<?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>Microscopic Analysis of Particle Regularity Effects on Cyclic Shear Behavior at Gravel–Geogrid Interfaces</title><link>http://pubsindex.trb.org/view/2701386</link><description><![CDATA[The apparent shape of particles is a key determinant of the mechanical properties of a gravel–geogrid interface. A quantitative analysis of particle shape was conducted to study the effect of particle regularity on the cyclic shear characteristics of a gravel–geogrid interface. The discrete-element method was utilized to establish particles of varying regularities in particle flow software and to simulate direct shear tests. Changes in porosity, coordination number, shear band proportion, particle rotation angle, and fabric anisotropy were analyzed for varying particle regularities and cycle numbers. The results demonstrate that the vertical displacement and shear stress of the gravel–geogrid interface increase with decreasing particle regularity. The interface exhibits reduced porosity and elevated coordination number for lower-regularity particles. The shear band proportion decreases with increasing particle regularity, with low-regularity particles having 1.4–1.7 times more shear band proportion than high-regularity particles. The rotation angle of a particle with a regularity of 0.707 is only 0.4–0.6 times that of a particle with a regularity of 0.975, showing an opposite trend to the shear band proportion. Simultaneously, particles within the shear band also show a significantly higher rotation angle than those outside. Additionally, the mean rotation angle decreases with increasing cycle number. The principal stress direction of the contact force at the gravel–geogrid interface shifts with the increase in shear displacement. The deflection of the direction of the principal stress axis between high-regularity particles is slightly less than that of low-regularity particles.]]></description><pubDate>Thu, 14 May 2026 17:01:53 GMT</pubDate><guid>http://pubsindex.trb.org/view/2701386</guid></item><item><title>Lessons Learned from the Real-World Deployment of Multisensor Fusion for Proactive Work Zone Safety Application</title><link>http://pubsindex.trb.org/view/2701385</link><description><![CDATA[Proactive safety systems that anticipate and mitigate traffic risks before incidents occur are increasingly recognized as essential for improving work zone safety. Unlike traditional reactive safety approaches, proactive systems rely on real-time sensing, trajectory prediction, and intelligent infrastructure to detect potential safety hazards. Existing simulation-based and real-world deployment studies often overlook and rarely discuss the practical challenges associated with deploying such proactive systems in operational settings, particularly those involving roadside infrastructure enabled multisensor integration and fusion. This study addresses that gap by presenting deployment insights and technical lessons learned from a real-world implementation of a proactive safety system utilizing multiple sensors mounted on a roadside infrastructure at an active bridge repair work zone along the N-2/US-75 corridor in Nebraska, USA. The deployed system combines lidar, radar, and camera sensors with an edge computing platform to support multimodal object tracking, trajectory fusion, and real-time predictive analytics. Specifically, this study presents key lessons learned across three critical stages of deployment: 1) sensor selection and placement; 2) sensor calibration, system integration, and validation; and 3) sensor fusion. Additionally, we propose a predictive digital twin framework that uses fused trajectory data to predict vehicle path for enabling early conflict detection and real-time warning generation, thereby enabling proactive safety interventions.]]></description><pubDate>Thu, 14 May 2026 17:01:53 GMT</pubDate><guid>http://pubsindex.trb.org/view/2701385</guid></item><item><title>A Comprehensive Review of Vehicle–Pedestrian Interactions: Crash Analysis and Conflict Assessment Approaches</title><link>http://pubsindex.trb.org/view/2701383</link><description><![CDATA[Pedestrian–vehicle collisions remain a critical issue in transportation safety, contributing disproportionately to global traffic-related fatalities and injuries. Unlike vehicle occupants, pedestrians lack physical protection and are therefore more susceptible to severe or fatal outcomes when involved in crashes. Understanding the mechanisms of vehicle–pedestrian interactions and contributing factors to pedestrian crashes is essential for uncovering the crash nature and informing the development of effective safety countermeasures and technologies. This review synthesizes recent advancements in the study of pedestrian safety research at intersections, covering historical crash data–based modeling and analysis, as well as conflict-based studies using field observations and simulation. Key topics include the application of statistical and machine-learning models in crash likelihood and severity analysis, the use of surrogate safety measures, and the integration of conflict analysis frameworks such as extreme value theory. Critical challenges related to pedestrian safety modeling methodologies and evaluation metrics, the evolving safety implications in connected and automated vehicle environments, and the practical applications of these insights for policy and infrastructure design are discussed in depth. By reviewing methodological innovations and highlighting emerging research directions, this paper offers a comprehensive foundation for advancing pedestrian safety research and guiding the development of data-driven, context-sensitive policy, operation, and infrastructure solutions.]]></description><pubDate>Thu, 14 May 2026 17:01:53 GMT</pubDate><guid>http://pubsindex.trb.org/view/2701383</guid></item><item><title>Dry Bulk Freight Rate Prediction and Interpretive Analysis: Transformer-Based Hybrid Model with Domain Adaptation</title><link>http://pubsindex.trb.org/view/2701382</link><description><![CDATA[Shipping is vital to global trade but the dry bulk market faces high volatility because of complex factors like economic conditions and geopolitics. Traditional models struggle to capture nonlinear rate fluctuations and offer interpretability. To overcome this, our study integrates shipping, financial, and commodity data. Using the Maximal Information Coefficient (MIC) algorithm, 4,281 time series (over 4.53 million points) were selected to pre-train a general maritime time series model (MTS) based on the Transformer. A fine-tuning dataset of 248 high-MIC series (&gt;0.2) and five binary event indicators (over 0.073 million points) was then used for domain adaptation, creating DA-MTS. To better model short-term changes, a long short-term memory (LSTM) network with Grey Wolf Optimization was applied to DA-MTS residuals, yielding a hybrid DA-MTS+LSTM model. Empirical results show DA-MTS+LSTM achieves high accuracy in forecasting the Baltic Dry Index (MAE = 0.16, RMSE = 0.21, MAPE = 5.26%), the Baltic Capesize Index (MAE = 0.21, RMSE = 0.27, MAPE = 6.67%), and the Baltic Panamax Index (MAE = 0.16, RMSE = 0.20, MAPE = 5.12%). The study also analyzes the short- and long-term impacts of exogenous variables and sudden events. This work enhances prediction accuracy and stability while extending Transformer applications to time series analysis, offering robust theoretical and practical decision-making support.]]></description><pubDate>Thu, 14 May 2026 17:01:53 GMT</pubDate><guid>http://pubsindex.trb.org/view/2701382</guid></item><item><title>Investigating Flexible Pavement Responses and Performance under Trunnion Axle Loading Using Three-Dimensional Finite Element Modeling and Field Validation</title><link>http://pubsindex.trb.org/view/2701381</link><description><![CDATA[The growing demand for high-capacity freight vehicles has heightened the need to evaluate the impact of heavy truck axle configurations on pavement behavior. This study employed a validated three-dimensional (3D) finite element (FE) model to evaluate the structural response of flexible pavements under trunnion and tandem axle configurations at two vehicle speeds (35 and 55 mph), using legal load levels of 60 and 34 kip, respectively. Then, key pavement responses (i.e., tensile strain, stress, and vertical displacement) were assessed. Also, pavement performance (i.e., fatigue cracking and subgrade rutting) was evaluated. The results showed that the trunnion axle generated higher tensile strain, von Mises stress, maximum principal stress, vertical stress, and vertical displacement than the tandem axle. This increase in pavement responses is primarily attributed to the trunnion’s shorter axle spacing, which causes overlapping stress zones and amplifies strain concentrations within the asphalt and subgrade layers. Concerning pavement performance, the trunnion axle exhibited lower fatigue life and lower resistance to subgrade rutting. Vehicle speed was also found to influence pavement response, with lower speeds producing higher stress, strain, and displacement levels for both axle types. Model predictions were validated using field measurements from the MnRoad test site in Minnesota, USA, confirming consistent trends in pavement responses under dynamic axle loading. These findings offer insight into the roles of axle configuration, maximum load, and truck speed in determining flexible pavement performance, thereby supporting better-informed design and evaluation practices.]]></description><pubDate>Thu, 14 May 2026 17:01:53 GMT</pubDate><guid>http://pubsindex.trb.org/view/2701381</guid></item><item><title>Strengthening Supply Chain Resilience from Dynamic Capability View: Role of Artificial Intelligence Assimilation</title><link>http://pubsindex.trb.org/view/2701380</link><description><![CDATA[Drawing on the hierarchical dynamic capabilities view, this study empirically examines the mechanism through which artificial intelligence (AI) assimilation translates into supply chain resilience (SCR). Analysis of data collected from 287 supply chain executives in China revealed that the resilience benefits of AI were realized primarily through the enhancement of specific organizational capabilities rather than direct technological application alone. The results demonstrated that the influence of AI assimilation on SCR was predominantly indirect, channeled through supply chain alertness (SCA) and supply chain disruption orientation (SCDO). A critical finding was that SCA mediated this relationship significantly more effectively than SCDO, suggesting that the immediate ability to sense and anticipate changes is a more vital driver of resilience than disruption orientation. These insights offer a strategic roadmap for managers, indicating that to maximize the resilience benefits of AI, organizations should prioritize the development of sensing-oriented capabilities.]]></description><pubDate>Thu, 14 May 2026 17:01:53 GMT</pubDate><guid>http://pubsindex.trb.org/view/2701380</guid></item><item><title>Express Charging Lanes for Electric Vehicle Evacuation</title><link>http://pubsindex.trb.org/view/2701379</link><description><![CDATA[The long-distance evacuation of electric vehicles (EVs) presents significant challenges for disaster management owing to their limited driving range and constrained charging infrastructure. EVs with long charging times can create negative externalities for all other EVs waiting in queues, especially during evacuation scenarios. This study investigates the use of express charging lanes to reduce overall evacuation delays. We propose optimization models to optimize the allocation of charging plugs for express and regular charging, considering both user-equilibrium and system-optimal scenarios. To account for heterogeneities and uncertainties, such as stochastic EV arrival patterns and variable charging demands, we further develop numerical simulation models to quantify the delay distribution. We found that separating EVs with lower charging demand had the potential to minimize total system delay. The proposed models identified the optimal level of express charging plug allocation to minimize the total charging delay without centralized enforcement of traffic distribution. In addition, the models could enable government agencies to estimate the required charging resources to fulfill an evacuation within a given time window. The insights generated by the proposed theoretical models were validated using agent-based simulation, in which uncertainties could be flexibly represented.]]></description><pubDate>Thu, 14 May 2026 17:01:53 GMT</pubDate><guid>http://pubsindex.trb.org/view/2701379</guid></item><item><title>Multi-Pedestrian Tracking Based on Improved YOLOv8 and OC-SORT</title><link>http://pubsindex.trb.org/view/2701297</link><description><![CDATA[Multi-pedestrian tracking is an important task for the environment perception systems of autonomous vehicles. In the multi-pedestrian tracking task, mutual occlusion, posture changes, small size, and poor lighting conditions usually pose challenges. To overcome these problems, we propose a detection-based multi-pedestrian tracking method, that is, combining the improved You Only Look Once (YOLO) v8 object detection algorithm with the improved observation-centric simple online and real-time tracking (OC-SORT) algorithm. Specifically, first, we improve the YOLOv8 pedestrian detector by constructing a C2f-Clo block, introducing an explicit visual center block, and designing a lightweight shared convolutional detection head. Second, we improve the OC-SORT tracker using a height-modified intersection over union. Results of experiments on the MOT17 and MOT20 pedestrian tracking datasets show that our method achieves 7.1% and 6.5% HOTA boosts, 8.5% and 8.8% MOTA improvements, 5.5% and 6.1% MOTP increases, 5.2% and 6.7% IDF1 boosts, and 648 and 692 IDSW decreases, respectively, compared with the baseline.]]></description><pubDate>Wed, 13 May 2026 17:00:16 GMT</pubDate><guid>http://pubsindex.trb.org/view/2701297</guid></item><item><title>A Multiobjective Optimization Method for Integrated Road Asset Management Considering Traffic Dynamics</title><link>http://pubsindex.trb.org/view/2701293</link><description><![CDATA[Traditional road asset management often operates in isolation, leading to suboptimal coordination. While integrated asset management enables multiasset maintenance and rehabilitation (M&amp;R) decisions on a unified platform, existing approaches typically do not systematically account for traffic redistribution effects. This study proposes a road M&amp;R planning method based on bilevel multiobjective optimization (MOO) that explicitly integrates user and environmental considerations with economic and performance objectives. The upper level optimizes network-level multiyear agency cost, network condition, user cost, and greenhouse gas (GHG) emissions. The lower level employs a traffic assignment model to address traffic dynamics caused by reduced link capacity during M&amp;R operations. The bilevel MOO model is solved using the Nondominated Sorting Genetic Algorithm III and Self-Regulated Method of Successful Averages to generate Pareto solutions, with an analytic hierarchy process-based weighted-sum method determining the final solution. A five-year case study on a road network in Liaoning Province, China, demonstrates the method’s effectiveness for pavement and bridge M&amp;R planning: 19.11% improvement in network condition, 9.07% reduction in GHG emissions, and 4.64% reduction in user costs, proving the method’s effectiveness for achieving cost-effective and sustainable M&amp;R decisions. Comparative analysis against a static traffic baseline reveals that explicitly modeling traffic redistribution reduces user costs by 99.39% and GHG emissions by 61.17%, demonstrating traffic dynamics alter optimal M&amp;R decisions. The methodology is validated for a regional network with asphalt pavements, reinforced concrete T-beam bridges, and passenger vehicle traffic under deterministic demand; extensions to heterogeneous vehicle types, elastic demand, and other infrastructures represent directions for future studies.]]></description><pubDate>Wed, 13 May 2026 17:00:16 GMT</pubDate><guid>http://pubsindex.trb.org/view/2701293</guid></item><item><title>Understanding Multi-Source Impacts on Airport Ride-Hailing Vehicle Queueing: Insights from a Time-Segmented Framework</title><link>http://pubsindex.trb.org/view/2701290</link><description><![CDATA[Ride-hailing drivers often face queueing challenges at airports owing to mismatches between passenger demand and vehicle supply, which can affect urban mobility efficiency. Queue length and waiting time are key performance metrics reflecting the interaction between airport passengers and ride-hailing services within the broader urban transport system. This study explores the impacts of multi-source exogenous factors on the queueing performance of airport ride-hailing vehicles and develops a generalizable analytical framework to support adaptive operation. The proposed optimal segmentation for circular samples (OSCS) algorithm enables time segmentation based on queueing dynamics while bypassing traditional calendar-based divisions. This segmentation allows precise regression and temporally interpretable analysis. Within each time segment, generalized additive models (GAMs) capture relationships between influencing factors and queueing metrics. Using Hangzhou Airport in China as a case study, we apply the OSCS-GAM framework, which divides the daily operational timeline into three statistically distinct segments, morning, afternoon, and night, revealing differentiated queueing characteristics. Regression results reveal that air passenger volume predominantly drives vehicle queueing dynamics, while other factor categories, including weather, urban traffic conditions, and weekday-weekend patterns, independently exhibit temporally heterogeneous impacts across the three daily segments. Our approach captures the temporal and contextual dependencies of airport ride-hailing vehicle queues, offering insights into optimizing resource utilization within urban transport systems. These findings inform municipal and airport authorities, as well as ride-hailing companies, supporting the development of adaptive operational strategies that promote efficient and sustainable urban transportation.]]></description><pubDate>Wed, 13 May 2026 17:00:16 GMT</pubDate><guid>http://pubsindex.trb.org/view/2701290</guid></item><item><title>Network-Level Bridge Condition Degradation Prediction and Interpretation Analysis Based on a Hybrid Ensemble Model</title><link>http://pubsindex.trb.org/view/2701291</link><description><![CDATA[Accurate prediction of network-level bridge conditions is crucial for informed maintenance decision-making. Traditional single algorithms struggle to extract bridge degradation features, leading to limited prediction accuracy. This study proposes a hybrid ensemble model approach that combines eXtreme Gradient Boosting (XGBoost), support vector regression (SVR), and artificial neural network (ANN) algorithms, significantly improving prediction performance. In addition, the SHapley Additive exPlanations (SHAP) method is utilized to analyze key influencing factors on bridge degradation, providing interpretability to the model’s predictions and offering valuable references for maintenance strategies. First, this study integrates inspection reports, design drawings, maintenance records, and technical condition ratings of 800 bridges to construct a comprehensive database. In total, 12 features, including bridge type, age, maximum span, maintenance frequency, and traffic volume, are extracted as inputs, with condition ratings as the output. Second, XGBoost, SVR, and ANN models are employed, and two hybrid ensemble models: (1) Inverse-Variance Weighted Hybrid Ensemble Prediction (HEP-IV), using inverse-variance weighting; and (2) artificial neural network-based Hybrid Ensemble Prediction (HEP-ANN), a meta-learner, are developed and tested for prediction accuracy. These models are tested for prediction accuracy. Finally, the SHAP method is applied to identify important factors, such as bridge age, maintenance frequency, and deck width, as well as their interactions. Experimental results indicate that the hybrid ensemble model (HEP-IV) outperforms other models, showing superior prediction accuracy and better generalization ability, with HEP-IV achieving the best performance across all evaluation metrics. The coefficient of determination for the test set is 0.982, the root mean square error is 0.066, and the mean absolute error is 0.04. The model’s interpretability quantifies the effect of key factors, enabling precise prioritization of maintenance interventions, which supports optimized budget allocation and policy-making for bridge network management.]]></description><pubDate>Wed, 13 May 2026 17:00:16 GMT</pubDate><guid>http://pubsindex.trb.org/view/2701291</guid></item><item><title>Toward an Autonomous Vehicle-Ready Workforce: Connecting Transportation Engineering with Core Competencies in Engineering and Computer Science</title><link>http://pubsindex.trb.org/view/2701289</link><description><![CDATA[Autonomous vehicles (AVs) are reshaping the transportation landscape and creating a growing demand for engineers with interdisciplinary expertise that bridges traditionally siloed domains. However, a review of existing AV-related workforce development programs shows that current engineering curricula often fail to integrate essential transportation engineering topics into core AV knowledge areas, such as electrical engineering, computer science, and mechanical engineering. This gap limits graduates’ preparedness for careers in AV development, deployment, and operations. In this study, current AV workforce development initiatives are reviewed, industry needs and technological trends are analyzed, and the core competencies required for AV systems are identified. Building on these insights, a comprehensive curriculum framework is proposed that introduces critical AV-related topics across engineering disciplines, with a particular emphasis on integrating transportation engineering concepts. The proposed framework combines theoretical foundations, systems-level thinking, and hands-on learning to prepare a workforce capable of addressing the technical, operational, and societal complexities of autonomous mobility. This interdisciplinary model provides a strategic roadmap for aligning engineering education with the rapidly evolving future of connected and automated transportation.]]></description><pubDate>Wed, 13 May 2026 17:00:16 GMT</pubDate><guid>http://pubsindex.trb.org/view/2701289</guid></item><item><title>Assessing Railway Track Embodied Carbon: Life Cycle Inventory Literature Review</title><link>http://pubsindex.trb.org/view/2701139</link><description><![CDATA[Efforts to reduce carbon emissions have intensified across the transportation sector, yet life cycle inventory (LCI) data describing the global warming potential (GWP) of railway infrastructure remain limited in the U.S. context. Existing literature is dominated by non-U.S. studies and often aggregates track infrastructure GWP with other civil structures, limiting transparency and applicability. This study synthesized LCI data from 116 case studies reported across 53 sources and isolated at-grade railway track infrastructure. Foreground and background inventory data were compiled for track components, construction and maintenance equipment, material transportation, and end-of-life (EOL) treatment. Case studies were harmonized to a common track-kilometer-per-year functional unit to enable cross-comparison of track GWP. More than 75% of case studies originate from Europe, with concrete-tie and slab-track systems accounting for 74% of all cases, while wooden-tie tracks are underrepresented (10%). EOL treatment data are reported in only 10 studies, and maintenance-cycles are included in approximately half of the sources. The Ecoinvent database is used in 26 studies, while only four incorporate U.S. LCI datasets. Material-level inventories show substantial variability in GWP factors, particularly for steel (0.4–5.7 kg CO₂e/kg), whereas aggregates exhibit the lowest impacts (∼10 g CO₂e/kg). Fuel emissions from maintenance-of-way equipment and material transportation are regarded minor contributors (2%–10% of track GWP). After normalization, overall track GWP converges to a narrow range (21.6–25.7 t CO₂e/km/year) across track systems, indicating that system boundaries and background data choices influence results more than track type. The review also highlighted a critical need for U.S.-specific LCI datasets and environmental product declarations.]]></description><pubDate>Tue, 12 May 2026 16:57:36 GMT</pubDate><guid>http://pubsindex.trb.org/view/2701139</guid></item><item><title>Long-Term Pavement Performance of Quiet Friction Course in Florida</title><link>http://pubsindex.trb.org/view/2701138</link><description><![CDATA[The main objective of this study was to evaluate the applicability of the “Quiet Pavement” concept under Florida’s unique weather and field conditions. The long-term field performance of three open-graded friction courses (OGFCs): FC-5 with PG 76-22 polymer-modified asphalt (PMA) binder (Control), FC-Q with ARB-12 binder, and FC-Q with PG 76-22 PMA binder was assessed using 16 years of comprehensive pavement data, including tire/pavement interaction noise (OBSI), rutting, cracking, raveling, friction, macrotexture (MPD), and ride quality (IRI). Results showed that FC-5 exhibited the highest initial noise levels and experienced the most severe surface deterioration, with crack ratings reaching failure thresholds and extensive raveling over time. FC-Q ARB-12 demonstrated stable, long-term performance, maintaining consistently lower noise, moderate macrotexture growth, and superior resistance to surface distress potentially because of its higher binder content. FC-Q PMA delivered moderate performance with more variability in noise, IRI, and surface distress indicators, falling between FC-5 and FC-Q ARB-12 with regard to performance. The observed performance justifies the adoption of the Quiet Pavement concept for Florida highways as an alternative of the traditional FC-5 mixture for reduced traffic noise and improved durability. The study also underscores the importance of ongoing monitoring in maximizing the benefits and lifespan of OGFC surfaces. Based on these results, FDOT has extended the Quiet Pavement study with new test sections using a finer 9.5 mm NMAS, seeking further improvements to the FC-Q mix design for even better performance in Florida’s challenging environment.]]></description><pubDate>Tue, 12 May 2026 16:57:36 GMT</pubDate><guid>http://pubsindex.trb.org/view/2701138</guid></item><item><title>Numerical Analysis of the Potential for Joint Separation in Round Concrete Culverts</title><link>http://pubsindex.trb.org/view/2701137</link><description><![CDATA[A common form of damage experienced by culverts is joint separation between culvert segments. Joint performance issues may allow water and soil to seep through the pipe leading to loss of soil support, which may ultimately result in roadway settlement or failure of the pipe. The factors that contribute to joint separation are unclear, and although past studies have investigated flexural demands across joints, no current studies are examining the axial tension demands that may develop across culvert joints. To this end, finite difference and finite element models of round concrete culverts were developed to examine the potential for separation from axial demands on culvert segments. The models investigated traffic loading, rise of the phreatic surface, freezing of the embankment, and dead load demands under the self-weight of the embankment. All of the above mechanisms led to axial tension along the length of the pipe. Of these, traffic loading caused the lowest separation forces, roughly 10% to 20% of the applied vertical load occurring under the roadway. Embankment self-weight caused built-in tensile demands under the driving surface. The rise of the phreatic surface and freezing of the embankment also caused significant separation forces, but near the embankment face. For untied pipe segments, increased depth to the culvert centerline and reduced embankment stiffness were the most critical parameters that increased the potential for joint separation. Further research focusing on detailed field observations to confirm the most likely locations of and conditions that lead to joint separation in culverts is recommended.]]></description><pubDate>Tue, 12 May 2026 16:57:36 GMT</pubDate><guid>http://pubsindex.trb.org/view/2701137</guid></item></channel></rss>