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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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      <title>A Lightweight Design Method for Internal Pavement Crack Recognition Models Using Ground Penetrating Radar</title>
      <link>https://trid.trb.org/View/2691065</link>
      <description><![CDATA[Transverse cracks are among the most prevalent forms of damage in Semi-rigid asphalt pavements, traditionally the crack identified through manual methods that are time-consuming and suffer from poor consistency. To improve the efficiency of crack disease identification, this study employs a self-developed high-speed and high-precision 3D ground-penetrating radar to characterize internal crack defects. The You Only Look Once-v8n algorithm is enhanced through lightweight design, optimizing its backbone network and loss function while incorporating an attention mechanism to strengthen crack feature extraction, detection accuracy, and training stability. Practical engineering evaluations demonstrate that the algorithm reduces manual identification time by 50%, with over 60% of results achieving high expert validation rates. The missed detection counts for visible cracks are comparable to manual methods. This algorithm enables automated identification of internal cracks in pavement rehabilitation projects.]]></description>
      <pubDate>Thu, 16 Jul 2026 16:39:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/2691065</guid>
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    <item>
      <title>Navigating the Future: Impact of Automation and Technology on Women’s Work-Life Balance</title>
      <link>https://trid.trb.org/View/2579053</link>
      <description><![CDATA[This chapter explores the profound effects of automation and technology on women’s work-life balance. As workplaces increasingly adopt automation, women are experiencing shifts in both professional and personal spheres. While technology has enabled greater flexibility through remote work and digital tools, it has also blurred the boundaries between work and home life. The chapter delves into the dual impact of these advancements, highlighting both opportunities and challenges. It examines how automation can reduce repetitive tasks, offering women more time for family and personal pursuits, yet it also raises concerns about job displacement and the intensification of work demands. By analyzing case studies and current trends, this chapter offers a comprehensive understanding of how automation is reshaping women's roles in the workforce and influencing their overall well-being.]]></description>
      <pubDate>Thu, 16 Jul 2026 09:08:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2579053</guid>
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      <title>A review of gangway design: Mechanical structures, modeling techniques, and control systems</title>
      <link>https://trid.trb.org/View/2720621</link>
      <description><![CDATA[Gangway is an equipment used to transport personnel and cargo between floating structures and platforms. It finds extensive applications in maritime rescue, offshore wind power operation, and offshore oil development. However, the design of gangways is a complex multidisciplinary challenge that integrates mechanical, electronic information, and control technologies. This paper reviews recent progress in mechanical structural design, system modeling techniques, control systems, and explores future possibilities such as applying birds’ head stability mechanisms to enhance the stability of gangway. The initial sections examine existing gangways, summarizing their structural types, manufacturing materials, and structural analysis methods. Subsequently, detailed discussions cover system modeling approaches, including wave modeling, ship modeling, and gangway modeling. Next, the control system is addressed, encompassing detection units, control units, and actuators. Finally, the study highlights the head stability control observed in birds or insects, which closely parallels the control requirements for gangways.]]></description>
      <pubDate>Thu, 16 Jul 2026 09:08:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2720621</guid>
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    <item>
      <title>Throughput-Delay Tradeoff Management for Partially Connected Networks Via Lyapunov Drift Optimization</title>
      <link>https://trid.trb.org/View/2717667</link>
      <description><![CDATA[Network-level traffic signal control is an effective way to increase throughput and reduce congestion. The max-pressure algorithm, known for maximizing network throughput, has been widely studied. However, it requires accurate queue length and turn ratio measurements, and its theoretical guarantee is limited to feasible demand (i.e., demand within the capacity region) under the assumption of infinite queue capacity. To overcome these limitations, this study proposes a distributed joint admission and signal control algorithm for finite-capacity networks with both connected and regular vehicles. By using feedback from connected vehicles, the algorithm estimates queue lengths and turn ratios, reducing reliance on precise measurements. It also adaptively adjusts input flow rates to prevent oversaturation and ensure demand feasibility, even under high-demand conditions, while optimizing signal phases to ensure analytic performance. Using a Lyapunov drift optimization approach, the authors analytically prove a [O(1/V), O(V)] tradeoff between throughput and delay and establish degradation bounds that quantify the impact of queue length estimation errors on network performance. Simulations in a network with 256 origin-destination pairs show up to a 16.3% increase in throughput and reduced delays, especially in high-demand settings. The method also demonstrates strong resilience to sudden demand changes and incidents, ensuring quick recovery.]]></description>
      <pubDate>Wed, 15 Jul 2026 16:27:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2717667</guid>
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    <item>
      <title>Parameter-Insensitive Non-Repetitive Iterative Learning Operation Control of High-Speed Train Subject to Safety Constraints</title>
      <link>https://trid.trb.org/View/2717664</link>
      <description><![CDATA[The periodic operation pattern of high-speed train (HST) grants the immense potential for iterative learning control (ILC) approach regulating the displacement and velocity, but the non-repetitive uncertainties caused by carrying loads, random disturbances, etc., may weaken the capability of controller. Further, the typical operating situations of rail transit, e.g., station entrance/exit, slowdown sections, can compress the safety margin of HST, increasing the difficulty of precise tracking. In this paper, an adaptive ILC scheme is proposed for HST subject to the safety constraints, where the unknown iteration-varying parameters and the modeling inaccuracies are handled deliberately. The technical route could be divided into two phases. The transformation mechanism of tracking errors, that can convert the control problem of constrained systems into an unconstrained form, is first established to guarantee that HST is always located within the safety zone. On this basis, the iterative learning controller is devised through integrating the hyperbolic tangent function and iteration-related sequence, where the neural network is leveraged to approximate the unmodeled lumps. The main innovative features lie in that, the iteration-dependent terms of control system are evolved into the parametric compensation components of controller and the iterative convergence parts, while the nested structure of control law is built to accommodate the iteration-variation of loads. As a result, the proposed approach can theoretically achieve the zero-error tracking of HST in the presence of the non-repetitive uncertainties and safety constraints, which indicates the better performance and practicability than the existing ones.]]></description>
      <pubDate>Wed, 15 Jul 2026 16:27:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2717664</guid>
    </item>
    <item>
      <title>Cosineopt: Optimization-Based Centralized Cooperative Speed Planning for Multiple CAVS Along Intersected Fixed Paths</title>
      <link>https://trid.trb.org/View/2717661</link>
      <description><![CDATA[This paper focuses on cooperative speed planning for multiple connected and automated vehicles (CAVs) traversing along intersected fixed paths. Nominally, this task is formulated as an optimal control problem incorporating logical operators to represent collision-avoidance constraints. This formulation requires solving a mixed-integer nonlinear programming (MINLP) problem, while handling non-differentiable integer variables remains challenging for gradient-based solvers. Instead of solving the MINLP, the authors propose a cosine-based method, a novel geometric strategy for formulating collision-avoidance constraints between CAVs. Constructing such a geometric model introduces potential approximation errors, which are mitigated by fitted correction terms designed to compensate for geometric deviations and refine the distance calculation. The authors propose a simulation-based planner to provide the speed profile with the globally optimal passing order, serving as a warm start for the solver. A lightweight iterative optimization strategy is also adopted to enhance robustness. Additionally, the authors propose a fault-tolerant strategy to ensure both system safety and operational efficiency. Extensive simulation results verify the proposed method, and comparative experiments demonstrate its efficiency.]]></description>
      <pubDate>Wed, 15 Jul 2026 16:27:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2717661</guid>
    </item>
    <item>
      <title>Distributed Composite Formation Tracking Control of High-Speed Trains under Safety Constraints</title>
      <link>https://trid.trb.org/View/2717522</link>
      <description><![CDATA[Safety and comfort are critical in the formation control of high-speed trains (HSTs). In this paper, a novel distributed composite adaptive control strategy with safety constraints is proposed to enhance the robustness and tracking precision of HSTs formation systems under unknown external disturbances. First, a universal nonlinear transformation technique is employed to handle position and velocity constraints, thereby removing the feasibility condition requirement in virtual controllers. Then, distributed control laws are designed using the command filter backstepping method, a disturbance observer, and an adaptive estimation technique. Furthermore, a rigorous analysis framework based on Lyapunov’s theorem is established, addressing state stability, prescribed tracking error performance, and Zeno behavior. Finally, simulation experiments are conducted to validate the effectiveness and superiority of the proposed control scheme.]]></description>
      <pubDate>Wed, 15 Jul 2026 16:27:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2717522</guid>
    </item>
    <item>
      <title>Unicorn: A Universal and Collaborative Reinforcement Learning Approach toward Generalizable Network-Wide Traffic Signal Control</title>
      <link>https://trid.trb.org/View/2717516</link>
      <description><![CDATA[Adaptive traffic signal control (ATSC) is crucial in reducing congestion, maximizing throughput, and improving mobility in rapidly growing urban areas. Recent advancements in parameter-sharing multi-agent reinforcement learning (MARL) have greatly enhanced the scalable and adaptive optimization of complex, dynamic flows in large-scale homogeneous networks. However, the inherent heterogeneity of real-world traffic networks, with their varied intersection topologies and interaction dynamics, poses substantial challenges to achieving scalable and effective ATSC across different traffic scenarios. To address these challenges, the authors present Unicorn, a universal and collaborative MARL framework designed for efficient and adaptable network-wide ATSC. Specifically, the authors first propose a unified approach to map the states and actions of intersections with varying topologies into a common structure based on traffic movements. Next, the authors design a Universal Traffic Representation (UTR) module with a decoder-only network for general feature extraction, enhancing the model's adaptability to diverse traffic scenarios. Additionally, the authors incorporate an Intersection Specifics Representation (ISR) module, designed to identify key latent vectors that represent the unique intersection's topology and traffic dynamics through variational inference techniques. To further refine these latent representations, the authors employ a contrastive learning approach in a self-supervised manner, which enables better differentiation of intersection-specific features. Moreover, the authors integrate the state-action dependencies of neighboring agents into policy optimization, which effectively captures dynamic agent interactions and facilitates efficient regional collaboration. Through comprehensive evaluations against other advanced ATSC methods across eight different traffic datasets, the empirical findings reveal that Unicorn consistently outperforms other methods across various evaluation metrics, highlighting its superiority and adaptability in optimizing traffic flows in complex, dynamic traffic networks.]]></description>
      <pubDate>Wed, 15 Jul 2026 16:27:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2717516</guid>
    </item>
    <item>
      <title>SERF: Spatiotemporal-Aware Event-Rgb Fusion for Steering Angle Prediction</title>
      <link>https://trid.trb.org/View/2717499</link>
      <description><![CDATA[Existing end-to-end methods for steering angle prediction (SAP) primarily rely on RGB imagery from conventional cameras as input; however, they suffer from limitations such as poor performance in low-light conditions and motion blur. Recently, event cameras have garnered attention as complementary to RGB imagery, providing advantages such as high dynamic range and low latency. Nevertheless, earlier SAP methods that integrate event and RGB data may not fully exploit the spatio-temporal characteristics of events, resulting in performance degradation in low-light scenarios affected by noise interference. To address this limitation, the authors present a novel spatiotemporal-aware event-RGB fusion method for SAP, referred to as SERF, which aims to enhance the accuracy of event-based SAP. Specifically, SERF introduces three key components: 1) An innovative multi-layer Interaction Module based on attention mechanisms to fuse the multi-frame data, enabling more fine-grained feature processing; 2) a dynamic spatiotemporal mask mechanism, focusing RGB’s attention on spatially proximate events while diminishing the influence of temporally distant events, thereby reducing the impact of noise; and 3) a Memory Module that utilizes learnable tokens to accumulate essential latent fusion features through dynamic feature consolidation. Extensive experiments conducted on a variety of real-world and simulated datasets demonstrate the superior performance of SERF compared to the state-of-the-art methods. The experiments also validate the advantages of SERF in terms of inference performance, meeting the real-time requirements for actual deployment.]]></description>
      <pubDate>Wed, 15 Jul 2026 16:27:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2717499</guid>
    </item>
    <item>
      <title>Fixed-Time Autonomous Berthing Control of Unmanned Surface Vehicles under Output Constraints Based on Barrier Lyapunov Function</title>
      <link>https://trid.trb.org/View/2717497</link>
      <description><![CDATA[Autonomous berthing is a critical step in realizing the full autonomy of unmanned surface vehicles (USVs), which can essentially be regarded as a trajectory-tracking task. It can be further transformed into a problem of nonlinear systems with output constraints. This paper proposes a novel adaptive fixed-time backstepping control scheme based on the barrier Lyapunov function (BLF) for autonomous berthing of USVs. Firstly, a new barrier Lyapunov function is designed to solve the output asymmetric constraint requirement of the autonomous berthing system, and it is also adaptive to the unconstrained system without changing the control structure. Secondly, the convergence of adaptive fixed-time control and bounded tracking of BLF are combined to conquer the long convergence time and nonlinear system uncertainty. Finally, simulation and field tests are conducted to verify the proposed scheme’s superiority.]]></description>
      <pubDate>Wed, 15 Jul 2026 16:27:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2717497</guid>
    </item>
    <item>
      <title>Adaptive Sliding Mode Control Strategy for High-Speed Train Platoon under Jointly Connected Switching Topologies</title>
      <link>https://trid.trb.org/View/2717479</link>
      <description><![CDATA[This study addresses the issue of communication link interruptions in train platoon control under the complex operating environment of high-speed railways. An adaptive trajectory tracking control approach based on a finite-time sliding mode is proposed under jointly connected switching topologies. The proposed framework ensures platoon consensus under jointly connected switching topologies, where the leader's information need not reach all followers in each topology but only collectively over a finite interval, thus relaxing connectivity requirements and enhancing practicality. Within this framework, the sliding surface is designed to incorporate relative error signals determined by the communication topology, and an adaptive mechanism is employed to effectively handle unknown external disturbances without requiring prior knowledge of their bounds or derivatives. Simulation results demonstrate that, compared with MPC and non-adaptive approaches, the proposed strategy reduces position errors by approximately 80% and velocity errors by around 40%, significantly improving platoon tracking accuracy. Furthermore, simulations under both periodic and random topology switching indicate that periodic switching achieves performance closer to that of a fully connected topology, further enhancing trajectory tracking effectiveness.]]></description>
      <pubDate>Wed, 15 Jul 2026 16:27:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2717479</guid>
    </item>
    <item>
      <title>Multi-Agent Model-Based Adaptive Cooperative Tracking Control of Railway Trains with Alleviating Coupling Force</title>
      <link>https://trid.trb.org/View/2717456</link>
      <description><![CDATA[Modern railway trains raise higher demands on stability, operational efficiency, and comfort. To this end, distributed tracking control is vital to improve tracking performance and reduce the coupling force between traction units. This paper presents a novel adaptive cooperative tracking control approach for high-speed trains based on a multi-agent model. The proposed method ensures accurate tracking of both displacement and velocity, and effectively mitigates the coupling forces between adjacent traction units. Specifically, an improved multi-agent model of the high-speed train is developed, in which uncertain nonlinear resistance is approximated using an adaptive neural network, and the coupler connecting adjacent agents (traction units) is modeled as a spring-damper system. To formulate a refined controller, prior knowledge of the train system, such as the inherent resistance and coupling structure, is fully utilized so that only a few unknown parameters need to be estimated. Furthermore, an adaptive cooperative controller is designed to achieve accurate speed and displacement tracking. With this controller, multiple agents are simultaneously coordinated, while nonlinearities and uncertainties are handled. The coupling force between adjacent agents is effectively mitigated compared to traditional methods. Comparative simulation studies are conducted to demonstrate the effectiveness of the proposed adaptive cooperative tracking controller.]]></description>
      <pubDate>Wed, 15 Jul 2026 16:27:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2717456</guid>
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    <item>
      <title>A Systematic Review of Adversarial Attacks and Defenses for Deep Reinforcement Learning in Autonomous Vehicle Applications</title>
      <link>https://trid.trb.org/View/2704047</link>
      <description><![CDATA[Deep reinforcement learning (DRL) is a significant component of autonomous vehicle (AV) systems, as it is involved in crucial aspects such as decision-making, motion planning, and control systems. Nevertheless, DRL exhibits inherent weaknesses that render it vulnerable to adversarial attacks. On the other hand, AV applications are classified as safety-critical applications. This work presents a systematic literature review of several forms of adversarial attacks and the corresponding response mechanisms developed in the literature on AVs. This study additionally examines several datasets utilized for benchmarking purposes and different evaluation factors employed to assess the resilience and efficacy of adversarial attacks. The survey adheres to the PRMA standards. Excluded from consideration are articles that failed to take into account adversarial assaults on DRL and Deep Learning (DL). A comprehensive search yielded a total of 2250 distinct peer-reviewed articles sourced from the ACM Digital Library, IEEE Xplore, Springer, and Elsevier databases. Upon fulfilling the predefined criteria for inclusion and exclusion, a sum of 151 articles is encompassed in the ultimate review. In the end, the paper examines the existing research gaps and proposes potential options for further investigations.]]></description>
      <pubDate>Wed, 15 Jul 2026 16:27:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2704047</guid>
    </item>
    <item>
      <title>Safety Assurance for Artificial Intelligence/ Machine Learning in Safety Critical Airborne Systems</title>
      <link>https://trid.trb.org/View/2724675</link>
      <description><![CDATA[This report addresses the challenges of integrating artificial intelligence (AI) and machine learning (ML) technologies into aviation while ensuring safety and reliability. AI/ML applications in areas such as autonomous flight control and collision avoidance show great potential, but there is uncertainty around how to employ them and certify them confidently. This report evaluates current verification and validation (V&V) methods and explores new approaches to safety assurance for AI/ML-enabled systems. The project assessed AI/ML assurance techniques, implementation frameworks, and risk mitigation strategies, including run-time assurance (RTA) and contingency management (CM). It highlights the need for improved methods to manage uncertainties in AI/ML models. Recommendations include the development of new AI-specific standards and certification processes, along with continued research into AI/ML safety, particularly in human-AI interaction and real-time monitoring. The findings emphasize that while AI/ML technologies have transformative potential for aviation, significant advancements in safety assurance frameworks and certification processes are required to support their safe and effective integration into safety-critical systems.]]></description>
      <pubDate>Tue, 14 Jul 2026 13:34:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2724675</guid>
    </item>
    <item>
      <title>Transportation Issues: From Energy Efficiency to Security and Traffic Automation</title>
      <link>https://trid.trb.org/View/2670834</link>
      <description><![CDATA[The book is devoted to the problems of transport and energy resources, functioning and development of energy and transport systems, and their impact on international economic processes and business performance. In a generalized form the book presents the development of the methodology for the economic justification of the energy supply cost management system in the technological complex of various transport, the results of testing the system of energy-economic certification of roads, and the criteria for assessing the energy efficiency of transport enterprises. This book also covers the issues of reducing the consumption of petroleum fuels by various modes of transport, the development of new energy sources, security, and traffic automation. This book is useful for specialists in the field of transport systems and energy, as well as for a wide range of readers interested in these issues.]]></description>
      <pubDate>Mon, 13 Jul 2026 13:51:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2670834</guid>
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