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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=PHNlYXJjaD48cGFyYW1zPjxwYXJhbSBuYW1lPSJkYXRlaW4iIHZhbHVlPSJhbGwiIC8+PHBhcmFtIG5hbWU9InN1YmplY3Rsb2dpYyIgdmFsdWU9Im9yIiAvPjxwYXJhbSBuYW1lPSJ0ZXJtc2xvZ2ljIiB2YWx1ZT0ib3IiIC8+PHBhcmFtIG5hbWU9ImxvY2F0aW9uIiB2YWx1ZT0iMCIgLz48L3BhcmFtcz48ZmlsdGVycz48ZmlsdGVyIGZpZWxkPSJpbmRleHRlcm1zIiB2YWx1ZT0iJnF1b3Q7UGFyYWxsZWwgcHJvY2Vzc2luZyZxdW90OyIgb3JpZ2luYWxfdmFsdWU9IiZxdW90O1BhcmFsbGVsIHByb2Nlc3NpbmcmcXVvdDsiIC8+PC9maWx0ZXJzPjxyYW5nZXMgLz48c29ydHM+PHNvcnQgZmllbGQ9InB1Ymxpc2hlZCIgb3JkZXI9ImRlc2MiIC8+PC9zb3J0cz48cGVyc2lzdHM+PHBlcnNpc3QgbmFtZT0icmFuZ2V0eXBlIiB2YWx1ZT0icHVibGlzaGVkZGF0ZSIgLz48L3BlcnNpc3RzPjwvc2VhcmNoPg==" rel="self" type="application/rss+xml" />
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    <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>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Schwarz decomposition for parallel minimum lap-time problems: evaluating against ADMM</title>
      <link>https://trid.trb.org/View/2646994</link>
      <description><![CDATA[The Minimum Lap Time Problem (MLTP) remains a significant area of research, particularly in the motorsport context. This form of Optimal Control Problem (OCP) aims to minimise lap times on a specific track with a given vehicle. Various complexities in both vehicle and track models are employed across the literature to address optimal trajectory planning. While previous works have tackled MLTP as a singular task using a serial approach, the increasing model complexity and horizon length demands the utilisation of parallelisation techniques. This paper introduces a novel application of the Overlapping Schwarz Decomposition algorithm to address the MLTP. The algorithm divides the problem into smaller sub-problems based on different sectors of a track, distributing them among multiple processors. We validate and compare the Schwarz approach against a serial approach and the Alternating Direction Method of Multipliers (ADMM) in solving MLTP with over 2.5 million variables. Despite the general efficiency improvement of parallelisation compared to the serial approach, the Schwarz algorithm demonstrates superior speed, accuracy and robustness compared to ADMM. As a result of our findings, it emerges as the preferred choice when large-scale MLTPs need to be solved.]]></description>
      <pubDate>Mon, 23 Mar 2026 09:45:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2646994</guid>
    </item>
    <item>
      <title>A graph vertex-coloring-based parallel block coordinate descent method for solving the traffic assignment problem</title>
      <link>https://trid.trb.org/View/2630889</link>
      <description><![CDATA[Traffic assignment is an essential component of the traditional four-step transportation planning methodology and significantly contributes to the prediction of traffic flow distribution and optimization of traffic planning. Existing algorithms for solving the user equilibrium traffic assignment problem typically rely on equal intervals and random sampling strategies to divide a set of origin–destination (OD) pairs. However, these sampling strategies fail to address the path overlap issue among OD pairs and often depend on sensitivity analyses to partition the OD set, hindering the efficiency of task parallelism. To address this challenge, the OD grouping problem was formulated as a vertex-coloring problem, which was translated into an integer linear programming (ILP) model. The largest degree first algorithm was proposed to solve the OD grouping problem, enabling the identification of OD pairs within each block with minimal path overlap. Thereafter, the results of the OD grouping based on vertex coloring were incorporated into the parallel block coordinate descent (PBCD) method, increasing the number of OD subproblems within each block and enhancing the parallel computation. An adaptive algorithm is further proposed to address the OD-based restricted subproblem depending on the number of paths for a given OD pair. The proposed method is evaluated based on various large-scale transportation networks and compared with existing algorithms, demonstrating its effectiveness in reducing path overlap within blocks and improving the efficiency of solving traffic assignment problems in large-scale networks.]]></description>
      <pubDate>Mon, 02 Mar 2026 08:56:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2630889</guid>
    </item>
    <item>
      <title>Dynamic Early Warning of Classified Information Security in Rail Transit Vehicle Communication Network Based on Parallel Interference Cancellation</title>
      <link>https://trid.trb.org/View/2642271</link>
      <description><![CDATA[To reduce the influence of intersymbol interference among multiple terminals in the communication network of rail transit vehicles on the security early warning effect for network-classified information, this paper proposes a dynamic early warning method based on Parallel Interference Cancellation (PIC) for rail transit vehicle communication network-classified information security. The proposed method adopts PIC technology to reduce multiple access interference in the communication network of rail transit vehicles by selectively reconstructing interference signals, accurately detecting classified information bits sent by each communication terminal, and introducing the reliability grouping strategy of first-level detection to reduce unnecessary interference reconstruction. We construct a deep learning-based dynamic early warning model for classified information security in the communication network of rail transit vehicles. The Gated Recurrent Unit (GRU) module is used to extract the time-series characteristics of the classified information. The Multilayer Perceptron (MLP) module performs nonlinear mapping on the classified information to detect abnormalities. It then converts the abnormal detection result into probabilities for different early warning categories using the softmax function in the output module, selecting the category with the highest probability as the final dynamic early warning result. The experimental results show that the proposed method can accurately identify and warn the potential leakage or intrusion risk of classified information, and accurately output the security warning level of classified information. Compared with the comparison methods, the proposed method achieves 97% accuracy of classified information detection, only 1.2% false alarm rate and only 95 ms warning time, which reduces the computational complexity by 45%, and can provide a strong guarantee for the security protection of the communication network of rail transit vehicles.]]></description>
      <pubDate>Tue, 17 Feb 2026 13:12:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/2642271</guid>
    </item>
    <item>
      <title>Prevention of Stress-Induced Failures of Prestressed Concrete Crossties of Railroad Track</title>
      <link>https://trid.trb.org/View/2666668</link>
      <description><![CDATA[Prestressed concrete crossties are vital for railroad stability and safety, but frequently suffer premature failures—such as splitting cracks and corrosion—due to increasing heavy freight and high-speed rail demands. To address these vulnerabilities, this project employs a dual approach of 3D numerical simulation and experimental material development. The research identifies specific failure mechanisms while simultaneously optimizing a new fiber-reinforced “Engineered Cementitious Material” (ECM) designed to bridge cracks and enhance strength. By delivering a material formula with superior resistance to impact and environmental degradation, this study aims to significantly improve the structural integrity of crossties, ensuring safer, more durable railroad infrastructure with an extended service life.]]></description>
      <pubDate>Tue, 10 Feb 2026 09:11:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/2666668</guid>
    </item>
    <item>
      <title>Distributed genetic algorithm for floating production unit’s mooring system optimization</title>
      <link>https://trid.trb.org/View/2627254</link>
      <description><![CDATA[Mooring systems ensure the structural integrity of Floating Production Units, especially in deep waters. These structures face environmental conditions that cause displacements along the waterline, resulting in significant stresses on risers and other subsystems. Minimizing such displacements and loads is crucial for safety. The design of mooring systems is complex, requiring a thorough analysis of project characteristics and environmental factors to fulfill safety requirements. Even though advances in computational tools have enhanced offshore system design by enabling a holistic assessment of critical parameters, optimization remains computationally intensive and often constrained by available resources. In this context, this work proposes the adoption of High-Performance Computing in offshore system design, allowing distributed optimization processes and accelerating the evaluation of solutions. Our procedure was successfully validated in a real-world case study involving the repositioning of an operational Floating Production Unit through a distributed implementation of the NSGA-II optimization algorithm, resulting in the reconfiguration of its mooring system. A viable repositioning was achieved with 32.94 m of displacement in the desired direction (10.98 % of water depth) and a reduction of 7 % in maximum line tension in the mooring system. Altogether, execution time was reduced by tenfold relative to the serial implementation of NSGA-II.]]></description>
      <pubDate>Tue, 03 Feb 2026 10:07:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2627254</guid>
    </item>
    <item>
      <title>Autonomous Driving in Underground Mines via Parallel Driving Operation Systems: Challenges, Frameworks and Cases Study</title>
      <link>https://trid.trb.org/View/2598781</link>
      <description><![CDATA[Autonomous driving plays a crucial role in the development of intelligent mines. However, the complex environments in mines present many challenges for the application of autonomous driving compared to urban scenes. Especially in underground mines, the environments such as dust, water mist, narrow roads, and sharp turnings bring additional difficulties for autonomous driving. In response to these issues, a framework of autonomous driving in underground mines based on parallel driving operation systems was proposed. It consists of the intelligent scheduling and management platform, autonomous trackless rubber-tyred vehicle, V2X cooperative perception system, and remote driving system. Field tests were conducted in two real mines to validate the effectiveness of the solution. The experiments demonstrate that our proposed solution boosts the automation level of transportation operations, ensuring operational efficiency and enhancing the safety of transportation processes in underground mines.]]></description>
      <pubDate>Mon, 08 Dec 2025 17:05:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2598781</guid>
    </item>
    <item>
      <title>DEMO-PAST: A Decentralized Multi-MAV Online Navigation System Using Parallel Strategy Acceleration</title>
      <link>https://trid.trb.org/View/2591946</link>
      <description><![CDATA[In this paper, a decentralized and asynchronous navigation system framework for multi Micro Aerial Vehicle (MAV) in unknown obstacle-rich scenarios is proposed. The developed framework strives to improve the performance of real-time by using parallel strategy on graphics processing units (GPU). In this way, we investigate parallel algorithms of map updating and trajectory planning. Specifically, map updating method based on spherical coordinate is initially presented. Then a hierarchical trajectory planning scheme is designed. Wherein the state lattice planning module as front-end is solved quickly in closed form, the trajectory optimization module based on model predictive path integral (MPPI) as back-end is utilized to refine trajectory. The optimization program is independent of gradient that might not exist when the cost function is composed of discontinuous components. Finally, the efficiency of the proposed method is validated by extensive numerical simulations and real-world experiments.]]></description>
      <pubDate>Thu, 13 Nov 2025 16:59:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/2591946</guid>
    </item>
    <item>
      <title>Ship structure corrosion detection using advanced image processing, active contour algorithm, and parallel processing</title>
      <link>https://trid.trb.org/View/2592572</link>
      <description><![CDATA[Corrosion poses significant risks to ship infrastructure, causing safety hazards and financial losses. This study introduces a hybrid corrosion detection algorithm combining advanced image processing and a Chan-Vese active contour model with parallel processing. Proposed method achieves classification accuracy of 98 %, precision of 97 %, and segmentation accuracy of 93.83 %. It delivers a sensitivity of 96 % and specificity of 89 % with an 89.2 % lower cutoff. GPU-based acceleration reduces normalization time by 68.49 %, feature extraction by 81.25 %, and matching by 45.01 %. Therefore, proposed method offers a robust and efficient tool for the maritime industry, significantly enhancing the reliability of ship structure monitoring.]]></description>
      <pubDate>Wed, 24 Sep 2025 15:31:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2592572</guid>
    </item>
    <item>
      <title>Parallel computing aided analyses of dynamic buckling for railway track infrastructure</title>
      <link>https://trid.trb.org/View/2588356</link>
      <description><![CDATA[his paper presents a scalable parallel computing framework for simulating track buckling under dynamic train loads, enabling large-scale railway track stability analysis. A three-dimensional (3D) track model is developed using finite element-based Euler–Bernoulli beam formulations for rails, dynamic force inputs, and nonlinear interactions at the sleeper–ballast interface to capture dynamic buckling behavior. To address computational challenges in simulating extended track sections, the framework employs message passing interface–based parallelization, optimizing load balancing, and minimizing interprocess communication overhead. Unlike approaches that simulate long tracks virtually by recycling a small domain, the proposed method maintains complete dynamic and structural detail across the entire track length. It dynamically adjusts lateral rail stiffness and incorporates thermal compression effects to enable simulation of buckling behavior, while efficiently scaling across high-performance computing clusters. Case studies demonstrate the framework's ability to simulate large-scale tracks under combined thermal gradients and dynamic train loads, achieving near-linear speedup and reducing runtime by up to 90% compared to serial approaches. Additionally, a machine learning–based buckling risk assessment is presented as a use case, where a model trained on long-track simulation results predicts buckling risk across extended sections. By integrating 3D track dynamics, parallel computing, and data-driven risk assessment, this work provides a powerful tool for evaluating railway infrastructure resilience under extreme operational conditions.]]></description>
      <pubDate>Tue, 23 Sep 2025 08:59:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2588356</guid>
    </item>
    <item>
      <title>Time Complexity of Training DNNs With Parallel Computing for Wireless Communications</title>
      <link>https://trid.trb.org/View/2553779</link>
      <description><![CDATA[Deep neural networks (DNNs) have been widely used for learning various wireless communication policies. While DNNs have demonstrated the ability to reduce the time complexity of inference, their training often incurs a high computational cost. Since practical wireless systems require retraining due to operating in open and dynamic environments, it is crucial to analyze the factors affecting the training complexity, which can guide the DNN architecture selection and the hyper-parameter tuning for efficient policy learning. As a metric of time complexity, the number of floating-point operations (FLOPs) for inference has been analyzed in the literature. However, the time complexity of training DNNs for learning wireless communication policies has only been evaluated in terms of runtime. In this paper, the authors introduce the number of serial FLOPs (se-FLOPs) as a new metric of time complexity, accounting for the ability of parallel computing. The se-FLOPs metric is consistent with actual runtime, making it suitable for measuring the time complexity of training DNNs. Since graph neural networks (GNNs) can learn a multitude of wireless communication policies efficiently and their architectures depend on specific policies, no universal GNN architecture is available for analyzing complexities across different policies. Thus, the authors first use precoder learning as an example to demonstrate the derivation of the numbers of se-FLOPs required to train several DNNs. Then, they compare the results with the se-FLOPs for inference of the DNNs and for executing a popular numerical algorithm, and provide the scaling laws of these complexities with respect to the numbers of antennas and users. Finally, they extend the analyses to the learning of general wireless communication policies. The authors use simulations to validate the analyses and compare the time complexity of each DNN trained for achieving the best learning performance and achieving an expected performance.]]></description>
      <pubDate>Thu, 26 Jun 2025 16:12:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2553779</guid>
    </item>
    <item>
      <title>A novel approach for longitudinal train dynamics simulations with multibody codes</title>
      <link>https://trid.trb.org/View/2543490</link>
      <description><![CDATA[The paper describes a novel approach to include longitudinal train dynamics within multibody codes by means of a dummy user force element. The latter solves the ordinary differential equations describing longitudinal train dynamics through the definition of additional dynamic states, and it calculates the in-train forces, which have a strong effect on the running safety of railway vehicles. The novel strategy allows to evaluate longitudinal train dynamics and multibody dynamics within the same computational framework, thus solving the dynamic states of both problems with the same numerical integrator. The method is first validated in simulation scenarios of an international benchmarking activity against traditional approaches and then it is applied to evaluate the dynamic safety of an emergency braking operation of a European freight train. With respect to developing multibody models with one body per vehicle in the train consist, the novel approach can be up to four times faster. Moreover, the novel method proves to be faster by up to one order of magnitude when compared to co-simulation strategies, whose accuracy is strongly related to a proper tuning of the communication rate.]]></description>
      <pubDate>Thu, 12 Jun 2025 13:46:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2543490</guid>
    </item>
    <item>
      <title>Multi Path Real-time Semantic Segmentation Network in Road Scenarios</title>
      <link>https://trid.trb.org/View/2525376</link>
      <description><![CDATA[Semantic segmentation is a critical task in computer vision. Existing methods often struggle to balance accuracy and computational efficiency when processing high-resolution images, limiting their application scenarios. To address these limitations, we introduce RepMPSeg, a novel re-parameterization-based multi-path real-time semantic segmentation network. RepMPSeg improves upon traditional dual-branch architectures. It features two independent yet interconnected branches: a high-resolution branch for detailed feature capture and a low-resolution branch for global semantic information extraction. By employing re-parameterization techniques, the basic convolutional blocks are optimized to enhance feature capture and local context information. During training, parallel convolution structures are utilized, which are then streamlined into a single kernel during inference to maintain performance while reducing computational complexity. The high-resolution branch leverages sub-pixel sampling and 1×\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times $$\end{document}1 convolutions to improve the receptive field and minimize computational load, while the low-resolution branch uses 4×\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times $$\end{document} downsampling to enhance semantic information extraction. Features from both branches are fused through the re-parameterization-based Hybrid Downsampling Module (RepHDM), which aligns and combines features effectively. Our experiments on the Cityscapes and CamVid datasets demonstrate that RepMPSeg achieves a balance between speed and accuracy, outperforming state-of-the-art methods. Specifically, RepMPSeg achieves an mIoU of 77.4 and an FPS of 91.4 on the Cityscapes dataset, and an mIoU of 78.6 and an FPS of 117.33 on the CamVid dataset, making it a highly efficient solution for real-time semantic segmentation in road scenarios.]]></description>
      <pubDate>Thu, 17 Apr 2025 09:14:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/2525376</guid>
    </item>
    <item>
      <title>Two-Tier Parallel Virtual Lattice Layers for Enhanced Efficiency in Video Traffic Management</title>
      <link>https://trid.trb.org/View/2525362</link>
      <description><![CDATA[In modern traffic management systems, efficient and accurate vehicle detection from video feeds plays a critical role. This paper introduces a novel approach utilizing Two-Tier Parallel Virtual Lattice Layers (TPL2) to significantly enhance the efficiency of vehicle detection in video-based traffic management applications. The TPL framework employs grid-like structures, processed in parallel to detect vehicles within traffic video streams. Multiple layers are dynamically configured based on the traffic scene’s perspective, employing parallel computation to improve both computational efficiency and detection accuracy. Experimental evaluations conducted on traffic video datasets demonstrate notable enhancements in efficiency and accuracy compared to traditional methods. TPL2 exhibits improved efficiency by approximately 26.55% which signifies a notable reduction in processing time compared to a full frame and sequential processing, indicating enhanced efficiency or optimization in the process. Also, it is important to acknowledge that this efficiency gain is accompanied by a moderate increase in memory usage, approximately 3.72% due to parallel processing technique. This novel framework ensures that it is highly robust, scalable and compatible to optimize diverse video traffic management systems.]]></description>
      <pubDate>Thu, 17 Apr 2025 09:14:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/2525362</guid>
    </item>
    <item>
      <title>Real Time Implementation of Inter-Car Distance Based on an Intelligent Stereovision System for Autonomous Vehicles</title>
      <link>https://trid.trb.org/View/2525356</link>
      <description><![CDATA[In recent years, the fusion of deep learning and computer vision technologies has significantly advanced the development of autonomous vehicles that are present more and more in road traffic. In this context, this paper proposes a vehicle vision system that combines two techniques, the first uses artificial intelligence algorithm to accurately identify vehicles in the path of vehicle’s trajectory, the second uses stereovision algorithm to precisely estimate inter-vehicle distances. This solution effectively reduces overall processing time by exploiting the advantages of the You Only Look Once real-time vehicle detection and limiting the region of interest in image to the computation area of the disparity map for the stereovision. Detection and distance estimation of numerous vehicles consumes an important computation time; therefore a parallel data processing based on the Open Multi-Processing library is used to optimize data processing performance. The proposed solution is implemented on an embedded platform, the experiment results show that the system successfully detects vehicle and estimate distance with an error rate of less than 10%, achieving a real-time processing of 30 frames per second.]]></description>
      <pubDate>Thu, 17 Apr 2025 09:14:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/2525356</guid>
    </item>
    <item>
      <title>Long railway track modelling – A parallel computing approach</title>
      <link>https://trid.trb.org/View/2475864</link>
      <description><![CDATA[This paper presents the development of a dynamics model for long track sections. It is based on an established short track model that utilises the Finite Element Method to describe rails and block models to describe sleepers, ballast and subballast. By implementing a parallel computing method, this innovation enables the construction of a true long track model: by segmenting the long track into shorter segments that are easier to compute. The model facilitates simulations to be run in parallel, thereby permitting simultaneous calculations of various numerical track variables. The model employs a Message Passing Interface framework to seamlessly link the track segments, handling the flow of data among the computing cores designated to each subdivided section. This strategic framework allows the long track model with the capability to simulate tracks of virtually any length, with the only constraints being the available computational resources and time. The claimed contribution about modelling capability is verified using two case studies on a 6km-long track involving different practical and conceptual train operational scenarios: emergency braking and constant braking force with constant train speed. These case studies show the flexibility and scalability of the method and its capability to handle complex track dynamic systems.]]></description>
      <pubDate>Mon, 27 Jan 2025 15:39:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2475864</guid>
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