions, encouraging them to optimize their driving speed and consequently reduce fuel consumption and emissions. However, there are barriers to making eco-driving a habit. To determine the elements that influence drivers’ intentions to practice eco-driving and their acceptance of eco-driving technology, the research team developed a theoretical model based on established theories on planned behavior, technology acceptance, and personal goals. The findings showed that drivers’ intention to practice eco-driving has an indirect effect on their intention to use the system via the factor of perceived ease of use. The research team also explored how cognitive distraction while using an eco-driving system can be a potential barrier to acceptance. The intent is to put forward a solution to improve drivers’ usage of eco-driving by turning off guidance when the system detects that the driver is experiencing serious distraction. To investigate how to detect a driver’s cognitive distraction status when they are interacting with an eco-driving system, this project used a driving simulator and leveraged machine learning algorithms to classify drivers’ attentional states. The findings showed that the glance features played a more important role than the driving features in cognitive distraction.