<?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?cdatein=1year" 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>Transportation Planning in Non-Metropolitan/Rural Areas</title><link>http://pubsindex.trb.org/view/2694542</link><description><![CDATA[This report presents the state of practice of state departments of transportation (DOTs) regarding transportation planning in non-metropolitan (rural) areas of their states. The synthesis includes information on the structure and roles of regional planning entities (RPEs), communication and engagement practices, planning responsibilities and products, support for rural planning capacity, integration of transit and multimodal planning, and challenges to coordination. Under NCHRP Project 20-05/Topic 56-11, “Practices for Transportation Planning in Non-Metropolitan Areas,” the University of South Florida was asked to synthesize information to document state DOT practices for transportation planning in non-metropolitan areas. 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 four case examples that highlight governance for planning in non-metropolitan areas, key coordination and support mechanisms used with RPEs or local governments, and barriers and challenges to non-metropolitan planning.]]></description><pubDate>Sun, 26 Apr 2026 17:37:53 GMT</pubDate><guid>http://pubsindex.trb.org/view/2694542</guid></item><item><title>Coordination of Highway Safety Improvement Program and Highway Safety Office Activities</title><link>http://pubsindex.trb.org/view/2694541</link><description><![CDATA[This report presents the state of practice of state departments of transportation (DOTs) on how they organize, manage, and align their Highway Safety Improvement Programs (HSIPs) and Highway Safety Offices (HSOs). The synthesis includes information on how state DOTs coordinate these functions through shared planning, data exchange, performance measures, and reporting processes. The synthesis also documents coordination practices related to funding, safety program implementation, public participation and engagement efforts, and the use of data tools and dashboards. Under NCHRP Project 20-05/Topic 56-19, “Practices on Coordination of HSIP and Highway Safety Office Activities,” the University of Missouri was asked to synthesize information to document current practices, challenges, and opportunities for improving coordination between HSIP and HSO management, practices, and associated funding. Information used in this study was attained through a literature review, a survey of state DOTs, and interviews to develop in-depth case examples. Chapter 4 provides six case examples that highlight how the interviewed state DOTs coordinate HSIP and HSO activities through shared performance measures, safety planning, crash data management, funding practices, and organizational structures, as well as challenges related to staffing, communication, and administrative processes.]]></description><pubDate>Sun, 26 Apr 2026 17:37:52 GMT</pubDate><guid>http://pubsindex.trb.org/view/2694541</guid></item><item><title>Development of Asphalt Concrete Rutting Model on Pavement Mechanistic–Empirical for Porous Asphalt Pavement Based on Accelerated Pavement Testing</title><link>http://pubsindex.trb.org/view/2694550</link><description><![CDATA[Porous asphalt mixtures are increasingly used in pavement infrastructure because of their environmental benefits and enhanced surface drainage. However, their unique open-graded structure and reduced stiffness present challenges for rutting prediction using traditional dense-graded asphalt models. This study developed and calibrated a rutting prediction model specifically for porous asphalt pavements using data from full-scale accelerated pavement testing with a Heavy Vehicle Simulator. The first section, consisting of a 2-in. modified open-graded friction course (MOGFC) over a 10-in. asphalt-stabilized drainage course (ASDC), was subjected to 1 million equivalent single axle loads (ESALs) under controlled conditions at 85°F. The second test section consisted of a 6-in. MOGFC layer over 13-in. ASDC, and was designed for 13 million ESALs. Laser profilometer measurements were used to track surface deformation. A power law model was fitted between the measured rut depth and ESALs, yielding a strong correlation (coefficient of determination = 0.97). Vertical compressive strains within each asphalt layer were computed using a structural response model that incorporated modulus values from laboratory testing. These strain values were input into the Pavement mechanistic–empirical viscoplastic rutting model, which revealed that over 64% of the rutting occurred in the MOGFC layer because of its high strain and shallow depth. The default global Pavement mechanistic–empirical coefficients significantly underpredicted the measured rutting, prompting calibration. Optimized model parameters aligned the predictions with observed performance. This calibrated model enhances the rutting prediction for porous asphalt systems and supports performance-based design under high-traffic loading.]]></description><pubDate>Thu, 23 Apr 2026 09:10:01 GMT</pubDate><guid>http://pubsindex.trb.org/view/2694550</guid></item><item><title>Optimizing Terminal and Vessel Selection for Shore Power Deployment: Case Study at the Port of Houston</title><link>http://pubsindex.trb.org/view/2694547</link><description><![CDATA[The maritime industry has a substantial impact on the environment and public health, particularly through ship operations and port-related activities. Shore power (SP) offers a promising solution by allowing docked ships to connect to local electrical grids, thereby reducing auxiliary engine usage during hotelling. One of the key challenges to SP adoption is the substantial amount of investment required from both port authorities and ship owners or operators. In this study, an optimization framework is developed to allocate a limited budget for SP deployment at terminals and subsidies, to encourage commercial ship retrofitting to maximize the environmental and economic benefits of SP. The framework takes account of the perspectives of ship owners and operators, port authorities, and the government to reflect the interactions in their decision-making. The framework is applied to the Port of Houston, based on commercial ship hotelling activities at its public terminals in 2022. The results demonstrate that, with an annualized budget of $5.5 million, up to 50% of SP-eligible hotelling activities can be powered by SP; this can generate substantial environmental and economic benefits. Additionally, the results indicate that the cost of SP electricity to ship operators plays a critical role in balancing economic incentives between ports and ship owners in the adoption of SP. Sensitivity analysis confirms the framework’s robustness to several key environmental and economic factors and assumptions. The proposed framework can serve as a practical decision-support tool for coordinate between stakeholders and ensure that limited resources generate the greatest possible environmental and economic benefit.]]></description><pubDate>Thu, 23 Apr 2026 09:10:01 GMT</pubDate><guid>http://pubsindex.trb.org/view/2694547</guid></item><item><title>A Heuristic Traffic Evacuation Approach for Emergency Vehicles in Mixed Traffic Flow in Urban Areas</title><link>http://pubsindex.trb.org/view/2694548</link><description><![CDATA[Emergency vehicles (EVs) play a pivotal role in promptly responding to time-sensitive situations, such as traffic accidents, medical emergencies, and fires in urban areas. Conventional traffic control methodologies primarily minimize EV travel time by granting them the highest road-use priority, potentially leading to delays for other nearby traffic participants and disrupting the flow of normal traffic. Connected and automated vehicles (CAVs) can help achieve the optimization of the transportation system. Thus, this work proposes an innovative heuristic approach aimed at reducing both EV travel time and the adverse impact on normal traffic by detouring CAVs adjacent to an EV’s path. Specifically, it periodically and dynamically establishes a subnet for each road segment along the EV route. Within the subnetwork, CAVs would be prohibited from utilizing the EV path, thus expediting emergency service delivery. Furthermore, the approach incorporates mechanisms for rerouting CAVs to alleviate congestion. Comprehensive experimental results demonstrate that compared with the heuristic traffic clear-out coordination algorithm (HTCC), the proposed approach reduces the negative impact of EVs on normal traffic by 8.5%, 8.1%, 6.2%, and 7.4%, respectively, across four metrics, including average travel time, average time loss, average waiting time, and average number of stops for EVs and regular vehicles. In addition, sensitivity analyses are conducted to evaluate the impact of penetration rates of CAVs and varying traffic densities. The results indicate that under different penetration rates of CAVs, the proposed approach outperforms the benchmark methods across all four metrics, with advantages becoming more pronounced at higher penetration rates of CAVs. Compared with the noncontrol strategy, our approach and HTCC reduce the response time of EVs by 73.1% and 72.6%, 76.3% and 74.7%, and 77.6% and 74.7% in low, medium, and high traffic densities, respectively. Our approach demonstrates overall superior performance to HTCC, particularly under high traffic densities. This work and its findings enhance the deployment of smart EV management systems.]]></description><pubDate>Thu, 23 Apr 2026 09:10:01 GMT</pubDate><guid>http://pubsindex.trb.org/view/2694548</guid></item><item><title>Dynamic Disruption Resilience in Intermodal Transport Networks: Integrating Flow Weighting and Centrality Measures</title><link>http://pubsindex.trb.org/view/2694546</link><description><![CDATA[Resilient intermodal freight networks are vital for sustaining supply chains amid increasing threats from natural hazards and cyberattacks. Transportation resilience has been widely studied; understanding how random and targeted disruptions affect structural connectivity and functional performance remains a key challenge. To address this, this study evaluates the robustness of the US intermodal freight network, which consists of rail and water modes, using a simulation-based framework that integrates graph-theoretic metrics with flow-weighted centrality measures. Disruption scenarios are examined, including random failures as well as targeted node and edge removals based on static and dynamically updated degree and betweenness centrality. To reflect more realistic conditions, flow-weighted degree centralities (WDC) and partial node degradation are considered. Two resilience indicators are used: (1) the size of the giant connected component to measure structural connectivity; and (2) flow-weighted network efficiency (NE) to assess freight mobility under disruption. The results show that progressively degrading nodes ranked by WDC to 60% of their original functionality causes a sharper decline in normalized NE, for up to approximately 45 affected nodes, than complete failure (100% loss of functionality) applied to nodes targeted by weighted betweenness centrality or selected at random. This highlights how partial degradation of high-tonnage hubs can produce disproportionately large functional losses. The findings emphasize the need for resilience strategies that go beyond network topology to incorporate freight flow dynamics.]]></description><pubDate>Thu, 23 Apr 2026 09:10:01 GMT</pubDate><guid>http://pubsindex.trb.org/view/2694546</guid></item><item><title>Emergency Material Scheduling in Hub-and-Spoke Networks Considering Resilience</title><link>http://pubsindex.trb.org/view/2694545</link><description><![CDATA[Emergency supply distribution networks face significant resilience challenges during large-scale disasters because of hub congestion and demand–supply mismatches. This study addresses this issue by proposing and comparing two congestion management strategies for hybrid hub-and-spoke rail–road intermodal networks: a waiting versus path redistribution strategy using backup hub mechanisms. A multi-objective optimization model was constructed to maximize network resilience, minimize transportation time, and reduce costs. Resilience was measured by the demand gap weighted by demand urgency. A rolling horizon optimization framework is established to address the temporal dynamics of disaster relief operations. A Q-learning-enhanced Non-dominated Sorting Genetic Algorithm II (NSGA-II) algorithm is developed to solve the optimization problem, constructing a 16-dimensional state space by integrating objective function values and population diversity metrics for intelligent local search. Using severely affected areas from the Wenchuan earthquake as a case study, the experimental results demonstrate that the improved algorithm reduces average objective function values by 17.75%, 49.82%, and 19.92%, respectively, compared with the standard NSGA-II. Incorporating demand urgency factors reduces the material shortage index by 53.41%, better reflecting humanitarian priorities. By comparing the average function values across Periods 1–6, the first four periods are suitable for the path reallocation strategy, while the subsequent two periods should adopt the “continue waiting” strategy. The study provides actionable insights for emergency managers in optimal strategy selection in disaster relief operations.]]></description><pubDate>Thu, 23 Apr 2026 09:10:01 GMT</pubDate><guid>http://pubsindex.trb.org/view/2694545</guid></item><item><title>Does Light Influence Route-Choice Behavior? Discrete-Choice Modeling Study</title><link>http://pubsindex.trb.org/view/2694544</link><description><![CDATA[Crowd management attempts to guide pedestrian crowds effectively and efficiently. Static crowd-management measures, such as fences, are often used to guide the crowd. Another way to steer pedestrian walking behavior is through “nudging,” that is, gently coaxing people into the “preferred” direction, for instance, by lighting conditions. This paper examines the impact of light intensity (brightness) and color on pedestrian route-choice behavior using data from a virtual reality (VR) experiment. The study develops two types of discrete-choice models—a panel mixed logit model and a latent class choice model—featuring the route-choice behavior of pedestrians under varying lighting conditions in a virtual maze in a controlled virtual reality experiment. We found that pedestrians avoided red and dark corridors and chose green and blue corridors. On average, the green light most effectively “pulled” people toward a specific route. In addition, this study uncovered three segments in the population: (1) light-sensitive individuals, (2) darkness-avoiding individuals, and (3) individuals with a severe right-handed bias. We found that the impact of color and brightness levels on route-choice behavior differed greatly across segments.]]></description><pubDate>Thu, 23 Apr 2026 09:10:01 GMT</pubDate><guid>http://pubsindex.trb.org/view/2694544</guid></item><item><title>Response Surface Methodology-Based Optimization of Rice Husk Ash and Ground Granulated Blast Furnace Slag Dosage for Alluvial Silty Soil Stabilization</title><link>http://pubsindex.trb.org/view/2694543</link><description><![CDATA[The low strength and large void ratio of alluvial soil (silty) represent a poor quality of subgrade that shows early distress, causing the premature failure of pavement. To enhance the performance of the soil in this context, a mixture of additives such as ground granulated blast furnace slag (GGBS) and rice husk ash (RHA) was applied. Researchers are now very interested in the optimal dosage of stabilizers for increased strength and cost-effectiveness in construction. The dosages analyzed were 5%–12.5% GGBS and 0%–7.5% RHA by weight of dry soil. The following response values were considered: optimum moisture content (OMC), elastic modulus (EM), California bearing ratio (CBR), and unconfined compressive strength (UCS). EM was derived using a novel method of light weight deflectometer testing on the CBR mold. To find the optimal dosage of RHA and GGBS to be added simultaneously for soil stabilization and to examine how these substances affect the soil, response surface methodology models were generated, giving the optimal RHA and GGBS dosages as 2.05% and 7.18%, respectively, to meet desired soil performance. According to the findings, adding GGBS and RHA increased the soil’s strength because of the production of cementitious compounds by pozzolanic reactions, as demonstrated by the mineralogical and morphological examinations of soil specimens. The stabilization resulting from optimal dosages improved the soil UCS by 177%, EM by 47%, and CBR by 242%, and reduced OMC by 4%. This study can provide a strong foundation for effective soil use in road building.]]></description><pubDate>Thu, 23 Apr 2026 09:10:01 GMT</pubDate><guid>http://pubsindex.trb.org/view/2694543</guid></item><item><title>Multi-Intersection Traffic Signal Control With Deep Q Network Softmax Cross-Entropy Algorithm Based on Attention Mechanism</title><link>http://pubsindex.trb.org/view/2694295</link><description><![CDATA[With the continuous increase of urban traffic flow, the intelligence of traffic signal control (TSC) has become an important means to improve traffic efficiency. Among them, the deep reinforcement learning (DRL) algorithm Deep Q-Network (DQN) has been successfully applied to the field of TSC. We focus on the problems of complex state representation of existing traffic models, insufficient performance of DQN algorithm when using multilayer perceptron (MLP) as an action network, and over-estimation of Q-value leading to degradation of convergence performance. To mine the potential traffic state information from limited features and to improve the efficiency of the model, we propose a DQN softmax cross-entropy (DQN-SCE) TSC algorithm. First, the model uses the current phase and queue length as the state representation and optimizes the reward function only by the queue length. Second, a multi-head self-attention mechanism is used to fuse the state features. Finally, an improved DRL algorithm DQN-SCE is proposed; that is, we add cross-entropy loss of current actions for the target network and the action network to DQN. The experimental results based on CityFlow show that the TSC algorithm has better performance in the metric of average travel time compared with some traditional methods and reinforcement learning methods. The proposed algorithm still performs well compared with the traditional DQN algorithms and several improved algorithms for DQN.]]></description><pubDate>Mon, 20 Apr 2026 18:10:32 GMT</pubDate><guid>http://pubsindex.trb.org/view/2694295</guid></item><item><title>Analysis of the Effect of Shield Tunnel Construction Method Under Viaduct on Tunnel Structure and Pile Foundations</title><link>http://pubsindex.trb.org/view/2693760</link><description><![CDATA[To ensure the safe operation of elevated bridges during shield tunneling, this study investigates the west extension of Shijiazhuang Metro Line 1, where the tunnel passes beneath an existing elevated bridge. Field monitoring data serve as validation. A numerical model was developed using FLAC3D based on site-specific geological conditions to examine three schemes: (1) a 0.7 m clearance between the tunnel and pile foundation; (2) earth pressure balance shield (EPBS) cuts through the bridge foundation piles; and (3) pre-reinforcement of the surrounding soil using a U-shaped Metro Jet System around the cut pile. In Scheme 1, the pile exhibits longitudinal deformation aligned with the tunnel’s advance direction. Vertical deformation presents an S-shaped profile, and lateral displacement remains negligible. The most pronounced pile deformation occurs from 2.4 m behind to 0 m ahead of the pile. In Scheme 2, lateral deformation remains constrained, but vertical and longitudinal deformations increase markedly. Maximum surface settlement occurs directly above the pile, and the tunnel lining becomes elliptical. In Scheme 3, relative to Scheme 2, longitudinal and lateral pile deformations exhibit little change, while vertical pile deformation, surface settlement, and tunnel lining distortion are significantly reduced.]]></description><pubDate>Fri, 17 Apr 2026 08:57:14 GMT</pubDate><guid>http://pubsindex.trb.org/view/2693760</guid></item><item><title>From Perception to Prediction: Modeling Pedestrian Satisfaction Using Multilevel Statistical and Sensitivity Methods</title><link>http://pubsindex.trb.org/view/2693758</link><description><![CDATA[This study presents an integrated modeling approach to evaluate pedestrian satisfaction in new urban cities characterized by rapid growth and limited multimodal connectivity. A structured questionnaire, distributed to stratified participants across residential, administrative, and service zones, captured user perceptions of 13 key urban design features, including safety, accessibility, visual coherence, and economic vibrancy. Descriptive statistics and visual analytics revealed that accessibility, protection from crime and traffic, and urban aesthetics were strong correlates of satisfaction. To model these relationships quantitatively, the study employed both ordinal and multinomial logistic regression, with the latter achieving 92.45% classification accuracy. K-means clustering and principal component analysis further uncovered latent user typologies, highlighting the heterogeneity of pedestrian priorities. Local and global sensitivity analyses, including mutual information metrics, identified easy access, protection from traffic, and crime prevention as the most influential features. Response surface modeling illustrated nonlinear interactions among key variables, emphasizing the multidimensional and synergistic nature of satisfaction outcomes. The findings showed that pedestrian experience is shaped not by isolated design features, but by their interactive effects across spatial, psychological, and infrastructural domains. The study offers actionable insights for human-centered urban design, while the presented analytical framework is scalable and supports evidence-based interventions in emerging urban contexts.]]></description><pubDate>Fri, 17 Apr 2026 08:57:14 GMT</pubDate><guid>http://pubsindex.trb.org/view/2693758</guid></item><item><title>Transportation Job Ads: Do They Align with the Sector’s Technology-Driven Transformation?</title><link>http://pubsindex.trb.org/view/2693757</link><description><![CDATA[This paper offers insights into the alignment of sought-after versus ideal transportation workforce skills through an analysis of 8,132 job advertisements in the United States. Required skill sets for various jobs were extracted from online job-posting websites by using text mining tools. These data were compared with (1) the Occupational Information Network (O*NET) database that documents the workforce skill expectations of transportation industry professionals, (2) an industry survey capturing expert insights into disciplinary knowledge needed in the future transportation workforce. The analysis also investigated emerging transportation job titles to assess the industry’s hiring landscape driven by technological transformation. The findings aligned with literature about the high percentage of opportunities for middle-skill jobs, and transportation jobs generally requiring a mix of soft and hard skills, reflecting the industry’s diverse demands. Comparison with the industry survey indicated that the ideal workforce skills and necessary integration of disciplines such as social sciences were not explicitly stated in job ads, especially under preferred/required skills that determine the main candidate pool. Some transportation job ads include technology-related skills such as Python and SQL. However, many emerging jobs are yet to appear in the transportation industry. For emerging job positions, salary offers were found to be noncompetitive with other industries. The advertisements on emerging job titles were also found to be more explicit about the required skills and disciplines compared with transportation job advertisements. Overall, this paper identifies the gaps between ideal transportation workforce needs and the hiring landscape and provides recommendations to bridge those gaps.]]></description><pubDate>Fri, 17 Apr 2026 08:57:14 GMT</pubDate><guid>http://pubsindex.trb.org/view/2693757</guid></item><item><title>Training Drivers in Advanced Driver Assistance Systems: How Training Content Shapes Driver Knowledge, Decision-Making, and Performance</title><link>http://pubsindex.trb.org/view/2693756</link><description><![CDATA[Driver understanding of Society of Automotive Engineers Level 1 and Level 2 automated driving systems is essential for safe human–automation interaction as these features are increasingly common in modern vehicles. Yet, little is known about how different training contents shape drivers’ knowledge and performance. This study investigates how variations in training content affect drivers’ knowledge, decision-making, driving performance, and subjective evaluations when using advanced driver assistance systems (ADASs), including adaptive cruise control, lane-keeping assist, and highway driving assist. Sixty participants were randomly assigned to one of three training groups: baseline training, driver-issue training, and feedback-based training. Pre- and post-training knowledge tests, simulator-based driving tests, and subjective questionnaires were used to evaluate outcomes. Results showed that feedback-based training significantly enhanced drivers’ knowledge compared with the other groups. Drivers trained with driver-issue content exhibited more stable lateral control. Training content did not meaningfully affect trust or perceived usefulness, although satisfaction differed across groups. These findings demonstrate that different contents of ADAS training influence driver understanding, driver performance, and subjective experience. This work provides guidance for designing future ADAS training programs that could help drivers to have safe driving experience.]]></description><pubDate>Fri, 17 Apr 2026 08:57:14 GMT</pubDate><guid>http://pubsindex.trb.org/view/2693756</guid></item><item><title>Assessing Sweden’s Greenhouse Gas Emissions from Road Maintenance using Environmental Product Declarations and Network Life-Cycle Optimization</title><link>http://pubsindex.trb.org/view/2693755</link><description><![CDATA[To achieve the Paris Agreement’s goal of limiting the average temperature rise to 1.5°C, greenhouse gas emissions must be reduced by 43% from 2010 levels by 2030. This paper quantifies greenhouse gas emissions from road maintenance in Sweden and evaluates reduction pathways by integrating three elements: (1) lifetime estimates of maintenance operations, (2) corresponding emissions estimated from published Environmental Product Declarations, and (3) simulation of a 10-year maintenance plan that maintains current condition distribution at minimum cost across three regions (Stockholm, Skåne, Norrbotten). We assess three scenarios: a 2010 fossil-fuel baseline; a 2024 practice with predominantly biofuel-fired production and higher reclaimed asphalt utilization; and a 2024 extension that additionally replaces 5% of bitumen with a biogenic binder and employs biofuels for transport and machinery. Relative to 2010, the simulated emissions required to maintain current network condition were 28% to 30% lower under 2024 practices and 60% to 61% lower with added biogenic binder. The largest emission reductions arose from switching production heat from fossil to biofuels and from increased reclaimed asphalt. Total life-cycle impacts remained sensitive to treatment longevity, underscoring the need to integrate performance into procurement and to monitor the durability of emerging low-carbon materials. Although Sweden has reduced emissions substantially, achieving the 43% reduction target by 2030 will require measures beyond current practice.]]></description><pubDate>Fri, 17 Apr 2026 08:57:14 GMT</pubDate><guid>http://pubsindex.trb.org/view/2693755</guid></item></channel></rss>