UC Irvine Institute of Transportation Studies

Insufficient charging/fueling infrastructure poses a major challenge to achieving U.S. policy goals for transitioning the heavy-duty vehicle (HDV) sector to zero-emission vehicles. Addressing the infrastructure needs of HDV fleet operators, who are key demand-side stakeholders, is crucial for developing effective solutions and strategies. This study investigates these needs through a fleet survey of California’s drayage sector, focusing on battery electric trucks. Key aspects examined include preferences for charging locations, access types, charging duration, time-of-day for charging, and innovative solutions like Truck-as-a-Service. Analyzing responses from 53 companies with varying fleet sizes, annual revenues, and operational characteristics, the study employed a comprehensive exploratory approach, utilizing descriptive analysis, thematic analysis, and hypothesis testing. Findings reveal that while most fleets preferred on-site charging, about a quarter, primarily smaller fleets with five or fewer trucks, preferred both on-site and off-site options. Private access was often favored for on-site facilities, though some respondents recognized the benefits of shared access for expanding operational coverage. The study also identified a need for faster charging solutions at both off-site and on-site locations, particularly for long-haul or mixed operations. Time-of-day preferences varied widely, driven by the need for efficient operations. Furthermore, a small proportion of participating fleets preferred Truck-as-a-Service over traditional procurement, predominantly among smaller fleets or those with lower revenues. The comprehensive research findings contribute to a deeper understanding of charging infrastructure needs and offer practical insights for policy practitioners and industry stakeholders committed to advancing zero-emission infrastructure.

Many U.S. states are supporting the transition of the heavy-duty vehicle (HDV) sector to zero-emission vehicles (ZEVs), with California leading the way through its policy and regulatory initiatives. Within various HDV fleet segments, California’s drayage fleets face stringent targets, requiring all vehicles newly registered in the Truck Regulation Upload, Compliance, and Reporting System to be ZEVs starting January 2024, and all drayage trucks in operation to be zero-emission by 2035. Understanding fleet operator behavior and perspectives is crucial for achieving these goals; however, it remains a critical knowledge gap. This study investigates the preferences and influencing factors for ZEVs among drayage fleet operators in California. We conducted a stated preference choice experiment survey, developed based on previous qualitative studies and literature reviews. With participation from 71 fleets of various sizes and alternative fuel adoption status, we collected 648 choice observations in a dual response design, consisting of a forced choice between ZEVs and a free choice between ZEVs and status quo alternatives. Multinomial logit model analyses revealed driving range and purchase costs as significant factors for ZEV adoption, with charging facility construction costs also critical in hypothetical choices between ZEVs and status quo alternatives. Fleet or organization size also influenced ZEV choices, with large fleets more sensitive to operating costs and small organizations more sensitive to off-site stations. These findings enhance our understanding in this area and provide valuable insights for policymakers dedicated to facilitating the transition of the HDV sector to zero-emission.

Throughout the ongoing COVID-19 pandemic, changes in daily activity-travel routines and time-use behavior, including the widespread adoption of telecommuting, have been manifold. This study considers how telecommuters have responded to the changes in activity-travel scheduling and time allocation. In particular, we consider how workers utilized time during the pandemic by comparing workers who telecommuted with workers who continued to commute. Commuters were segmented into those who worked in telecommutable jobs (potential telecommuters) and those who did not (commuters). Our empirical analysis suggested that telecommuters exhibited distinct activity participation and time use patterns from the commuter groups. It also supported the basic hypothesis that telecommuters were more engaged with in-home versus out-of-home activity compared to potential telecommuters and commuters. In terms of activity time-use, telecommuters spent less time on work activity but more time on caring for household members, household chores, eating, socializing and recreation activities than their counterparts. During weekdays, a majority of telecommuters did not travel and in general this group made fewer trips per day compared to the other two groups. Compared to telecommuters, potential telecommuters made more trips on both weekdays and weekends while non-telecommutable workers made more trips only on weekdays. The findings of this study provide initial insights on time-use and the associated activity-travel behavior of both telecommuter and commuter groups during the pandemic.

Trucks have a significant impact on infrastructure, traffic congestion, energy consumption, pollution and quality of life. To better understand truck characteristics, comprehensive high resolution truck data is needed. Higher quality truck data can enable more accurate estimates of GHGs and emissions, allow for better management of infrastructure, provide insight to truck travel behavior, and enhance freight forecasting. Currently, truck traffic data is collected through limited means and with limited detail. Agencies can obtain or estimate truck travel statistics from surveys, inductive loop detectors (ILD) and weigh-in-motion (WIM) stations, or from manual counts, each of which have various limitations. Of these sources, WIM and ILD seem to be the most promising tools for capturing detailed truck information. Axle spacing and weight from existing WIM devices and unique inductive signatures indicative of body type from ILDs equipped with high sampling rate detector cards are complementary data sources that can be integrated to provide a synergistic resource that otherwise does not exist in practice, a resource that is able to overcome the drawbacks of the traditional truck data collection methods by providing data that is detailed, link specific, temporally continuous, up-to-date, and representative of the full truck population. This integrated data resource lends itself very readily toward detailed truck body classification which is presented as a case study. This body classification model is able to predict 35 different trailer body types for FHWA class 9 semi-tractors, achieving an 80 percent correct classification rate. In addition to the body classification model, the large data set resulting from the case study is itself a valuable and novel resource for truck studies.

Monitoring Commerical Vehicle Activities is very important for developing and  maintaining efficient freight transport systems. In the existing Literature this is broadly done through vehicle classification and reidentification problems using various sensing technologies. Lidar is an emerging traffic sensing technology which could potentially serve as a multi functional sensor for transport systems. In out current work we mainly focused on developing and qualitatively assessing a Lidar based Reconstruction framework for Truck surveillance purpose. We proposed a two stage Truck body reconstruction framework and found the results of reconstructed Truck bodies are quite promising for several truck-trailer configurations. For certain types of Truck-Trialer configurations such as containers due to the sparsity of scanned points in lateral direction, the wheel portion of reconstructed body still has noticeable deformations. We would like to address the same in our future work.

Real-time peer-to-peer ridesharing is a promising mode of transportation that has gained popularity during the recent years, thanks to the wide-spread use of smart phones, mobile application development platforms, and online payment systems. An assignment of drivers to riders, known as the ride-matching problem, is the central component of a peer-to-peer ridesharing system. In this paper, we discuss the features of a flexible ridesharing system, and propose an algorithm to optimally solve the ride-matching problem in a flexible ridesharing system in real-time. We generate random instances of the problem, and perform sensitivity analysis over some of the important parameters in a ridesharing system. Finally, we introduce the concept of peer-to-peer ride exchange, and show how it affects the performance of a ridesharing system.

Studies on the economic impact of air cargo traffic have been gaining traction in recent years. The slowed growth of air cargo traffic at California’s airports, however, has raised more pressing questions amongst airport planners and policy makers regarding the determinants of air cargo traffic. Specifically, it would be useful to know howCalifornia’s air cargo traffic is affected by urban economic characteristics surrounding airports. Accordingly, this study estimates the socioeconomic determinants of air cargo traffic across cities in California. We construct a 7-year panel (2003-2009) using quarterly employment, wage, population, and traffic data for metro areas in the state. Our results reveal that the concentration of service and manufacturing employment impacts the volume of outbound air cargo. Total air cargo traffic is found to grow faster than population, while the corresponding domestic traffic grows less than proportionally to city size. Wages play a significant role in determining both total and domestic air cargo movement. We provide point estimates for the traffic diversion between cities, showing that 80 percent of air cargo traffic is diverted away from a small city located within 100 miles of a large one. Using socioeconomic and demographic forecasts prepared for California’s Department of Transportation, we also forecast metro-level total and domestic air cargo tonnage for the years 2010-2040. Our forecasts for this period indicate that California’s total (domestic) air cargo traffic will increase at an average rate of 5.9 percent (4.4 percent) per year.

The solution algorithms for the family of flow distribution problems, which include (1) the trip distribution problem of travel forecasting, (2) the OD estimation from link counts problem, and (3) the trip matrix disaggregation problem, are usually based on the Maximum Entropy (ME) principle. ME-based optimization problems are hard to solve directly by optimization techniques due to the complexity of the objective function. Thus, in practice, iterative procedures are used to find approximate solutions. These procedures, however, cannot be easily applied if additional constraints are needed to be included in the problem. In this paper a new approach for balancing trip matrices with application in trip matrix disaggregation is introduced. The concept of generating the most similar distribution (MSD) instead of the Most Probable Distribution of Maximum Entropy principle is the basis of this approach. The goal of MSD is to minimize the deviation from the initial trip distribution, while satisfying additional constraints. This concept can be formulated in different ways. Two MSD-based objective functions have been introduced in this paper to replace the ME-based objective function. One is the Sum of Squared Deviations MSD (SSD-MSD), and the other is Minimax-MSD. While SSD-MSD puts more emphasis on maintaining the base year trip shares as a whole, Minimax-MSD puts more emphasis on maintaining the share of each individual element in the trip table. The main advantage of replacing the entropy-based objective functions with any of these functions is that the resulting problems can include additional constraints and still be readily solved by standard optimization engines. In addition, these objective functions could produce more meaningful results than entropy-based functions in regional transportation planning studies, as shown in the case study and some of the examples in the paper. Several examples and a case study of the California Statewide Freight Forecasting Model (CSFFM) are presented to demonstrate the merits of using MSD-based formulations.