conference paper
research report
This research project investigates the integration of point detector data to enhance vehicle tracking for heavy-duty trucks in Southern California. Leveraging a combination of automated license plate readers (ALPR) and inductive signature data, we aim to enhance the accuracy of vehicle monitoring, addressing limitations of traditional telematics data, such as sampling bias and data quality issues. Our research draws upon a dataset from sites deployed in the Freight Mobility Living Laboratory (FML2) testbed, enabling a comprehensive analysis of truck activity. Employing a Bayesian Logit model and principal component analysis, we developed an innovative framework aimed at improving truck tracking accuracy across key routes by matching inductive signatures. Findings indicate that integrating these advanced data sources leads to more accurate classifications and tracking of freight vehicles, ultimately contributing to better-informed planning and environmental sustainability efforts by Caltrans, the California Air Resources Board, and local agencies. By focusing on critical highways like I-710 and SR-60, we identify significant opportunities for optimizing freight mobility and addressing the increasing pressures on transportation infrastructure in urban settings. This research highlights the importance of innovative data integration techniques in developing effective traffic management strategies that meet the evolving demands of urban freight operations.
published journal article |
Jan 2025
policy brief
Researchers at the University of California, Irvine, are looking into what may become the “new normal” in work and work-related travel and the consequences that could have on traffic conditions, efforts to address climate change, and the future of our urban areas, as well as our daily lives. They find, for instance, that current research is largely equivocal about the consequences of telecommuting on where individuals choose to live, their day-to-day travel, and urban/metropolitan development. Equally unclear is how increased telecommuting may impact efforts to create more sustainable and inclusive communities. In light of this uncertainty, they suggest planners and researchers need to pay more attention to the changing nature of urban commuting and how it can play an important role in shaping a more desirable future.
research report |
Jan 2024
conference paper
Road accidents involving heavy duty trucks have long been of concern but detecting the impact on accidents of various policies is typically challenging. The objective of this study is to understand if there was a change in accident rates on a busy freight corridor (the I-710 freeway in Los Angeles County, California) connecting the Ports of Los Angeles and Long Beach to nearby intermodal rail, trans-loading facilities, and warehouses after the implementation of the PierPASS program on July 23, 2005. The authors analyzed 2,043 accidents that occurred in 2005 on the I-710 freeway; approximately 27.8 percent of these accidents involved trucks. The authors estimated a three stage hierarchical Bayesian change point model with MCMC developed by Carlin et al. (1992) to evaluate whether the implementation of the PierPASS program resulted in a change of accident rates on the I-710. After successfully verifying and validating the authors’ model on the dataset used by Carlin et al. (1992), and Raftery and Akman (1986), the authors analyzed road accidents on the I-710 for 2005 filtered by four (approximately 6 mile) segments and four time periods corresponding to different traffic regimes. The authors generated the probability distribution of the difference between accident rate parameters and built 95% High Density Intervals. Results indicate that there was no significant change in accident rate in 2005 following the implementation of PierPASS. To the authors’ knowledge, this is the first time that a three stage hierarchical Bayesian change point model with MCMC was applied to a transportation problem.
published journal article
Conventional traffic assignment methods assume that the origin-destination (OD) demand is uniformly distributed over time to estimate the traffic pattern. This assumption does not hold for modeling peak periods of congestion in which the OD demand is time varying. In this paper, we present a dynamic traffic assignment model with traffic-flow relationships based on a bi-level optimization framework. Our assignment variable is the number of vehicles present on a link during a time step, rather than traffic flow, which is used in static assignment. Using the modified Greenshields speed-density relationship, we derive a link-cost function that is monotonically nondecreasing and convex with respect to density. To capture traffic dynamics, we use short time-steps. The model prevents violations of the first-in-first-out (FIFO) condition using constraints on the distances moved by vehicles during each time step. A solution algorithm which resembles a Stackelberg leader-follower problem is presented, and numerical results from networks of different sizes demonstrate that the proposed model performs satisfactorily.
conference paper
In recent years a Clean Trucks Program (CTP) has been enacted at California’s San Pedro Bay Ports (SPBP) of Long Beach and Los Angeles, to help address major environmental issues associated with port operations. “Clean trucks” (meeting 2007 model year emission standards) that utilized public funds to replace older, polluting drayage trucks were required to be fitted with GPS units for compliance monitoring. In late 2010, 94% of cargo moves at SPBP were reportedly made by clean trucks. The study reported in this paper is based on a year of such GPS data for a sample that in December, 2010 comprised 545 clean drayage trucks. Drayage trucks carry port cargo (mostly containers) to and from the ports and intermodal rail and transloading facilities (drayage trucks have at least one trip end on port or intermodal rail facility property). An analytical framework is introduced for processing the GPS data from these trucks to both interpret the trip chaining or tour behavior of clean drayage trucks, and to prepare sufficient tour data for clean truck modeling at the SPBP. It is found that SPBP clean trucks make an average of 3.1-3.9 stops per tour, depending on the open or closed nature of the tour, fuel type and monthly cargo type. Furthermore, newer diesel trucks tend to travel further and longer than liquefied natural gas (LNG) trucks, and monthly cargo volume changes have an effect on clean truck tour behaviors. An important finding is that clean trucks at the SPBP rely on daily-based operations and one day of travel behavior is not necessarily representative of any other day. These insights into clean truck behavior at the SPBP potentially provide more accurate depictions of current conditions and better projections of future conditions for freight related improvement plans and models.
conference paper
working paper
working paper
In the next section, a summary and critique of the convention trip-based approach is presented, followed by an overview of activity-based approaches, focusing on how these approaches address the various limitations of the conventional model. This is followed by a review of representative examples of activity-based approaches, including several perhaps best considered as contributions to understanding travel behavior, and several oriented toward direct application in forecasting and policy analysis. Some summary comments are then provided including an assessment of the future of both trip-based and activity-based approaches.
research report
This report provides an overview of the federally-sponsored Anaheim Advanced Traffic Control System Field Operations Test, and of the technical issues associated with the evaluation of SCOOT performance during this test. The primary FOT objective was the implementation and performance evaluation of adaptive traffic signal control technologies including an existing second generation approach, SCOOT, and a 1.5 generation control (1.5GC) approach under development. Also selected for implementation was a video traffic detection system (VTDS). The SCOOT evaluation was defined relative to existing, first generation UTCS-based control but using standard field detectorization rather than that normally associated with SCOOT. Furthermore, SCOOT was installed to operate in parallel to UTCS. The 1.5GC system was planned to be efficiently utilized to update baseline timing plans. The VTDS was planned for use as a low cost system detector for deployment in critical areas.Both SCOOT and the VTDS were implemented with some degree of success, with technical and institutional issues limiting expected performance. Technical issues that limited SCOOT performance included existing communication and controller systems of lower quality than anticipated. Corresponding institutional factors included inconsistent project management due to staff changes and delays due to contractual issues. Both SCOOT and a modified version of the VTDS are in current use in selected areas, with plans for system expansion.This evaluation report summarizes an introduction to the project, the evaluation objectives for Tasks A and B, and Task A of the three part evaluation project. Separate reports summarize Tasks B and C, assessment of institutional issues and the advanced Video Detection Systems, respectively
