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Open Access Publications from the University of California


The Institute of Transportation Studies (ITS) at the University of California, Irvine (UCI) is part of the University of California Multicampus Research Unit (MRU) of the same name (UC ITS), which has branches at the Irvine, Davis, Berkeley and UCLA campuses.  The UC ITS mission is to serve as the premier university-based transportation research center in the world, recognized for advancing the state of the art in transportation engineering and planning, delivering multidisciplinary research and education, and informing policy through direct engagement with leaders from the public sector, industry, and nongovernmental organizations.

Research at ITS Irvine involves faculty and students from The Henry Samueli School of Engineering, the School of Social Sciences, the School of Social Ecology, the Paul Merage School of Business, the School of Law, and the Bren School of Information and Computer Science. The Institute also hosts visiting scholars from the U.S. and abroad to facilitate cooperative research and information exchange, and sponsors conferences and colloquia to disseminate research results. ITS is a member of the Council of University Transportation Centers, (CUTC), and is a regular participant in the USDOT's University Transportation Centers program.

Research at ITS covers a broad spectrum of transportation issues. Currently funded research projects at Irvine focus upon:

  • energy and the environment
  • alternative-fueled vehicles
  • transportation pricing and demand management
  • transportation/land use relationships
  • transportation safety
  • freight and logistics
  • advanced transportation management systems
  • advanced traveler information systems
  • transportation network optimization
  • real-time simulation of intelligent transportation systems
  • microsimulation models for transportation planning
  • activity-based approaches to travel behavior
  • GPS/GIS for transportation data collection and analysis

ITS Irvine is part of a University of California (UC) multicampus organized research unit with branches on the Berkeley, Davis, Irvine and Los Angeles campuses.  The four branches collaborate in the statewide transportation research program funded by the Road Repair and Accountability Act of 2017 (SB1).  In addition, ITS Irvine is a member of the Pacific Southwest Region 9 University Transportation Center (PSR-UTC), a federally-designated center for research on transportation systems and policy. The Institute also plays a major role in the intelligent transportation and telematics research component of the California Institute for Telecommunications and Information Technology (Cal IT2).

UC Irvine Institute of Transportation Studies

There are 389 publications in this collection, published between 1976 and 2024.
Policy Briefs (18)

Will COVID-19 Worsen California’s Truck Driver Shortage?

The trucking industry serves as the backbone of the nation’s economy. In 2018, approximately 3.5 million truck drivers were delivering over 70% of all freight tonnage in the United States, generating close to $800 billion in gross revenue annually.1 While 3.5 million truck drivers represents a significant number of jobs, it is not enough to satisfy demand. The trucking industry suffers from a chronic shortage of drivers. Nearly 70,000 additional heavy-duty tractor-trailer drivers in the United States were needed at the end of 2018, according to the American Trucking Associations. And COVID-19 has brought new challenges that may amplify or dampen the driver shortage and in turn impact supply chains. For example, what if a small percentage of long-haul truck drivers became ill? Would it cripple the industry? Would it significantly delay the delivery of essential medical supplies and equipment? New research from UC Irvine explored the challenges imposed by COVID-19 on truck drivers by conducting a literature review, looking at past crises, and interviewing academic and industry experts.

A Review of Reduced and Free Transit Fare Programs in California

Free or reduced-fare transit passes have the potential to increase transit ridership, enhance the mobility of underserved groups (e.g., low-income, seniors, and youth), and reduce the environmental footprint of transportation. Under the right conditions, these programs can also help reduce traffic congestion and motor vehicle use. Transit agencies in different parts of the world have been experimenting with free or reduced-fare transit for decades, yet there are still substantial concerns about the impacts of free or reduced-fare transit on ridership as well as on the fiscal health of transit agencies. Some of these concerns linger partly because rigorous academic studies on free and reduced-fare transit passes are still rare.

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Research Reports (22)

New Methods for Monitoring Spatial Truck Travel Patterns in California Using Existing Detector Infrastructure

This study developed a methodology to accurately estimate network-wide truck flows by leveraging existing point detection infrastructure, namely inductive loop detectors. The tracking model identifies individual trucks at detector locations using advanced inductive signatures and matches vehicle pairs at detector locations, using an extended form of the Bayesian classification model to estimate matching and non-matching probabilities of the vehicle pairs  Several vehicle feature selection and weighting methods including Self Organizing Map and K-means clustering were applied to better identify individual vehicles from signature data.  It was shown that the proposed extensive feature processing enhanced vehicle identification performance even among vehicle pools sharing similar physical configurations. The developed model was tested along an approximately 5.5-mile freeway segment on I-5 and CA-78 in San Diego, California where only 67 percent of the total trucks were observed at both up- and down-stream detector sites. Results showed balanced performances in exactness and completeness of matching with 91 percent of correct outcomes for multi-unit trucks

Improved California Truck Traffic Census Reporting and Spatial Activity Measurement 

The Federal Highway Administration (FHWA) vehicle classification scheme is designed to serve various transportation operational and planning needs. Many transportation agencies rely on Weigh-In-Motion and automatic vehicle classification sites to collect vehicle classification count data. However, these systems are not widely deployed due to high installation and operations costs. One cost-effective approach investigated by researchers has been the use of single inductive loop sensors as an alternative to obtain FHWA vehicle classification data. However, most models do not accurately classify under-represented classes, even though many of these minority classes pose disproportionally adverse impacts on pavement infrastructure and the environment. As a consequence, previous models have not been able to adequately classify under-represented classes, and the overall performance of the models are often masked by excellent classification accuracy of the majority classes, such as passenger vehicles and five-axle tractor trailers. This project developed a bootstrap aggregating (bagging) deep neural network (DNN) model on a truck-focused dataset obtained from Truck Activity Monitoring System (TAMS) sites, which leverage existing inductive loop sensor infrastructure coupled with deployed inductive loop signature technology, and already deployed statewide at over ninety locations across all Caltrans Districts. The proposed method significantly improved the model performance on truck-related classes, especially minority classes such as Classes 7 and 11 which were overlooked in previous research studies. Remarkably, the proposed model is also capable of distinguishing classes with overlapping axle configuration, which is generally a challenge for axle-based sensor systems. 

Risk Assessment for Security Threats and Vulnerabilities of Autonomous Vehicles

Autonomous vehicles (AVs) heavily rely on machine learning-based perception models to accurately interpret their surroundings. However, these crucial perception components are vulnerable to a range of malicious attacks. Even though individual attacks can be highly successful, the actual security risks such attacks can pose to our daily life are unclear. Various factors, such as lack of stealthiness, cost-effectiveness, and ease of deployment, can deter potential attackers from employing certain attacks, thereby reducing the actual risk. This research report presents the first quantitative risk assessment for physical adversarial attacks on AVs. The specific focus is on attacks on AV’s perception components due to their highly critical function and representation in existing research. The report defines the daily-life risk as the likelihood that a given type of attack will be employed in real life and the authors develop a problem-specific risk scoring system and accompanying metrics. They perform an initial evaluation of the proposed risk assessment method for all the reported attacks on AVs from 2017 to 2023. They quantitatively rank the daily-life risks posed by each of eight different categories of attacks s and find three attacks with the highest risks: 2D printed images, 2D patches, and coated camouflage stickers, which deserve more focused attention for potential future mitigation strategy development and policy making.

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Working Paper Series (292)

An Analysis of PM and NOx Train Emissions in the Alameda Corridor, CA.

The Alameda corridor provides a crucial rail link for moving freight in and out of the Ports of Los Angeles and Long Beach, also known as the San Pedro Bay Ports (SPBP). While the benefits of this trade are enjoyed by the whole nation, the associated air pollution costs are born mostly by the people who live in the vicinity of the Alameda corridor and the two freeways (the I-710 and the I-110) that serve the Ports. Although they are more energy efficient than trucks, trains contribute heavily to regional air pollution; in addition, rail traffic in the South Coast Air Basin is projected to almost double in the next twenty years. This paper presents an analysis of the emissions and the dispersion of PM and NOx emitted by train operations in and around the Alameda corridor. We find spatial and temporal variations in the dispersion of these pollutants, which justifies our approach. Moreover, the railyards in our study area are responsible for the bulk of PM and NOx emissions (compared to line haul operations). While PM emissions from train operations contribute only a fraction of the recommended maximum concentration, NOx emissions go over recommended guidelines in different areas. The affected population is mostly Latino or African American. Our approach is also useful for better understanding trade-offs between truck and rail freight transport.

A Personal Vehicle Transactions Choice Model for Use in Forecasting Demand for Future Alternative-Fuel Vehicles

A discrete choice model has been developed in which the choice alternative consist of vehicle transactions rather than portfolios of vehicle holdings. The model is based on responses to customized stated preference questions involving both hypothetical future vehicles and the household's current vehicle holdings. The stated choices were collected from 4747 survey respondents located throughout most of the urbanized portions of the state of California. Respondents were asked what their next vehicle transaction would most likely be (replace a current vehicle, add another vehicle, or dispose of a current vehicle), and respondents who wanted to replace or add vehicles were asked to indicate their most preferred vehicle from a set of six hypothetical vehicles. The hypothetical vehicles were described in terms of fourteen attributes, manipulated according to an experimental design. 

The transactions model is a multinomial logit model of the choice of the hypothetical vehicles and whether or not the hypothetical vehicle will be a replacement or addition to the household fleet. The model is conditioned on the household's current vehicle stock. and the characteristics of the current vehicles are important explanators of the stated preference choices. In addition to the model estimates, forecasts are given for a base case scenario in 1998. 

This model is one component in a micro-simulation demand forecasting system being designed to produce annual forecasts of new and used vehicle demand by type of vehicle and geographic area. The system will also forecast annual vehicle miles traveled for all vehicles and recharging demand by time of day for electric vehicles. These results are potentially useful to utility companies in their demand-side management planning, to public agencies in their evaluation incentive schemes, and to manufacturers faced with designing and marketing alternative-fuel vehicles. 

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