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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
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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>
    <image>
      <title>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Distributed Adaptive Tracking Control of an Underactuated High-Speed Train With Completely Unknown System Parameters</title>
      <link>https://trid.trb.org/View/2672821</link>
      <description><![CDATA[A high-speed train (HST) is a physically interconnected underactuated system consisting of both motor cars and trailer cars. During operation, all cars experience varying degrees of aerodynamic resistance, which imparts nonlinear characteristics to each car, posing significant challenges for controller design and stability analysis. Investigating the distributed tracking control problem for underactuated HSTs, where aerodynamic resistance acts on every car, remains a long-standing open problem. The challenge is further compounded when actuator faults are involved. Additionally, accurately obtaining the system parameters for a HST is difficult. To address these challenges, we propose a distributed tracking control approach that does not rely on system parameters, where each motor car uses only its own information, as well as that of the cars in front and behind. In this paper, a new Lyapunov function is innovatively established by incorporating elastic potential energy and relative kinetic energy into its construction. Based on this function, it is rigorously proved that the closed-loop tracking error system remains stable as long as at least one motor car exists, and that the velocity-tracking errors of the motor cars are guaranteed to asymptotically converge to zero. Furthermore, an innovative algorithm is proposed, which effectively reduces the cumulative position-tracking error by adjusting the desired trajectory. Compared with the existing results, the proposed method does not depend on any system parameters, and the resulting closed-loop tracking error system is guaranteed to be stable. Finally, we provide simulations on two HSTs to verify our theoretical results.]]></description>
      <pubDate>Thu, 09 Jul 2026 13:32:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/2672821</guid>
    </item>
    <item>
      <title>A Self-Adaptive Monte Carlo Tree Search Algorithm for Generalized Quay Crane Scheduling Problem</title>
      <link>https://trid.trb.org/View/2686235</link>
      <description><![CDATA[Efficient quay crane (QC) handling is crucial for enhancing service levels and competitiveness in container terminals, particularly with the advent of ultra-large vessels necessitating rapid container turnover. As the complexity of terminal operations escalates, this paper addresses the generalized quay crane scheduling problem (GQCSP), aiming to minimize the makespan for discharging and loading operations. In this problem, both 20-ft and 40-ft containers are mixed and stacked above and below the hatch covers, and QCs operate multidirectionally under safety distance and noncrossing constraints. A solution approach is proposed that delivers swift, feasible solutions over protracted optimal ones, which is essential for adapting to last-minute operational changes. The problem is formulated as a Markov decision process model, and a self-adaptive Monte Carlo tree search (MCTS) algorithm is proposed that can handle up to 11 QCs and 280 container groups. For algorithm acceleration, problem-specific methods are developed, achieving an improvement of approximately 1.2% of the makespan and a reduction of two-thirds of the computation time. Specifically, an adaptive lower bound is introduced for node selection and subtree pruning, accompanied by a lower-bound-based reward function to facilitate backpropagation. The experiments demonstrate that the proposed MCTS algorithm significantly outperforms existing heuristic algorithms, achieving average improvements of 20.84%, 14.40%, and 11.02% in terms of makespan compared with the strategy-based approach, while also surpassing the dynamic programming algorithm by 12.88% and the memetic algorithm by 72.79%. Furthermore, compared with the most recent Benders decomposition method assuming a homogeneous container size, the proposed MCTS algorithm also demonstrates comparable performance where the difference is less than 0.4%. The significance of the results demonstrates that the proposed approach has great universality, thus improving terminal productivity and responsiveness.]]></description>
      <pubDate>Tue, 30 Jun 2026 17:03:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2686235</guid>
    </item>
    <item>
      <title>Convergence-triggered reinforcement adaptive relearning control for virtually-coupled trains</title>
      <link>https://trid.trb.org/View/2684788</link>
      <description><![CDATA[This paper proposes a convergence-triggered reinforcement adaptive relearning control (CTRAR) framework for virtual coupling (VC) train formation systems, integrating a novel dual-criteria performance monitor mechanism to ensure optimal and safe operation. Distinct from prior approaches, the proposed mechanism enforces boundary constraints and minimum dwell-time conditions, triggering either convergence to the approximate optimal mode with suspended actor-critic (AC) weight updates or nonconvergence to the nonoptimal mode which indicates a restart of the reinforcement learning (RL) procedure upon violation, termed CTRAR. To enhance learning efficiency visualization, a coefficient-enhanced Gaussian learning rate is developed for AC weight updates, ensuring persistent excitation of tracking error signals without compromising control performance. By leveraging train in-transit data from Beijing South Railway Station (BSRS) to Jinan West Railway Station (JWRS), the simulations successfully demonstrate the execution of reinforcement relearning under convergence-triggered conditions, as well as rigorous validation of the CTRAR algorithm through both error-convergence data illustration and computational resource savings quantification.]]></description>
      <pubDate>Tue, 30 Jun 2026 17:02:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2684788</guid>
    </item>
    <item>
      <title>Pareto optimality control of traffic signal and vehicle speed considering mixed traffic stream at isolated intersection</title>
      <link>https://trid.trb.org/View/2680761</link>
      <description><![CDATA[Most existing research on the cooperative control of traffic signals and vehicle speed encounters challenges in achieving a balance between traffic efficiency and vehicle fuel consumption at intersections. The pareto optimality control of traffic signal and vehicle speed is proposed to solve this balance problem considering mixed traffic stream at isolated intersection. This control method encompasses three main components: a multi-criteria signal control method, a vehicle speed trajectories multi-objective optimization model based on Pareto optimality, and a macro control strategy. Multi-criteria signal control method is designed based on multiple criteria to divide vehicles into groups optimally. The proposed vehicle speed trajectories multi-objective optimization model is solved by NSGA-II (Non-dominated Sorting Genetic Algorithm II) method to obtain pareto optimality. In order to illustrate the efficiency, this control method is tested in SUMO software compared with ASC (Adaptive Signal Control) method and bilevel optimization method. Average delay and average fuel consumption of this control method are reduced by 44.49% and 25.31% compared with ASC method. On the other hand, this control method also shows obvious balance effect compared with bilevel optimization method under different penetration level and demand level. Furthermore, average delay and average fuel consumption of this control method are reduced simultaneously under 250veh· l⁻¹·h⁻¹ level and 40% penetration level, 100% penetration level and 250veh· l⁻¹·h⁻¹ level. Experimental results illustrate the effectiveness of the proposed Pareto optimality-based control of traffic signals and vehicle speed.]]></description>
      <pubDate>Tue, 30 Jun 2026 10:21:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2680761</guid>
    </item>
    <item>
      <title>Federated Learning and Digital Twin-Enabled Distributed Intelligence Framework for 6G Autonomous Transport Systems</title>
      <link>https://trid.trb.org/View/2617929</link>
      <description><![CDATA[The rapid improvement in 6G-enabled Autonomous Transport Systems (ATS) has enhanced operational efficiency in terms of communication speed, data processing, and vehicle coordination. However, it presents a critical challenge in enabling vehicles to handle unforeseen, real-time adverse conditions. Despite these advancements, the challenge of adapting to unpredictable traffic scenarios and operational anomalies persists, and there is still room for improvement in managing these situations without compromising decision-making or resource management. We propose the Distributed Intelligence Framework (DIF), which leverages Federated Learning (FL) and Digital Twins (DTs) to enhance decision-making and network resilience. FL enables collaborative learning among vehicles while ensuring sensitive data remains localized, and DTs simulate adverse traffic scenarios in real time, allowing proactive adjustments to resource allocation and traffic management. The DIF framework enables vehicles to learn from the experiences of others, allowing them to handle unique or adverse conditions that individual vehicles may not have encountered before. This collaborative approach strengthens the system’s ability to adapt to new challenges while safeguarding data integrity and ensuring operational efficiency. Experimental results show that DIF achieves a 65% reduction in convergence error within just five epochs, demonstrating significant improvements in both network resilience and decision-making, making it a critical advancement for the future of 6G-enabled ATS networks.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2617929</guid>
    </item>
    <item>
      <title>An Adaptive Computing Offloading and Resource Allocation Strategy for Internet of Vehicles Based on Cloud-Edge Collaboration</title>
      <link>https://trid.trb.org/View/2617862</link>
      <description><![CDATA[With the development of the Internet of Vehicles (IoV) industry, the introduction of cloud-edge collaboration has greatly enhanced the computing capabilities of vehicle networks. However, optimizing computing offloading and resource allocation strategies in IoV to reduce latency and energy consumption at the vehicle terminals remains a challenge. This paper proposes an Adaptive Computing Offloading and Resource Allocation Strategy (ACORAS) for IoV based on cloud-edge collaboration. Firstly, a Vehicles-Collaborative Road Side Units-Cloud (VCRSUC) system architecture is constructed by considering the use of idle resources on edge servers at remote Road Side Unit (RSU) to reduce the total cost at the vehicle terminals. Secondly, the discrete particle swarm optimization algorithm is combined with chaotic mapping and Cauchy mutation, and dynamically adjusts weights and learning factors based on variable updates. Finally, our proposed ACORAS gradually approaches the optimization of the computing offloading decisions and resource allocation decisions through iterative calculations. Simulation results show that our proposed ACORAS can effectively reduce the total cost while considering latency and energy consumption, demonstrating superior performance compared to traditional algorithms.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/2617862</guid>
    </item>
    <item>
      <title>Adaptive Control of Bidirectional Platoons With Actuator Saturation and Discontinuous Trajectory Tracking</title>
      <link>https://trid.trb.org/View/2617818</link>
      <description><![CDATA[With the rapid development of V2V and V2I communication technologies and autonomous control systems, autonomous vehicles (AVs) are gaining increasing popularity. Small-spacing AV platoons offer advantages such as enhanced road capacity and energy efficiency. However, in non-ideal communication environments, packet loss can cause partial loss of trajectory information, resulting in discontinuous tracking. This may induce significant transients and trigger actuator saturation, aggravating traffic disturbances. In bidirectional platoons, where control signals propagate in both directions, the impact of such disruptions is further amplified due to mutual vehicle interdependence. This paper addresses these challenges by considering asymmetric actuator saturation, discontinuous tracking trajectories, and non-zero initial spacing errors in bidirectional AV platoons. A continuous control law is designed based on coupled sliding mode control, and Lyapunov stability theory is employed to ensure both trajectory tracking stability and string stability. Our contributions include the development of a modified spacing policy that not only eliminates large transients and string instability caused by non-zero initial spacing errors but also ensures rapid convergence to the desired spacing within a finite and adjustable time frame. Furthermore, a variant sigmoid function is introduced to actively smooth the discontinuous tracking trajectories, thereby reducing communication demands and suppressing transients. An auxiliary system is also designed to manage actuator saturation effectively, ensuring provable stability and fully leveraging actuator capabilities. Results demonstrate that the control strategy achieves both trajectory tracking stability and string stability, while also enabling rapid tracking performance and maintaining small spacing errors by making full use of actuator potential.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2617818</guid>
    </item>
    <item>
      <title>Automated Adaptive Traffic Network: Adapting the M50 in Real-Time by Optimizing Speed Limits Using a Proposed Intelligent Agent</title>
      <link>https://trid.trb.org/View/2671052</link>
      <description><![CDATA[Traffic congestion has been one of the most important issues in urban areas, which results in pollution, fuel cost, loss of time (work hours), stress and anxiety. It is possible to increase the traffic network efficiency through solutions such as Intelligent Transport Systems (ITS) by adapting the existing network to ongoing operational conditions, especially in bottle neck conditions. In this study to minimize travel time losses, speed limits are optimized to adapt the traffic network to its operational conditions in real-time. To do so, an intelligent agent is developed to estimate the traffic in part of the M50 motorway in Dublin and is given the capability to learn and change the operational scenarios of the motorway that allow it to perform online management of its speeds. Results, tested in SUMO, indicate that the intelligent agent can reduce the travel time at peak congestion by a maximum of 60% in average travel times for a period of 10 min, and it has an overall significant benefit to alleviate congestion in the M50 section of interest during peak morning and afternoon times.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:09 GMT</pubDate>
      <guid>https://trid.trb.org/View/2671052</guid>
    </item>
    <item>
      <title>Learning human collision avoidance behavior for autonomous ships using adaptive neuro-fuzzy inference system</title>
      <link>https://trid.trb.org/View/2712882</link>
      <description><![CDATA[Collision avoidance for Maritime Autonomous Surface Ships (MASS) remains challenging due to the need for safe, COLREGs-compliant decisions in close encounters. This study proposes a human-centric collision avoidance framework based on a computational intelligence approach called Adaptive Neuro-Fuzzy Inference Systems (ANFIS), trained using high-fidelity bridge simulator data that capture realistic navigator behavior. The fuzzy inference structure is generated using Fuzzy C-Means (FCM) clustering, with the optimal number of clusters determined using the Fuzzy Partition Coefficient (FPC) and Xie-Beni (XB) index. Separate ANFIS models are developed for crossing, head-on, and overtaking scenarios, and the effectiveness of the proposed method is validated by closed-loop validations using a second-order Nomoto model. Analysis of three simulation cases demonstrates that ANFIS models generate smooth and stable maneuvers, achieve collision free trajectories with safe passing distances, and comply with COLREGs. Moreover, the predicted rudder commands closely resemble human navigator actions, indicating the capability of the approach to capture underlying patterns. A global sensitivity analysis based on Sobol indices is conducted to quantify the influence of input variables on the ANFIS model output. The results show that different encounter scenarios are governed by distinct dominant features, with crossing and overtaking primarily influenced by geometric variables, while the head-on model exhibits stronger interaction effects among multiple inputs.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2712882</guid>
    </item>
    <item>
      <title>A stable adaptive control method for the four-wheel independent steering vehicle</title>
      <link>https://trid.trb.org/View/2677532</link>
      <description><![CDATA[Vehicle stability control is crucial for securing vehicle driving safety, while precise stability classification can assist in enhancing the performance of vehicle control. A stable adaptive model predictive controller (MPC) of the four-wheel independent steering (4WIS) vehicle is introduced in this paper. Firstly, a new vehicle dynamic model derived from a long short-term memory (LSTM) network is established, and an attribute dataset representing vehicle stability is procured. Subsequently, we employ the Gaussian mixture model (GMM)-hidden Markov model (HMM) to classify the stability of the 4WIS vehicle. According to the different classification results, the stable adaptive MPC integrated with Bayesian optimisation (BO) is designed to track an optimal trajectory with high accuracy while working under different conditions. Through the simulation tests in various typical driving scenarios, the advantages of the stability classification strategy and the stable adaptive control method proposed in this paper are confirmed.]]></description>
      <pubDate>Wed, 24 Jun 2026 13:22:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2677532</guid>
    </item>
    <item>
      <title>Optimize information sharing locations in locally connected mobility systems</title>
      <link>https://trid.trb.org/View/2679107</link>
      <description><![CDATA[Information collection and communication are fundamental to smart mobility, but designing an optimal information-sharing system for transportation networks presents challenges due to the intricate interplay between travelers’ routing behavior, cyber networks, and physical transportation infrastructure. The goal of this paper is to optimize the locations for sharing traffic information, considering en-route information sharing and adaptive routing behavior in response to en-route information updates. To achieve this goal, we propose a bilevel modeling framework, where the upper-level problem aims to optimize the transportation system mobility by deciding the optimal information-sharing locations, while the lower-level problem models the stochastic traffic equilibrium with en-route information updates. To solve the nonlinear and mixed-integer bilevel programming problem, we propose two novel value-decomposition algorithms within cutting-plane and branch-and-cut frameworks, respectively. These proposed models and algorithms are implemented using various scales of test networks to offer practical insights into information system design in edge-enabled mobility systems and to evaluate computational performance.]]></description>
      <pubDate>Wed, 24 Jun 2026 13:22:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/2679107</guid>
    </item>
    <item>
      <title>Adaptive multi-SWAL: Real-time intersection control with flexible lane design</title>
      <link>https://trid.trb.org/View/2676481</link>
      <description><![CDATA[The growing conflict between rising vehicle volumes on roads and limited road capacity has drawn significant attention to optimizing space allocation for individual vehicles. The lane-free approach, which allows vehicles to move without the constraints of lane boundaries, shows the potential to accommodate higher traffic densities. However, its reliance on a high penetration rate of autonomous vehicles (AVs) poses safety challenges during the early stages of AV deployment. To address this limitation, we propose a novel multi-Special Width Approach Lane (multi-SWAL) management method, which uses SWALs to reduce lane widths while maintaining a structured, lane-based framework. This approach is adaptable to various scenarios, ranging from limited AV penetration during initial deployments to full AV adoption in mature systems. The proposed method integrates lane change guidance with adaptive signal control to enhance both spatial and temporal utilization of intersection resources. By dynamically optimizing vehicle positioning across multiple lanes and coordinating discharge timing, the method minimizes average delays and improves intersection efficiency. Simulation results indicate that the multi-SWAL approach significantly outperforms conventional lane designs and other existing methods across various traffic demand levels. Sensitivity analyses further demonstrate its robustness in effectively managing intersections under diverse traffic conditions.]]></description>
      <pubDate>Wed, 24 Jun 2026 13:22:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/2676481</guid>
    </item>
    <item>
      <title>Joint Optimization of Vehicle and Pedestrian Traffic Signals Using Multi-Objective Deep Reinforcement Learning</title>
      <link>https://trid.trb.org/View/2672811</link>
      <description><![CDATA[Traffic signal control (TSC) at urban intersections is crucial for optimizing vehicle traffic flow and ensuring pedestrian safety. Advances in Internet of Things (IoT) and Internet of Vehicles (IoV) technologies have significantly improved traffic monitoring. However, most existing TSC studies primarily focus on optimizing vehicular traffic flow metrics such as waiting time and queue length, often overlooking crucial factors like lane fairness, emergency vehicle priority, and pedestrian convenience and safety at crosswalks. This paper proposes a novel deep reinforcement learning (DRL)-based TSC framework that jointly optimizes vehicular and pedestrian requirements. Two algorithms are introduced to address these multi-objective goals: DFASD (Dynamic Feasible Action Set Derivation), which guarantees pedestrian crosswalk requirements by leveraging a set of feasible actions during action selection, and ACSCS (Adaptive Crosswalk State Combined System), which integrates crosswalk sub-states into the state representation. Simulation results demonstrate that the proposed methods outperform conventional DRL-based dynamic signal control and cycle-based approaches, achieving superior performance in both vehicle traffic flow and pedestrian crosswalk management. These results underscore the potential of the proposed framework to effectively balance vehicular and pedestrian needs, enhancing urban intersection management.]]></description>
      <pubDate>Wed, 24 Jun 2026 11:31:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2672811</guid>
    </item>
    <item>
      <title>Engine Park Angle Control via Electric Machine under Varying Deceleration Limits</title>
      <link>https://trid.trb.org/View/2717256</link>
      <description><![CDATA[Accurate control of the engine park angle during Autostop in hybrid vehicles is critical for enabling rapid and smooth Autostarts, reducing start-up vibrations, and enhancing overall driving comfort. However, in real-world scenarios, the available torque for engine positioning is often limited by competing driver torque demands, battery discharge constraints, and the state of charge (SoC). Under these conditions, conventional position-speed control strategies frequently fail to achieve the desired precision.This paper introduces an adaptive control strategy for the electric machine (EM) that drives the internal combustion engine, ensuring precise alignment of the crankshaft at a predefined angle to optimize restart conditions. Upon receiving an engine shutdown request, the proposed controller computes an adaptive deceleration profile that respects the EM’s torque and deceleration limits while guiding the crankshaft toward the target park position.The core of the approach lies in generating a theoretical speed trajectory and tracking it through an adaptive nonlinear control law that dynamically adjusts in real time to compensate for disturbances and eliminate residual angular error at the end of the maneuver. Unlike conventional methods, the proposed solution maintains robustness under stringent deceleration constraints and varying operating conditions.Simulation and experimental results on a hybrid powertrain test bench demonstrate that the proposed method significantly improves park angle accuracy and consistency even under limited deceleration scenarios.]]></description>
      <pubDate>Tue, 23 Jun 2026 13:43:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/2717256</guid>
    </item>
    <item>
      <title>Intelligent Control of Automobile Spiral Bevel Gear Grinding Process Based on Force Feedback</title>
      <link>https://trid.trb.org/View/2706233</link>
      <description><![CDATA[To enhance the grinding quality of spiral bevel gears, an intelligent control model for the grinding process of automotive helical conical gears based on force feedback has been designed. This model outputs the control voltage for the machine tool's permanent magnet synchronous motor (PMSM), ensuring that the motor speed constantly tracks the desired value. By adjusting the grinding generating speed, the grinding force is controlled, and the tooth surface roughness is reduced. Firstly, the state equation of a permanent magnet synchronous AC servo motor is established. By employing the second method of Lyapunov, an RM adaptive control algorithm is developed. It is found that the model output can efficiently track the reference model (RM) and adjust to variations in torque due to load. To further enhance the controller, a generalized regression neural network (GRNN) was developed; subsequently, training data were generated using the output voltage of the RM self-adjusting controller to achieve velocity regulation of the machine tool's servo motor. Finally, the results indicate that the GRNN controller is superior. It uses RM self-adjusting control data as samples for regression analysis, outputs control signals, and controls the angular velocities of each axis of the machine tool to control the grinding force within a reasonable threshold range, reducing the complexity of the controller and achieving lightweight. At the same time, the feasibility of the controller has been experimentally verified. This improves the roughness of the tooth surface during the grinding of spiral bevel gears and enhances the quality of vehicle operation.]]></description>
      <pubDate>Mon, 22 Jun 2026 07:29:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/2706233</guid>
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