<?xml version="1.0" encoding="utf-8"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>TRB Publications Index</title><link>http://pubsindex.trb.org/</link><atom:link href="http://pubsindex.trb.org/common/TRIS Suite/feeds/rss.aspx?s=PHNlYXJjaD48cGFyYW1zPjxwYXJhbSBuYW1lPSJzdWJqZWN0aWQiIHZhbHVlPSIxNzc1IiAvPjxwYXJhbSBuYW1lPSJsb2NhdGlvbiIgdmFsdWU9IjIiIC8%2BPHBhcmFtIG5hbWU9InN1YmplY3Rsb2dpYyIgdmFsdWU9Im9yIiAvPjxwYXJhbSBuYW1lPSJ0ZXJtc2xvZ2ljIiB2YWx1ZT0ib3IiIC8%2BPC9wYXJhbXM%2BPGZpbHRlcnMgLz48cmFuZ2VzIC8%2BPHNvcnRzPjxzb3J0IGZpZWxkPSJwdWJsaXNoZWQiIG9yZGVyPSJkZXNjIiAvPjwvc29ydHM%2BPHBlcnNpc3RzPjxwZXJzaXN0IG5hbWU9InJhbmdldHlwZSIgdmFsdWU9InB1Ymxpc2hlZGRhdGUiIC8%2BPC9wZXJzaXN0cz48L3NlYXJjaD4%3D" rel="self" type="application/rss+xml" /><description></description><language>en-us</language><copyright>Copyright © 2015. National Academy of Sciences. All rights reserved.</copyright><docs>http://blogs.law.harvard.edu/tech/rss</docs><managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor><webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster><image><title>TRB Publications Index</title><url>http://pubsindex.trb.org/Images/PageHeader-wTitle.png</url><link>http://pubsindex.trb.org/</link></image><item><title>NCHRP Research Report 1115: In Case of Emergency: Easing Weight Limits for Commercial Trucks</title><link>http://pubsindex.trb.org/view/2678790</link><description><![CDATA[The Texas A&amp;M Transportation Institute recently concluded a comprehensive study to address the challenges state agencies face in managing regulatory relief for overweight commercial motor vehicles transporting supplies during emergencies. The research aimed to establish a unified approach to emergency management, focusing on the definitions of emergency and emergency commodities, identifying successful practices, and formulating a decision framework tailored to different emergency scenarios. The study, conducted under National Cooperative Highway Research Program (NCHRP) Project 3-13(05), “Regulatory Relief of Commercial Vehicle Weight Requirements for Emergency Transportation of Critical Commodities,” and resulting guide highlighted the importance of streamlining special permits in emergency situations. This need became apparent during the COVID-19 pandemic, when rapid changes in commerce increased demand on the freight community to deliver emergency supplies and goods amid uncertainty. The study noted the challenges posed by the lack of uniformity in permit regulations between neighboring states and within states that have multiple entities responsible for infrastructure ownership, operation, and local oversight. The study culminated in the creation of a comprehensive guide—NCHRP Research Report 1115: Transporting Freight in Emergencies: A Guide on Special Permits and Weight Requirements—that integrates best practices and essential resources.]]></description><pubDate>Thu, 02 Apr 2026 15:16:37 GMT</pubDate><guid>http://pubsindex.trb.org/view/2678790</guid></item><item><title>Micro- to Macro-Level Fuel Consumption Modeling of Diesel Heavy-Duty Trucks: A Hierarchical Bayesian Approach</title><link>http://pubsindex.trb.org/view/2613376</link><description><![CDATA[The transportation sector faces growing pressure to reduce fuel consumption, driving the need for highly accurate and stable energy-use prediction models. However, developing these models is often constrained by limited data for certain vehicle categories, leading to prediction bias. This study introduces a Bayesian hierarchical modeling approach for predicting fuel consumption, leveraging extensive data from trucks operating throughout China. We assess the model’s applicability across varying data durations (from 100 to 100,000?s) and across analytical levels, from micro (detailed vehicle-level data) to macro (aggregate data). Our approach is compared with conventional models, including ordinary least squares regressions for individual vehicles and fleet-level data. Findings indicate that the Bayesian hierarchical model provides robust estimates, performing well in scenarios that require detailed, vehicle-specific data and in broader contexts with high-level aggregate data. Additionally, this model effectively mitigates prediction bias in small samples while preserving unique fuel consumption characteristics for each vehicle. This paper thus offers a strong framework for precise fuel consumption prediction in heavy-duty trucks, particularly advantageous in small-sample contexts.]]></description><pubDate>Tue, 28 Oct 2025 09:47:49 GMT</pubDate><guid>http://pubsindex.trb.org/view/2613376</guid></item><item><title>Strategic Location Planning for Electric Truck Charging Stations using Truck Trajectory Data</title><link>http://pubsindex.trb.org/view/2606572</link><description><![CDATA[In the U.S.A., the drive toward electric vehicle adoption seeks to reduce greenhouse gas emission and achieve the national goal of net-zero emissions by 2050. The significant contribution of heavy-duty semi-trailer trucks in transport-related carbon emissions and energy use motivate the environmental benefits of their electrification. The electrification of heavy-duty semi-trailer trucks, which significantly contribute to tailpipe emissions, presents an opportunity to mitigate these impacts while improving overall air quality. To facilitate the shift to electrification, this study presents a methodology for identifying locations for electric truck charging stations (ETCSs), utilizing data-driven approaches applicable to electric truck deployment needs. A tiered site selection strategy for ETCS sites ensures access coverage, identifying eligible ETCS sites and emphasizing arterials’ and interstates’ significance. Comparison with existing truck parking facilities shows the need for strategic ETCS placement. In addition, the study investigates the impact of advancements in battery technology on infrastructure requirements, analyzing how improved travel ranges may affect the distribution and number of charging stations required. Finally, an analysis comparing different vehicle range scenarios shows an inverse relation between travel range enhancements and ETCS requirements, providing insights for future infrastructure planning that balances technological advancements with charging network development.]]></description><pubDate>Wed, 08 Oct 2025 09:29:42 GMT</pubDate><guid>http://pubsindex.trb.org/view/2606572</guid></item><item><title>Optimizing Sustainable Relay Freight Transport Networks: A Case Study in Japan</title><link>http://pubsindex.trb.org/view/2604108</link><description><![CDATA[Relay transport networks design plays an important role in improving logistics efficiency, involving the determination of the number of relay points, costs of constructing these points, and constraints on the distance between relay points. The configuration of these unique characteristics must consider multiple aspects of sustainability, including economic, environmental, and social factors. In this study, we aim to determine the optimal relay network configurations and transportation routes through a multiobjective optimization model, considering three key objectives: maximizing freight demand for relay transport, minimizing total costs, and maximizing CO2 emission reduction. The proposed model was solved using the Gurobi optimization solver. A case study conducted in Japan revealed that the optimal relay network configuration consists of 23 relay points, including 21 small, 1 medium, and 1 large point. This optimized configuration yielded three significant contributions for environmental, economic, and social aspects, namely 34.72% reduction in CO2 emissions, 3.65% reduction in transportation costs, 27.47% reduction in overtime for short-haul trips, and 14.92% reduction in overnight stays for long-haul trips. Our findings recommend the following policies: (i)?trucking companies should set the distance constraints between two adjacent relay points as 150?km for short-haul trips and 450?km for long-haul trips to balance environmental, social, and economic priorities; (ii) the Japanese government’s proposed budget (2.5 billion?yen) for relay point construction is relatively sufficient, exceeding our estimated construction cost (2.095 billion?yen); and (iii) relay transport implemented as a standalone measure has the potential to help achieve the Japanese trucking industry’s CO2 emission reduction target.]]></description><pubDate>Fri, 26 Sep 2025 16:33:17 GMT</pubDate><guid>http://pubsindex.trb.org/view/2604108</guid></item><item><title>Routing and Scheduling of Fresh Product Distribution for Hybrid Truck–Drone Delivery with Temporary Drone Stations</title><link>http://pubsindex.trb.org/view/2577189</link><description><![CDATA[To save costs and increase profit in the last mile of fresh product distribution, considering the distribution characteristics of urban customers, the perishability of fresh products, and the cooperative mode of trucks and drones, a temporary drone station is introduced into a traditional logistics network, and the routing and scheduling problem of hybrid truck–drone cooperative delivery is proposed. First, a multi-objective mixed-integer linear programming model is constructed to minimize the total operating cost and maximize customer satisfaction. Second, a two-layer programming solution method based on density-based spatial clustering of applications with noise (DBSCAN) and multi-objective genetic algorithm with cooperative strategy (MOGA-CS) is designed to solve this problem. Moreover, DBSCAN is used to determine the location of the temporary drone station. MOGA-CS embeds a collaborative evolutionary strategy with two populations under the framework of non-dominated sorting genetic algorithm II (NSGA-II) and incorporates local search to solve the path optimization problem of truck–drone cooperative distribution. Finally, the superiority of the hybrid distribution mode proposed in this paper is verified by numerical experiments, and the efficiency of MOGA-CS is further verified by comparing it with NSGA-II, multi-objective differential evolution (MODE), and non-dominated sorting whale optimization algorithm (NSWOA).]]></description><pubDate>Tue, 22 Jul 2025 10:31:00 GMT</pubDate><guid>http://pubsindex.trb.org/view/2577189</guid></item><item><title>TRB’s Freight Systems Group Drives Truck Parking Solutions</title><link>http://pubsindex.trb.org/view/2554115</link><description><![CDATA[The Transportation Research Board (TRB) Freight Systems Group held a joint session at the 2025 TRB Annual Meeting. The Group was composed of members from several standing committees including Intermodal Freight Transportation, Trucking Industry Research, Freight Transportation Planning and Logistics, Urban Freight Transportation, and Agriculture and Food Transportation. The Group discussed nontraditional solutions to meet the demand for truck parking and developed future research topics to explore. One clear outcome of the joint truck parking session was the need to address this issue as one holistic, cross-cutting effort.]]></description><pubDate>Wed, 16 Jul 2025 08:47:36 GMT</pubDate><guid>http://pubsindex.trb.org/view/2554115</guid></item><item><title>NCHRP Project 20-68A, SCAN 20-02: SCAN Team Report: Safe Parking for Commercial Truck Drivers</title><link>http://pubsindex.trb.org/view/2554114</link><description><![CDATA[National Cooperative Highway Research Program's (NCHRP's) U.S. Domestic Scan Program assembled a team of subject matter experts from several state departments of transportation (state DOTs) and the Federal Highway Administration (FHWA) to identify successful and emerging systems and technologies in assisting truck drivers with finding truck parking along major freight corridors. The scan team was charged with documenting the following: the agency’s process and successful strategy for developing a truck parking information system; technologies that might be candidates to support sharing information on parking availability, (e.g., alerting truckers to available parking in advance of a rest area or truck stop); and case studies of systems that may be transferable to other agencies.  The team also focused on and presented recommendations on potential strategies for addressing issues such as identification of parking availability, overcoming legal barriers, and potential funding mechanisms. Based on discussions with agencies having success in addressing truck parking needs, the scan team determined that state DOTs should choose one of the following three paths: (1) initiate a truck parking management system on their own; (2) band together with surrounding states, and take a corridor approach to implementing a truck parking management system; or (3) address the parking issue, ideally with the assistance of a metropolitan planning organization (MPO) or other group affiliated or associated with the freight motor carrier industry. This article outlines each of these three paths, provides examples of states utilizing these approaches, and highlights key components of success.]]></description><pubDate>Wed, 16 Jul 2025 08:47:36 GMT</pubDate><guid>http://pubsindex.trb.org/view/2554114</guid></item><item><title>The Market Potential of Autonomous Trucks in the United States: An Industry Review</title><link>http://pubsindex.trb.org/view/2572987</link><description><![CDATA[The U.S. trucking industry has the potential to be an early adopter of autonomous vehicles. The trucking industry hauls the majority of U.S. freight by weight and has a vast infrastructure network. Numerous business cases and routes may be able to utilize autonomous trucks. The trucking industry consumes more fuel and has more crashes than other modes of freight transportation. Trucking companies may be able to reduce costs through labor savings and increased utilization of trucks. All the dynamics of market size, infrastructure, fuel consumption, safety, and cost savings make the trucking industry a potential early adopter of autonomous vehicles. For autonomous technology to be applied to trucks in the United States there needs to be interest from technology developers, truck manufacturers, and trucking companies; government support to allow the testing and deployment of autonomous trucks on public roads; and acceptance from the public who will share the road with autonomous trucks. Given that autonomous truck deployments are in the beginning stages of being tested on public roads, there needs to be a comprehensive review of the market potential of autonomous trucks from the perspective of all involved stakeholders. To provide this comprehensive overview, this paper reviews the current dynamics of autonomous truck deployments in the United States, including deployment markets, business cases, adoption timelines, and logistics-, manufacturing-, operations-, technology-, and government partnerships. The paper concludes with the possible benefits of and barriers to autonomous trucks to illustrate what may drive or impede their U.S. deployment and market potential.]]></description><pubDate>Fri, 11 Jul 2025 08:39:01 GMT</pubDate><guid>http://pubsindex.trb.org/view/2572987</guid></item><item><title>Impact of Truck Platoon Rest Period on Mechanistic-Empirical Flexible Pavement Design</title><link>http://pubsindex.trb.org/view/2556774</link><description><![CDATA[Truck platoons could reduce fuel consumption and improve safety; however, they may increase pavement damage because of potential channelized traffic and a reduced rest period. The rest period is a critical parameter, and it is not included in the AASHTOWare Mechanistic-Empirical Pavement Design Guide (MEPDG) framework. This study’s objective was to include the impact of a rest period in the MEPDG framework, utilizing repeated-load permanent-deformation test results. A shift model was developed by extending the time–temperature superposition concept to incorporate rest period using experimental data. A three-dimensional finite-element pavement model was used, and proper pavement material characteristics and loading configurations were considered. A holistic response framework was used to compute pavement distresses as a function of both the wander and the rest period (based on the shift model). To illustrate the holistic framework, a case study of truck platoons distributed uniformly on sublanes was considered. The results indicated platoons could result in lower damage than a conventional trucking operation, with 60-ft spacing between trucks being optimal.]]></description><pubDate>Fri, 23 May 2025 15:34:15 GMT</pubDate><guid>http://pubsindex.trb.org/view/2556774</guid></item><item><title>Analysis, Modeling, and Simulation of Connected and Automated Trucks: State-of-the-Art and Identified Gaps</title><link>http://pubsindex.trb.org/view/2516928</link><description><![CDATA[Connected and automated trucks (CATs) have the potential to transform the transportation system and logistics industry. Their unique features, such as operational strategies and truck driving behaviors, can affect transportation system performance. For successful development, testing and deployment of CATs, analysis, modeling, and simulation (AMS) plays an important role, especially in evaluating the impacts of CAT technologies on existing transportation systems. This paper presents a comprehensive review and assessment of up-to-date studies related to CAT AMS, focusing on three correlated elements: CAT applications, data, and tools. The research delves into CAT applications from individual CAT and CAT fleet to CAT-involved traffic. It explores available data sources relevant to CAT system use cases, assessing their potential issues and opportunities. The study also reviews existing AMS tools used to analyze CAT applications at both operational performance and network integration levels, emphasizing research needs in CAT-specific tools development. The findings identify the data needs and point out that existing AMS tools may not capture the complexity of CAT operation, which involves driving behaviors, vehicle-to-everything communications, autonomous capabilities, and response to truck-specific scenarios. The study will lay a solid foundation for further development of the AMS framework for CATs and provide guidance to future research of CAT applications.]]></description><pubDate>Mon, 03 Mar 2025 12:22:36 GMT</pubDate><guid>http://pubsindex.trb.org/view/2516928</guid></item><item><title>An Exploratory Assessment of Driver Injury Severities in Large-Truck Crashes Involving Fatigued and Non-Fatigued Driving</title><link>http://pubsindex.trb.org/view/2449568</link><description><![CDATA[Fatigue poses a persistent safety challenge for long-haul truck drivers, significantly elevating the risk of highway crashes and compromising daily work performance. This study investigates the factors influencing driver injury severity in single-large truck crashes due to fatigued driving, compared with non-fatigued driving (normal driving). Using Florida crash data from 2011 to 2019 (inclusive), covering both fatigue and non-fatigue-related crashes, this analysis employs random parameters logit models to account for potential unobserved heterogeneity in means and variances. A comprehensive array of factors affecting driver injury severity, including spatial and temporal characteristics, vehicle and traffic attributes, roadway conditions, and driver-specific characteristics, are examined. Strikingly, different parameter estimates emerge for fatigue and non-fatigue-related crashes, indicating fundamental dissimilarities in unobserved heterogeneity in large truck crash data. The estimated model results unveil distinct marginal effects between fatigued and non-fatigued driving, particularly concerning severe injury crashes, suggesting significant variation in driver behavior based on fatigue levels. These findings contribute substantially to the growing literature on the intrinsic disparities between fatigued and non-fatigued driving behaviors. Moreover, this research underscores the potential ramifications of these distinctions on the safety performance of commercial trucks, highway safety technologies, and policy-related safety countermeasures. Understanding the unique characteristics of fatigued driving can inform targeted interventions to bolster safety within the commercial trucking industry and mitigate the impact of severe crashes resulting from fatigued driving.]]></description><pubDate>Mon, 11 Nov 2024 10:48:11 GMT</pubDate><guid>http://pubsindex.trb.org/view/2449568</guid></item><item><title>Intermodal Chassis Provisioning and Supply Chain Efficiency: Equipment Availability, Choice, and Quality</title><link>http://pubsindex.trb.org/view/2427792</link><description><![CDATA[This study by an independent committee was conducted at the request of Congress and sponsored by the Federal Maritime Commission (FMC) to review existing approaches in the United States for provisioning intermodal chassis for the drayage or short-distance movement of shipping containers. Most general cargo in international commerce moves in standardized, 20-ft, 40-ft, or 45-ft steel box containers. Designed and sized specifically for transporting these containers on the highways, chassis are truck trailers consisting of a frame, two or three axles, and mechanisms for locking the container in place. Chassis are used by motor carriers to transport imported containers arriving at seaports and rail terminals to their final destinations. They are also used to transport containers used for exporting containerized cargo from their origins to railroad terminals and international gateway ports. This report synthesizes the views on chassis provisioning practices offered by dozens of consulted participants in the intermodal container shipping enterprise, including ocean carriers, railroads, motor carriers, IEPs, pool managers, and shippers of containerized cargo, known as beneficial cargo owners (BCOs). The purpose of the consultations was to inform the study committee’s identification of key advantages and disadvantages of alternative approaches for chassis provisioning and fleet management when taking into account different drayage market  circumstance and conditions, such as port size, geography, traffic activity, and operational complexity. The goal was to evaluate whether the approaches have aligned incentives with regard to equipment ownership, management, maintenance, and provisioning that promote supply chain efficiencies.]]></description><pubDate>Sun, 15 Sep 2024 17:48:23 GMT</pubDate><guid>http://pubsindex.trb.org/view/2427792</guid></item><item><title>Transporting Freight in Emergencies: A Guide on Special Permits and Weight Requirements</title><link>http://pubsindex.trb.org/view/2417414</link><description><![CDATA[This report provides a guide to state departments of transportation (DOTs) to consider options to better anticipate and respond to state and federal emergencies, specifically related to the movement of overweight commercial vehicles carrying emergency commodities within a state or across a region. The guide documents (1) current legal requirements on emergency declarations and options for state DOTs to address emergency movement of commodities, (2) options to prepare for emergency overweight special permitting, and (3) tools and resources. The guide identifies the challenge of navigating the overlays of federal, state, and local laws and regulations on truck movements during emergencies. The guide will be of interest to elected officials and permitters at state DOTs and other public agencies.]]></description><pubDate>Sun, 18 Aug 2024 18:13:50 GMT</pubDate><guid>http://pubsindex.trb.org/view/2417414</guid></item><item><title>Development of a Practical Procedure for Data-Driven Weigh-in-Motion Equipment Calibration Scheduling to Assure Data Accuracy and Consistency over Time</title><link>http://pubsindex.trb.org/view/2408319</link><description><![CDATA[Weigh-in-motion (WIM) technology is a traffic monitoring technology that highway agencies use to obtain information about the weight, axle loading, and configuration of heavy vehicles moving at operational speed. To ensure high-quality WIM data, the Federal Highway Administration (FHWA) recommends regular calibration of WIM equipment. This study addresses the need to optimize the allocation of the limited resources that agencies have for WIM equipment calibration by developing a procedure for data-driven calibration scheduling. This was accomplished through an analysis of WIM measurement errors from test truck data collected during field performance validation and calibration events and an analysis of monthly changes in truck weight and axle loading characteristics, based on WIM data collected between calibration events. The analysis results were used to draw conclusions on the functional performance of different WIM sites. The study also demonstrates how the newly developed National Cooperative Highway Research Program (NCHRP) WIM Data Quality Assurance Analysis Tool can be used to compute truck weight and axle loading parameters and visualize data analysis results using four case studies: two WIM sites with piezo quartz sensors in asphalt pavements and two WIM sites with bending plate sensors in concrete pavements. This paper provides a practical procedure and recommendations that highway agencies can use to develop data-driven WIM calibration schedules that will ensure consistent high-quality WIM data for sites managed by an agency with the aid of the NCHRP WIM Data Quality Assurance Analysis Tool.]]></description><pubDate>Tue, 30 Jul 2024 09:53:05 GMT</pubDate><guid>http://pubsindex.trb.org/view/2408319</guid></item><item><title>Research on the Method and Application of Truck Type Recognition Based on Deep Learning from the Perspective of Unmanned Aerial Vehicles</title><link>http://pubsindex.trb.org/view/2408320</link><description><![CDATA[A lightweight detection model for truck models based on improved YOLOv5s (MobileNetV3-YOLOv5s) is proposed to meet the requirement for real-time detection under the limited embedded device resources carried by drones. Firstly, we use MobileNetV3 to replace the backbone feature extraction network and use deep separable convolution to replace traditional convolution to reduce the model’s parameter count. Secondly, we use DIOU loss as the regression loss of the bounding box to enhance the convergence speed of the model and improve the ability to fit data. Finally, we use the K-means clustering method to reset the prior box. The experimental results show that the mAP value of the improved model is 89.6%, which is 0.2 percentage points lower than before, but the volume is only 3.98MB, which is about half of the original model. The detection speed is also significantly improved compared with before the improvement. Therefore, the lightweight model based on improved YOLOv5s improves detection speed and significantly reduces model volume while ensuring detection accuracy. It enables efficient, real-time recognition of truck models under complex road conditions on embedded devices.]]></description><pubDate>Tue, 30 Jul 2024 09:53:05 GMT</pubDate><guid>http://pubsindex.trb.org/view/2408320</guid></item></channel></rss>