<?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" 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>Cooperative Lane-Changing Decision-Making Method for Connected and Automated Vehicles in Highway Merging Areas Based on Multi-Agent Deep Deterministic Policy Gradient</title><link>http://pubsindex.trb.org/view/2790766</link><description><![CDATA[Mandatory merging at expressway lane reductions often involves intensive inter-vehicle interactions, triggering lateral conflicts and longitudinal fluctuations that lead to traffic instability. This paper proposes a cooperative lane-changing decision-making framework for connected and automated vehicles (CAVs) using the multi-agent deep deterministic policy gradient (MADDPG) algorithm. The method integrates ego-vehicle states with the relative motion data of surrounding vehicles to establish a collaborative perception state space. Within the action space, the framework jointly optimizes lane-changing and car-following decisions of the merging vehicle while coordinating the longitudinal control of the target-lane following vehicle. A novel multi-objective reward function is introduced, balancing collaboration, safety, efficiency, and passenger comfort. Using a Python-SUMO co-simulation platform, the paper evaluates the proposed method against multi-agent proximal policy optimization and deep Q-network under free-flow and synchronized-flow conditions. Results indicate that, under free-flow conditions, the proposed method reduces collision risk by up to 1.27%, shortens lane-change decision time by up to 2.51 s, and increases the lane-change execution rate by up to 4.59%. Under synchronized-flow conditions, the method reduces collision risk by up to 1.16%, shortens lane-change decision time by up to 2.13 s, and increases the lane-change execution rate by up to 5.32%. Furthermore, the approach significantly enhances the level of coordination among CAVs. These findings demonstrate that the proposed method effectively balances safety, efficiency, and stability across varying traffic conditions, providing a practical technical solution for vehicle-infrastructure coordination and autonomous driving decisions in bottleneck zones.]]></description><pubDate>Wed, 21 Oct 2026 15:29:18 GMT</pubDate><guid>http://pubsindex.trb.org/view/2790766</guid></item><item><title>Evaluating the Alignment Between Greenway Supply and Cycling Demand in a High-Density City</title><link>http://pubsindex.trb.org/view/2790765</link><description><![CDATA[Urban greenways are increasingly promoted to support active mobility and sustainable transport in high-density cities. However, greenway development is often shaped by standardized planning frameworks and land availability rather than cycling demand. This study evaluates the alignment between greenway supply and recorded cycling demand in Shenzhen, China, using large-scale trajectory data and a supply and demand coordination framework. Coupling coordination analysis and spatial autocorrelation were used to assess their spatial correspondence. The results reveal uneven alignment between greenway supply and cycling demand across the city. The average coupling coordination degree was 0.575, indicating a basic but barely coordinated relationship. A Moran’s I value of 0.452 further indicates significant positive spatial autocorrelation in coordination across subdistricts. These findings highlight the need for demand-sensitive investment, cross-sectoral governance, and performance-based infrastructure appraisal. Embedding trajectory-based mobility data into transport policy frameworks may improve allocative efficiency and support more equitable and effective active mobility systems in rapidly urbanizing contexts.]]></description><pubDate>Wed, 21 Oct 2026 15:29:18 GMT</pubDate><guid>http://pubsindex.trb.org/view/2790765</guid></item><item><title>Do Longer Commutes Lead to Depression? Evidence from a Longitudinal Study</title><link>http://pubsindex.trb.org/view/2790764</link><description><![CDATA[A growing number of studies have investigated the relationship between commuting to work and mental health, usually with cross-sectional data. In these studies, lengthy commutes are typically associated with stress, time scarcity, and lower well-being. However, the more salient measures of mental health, such as the likelihood of depression, have been more rarely studied, and longitudinal data, which have the potential to provide more robust causal inference, have typically not been used. Using data from the 2016 and 2018 China Family Panel Studies, I examine the relationship between commuting and depression employing multinomial logistic regressions. My findings challenge the existing literature. Controlling for life events, sociodemographic characteristics, and neighborhood satisfaction, individuals who increased their commute distances were more likely to enter any level of depression and major depression than those without a change, consistent with current studies. However, a reduction in commute distance was also significantly associated with an increased onset of major depression and a lower probability of recovery. Higher income moderates these pathways by reducing the risk of an increase in commute distance on entering depression in general and a decrease in commute distance on entering major depression. Furthermore, major life events confound these relationships. For example, individuals who changed jobs and moved house were more likely to enter general depression. These results suggest the complexity of the relationship between travel and mental health, a complexity the literature needs to address.]]></description><pubDate>Wed, 21 Oct 2026 15:29:18 GMT</pubDate><guid>http://pubsindex.trb.org/view/2790764</guid></item><item><title>Modeling the Spatiotemporal Implications of Autonomous Vehicles’ Empty Cruising on Parking Dynamics and Traffic Flow</title><link>http://pubsindex.trb.org/view/2790763</link><description><![CDATA[Autonomous vehicles’ (AVs) ability to reposition themselves can transform parking dynamics. We focus on empty cruising as an alternative to short-term parking. This capability affects parking space occupancy and traffic flow because AVs use road space instead of parking spaces. This research models the spatiotemporal implications of empty cruising on parking dynamics and traffic flow in a mixed environment of AVs and conventional vehicles (CVs). The contributions of this research compared with existing research include: (1) analyzing how empty cruising influences parking dynamics and traffic flow across diverse urban zones; (2) examining its temporal effect on parking search and traffic congestion; and (3) assessing how CVs are affected when this strategy is adopted by varying numbers of AVs. The methodological framework of this research is based on agent-based modeling to investigate the spatiotemporal implications of AVs empty cruising at different adoption rates, compared with a full CV environment. The findings of this research show that when empty cruising is performed to address short-term parking demand of up to 45 min, it can reduce overall parking search by approximately 2.5%, with central business district (CBD) zones experiencing around 33% higher reduction compared with suburban Traffic Analysis Zones. However, this reduction in parking search ratio exacerbates congestion, increasing travel time in the CBD zones by around 56% and in suburban zones by around 21%. The insights of this research can guide policy formulation for balanced use of road and parking infrastructure in the age of AVs.]]></description><pubDate>Tue, 20 Oct 2026 13:32:52 GMT</pubDate><guid>http://pubsindex.trb.org/view/2790763</guid></item><item><title>User Perceptions of Usage-Based Insurance: Benefits, Challenges, and Impacts on Driving Behavior</title><link>http://pubsindex.trb.org/view/2790762</link><description><![CDATA[Usage-based insurance (UBI) programs have emerged as potential tools for promoting safe driving behaviors and enhancing road safety. These programs utilize advanced technology and data-driven methods to track the driving behaviors of their users. Understanding the benefits and challenges users face when interacting with these programs requires a systematic analysis of user feedback. This study aims to evaluate user feedback on UBI programs implemented by US insurance companies to identify benefits and challenges facing users of the program. The study analyzes reviews collected from Google Play Store and Apple App Store between 2019 and 2024. The study utilizes logistic regression and text mining techniques to accomplish the study objectives. Logistic regression results show speeding behavior to be statistically significant compared with other driving behaviors. Furthermore, text analysis and thematic-analysis-associated statistical quantifiers evaluated reviews associated with speeding behavior. The findings of the analysis reveal positive feedback, indicating that programs are user-friendly applications that help to improve driving behavior. Text mining analysis revealed that each of the programs had specific recommended improvements that were identified from the user reviews. The improvements were associated with improving location service issues, causing inaccurate citations, a need for better awareness, and updating Google Maps integration to address outdated speed limits. These findings offer actionable insights for enhancing UBI programs by addressing usability gaps, refining application functionalities, and improving behavioral monitoring accuracy. Strengthening these areas can improve user satisfaction, promote safer driving habits, and advance UBI’s role in road safety.]]></description><pubDate>Tue, 20 Oct 2026 13:32:52 GMT</pubDate><guid>http://pubsindex.trb.org/view/2790762</guid></item><item><title>Use of the Monotonic Shear Strength Test to Characterize Asphalt Binders</title><link>http://pubsindex.trb.org/view/2790761</link><description><![CDATA[The Monotonic Shear Strength (MOSS) Test was developed during Asphalt Institute Foundation (AIF) research project AIF 22-03 as a means of evaluating the strength and strain tolerance of asphalt binders in relation to asphalt shingle durability. Since that time, the procedure has been refined and used in a variety of applications in binder research. This paper attempts to frame a “proof of concept” for the use of the MOSS test as a viable option for (a) evaluating the ductile-to-brittle transition range of asphalt binders; (b) discriminating between yield energy for binders at isothermal conditions; (c) highlighting the strength-enhancing properties of modification not captured by testing in the linear viscoelastic range; (d) assessing aging effects and durability; and (e) providing an initial estimation of test variability. By evaluating several material types (roofing and paving grade binders with and without modification with polymers) with varying methods of analysis for fitting and interpreting data (such as toughness comparisons and indexing of materials using Critical Strain determinations), several options for use of the MOSS procedure as a failure test are presented for consideration and use.]]></description><pubDate>Tue, 20 Oct 2026 13:32:52 GMT</pubDate><guid>http://pubsindex.trb.org/view/2790761</guid></item><item><title>Airport Energy Preparedness</title><link>http://pubsindex.trb.org/view/2790146</link><description><![CDATA[The Airport Energy Preparedness Insight Event held April 30–May 1, 2025, at the Keck Center of the National Academics of Sciences, Engineering, and Medicine in Washington, DC, addressed the increasing challenges with expanding and enhancing airport energy preparedness. Energy preparedness is defined as the ability to prepare energy resources to adapt to changing short- and longer-term conditions and to withstand and recover rapidly from disruptions, whether from deliberate attacks, accidents, or naturally occurring threats or incidents. This Insight Event summary is a compilation of the presentations and a factual summary of the ensuing discussions at the event. The goals of the event included the following: Explore the tangible and intangible value of energy preparedness, including the impact on operational costs and risk mitigation; Identify current challenges and gaps, including how to balance the unique needs of internal and external stakeholders while driving progress toward shared objectives; Assess common tools, metrics, and funding mechanisms that drive energy preparedness; and Develop future areas of research that have the potential to better prepare airports of all sizes to address challenges and opportunities associated with energy preparedness.]]></description><pubDate>Tue, 20 Oct 2026 12:47:10 GMT</pubDate><guid>http://pubsindex.trb.org/view/2790146</guid></item><item><title>Traffic Analysis Practices for Nonmotorized Modes</title><link>http://pubsindex.trb.org/view/2790145</link><description><![CDATA[This synthesis presents the state of practice of state departments of transportation (DOTs) on traffic analysis for nonmotorized modes of transportation. The synthesis includes information on how state DOTs define and analyze nonmotorized modes, including pedestrians, bicyclists, mobility device users, and micromobility; the tools, data sources, and performance measures used in practice; and the institutional, technical, and operational challenges associated with integrating nonmotorized considerations into planning, design, and operations. The report also documents emerging approaches such as level of traffic stress, multimodal level-of-service methods, statewide count programs, and the use of geographic information system-based and data-driven analytical tools. Under NCHRP Project 20-05/Topic 56-05, “Traffic Analysis Practices for Non-Motorized Modes,” Texas State University was asked to synthesize the information to document current state DOT practices for analyzing nonmotorized traffic across planning, design, and operations, including definitions, legal and funding frameworks, tools and data sources, applications in project development, and implementation challenges. Information used in this study was obtained through a literature review, a survey of state DOTs, and interviews to develop in-depth case examples. The synthesis provides seven case examples that highlight how state DOTs are institutionalizing nonmotorized traffic analysis through statewide count repositories, policy-driven design mandates, adaptation of national standards to local contexts, use of proxy and crowdsourced data, and tiered analytical frameworks tailored to project scale and conditions.]]></description><pubDate>Tue, 20 Oct 2026 12:47:10 GMT</pubDate><guid>http://pubsindex.trb.org/view/2790145</guid></item><item><title>Transportation Systems Management and Operations: Implementation Guide</title><link>http://pubsindex.trb.org/view/2790144</link><description><![CDATA[The American Association of State Highway and Transportation Officials (AASHTO) publishes the Transportation Operations Manual (TOM). The TOM provides a holistic view of transportation systems management and operations (TSMO) and encompasses a broad range of topics and tactics that exist within this discipline. Preparing the TOM involved extensive outreach to industry stakeholders to build consensus on the manual’s content and presentation, which provides a coherent vision for TSMO across the nation and for all modes of accessing the surface transportation system. This Implementation Guide complements the TOM by providing effective practices and other resources that illustrate the practical application of operations topics, concepts, and strategies found in the TOM. The purposes of the Guide consist of the following: Provide TSMO effective practices; Assist with TSMO implementation; and Support guidance in the TOM.]]></description><pubDate>Tue, 20 Oct 2026 12:47:10 GMT</pubDate><guid>http://pubsindex.trb.org/view/2790144</guid></item><item><title>Understanding Safety of Navigation Deficiencies Leading to Vessel Detention: A Data-Driven Analysis of Black Sea Memorandum of Understanding Inspections</title><link>http://pubsindex.trb.org/view/2790042</link><description><![CDATA[Preventing safety of navigation (SN) deficiencies is of critical importance in waterborne transport since the SN Code constitutes one of the most recurrent deficiency categories that serve as a basis for detention under port state controls (PSC). In pursuit of this objective, this study initially delineates and critically evaluates the interdependencies between a spectrum of influential determinants and deficiency types, as well as their respective contributions to detention occurrences attributable to the SN code. For this purpose, a data-driven Bayesian network model is developed based on 5,979 vessel detentions documented in the Black Sea Memorandum of Understanding (MoU) archives between January 2003 and December 2024. The novelty of this study lies in the integration of a probabilistic graphical modeling approach (tree augmented naïve (TAN)-based Bayesian networks) with association rule mining to jointly capture both causal dependency structures and frequent co-occurrence patterns in PSC detention data, which has not been previously explored in the context of SN-focused deficiency analysis. Findings indicate that deficiencies related to labor conditions have the highest impact to explain ship detentions under the SN Code, increasing the likelihood by 16.1%, highlighting a strong interrelationship between crew welfare conditions and navigational safety performance. The study contributes methodologically by integrating Bayesian network learning (TAN) with association rule mining in a unified analytical framework for PSC detention analysis, and practically by providing a data-driven basis for targeted inspection prioritization and risk mitigation strategies for port authorities and maritime operators.]]></description><pubDate>Mon, 19 Oct 2026 16:25:56 GMT</pubDate><guid>http://pubsindex.trb.org/view/2790042</guid></item><item><title>Reducing Serious Injuries and Fatalities Among State DOT Employees</title><link>http://pubsindex.trb.org/view/2789934</link><description><![CDATA[This synthesis presents the state of the practice of state departments of transportation (DOTs) on the broad range of safety strategies they implement to reduce serious injuries and fatalities (SIFs) among their employees. This synthesis includes information on definitions and classification, data collection and analysis, high-risk work activities and tasks, fatigue and distraction management, employee engagement and awareness, and technologies and procedures to mitigate SIFs. Under NCHRP Project 20-05/56-10, “Practices to Reduce Serious Injuries and Fatalities (SIFs) for State DOT Employees,” Blue Hardhat Consulting, LLC, was asked to synthesize information to document state DOT practices regarding efforts to reduce SIFs for state DOT employees. The information used in this study was obtained through a literature review, a survey of state DOTs, and interviews to develop in-depth case examples. Chapter 4 provides five case examples that highlight similar approaches to reducing SIFs, including analyzing collected incident data to identify hazards with the potential for SIFs and to raise risk awareness. This synthesis is an immediately useful document and resource for state DOTs’ employee safety programs and overall safety cultures to better understand implemented strategies to reduce SIFs. Its findings can be used by practitioners as they may seek to refine their practices. The synthesis also notes gaps in knowledge and practice that could be addressed by future researchers.]]></description><pubDate>Sat, 17 Oct 2026 17:51:57 GMT</pubDate><guid>http://pubsindex.trb.org/view/2789934</guid></item><item><title>Vehicle-Based Telemetric Data for State and Local Transportation Agencies</title><link>http://pubsindex.trb.org/view/2789933</link><description><![CDATA[This report provides a guide on vehicle-based telemetric data, including an overview of the state of the practice, a summary of challenges and opportunities, and case studies. The guide describes how data generated by vehicles and in-vehicle devices can support transportation planning, operations, safety, and performance monitoring. It describes implementation considerations such as data access, governance, procurement, integration, privacy, and appropriate use. This guide will be of interest to practitioners responsible for transportation data management, system performance monitoring, and operational decision-making within state and local departments of transportation (DOTs).]]></description><pubDate>Sat, 17 Oct 2026 17:51:57 GMT</pubDate><guid>http://pubsindex.trb.org/view/2789933</guid></item><item><title>Strengthening the Transit Workforce Through Operator Health and Wellbeing</title><link>http://pubsindex.trb.org/view/2789937</link><description><![CDATA[This synthesis presents the current state of practice among public transportation agencies for understanding how exposure to psychosocial hazards affects transit operators’ health and wellbeing. The synthesis provides information for agencies on mitigating these hazards, supporting operator wellbeing, and strengthening workforce recruitment and retention. Under TCRP Project J-07/Topic SA-63, Texas A&amp;M Transportation Institute was asked to synthesize current practices for improving operators’ quality of life as a means of addressing workforce shortages. Information used in this study was obtained through a literature review, a survey of 24 public transportation agencies, interviews with industry practitioners, and case examples. The synthesis provides five case examples that highlight psychosocial hazards affecting transit operators; the impacts of these hazards on health, wellbeing, and workforce outcomes; the role of labor representation and employee benefits; agency policies and programs; implementation challenges and opportunities; and innovative and effective practices. This synthesis is an immediately useful document for transit agencies seeking to strengthen operator health and wellbeing and address related workforce challenges. The synthesis also identifies gaps in knowledge and practice that could be addressed by future researchers.]]></description><pubDate>Sat, 17 Oct 2026 17:51:56 GMT</pubDate><guid>http://pubsindex.trb.org/view/2789937</guid></item><item><title>Machine Learning–Driven Pavement Management System Framework for Bleeding Severity Classification Using Long-Term Pavement Performance Data</title><link>http://pubsindex.trb.org/view/2789790</link><description><![CDATA[This study developed a machine learning–based framework to detect and classify bleeding severity in flexible pavements. It utilized nationally representative pavement performance data obtained from the Long-Term Pavement Performance program. Bleeding significantly reduces surface friction and increases crash risk, yet it remains difficult to detect through traditional inspection methods. The analysis was implemented on 3,781 observations from 658 pavement sections across 35 US states, incorporating structural, environmental, and traffic parameters. The methodology included feature selection, a new bleeding severity classification based on affected surface area, and comparative evaluation of seven classification models. Across all evaluated models, Light Gradient Boosting (LGB) and Extreme Gradient Boosting (XGB) demonstrated the strongest predictive performance. LGB achieved a Precision of 0.85, while XGB closely followed at 0.84; both models recorded a Recall of 0.83, with corresponding F1-scores of 0.83 and 0.82. The subsequent interpretability assessment, using feature importance, a representative decision tree, and partial dependence plots, confirmed that air voids, voids filled with asphalt, pavement age, and total annual precipitation consistently emerged as the dominant variables influencing bleeding severity. The results demonstrated the potential of machine learning to support Pavement Management Systems by enabling accurate identification of severe bleeding cases, thereby reducing inspection efforts and enabling targeted maintenance planning. Although some regions remained underrepresented in the available data, the framework provides a foundation for enhanced bleeding detection and classification. Future work should focus on expanding regional coverage, incorporating advanced sensor and imaging technologies, and developing predictive models for long-term maintenance planning and roadway safety.]]></description><pubDate>Thu, 15 Oct 2026 16:55:28 GMT</pubDate><guid>http://pubsindex.trb.org/view/2789790</guid></item><item><title>Quantifying Traffic Conflict Severity Using a Multi-Factor Nonlinear Finite Element Framework: The Role of Vehicle Mass, Impact Geometry, and Occupant-Compartment Proximity</title><link>http://pubsindex.trb.org/view/2789789</link><description><![CDATA[Traffic conflict analysis offers a proactive alternative to crash-based safety assessments by identifying and evaluating near-crash events before they escalate into actual collisions. Previous research established a conflict detection framework capable of identifying critical conflict points, anticipating likely crash types, and defining corresponding crash influence points (CIPs), while demonstrating the value of kinetic energy (KE) as a meaningful indicator of conflict severity. Building on this foundation, the present study introduces a complementary analytical framework which isolates the influence of vehicle mass, arrangement, and geometry in shaping severity outcomes. To control for speed effects, the initial velocity in all simulations was standardized at 35 mph, except for rear-end scenarios in which the lead vehicle was modeled as stationary to reflect realistic pre-impact conditions. This design enabled focused analysis of mass disparity, vehicle positioning within the crash-prone zone (leading versus following), angle of impact (AOI), and CIP location. Sixty-three finite element crash simulations were conducted involving a sedan, minivan, and single-unit truck (SUT) across diverse crash types, including head-on, angle, right-angle, and side-swipe collisions. Severity was evaluated using KE dissipation, deformation proximity to occupant compartments, and detailed examination of crash dynamics. Findings indicate that mass configuration, AOI, and CIP location are decisive in determining severity, with SUT-to-SUT head-on crashes producing the most severe outcomes and non-driver-side side-swipes exhibiting the lowest severity. These insights provide a physics-grounded framework for prioritizing safety countermeasures, enabling decision-makers to target the most critical conflict configurations in high-risk traffic environments.]]></description><pubDate>Thu, 15 Oct 2026 16:55:27 GMT</pubDate><guid>http://pubsindex.trb.org/view/2789789</guid></item></channel></rss>