UC Irvine Institute of Transportation Studies

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.

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.

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

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. 

While a large amount of effort has been devoted to making and updating local transportation plans, little is known about the informational contents of these plans and their use patterns.  This project attempted to identify key informational contents of Californian cities’ transportation plans and to investigate how the plan contents can be used by various stakeholders through (i) a plan content analysis of a sample of general plans (recently adopted by eight municipalities in Orange County, California) and (ii) a plan use survey and follow-up analysis of survey responses. All plans analyzed were found to convey a variety of information about their visions, goals, policies, and implementation strategies, but the plan content analysis revealed substantial variation in the way cities composed their general plans and integrated them with other plans/players. Compared to land use elements, circulation elements tended to focus more on their connections with other agencies (external consistency) than on internal consistency. The plan use survey yielded a low response rate which may indicate limited use of plans in the field. However, a majority of the survey responses were positive about the usefulness and usability of general plans. In particular, the survey participants reported that they found the plans comprehensive, visionary, and well-organized, while relatively lower scores were obtained for two evaluation criteria: ‘[the plan] clearly explains what actions will be taken and when’ and ‘[the plan] is relevant to my everyday life and/or work’.  Furthermore, some respondents reported that they used general plans not for their professional duties but for other (non-conventional) purposes, suggesting that plan contents could be used for a variety of decision-making processes.

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 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.