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    <title>Transport Research International Documentation (TRID)</title>
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    <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>
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      <title>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Network-based risk assessment of ship-mediated dispersal of non-native species across Chilean and international ports</title>
      <link>https://trid.trb.org/View/2604556</link>
      <description><![CDATA[Marine biological invasions threaten global biodiversity, making it essential to identify high-risk areas for effective management and prevention. This study assesses the risk of non-native species (NNS) dispersal through maritime transport using network analysis to examine connectivity patterns among Chilean and international ports, complemented by an environmental similarity approach for estimation. The results highlight San Antonio, San Vicente, and Concepcion Bay as key nodes within the national network, facilitating maritime traffic redistribution and linking the Central Chile and Araucanian ecoregions, identified as critical corridors for NNS introduction and spread. In contrast, secondary ports such as Corral and Mejillones have limited connectivity and play minor roles in the network. Internationally, Chilean ports maintain frequent connections with global hubs such as Panama and Shanghai, emphasizing their role in global maritime traffic. Procrustes analysis reveals strong environmental similarity between connected regions, suggesting that shared conditions enhance NNS survival and establishment. Alluvial diagrams and network illustrate high-risk routes and port, aiding in the identification of critical areas for monitoring and management. This study underscores the importance of integrating network analysis with environmental data as a key tool for assessing invasion risk, prioritizing strategic areas, and strengthening preventive strategies.]]></description>
      <pubDate>Tue, 21 Apr 2026 09:29:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/2604556</guid>
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    <item>
      <title>A Cluster-Based Channel Model Incorporating Quasi-Stationary Segmentation for Vehicle-to-Vehicle Communications</title>
      <link>https://trid.trb.org/View/2658668</link>
      <description><![CDATA[Vehicle-to-vehicle (V2V) wireless communication is vital for intelligent transportation systems (ITSs). The high mobility of transceivers, along with the complex 3D propagation caused by low antenna heights and short communication ranges, present challenges to propagation modeling. Accurate V2V channel models are crucial for capturing these characteristics to design reliable V2V systems. Existing cluster-based V2V channel models neglect Doppler frequency variations in cluster classification, reducing classification and model accuracy. They describe clusters in single snapshot, missing temporal channel stationarity, and their complex structures slow model generation, hampering ITS applications. This paper presents a cluster-based V2V channel model incorporating quasi-stationary segmentation. First, SAGE algorithm extracts Multipath components (MPCs), followed by clustering and tracking. By analyzing clusters' Doppler frequency variations alongside angle, delay, and power changes, clusters are more accurately classified into global, static and dynamic types. Next, the model uses Correlation matrix distances (CMDs) to perform quasi-stationary segments for each cluster type, characterizing their distributions within each segment via inter- and intra-cluster parameters. This simplifies the model structure compared to single-snapshot models, improving generation efficiency. Segment duration and quantity statistics characterize channel stationarity. The model is validated by comparing simulated second-order channel statistics with comparable models and measured data. Its complexity is evaluated by comparing model generation time with alternative models in the literature.]]></description>
      <pubDate>Mon, 20 Apr 2026 09:24:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2658668</guid>
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    <item>
      <title>Maritime Transport and Supply Chain Resilience</title>
      <link>https://trid.trb.org/View/2670825</link>
      <description><![CDATA[This book addresses maritime transport and supply chain resilience, offering analysis of emerging topics as well as case studies from different countries and regions. Understanding resilience as planning and preparing for changes, and absorbing, recovering from, and adapting to such changes, it offers readers an insightful outlook on how maritime transport and supply chain stakeholders develop strategies in achieving resilience, and mobilizes knowledge between them. As such, this book investigates the impacts and effects of the several disruptive events that have taken place in the past decade in the maritime transport and supply chain sectors, and in the process, provides advice on the best practice for the betterment of the future. Intended audiences include scholars, industrial practitioners, policymakers, and other professionals in the maritime transport and supply chain sectors.]]></description>
      <pubDate>Thu, 16 Apr 2026 16:54:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/2670825</guid>
    </item>
    <item>
      <title>GPS-Denied ISAC Vehicle Localization Based on mmWave Radar and Identification</title>
      <link>https://trid.trb.org/View/2658664</link>
      <description><![CDATA[Millimeter wave (mmWave) radar has become a widely adopted technology in vehicles and advanced driver assistance systems (ADAS). Meanwhile, as wireless communication progresses to higher frequencies, the role of the mmWave band has expanded, now supporting both sensing and 5G communication, which has given rise to integrated sensing and communication (ISAC). In this paper, we propose a novel mmWave ISAC vehicle localization system, which innovatively integrates emerging mmWave identification (MMID) technology into conventional automotive mmWave radar, enabling automotive radar to interact with roadside MMID tags, thereby achieving lane-level vehicle localization independent of the global positioning system (GPS). Unlike existing RFID-based vehicle localization solutions, the proposed solution is more practical for real-world deployment, as it eliminates the need to install additional large RFID antennas on vehicles. To achieve this, we first analyze the backscatter modulation characteristics of MMID tags and propose a novel frequency modulation strategy that lays the foundation for distinguishing signals from different tags within radar echoes that contain various tags and other objects. Subsequently, based on the distance, relative velocity, and azimuth of the tags, we perform static parameter estimation of the vehicle using the least squares (LS) algorithm. Finally, we construct a vehicle motion model and introduce a novel mutation particle filter (MPF) algorithm to estimate the dynamic motion state of the vehicle, ultimately achieving precise vehicle position tracking. The proposed system offers a practical solution for GPS-denied vehicle localization, aligning with the future vision of 6G-enabled intelligent transportation systems (ITS) and IoT-driven smart cities.]]></description>
      <pubDate>Thu, 16 Apr 2026 13:54:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2658664</guid>
    </item>
    <item>
      <title>Auto-correlation based spatio-temporal adaptive transformer traffic flow prediction</title>
      <link>https://trid.trb.org/View/2691772</link>
      <description><![CDATA[Traffic flow prediction is a crucial technology in intelligent transportation systems. To effectively handle intricate spatio-temporal relationships and dynamic features of traffic flow, an Auto-Correlation Based Spatio-Temporal Adaptive Transformer Prediction Model (Auto-STAT) is established, which considers the periodicity of traffic flow. Auto-STAT encompasses such components as Auto-Correlation, Encoder-Decoder, Dynamic Halting, and Cross-Attention. Auto-Correlation is employed to capture the periodic characteristics of traffic flow. The encoder-decoder architecture incorporates Spatial-Adaptive Transformer (SA-Trans) and Temporal-Adaptive Transformer (TA-Trans) to extract intricate spatio-temporal dynamics. Dynamic Halting is integrated into the encoder to enhance computational efficiency. Cross-Attention module is constructed to mitigate error propagation between the encoder-decoder. Furthermore, two decoders are utilized to simultaneously tackle the Historical Traffic Reconstruction (HTR) task and the Future Traffic Forecasting (FTF) task to recollect historical traffic patterns and predict future traffic patterns. Experimental results demonstrate the proposed Auto-STAT achieves exceptional prediction performance on two datasets.]]></description>
      <pubDate>Thu, 16 Apr 2026 09:25:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/2691772</guid>
    </item>
    <item>
      <title>Effect of Passive Pre-Chamber Geometry on Performance and Emissions of a Single-Cylinder Natural Gas Large Bore Engine</title>
      <link>https://trid.trb.org/View/2692231</link>
      <description><![CDATA[The maritime industry is one of the most energy-intensive sectors, characterized by high fuel consumption and significant environmental impact. As global trade relies on shipping, the challenge of reducing pollutants and greenhouse gas emissions becomes ever more pressing. Natural gas (NG) is considered as a transitional fuel, capable of lowering CO₂ emissions by 20–30% compared to conventional marine fuels. However, to fully harness this potential, significant advances in combustion technology are necessary, particularly with ultra-lean combustion strategies. One of the most promising pathways is pre-chamber combustion, a solution that can simultaneously improve the efficiency and sustainability of NG marine engines. In this scenario, the passive pre-chamber geometry plays a key role, as it directly influences ignition behavior, combustion stability, and exhaust emissions.This work presents an experimental study conducted on a single-cylinder marine engine prototype, retrofitted from a diesel baseline, and equipped alternatively with four passive pre-chambers featuring different geometrical configurations. The tests were conducted at an engine speed of 1500 rpm and different loads to evaluate the influence of pre-chamber geometry on engine performance and exhaust emissions. Key parameters such as combustion phasing, efficiency, and pollutant formation were analyzed and compared between the four setups. Results showed that pre-chamber design affects the interaction between the turbulent jets and the main chamber mixture, leading to significant variations in both combustion efficiency and emission trends.These findings provide new insights into the role of passive pre-chamber geometry in optimizing large-bore NG marine engines, offering a valuable contribution to the development of cleaner and more efficient propulsion systems for the maritime sector.]]></description>
      <pubDate>Tue, 14 Apr 2026 15:11:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692231</guid>
    </item>
    <item>
      <title>Collaborative Development Framework for Electric-Based Software-Defined Vehicles – The European Research Project CODE4EV</title>
      <link>https://trid.trb.org/View/2691913</link>
      <description><![CDATA[The automotive industry is subject to major transformation initiated by societal and economical pull (reducing emissions, zero fatalities, European competitiveness) and accelerated by technology push (electrification, Cooperative, Connected and Automated Mobility (CCAM), and Cooperative Intelligent Transport Systems (C-ITS)). Following this trend, the Software-Defined Vehicle (SDV) targets the integration of software (SW) development methodologies for vehicle development as well as the value delivery shift toward customers along the entire lifecycle. It promises to create benefits for the car manufacturers in terms of faster time to market, easier update – as well as for the car users (private persons, fleet operators) in terms of personalized user experience, upgradability. At the same time, SDV requires a much more integrated and continuous development framework to enable different experts to efficiently develop and validate concurrently the different parts of the vehicles, to gather information about real operation, and to support update in the field. This paper introduces the collaborative development framework introduced in the European research program Collaborative Development Framework for electric-based Software-Defined Vehicles (CODE4EV).]]></description>
      <pubDate>Tue, 14 Apr 2026 15:11:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2691913</guid>
    </item>
    <item>
      <title>A Dataset for Visual Classification of Flat Tires</title>
      <link>https://trid.trb.org/View/2691907</link>
      <description><![CDATA[Flat tires represent a common yet serious issue in vehicle safety, leading to compromised control, increased braking distance, and potential rim or structural damage when undetected. Conventional tire pressure monitoring systems (TPMS) rely on embedded sensors that can fail, incur high replacement costs, and are not always equipped in older or low-cost vehicles. To address these limitations, this study presents a comprehensive visual dataset for flat-tire classification using computer vision and machine learning techniques. The dataset comprises 600 labeled images—300 flat-tire and 300 non-flat-tire samples—collected from diverse vehicle types, lighting conditions, and viewpoints. This dataset is designed to support the training and benchmarking of lightweight edge-AI models suitable for real-time deployment on embedded platforms. A set of supervised learning models were evaluated. Results demonstrate that visual-based classification provides a cost-effective and scalable pathway toward automated tire health monitoring and contributes to safer and more sustainable intelligent transportation systems.]]></description>
      <pubDate>Tue, 14 Apr 2026 15:11:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2691907</guid>
    </item>
    <item>
      <title>High Order Control Lyapunov Function - Control Barrier Function - Quadratic Programming Based Autonomous Driving Controller for Bicyclist Safety</title>
      <link>https://trid.trb.org/View/2691831</link>
      <description><![CDATA[Ensuring the safety of Vulnerable Road Users (VRUs) is a critical challenge in the development of advanced autonomous driving systems in smart cities. Among vulnerable road users, bicyclists present unique characteristics that make their safety both critical and also manageable. Vehicles often travel at significantly higher relative speeds when interacting with bicyclists as compared to their interactions with pedestrians which makes collision avoidance system design for bicyclist safety more challenging. Yet, bicyclist movements are generally more predictable and governed by clear traffic rules as compared to the sudden and sometimes erratic pedestrian motion, offering opportunities for model-based control strategies. To address bicyclist safety in complex traffic environments, this study proposes and develops a High-Order Control Lyapunov Function–High-Order Control Barrier Function–Quadratic Programming (HOCLF-HOCBF-QP) control framework. Through this framework, CLFs constraints guarantee system stability so that the vehicle can track its reference trajectory, whereas CBFs constraints ensure system safety by letting vehicle avoiding potential collisions region with surrounding obstacles. Then by solving a QP problem, an optimal control command that simultaneously satisfies stability and safety requirements can be calculated. Three key bicyclist crash scenarios recorded in the Fatality Analysis Reporting System (FARS) are recreated and used to comprehensively evaluate the proposed autonomous driving bicyclist safety control strategy in a simulation study. Simulation results demonstrate that the HOCLF-HOCBF-QP controller can help the vehicle perform robust, and collision-free maneuvers, highlighting its potential for improving bicyclist safety in complex traffic environments.]]></description>
      <pubDate>Tue, 14 Apr 2026 15:11:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2691831</guid>
    </item>
    <item>
      <title>A Novel Attention-Weighted VMD-LSSVM Model for High-Accuracy Short-Term Traffic Prediction</title>
      <link>https://trid.trb.org/View/2686147</link>
      <description><![CDATA[To improve the management and operational efficiency of Intelligent Transportation Systems (ITS), address the nonlinear complexity of short-term traffic flow, mitigate the issue of significant noise in traffic flow datasets, and tackle the challenges in determining parameters for Support Vector Machines (SVM) and Long Short-Term Memory (LSTM) networks, this paper proposes a short-term traffic flow prediction model based on Variational Mode Decomposition (VMD) and Least Squares Support Vector Machine (LSSVM) integrated with an attention mechanism. Multiple intrinsic mode functions (IMFs) decomposed by VMD are input into the LSSVM model, and the parameters and weights of the model are automatically adjusted using the attention mechanism. Experimental results on the Italian highway traffic flow dataset show that the prediction accuracy of the VMD-LSSVM-Attention model is improved by an average of about 38.6% compared with the traditional VMD-SVM, VMD-LSTM-Attention, VMD-LSSVM and LSSVM-Attention models, and the model is more stable. Furthermore, in generalisation validation experiments on the Rotterdam and Madrid datasets, the model improved prediction accuracy by 5.17% to 20.97% compared to the best-performing advanced models. This model provides a prediction method for the traffic flow prediction module in the intelligent transportation system (ITS) architecture.]]></description>
      <pubDate>Tue, 14 Apr 2026 14:31:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2686147</guid>
    </item>
    <item>
      <title>EU Transport Infrastructure: Further Delays and Some Cost Increases, but a Reinforced Governance Framework Is in Place for the Future (An Update of ECA Special Report 10/2020)</title>
      <link>https://trid.trb.org/View/2657020</link>
      <description><![CDATA[Megaprojects are key to the completion of the EU trans-European transport network. In 2020, we published a special report showing major delays, cost increases, weak coordination between member states, and weaknesses in the Commission’s oversight. This report provides an update, taking into account developments since then. We observed a further increase in the combined cost of the megaprojects, mainly driven by two of them, and additional delays which imply that the EU core network will not be completed by the 2030 deadline. In 2024, new legal provisions were introduced with the potential to improve the Commission’s oversight of the implementation of the network, although the changes will mostly be relevant for projects that started later than the megaprojects we audited. ECA special report pursuant to Article 287(4), second subparagraph, TFEU.]]></description>
      <pubDate>Tue, 14 Apr 2026 10:11:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2657020</guid>
    </item>
    <item>
      <title>The ARTS Compendium</title>
      <link>https://trid.trb.org/View/2683241</link>
      <description><![CDATA[In order to meet the needs of travelers and the agencies responsible for the operation and maintenance of the rural transportation system, the U.S. Department of Transportation has developed the Advanced Rural Transportation Systems (ARTS) program. This publication describes the ARTS Compendium which is a computer-based clearinghouse of information, providing a manageable way to store and retrieve information about the wide range of ARTS and ARTS-related projects. The ARTS Compendium is a "living" document, additions and modifications are made whenever necessary to keep the database current. Users are encouraged to provide additions and updates through an automated process online. The compendium consists of a variety of project types, from planning studies to federally funded field operational tests. The compendium includes projects both within and outside of the ITS umbrella; all have implications within the ARTS program. In addition, not all of the projects listed are strictly rural in nature. For instance, some are vehicle-based, operating independently of the road type, and others are urban with rural applications. The first version includes approximately 170 projects.]]></description>
      <pubDate>Mon, 13 Apr 2026 16:27:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2683241</guid>
    </item>
    <item>
      <title>Advanced Transportation Weather Information System (ATWIS)</title>
      <link>https://trid.trb.org/View/2683246</link>
      <description><![CDATA[According to the U.S. Department of Transportation, 80 percent of the road mileage in the United States is located in rural or small urban areas and accounts for about 60 percent of all traffic fatalities. With these facts in mind, the Advanced Transportation Weather Information System (ATWIS) was developed to enhance the efficiency and safety along these rural highways through America. The primary purpose of the ATWIS research program is to demonstrate how current technologies in weather forecasting, weather analysis, telecommunications and road condition monitoring can be merged effectively to produce a safer and more efficient transportation system for both commercial and general travel.]]></description>
      <pubDate>Mon, 13 Apr 2026 16:27:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2683246</guid>
    </item>
    <item>
      <title>Technology in Rural Transportation: "Simple Solutions"</title>
      <link>https://trid.trb.org/View/2683242</link>
      <description><![CDATA[The Rural ITS "Simple Solutions" Project, which was performed within the ENTERPRISE pooled-fund study program, aimed to identify and describe proven, cost-effective, "low-tech" solutions for rural transportation-related problems or needs. These projects, referred to as "Simple Solutions," focus on practical applications of technologies that could serve as precursors to future applications of more advanced systems or intelligent transportation systems (ITS). More than 50 solutions were initially identified and documented, and then categorized according to the 7 Critical Program Areas (CPAs) defined within the U.S. Department of Transportation's Advanced Rural Transportation Systems (ARTS) Strategic Plan. Of all the projects, 14 solutions were selected to be documented and analyzed in detail. Project selection was based on representing all of the CPAs, as well as the ability of a project to transfer to other locations. A report was written as part of this 6-month study that contains detailed descriptions of the 14 solutions, which include benefits of the technology; the expected implementation process; the potential issues associated with each technology; and each technology's role in a larger scale, fully integrated rural intelligent transportation system. The report also describes 42 other feasible solutions, examines broader rural ITS developments, and discusses other findings, such as transportation practitioners' perceptions of ITS. The 14 solutions are also published as stand-alone technical briefs.]]></description>
      <pubDate>Mon, 13 Apr 2026 16:27:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2683242</guid>
    </item>
    <item>
      <title>Modeling and application of navigation pattern for container ships in coastal waters of China</title>
      <link>https://trid.trb.org/View/2656394</link>
      <description><![CDATA[Understanding container ships' navigation pattern is crucial for predicting the estimated time of arrival accurately, which is essential for intelligent maritime transport management. The navigation pattern, characterized by typical, general, and temporal navigation features, is modeled using the proposed CSM-CSNP (Clustering and Statistical Modeling for Container Ships Navigation Pattern) method. Firstly, typical navigation features are characterized by the identification of behavioral nodes and feature trajectories. Behavioral nodes are clustered by recognizing the typical behaviors of container ships, such as dwelling, turning, and berthing/departing. Meanwhile, feature trajectories are derived through a hierarchical strategy within adjacent matrix. Secondly, general navigation features are described by classifying feature trajectories based on traffic volume. Finally, temporal navigation features are obtained through statistical analysis and probabilistic modeling. The whole ship trajectories are segmented into sub-trajectories using typical behavioral nodes as division points. The statistical features of the average time between adjacent trajectory point are calculated. Take the navigation time of the sub-trajectories as a random variable, the probability density function is estimated. Using AIS data from container ships navigating along China's coast to Tianjin Port, the navigation pattern was modeled. Results show that container ships arriving at Tianjin Port have multiple paths to choose from. The key waterway hub, such as Chengshanjiao waters, which is divided into three channels: inner, outer, and eastern. The inner channel experiences the highest traffic volume, while the outer and eastern channels have comparatively lower volumes. The navigation times of most feature trajectories follow a regular distribution. The modeling method proposed in this study can provide crucial data support for estimated time of arrival prediction, and lay the foundation for the development of intelligent maritime transport management.]]></description>
      <pubDate>Mon, 13 Apr 2026 09:40:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2656394</guid>
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