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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
    <atom:link href="https://trid.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
    <description></description>
    <language>en-us</language>
    <copyright>Copyright © 2026. 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>Transport Research International Documentation (TRID)</title>
      <url>https://trid.trb.org/Images/PageHeader-wTitle.jpg</url>
      <link>https://trid.trb.org/</link>
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    <item>
      <title>Hybrid FEM–machine learning framework for back-analysis of spatially varying soil parameters in super-large caisson foundation</title>
      <link>https://trid.trb.org/View/2667066</link>
      <description><![CDATA[Accurate and efficient estimation of soil parameters is critical for the safe and successful construction of super-large caisson foundations, which are increasingly utilized in major infrastructure projects. Conventional in situ and laboratory methods are often slow, costly, and unable to capture dynamic soil–structure interactions during the sinking process. This study proposes a novel hybrid framework that integrates 3D finite element modeling (FEM), Uniform Design theory, and advanced machine learning (ML) for high-precision back-analysis of soil parameters. The approach is validated using the south anchorage of the super-large rectangular caisson in the Nanjing Longtan Yangtze River Bridge project. A total of 550 FEM simulations were conducted under varying soil parameter scenarios, generating corresponding stress responses. These stress–parameter pairs trained ML models to predict soil parameters from new stress data, enabling efficient back-analysis. The dataset was further augmented to 1550 samples using an ML-based synthetic data generation scheme that preserved key parameter correlations. Eighteen ML algorithms were compared; Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), and Target-Specific Extra Trees (TSET) achieved the highest predictive accuracy (R² ≥ 0.98), with LightGBM performing best (R² = 0.987, MAPE = 1.68%, RSR = 0.016, VAF = 98.66%). The framework successfully captured the complex nonlinear relationships between stress responses and underlying soil properties, yielding results that aligned closely with independent geotechnical investigation reports. This validated approach provides a powerful tool for the proactive failure analysis of design assumptions, offering significant practical implications for risk assessment, failure prevention, and risk mitigation in large-scale foundation engineering.]]></description>
      <pubDate>Tue, 01 Sep 2026 14:02:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2667066</guid>
    </item>
    <item>
      <title>Discourses on the Lower Thames Crossing: rationality or rationalisation?</title>
      <link>https://trid.trb.org/View/2753236</link>
      <description><![CDATA[This paper examines the Lower Thames Crossing in discursive terms, seeking to understand the multiple socially-constructed realities associated with the project. Nine in-depth interviews are carried out, with transport planners, academics and environmental activists, to examine the project in relation to the discursive formation, discursive practice, discursive meaning; and related concepts of truth, power/knowledge and ethics. The analysis finds that the project is rationalised and presented as the obvious technical solution, overlooking wider contestation and the lack of contribution towards environmental and social equity goals. This represents a process of depoliticisation, serving some interests over others, including using infrastructure provision for capital accumulation. Broader implications for transport planning are discussed, including how the process may align more effectively with public policy.]]></description>
      <pubDate>Tue, 01 Sep 2026 14:02:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/2753236</guid>
    </item>
    <item>
      <title>Three Dimensional Flow Analysis Methodology for Assessing Stream Stability and Channel Migration</title>
      <link>https://trid.trb.org/View/2764029</link>
      <description><![CDATA[Increases in the frequency and magnitude of weather events that deviate significantly from average are being observed both globally and in the U.S. Adaptation strategies need to account for and mitigate increased risks to transportation infrastructure that result from more extreme weather. The present study is a part of a project that covers a sensitivity study of the potential impacts of increases in stream flows due to rain events and floods on an active section of the Maple River near Iowa Highway 175 and Danbury, Iowa, which is shown. This study focuses on the use of advanced three-dimensional (3D) computational fluid dynamics (CFD) techniques to enhance the assessment of increased risks to stream stability of a section of the Maple River that may erode into Iowa Highway 175. The methodology developed and presented can be applied to a wide variety of streams and rivers that may face increased risk of migration due to changes in weather patterns and severity of weather events. The Maple River is a laterally active channel flowing through agricultural land that has migrated several hundred feet in recent decades and is currently within approximately 100 feet of the Highway 175 Right-of-Way. In addition to the near-term threat to the highway from the closest river meander, there is also a concern that other river meanders could develop into longer-term threats, especially if increasing intensity or frequency of rain events increase the rate of meander development and channel migration by changing the long-term hydrology of the Maple River.]]></description>
      <pubDate>Mon, 31 Aug 2026 08:38:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2764029</guid>
    </item>
    <item>
      <title>Five-state seismic damage classification of steel-girder bridges on soft soil using directional intensity measures and multi-parameter damage indices</title>
      <link>https://trid.trb.org/View/2696272</link>
      <description><![CDATA[Rapid yet accurate post-earthquake damage assessment of highway bridges is essential to ensure transportation network resilience. Traditional inspection, fragility analysis, and finite element modeling are time-consuming or inaccurate for real-time decision-making. This study compares ten machine-learning classifiers for the seismic damage states of highway steel-girder bridges that incorporate directional intensity measures to represent the variance of ground motions. Based on the nonlinear time-history analyses of two Missouri bridges, one Michigan bridge, and one Wisconsin bridge, multi-parameter structural damage indices were proposed and mapped to five damage states from none to collapse. Among ten supervised algorithms studied, the artificial neural network trained using the Missouri bridge data set was most generalizable on the unseen test set from the same bridges, with a prediction accuracy of 0.95 and an area-under-the-curve (AUC) of 0.98. When applied to two unseen bridges from Michigan and Wisconsin, the neural network’s average accuracy decreased to approximately 0.81. The directional intensity measures and the multi-parameter damage indices enabled robust and scalable classifications of highway bridges, even with domain shifting, and thus the rapid post-earthquake damage assessment of a regional transportation network.]]></description>
      <pubDate>Fri, 28 Aug 2026 08:34:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2696272</guid>
    </item>
    <item>
      <title>Prediction of typhoon wind speeds at bridge site using a hybrid of in-situ measured data and meteorological forecast data</title>
      <link>https://trid.trb.org/View/2697978</link>
      <description><![CDATA[The prediction of typhoon wind speeds at the bridge site is a critical consideration for the safe operation of long-span bridges. Meteorological forecasts typically provide typhoon parameters at large spatial scales, which are insufficiently accurate for the structural assessment of long-span bridges. To address this limitation, a physics-data hybrid method integrating in-situ measured data and meteorological forecast data is proposed. Central to this method is the step-wise updating of the maximum wind speed radius using in-situ measured data, followed by a subsequent prediction using a wind field model established from both data types. This method enables the conversion of spatially coarse meteorological forecasts into site-specific wind speed predictions. To validate the effectiveness of the proposed physics-data hybrid method, the wind speed of Typhoon Rumbia at the Sutong Bridge site is predicted and compared with results obtained from data-driven and physics-driven methods. Analytical results indicate that the physics-data hybrid method exhibits strong generalizability in both single-step and multi-step predictions of typhoon wind speeds, with minimal dependence on the selection of basic wind-field models. The predicted results are well aligned with the measured values, confirming the efficacy of the proposed method for predicting typhoon wind speeds at the bridge site.]]></description>
      <pubDate>Fri, 28 Aug 2026 08:34:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2697978</guid>
    </item>
    <item>
      <title>Closed-Loop Decision-making Framework for Electric Vehicle Battery Recycling: Synchronizing Reverse Logistics Network Optimization with Disassembly Line Design</title>
      <link>https://trid.trb.org/View/2699037</link>
      <description><![CDATA[The widespread adoption of electric vehicles (EVs) has resulted in a growing wave of retired power batteries, making the development of efficient reverse logistics networks (RLNs) essential for sustainable resource management and environmental protection. Considering the impact of recycling volumes on RLN, this study presents an integrated prediction-optimization framework to address the critical challenges in waste battery recycling. Specifically, the battery retirement volume prediction model is constructed and a machine learning-based decomposition-integration method is designed to achieve EV sales forecasting. Subsequently, a mixed-integer nonlinear programming model is formulated to jointly address the recycling network design and the configuration of the disassembly line, two aspects that have often been discussed separately in prior research ignoring their inherent interconnections. The formulated model incorporates a multi-operator workstation mechanism to better reflect the modular characteristics of waste batteries, with the goal of achieving more coordinated system optimization. Besides, an improved multi-stage adaptive large neighborhood search (MS-ALNS) algorithm is designed to solve the integrated optimization model. Finally, a practical case is performed to verify the effectiveness of the formulated model and the proposed decision-making framework by comparing with commercial solver Gurobi as well as the ALNS, GA, and HGA algorithms.]]></description>
      <pubDate>Thu, 27 Aug 2026 16:32:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2699037</guid>
    </item>
    <item>
      <title>Synthesis: Flood Early Warning Systems in Texas and the United States</title>
      <link>https://trid.trb.org/View/2768432</link>
      <description><![CDATA[The research team will synthesize Flood Early Warning Systems (FEWS) deployed across Texas and the United States, focusing on systems applicable to State Departments of Transportation (DOTs). Researchers will evaluate automated gates, flashing beacons, rainfall sensors, and stream gauges, and will assess their performance during documented flood events. The research team incorporate guidance from the Texas Water Development Board (TWDB), the 2024 State Flood Plan, and local initiatives including the City of Austin beacon systems and the Houston TranStar network, and will benchmark practices from comparable states.]]></description>
      <pubDate>Fri, 28 Aug 2026 10:01:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2768432</guid>
    </item>
    <item>
      <title>Multi-Source Data-Driven Structural Equation Modeling Analysis of Intercity Travel Behavior under the Toll-Free Highway Policy during Holidays</title>
      <link>https://trid.trb.org/View/2767452</link>
      <description><![CDATA[During public holidays in China, intercity travel surges sharply, causing widespread highway congestion. This study investigates the mechanisms behind holiday-related congestion, with a focus on the toll-free highway policy implemented during national holidays. Using data from the Yangtze River Delta, this study combined Baidu migration data, time-series analysis, and structural equation modeling to examine the direct and indirect effects of toll-free highway policy, transport accessibility, urban spatial structure, and socioeconomic factors on travel behavior. Results showed that the toll-free policy had the strongest direct effect on travel intensity, followed by urban spatial structure, while transport accessibility played a relatively limited role. Socioeconomic attributes had the most pronounced indirect effect through their effects on urban spatial structure. Based on these findings, we recommend targeted strategies such as time-based adjustment, distance-based graduated tolling, and city-specific tolling schemes. This study provides empirical evidence and policy implications for optimizing highway management during national holidays.]]></description>
      <pubDate>Thu, 27 Aug 2026 14:26:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2767452</guid>
    </item>
    <item>
      <title>An Automated Reinforcement Learning Reward Design Framework with Large Language Model for Cooperative Platoon Coordination</title>
      <link>https://trid.trb.org/View/2735051</link>
      <description><![CDATA[Reinforcement Learning (RL) has demonstrated excellent decision-making potential in platoon coordination problems. However, due to the variability of coordination goals, the complexity of the decision problem, and the time-consumption of trial-and-error in manual design, finding a well performance reward function to guide RL training to solve complex platoon coordination problems remains challenging. In this paper, we formally define the Platoon Coordination Reward Design Problem (PCRDP), extending the RL-based cooperative platoon coordination problem to incorporate automated reward function generation. To address PCRDP, we propose a Large Language Model (LLM)-based Platoon coordination Reward Design (PCRD) framework, which systematically automates reward function discovery through LLM-driven initialization and iterative optimization. In this method, LLM first initializes reward functions based on environment code and task requirements with an Analysis and Initial Reward (AIR) module, and then iteratively optimizes them based on training feedback with an evolutionary module. The AIR module guides LLM to deepen their understanding of code and tasks through a chain of thought, effectively mitigating hallucination risks in code generation. The evolutionary module fine-tunes and reconstructs the reward function, achieving a balance between exploration diversity and convergence stability for training. To validate our approach, we establish six challenging coordination scenarios with varying complexity levels within the Yangtze River Delta transportation network simulation. Comparative experimental results demonstrate that RL agents utilizing PCRD-generated reward functions consistently outperform human-engineered reward functions, achieving an average of 10% higher performance metrics in all scenarios.]]></description>
      <pubDate>Wed, 26 Aug 2026 14:11:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2735051</guid>
    </item>
    <item>
      <title>Navigating Possibilities: Unlocking Tennessee’s Waterways for Interstate Freight Transportation</title>
      <link>https://trid.trb.org/View/2752389</link>
      <description><![CDATA[The study evaluated the inland waterways and port facilities of the State and analyzed their potential for transportation services as part of the overall State Transportation plan. The focus of the study evaluated the current flows and costs of cargo movement and commodity class; the path related to origin and destination; the viability of potential port assets that could be utilized; the effectiveness of optimizing current State infrastructure and connecting waterways for enhancing the transportation network. By leveraging its inland waterways and investing in transportation infrastructure, Tennessee can enhance its competitiveness, promote sustainable development, and build a more resilient and prosperous future. Tennessee's geographical location creates both challenges and opportunities as a strategic multi-modal hub for national commerce. By developing its waterways for freight transportation, the state can capitalize on its central location to facilitate the movement of goods well beyond the state which would serve to attract businesses seeking efficient transportation routes, expand market access for Tennessee-based industries, as well as connect other industries to Tennessee.]]></description>
      <pubDate>Mon, 24 Aug 2026 09:03:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2752389</guid>
    </item>
    <item>
      <title>Marine Investigation Report: Explosion aboard Sludge Vessel Hunts Point, May 24, 2025</title>
      <link>https://trid.trb.org/View/2742792</link>
      <description><![CDATA[​​On May 24, 2025, about 1020 local time, the sludge vessel Hunts Point was moored at the North River Wastewater Treatment Plant on the Hudson River, in New York, New York, when an explosion occurred aboard the vessel. One crewmember was killed, and three crewmembers sustained minor injuries. No pollution was reported. Vessel damage was estimated to exceed $10 million. The National Transportation Safety Board (NTSB) determined that the probable cause of the explosion on board the sludge vessel Hunts Point was unapproved hot work being conducted above a tank that contained a cargo that produced flammable methane gas, resulting in the gas igniting and the subsequent overpressurization of the cargo tank.]]></description>
      <pubDate>Mon, 24 Aug 2026 08:44:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/2742792</guid>
    </item>
    <item>
      <title>An anomalous behavior detection framework for inland vessels using automatic identification system data</title>
      <link>https://trid.trb.org/View/2706421</link>
      <description><![CDATA[To address the challenges of inadequate parameter optimization and the latency caused by reliance on manual rules, this study proposed a BOA-XGBoost framework integrating trajectory clustering and supervised learning. The Butterfly Optimization Algorithm (BOA) is introduced to adaptively optimize key parameters for both DBSCAN clustering and the XGBoost classifier. In addition, an adaptive and hybrid DP-based vessel trajectory compression strategy is designed to reduce noise while preserving navigational semantic features. To detect anomalous behaviors in inland waterways, a vessel behavior representation system is constructed by jointly characterizing trajectory morphology and multidimensional lateral motion features. Comparative experiments conducted in the Hanjiang River Confluence in the Wuhan reach of the Yangtze River (HRC-Wuhan) and the WISCO Transverse Crossing Area (WTCA) demonstrate that the proposed BOA-XGBoost model achieves detection accuracies of 98.06% and 93.40% for two representative anomalous behaviors—illegal downstream navigation and early river-crossing, respectively—outperforming all baseline models across multiple performance metrics. Furthermore, ten-fold cross-validation and SHAP-based interpretability analysis confirm the robustness and interpretability of the proposed model. The proposed framework effectively identifies anomalous vessel behaviors in complex inland waters, providing reliable technical support for intelligent inland waterway traffic supervision.]]></description>
      <pubDate>Tue, 18 Aug 2026 14:11:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2706421</guid>
    </item>
    <item>
      <title>EAC-based ensemble clustering with four-dimensional features for upbound and downbound route extraction in inland waterways</title>
      <link>https://trid.trb.org/View/2733007</link>
      <description><![CDATA[Confined inland waterways are frequently characterized by dense vessel traffic and highly overlapping bidirectional trajectories, presenting significant challenges for maritime surveillance. To address these challenges, the primary objective of this study is to develop a robust, data-driven framework capable of identifying the common navigation corridors and key navigation-state points repeatedly selected by different types of vessels from massive, unlabeled Automatic Identification System (AIS) observations. Unlike conventional single-method approaches, the proposed methodology synergistically integrates a four-dimensional spatial-heading feature space with an evidence accumulation clustering ensemble and k-dimensional tree (KD-tree) consistency filtering. The extracted consensus route templates can support waterway managers in delineating feasible navigation areas and recommended routes, while also providing vessel operators with data-driven references for route selection and key-point speed and heading settings. A case study using data from a navigable reach of the Yangtze River demonstrates the robust quantitative superiority of the proposed framework. Experimental evaluations reveal that the ensemble approach achieves an outstanding average comprehensive clustering performance metric score of 6.0096 across diverse navigational regions. This result significantly outperforms traditional single-method clustering baselines whose average scores merely range from 4.2126 to 5.4673, as well as all dual-model combinations.]]></description>
      <pubDate>Fri, 14 Aug 2026 15:04:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2733007</guid>
    </item>
    <item>
      <title>SEAHIVE® Solutions to Mitigate Bridge Scour - Phase 1</title>
      <link>https://trid.trb.org/View/2736747</link>
      <description><![CDATA[Protecting coastal regions is crucial because of high population density and important economic significance. Numerous strategies have been suggested to safeguard coastal regions and bridge piers from scouring, encompassing natural and man-made approaches. Given the constraints of existing techniques, this study examines a new method named SEAHIVE®, which is designed to improve the performance of engineered structures. This method incorporates hexagonal, hollow, and perforated concrete elements, which are reinforced with glass fiber-reinforced polymer (GFRP) bars or wraps. To investigate the load-bearing capacity, SEAHIVE® specimens were tested under pure compression (cut-off samples) and flexure (full samples). For specimens under pure compression, analysis, and experimentation showed that cracks started due to exceeding the concrete tensile strength in the inclined leg of the hexagon and eventually led to failure in both elements reinforced with GFRP bars or wraps. In elements reinforced with GFRP bars tested under flexure, the strut-and-tie analysis confirmed that SEAHIVE® beam-like specimens failed because of inadequate development length of longitudinal bars and toe crushing. As for the sample reinforced with GFRP wraps under flexure, cracks initiated due to the slipping and loss of the longitudinal GFRP strips.]]></description>
      <pubDate>Tue, 11 Aug 2026 16:57:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2736747</guid>
    </item>
    <item>
      <title>SEAHIVE Project Data [supporting dataset]</title>
      <link>https://trid.trb.org/View/2736749</link>
      <description><![CDATA[The uploaded file contains the raw data from the structural tests I conducted on the SEAHIVE model. These tests were performed to evaluate the structural integrity and resilience of the SEAHIVE design under various conditions. The results, along with the corresponding photos and detailed analysis in Excel format, are available in the provided link for further review and interpretation.]]></description>
      <pubDate>Tue, 11 Aug 2026 16:57:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2736749</guid>
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