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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
    <atom:link href="https://trid.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
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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>
      <url>https://trid.trb.org/Images/PageHeader-wTitle.jpg</url>
      <link>https://trid.trb.org/</link>
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    <item>
      <title>Scenario-to-Zone Assignment and Route Planning for Autonomous Vehicle Testing: Hybrid Genetic Algorithm-Integer Programming Approach</title>
      <link>https://trid.trb.org/View/2701140</link>
      <description><![CDATA[This study proposes a hybrid optimization framework for efficient execution planning of scenario-based autonomous vehicle (AV) testing in physical proving grounds. Using K-City, Korea’s national AV testbed, as a case study, the framework addresses the practical challenge of executing many predefined scenarios under spatial feasibility constraints and operational limitations. A genetic algorithm (GA) is integrated with an integer programming (IP) model to jointly determine scenario-to-zone assignments and execution routes, explicitly capturing the dependency between scenario allocation and routing efficiency. Unlike conventional sequential approaches, IP-based route optimization is embedded within the GA fitness evaluation, allowing route cost to directly guide the search process. The methodology was validated on both a simplified grid network and the full K-City road network comprising 47 scenarios. Results showed substantial reductions in total travel distance compared with a naive sequential baseline, demonstrating the effectiveness of the integrated approach. To reflect real testing conditions, execution constraints on the number of scenarios per cycle were incorporated, producing repeatable and operationally feasible routes. The proposed framework improves execution efficiency and consistency and can be readily adapted to other AV testbeds with similar constraints.]]></description>
      <pubDate>Mon, 11 May 2026 12:24:46 GMT</pubDate>
      <guid>https://trid.trb.org/View/2701140</guid>
    </item>
    <item>
      <title>Multiobjective Optimization of a Single Slotted Flap Using Artificial Neural Network and Metaheuristic Algorithms</title>
      <link>https://trid.trb.org/View/2562299</link>
      <description><![CDATA[This research aims to improve the aerodynamic performance of the high-lift device used during landing in a general aviation aircraft. Our approach involved conducting a multiobjective aerodynamic optimization of the single slotted flap on the aircraft’s wing. The objective was to simultaneously maximize the lift and drag coefficients while minimizing the moment coefficient. To achieve this, we performed several simulations with varying gap and overlap values, resulting in a primary data set. The simulations were conducted using the k-ε turbulence model to simulate the incompressible turbulent flow around the NACA-23012 airfoil at a Reynolds number of Rec=3.6×106. However, due to the extensive computational resources and time required for solving, analyzing the data posed significant challenges. To address this, we utilized a surrogate model to generate data, reducing both cost and time. During the machine learning process, an artificial neural network (ANN) was trained on the initial data set. To solve this type of problem, we employed the multiobjective genetic algorithm (MOGA) as the most suitable algorithm. By utilizing MOGA, we were able to determine the optimal configuration for the single slotted flap. The training of computational fluid dynamics data in an ANN significantly reduced computational time and cost, enabling the generation of a continuous response surface in less than one minute. Additionally, the multiobjective genetic algorithm efficiently produced the Pareto front in just 45 s. Finally, based on the design requirements of the proposed configurations, the best option was selected. Subsequently, an aerodynamic analysis of the final configuration was conducted to evaluate its performance.]]></description>
      <pubDate>Tue, 28 Apr 2026 11:20:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/2562299</guid>
    </item>
    <item>
      <title>Optimization method for jacket platforms using random forest surrogate model</title>
      <link>https://trid.trb.org/View/2638272</link>
      <description><![CDATA[The optimization of jacket platforms typically relies on computationally intensive finite element analysis (FEA), which is relatively time-consuming. Surrogate models are widely used in multi-domain optimization problems, while machine learning algorithms are employed to construct surrogate models, demonstrating promising applications. In this study, an optimization method that integrates machine learning surrogate models with an improved genetic algorithm (GA) is employed to enhance structural optimization efficiency. Then, the proposed method is applied to a typical jacket platform in the Bohai Sea using a seven-dimensional optimization space designed to minimize the total structural weight. A surrogate model is derived through a random forest algorithm to replace FEA. The surrogate model is combined with a real-coded GA featuring adaptive crossover and mutation for iterative optimization. Moreover, surrogate models are developed using Decision Tree and KNN methods. Evaluation results show that the random forest algorithm outperforms Decision Tree and KNN methods. The random forest surrogate model can save significant computation time while maintaining high accuracy, providing an efficient and effective alternative to the FEA optimization method.]]></description>
      <pubDate>Mon, 27 Apr 2026 15:01:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2638272</guid>
    </item>
    <item>
      <title>Optimizing road side units connectivity in intelligent transportation systems with optical network solutions</title>
      <link>https://trid.trb.org/View/2606780</link>
      <description><![CDATA[A fundamental component of Intelligent Transportation Systems (ITS) is connectivity. For connected vehicles to be aware of events occurring nearby, or even far from them, roadside infrastructure is essential. Roadside Units (RSUs) are electronic equipment placed along highways to provide connectivity and share data with vehicles, other RSUs, and networks. Connected vehicles require wireless communication with RSUs; however, depending on the complexity of tasks and the number of users, spectrum resources may be insufficient to handle all required communication between vehicles, RSUs and external networks. Since RSUs are stationary, optical fiber is an ideal technology for interconnecting them and linking them to the Internet and the cloud, providing reliable, high-performance connectivity, with low signal attenuation and high bandwidth. This paper proposes a model for deploying fiber networks to connect RSUs, with a focus on minimizing capital expenditures, including costs for civil works, cables, and devices, which are critical considerations given the large distances involved. Specifically, we consider and compare two established optical network technologies: point-to-point (PtP) and passive optical networks (PON). To support this comparison, we present and test two novel Integer Linear Programming (ILP) formulations: one for PtP and one for PON. Additionally, we introduce a genetic algorithm that improves upon a previously proposed heuristic, achieving near-optimal results comparable to the ILP formulation, while efficiently solving large-scale scenarios. The results show that the optimal choice between PtP and PON depends on the deployment area and density of RSUs.]]></description>
      <pubDate>Mon, 27 Apr 2026 14:58:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2606780</guid>
    </item>
    <item>
      <title>Multi-target dynamic allocation and adaptive path planning for unmanned surface vehicles under marine surface with water flows</title>
      <link>https://trid.trb.org/View/2661524</link>
      <description><![CDATA[This paper addresses the path planning challenges faced by multiple unmanned surface vehicles (USVs) traversing multiple targets in the presence of marine surface water flows. We propose a novel method for multi-target traversal that takes into account dynamic allocation of targets for linear water flows and adaptive paths for vortex water flows. Our approach comprises two main components: a priority bundled (PB) genetic target allocation Algorithm and a direction varying fast marching (DV-FM) Q-Learning path planning algorithm. Comparative studies with existing algorithms demonstrate the efficiency of our proposed method. Specifically, the novel target allocation algorithm incorporates a predictive function for linear water flows, increasing the speed of obtaining the final allocation scheme by 26% and reducing the distance traveled by 4.6%. Additionally, the adaptive function of the proposed Q-Learning path planning algorithm for vortex flows reduces the unsafe factor by 68.8%. Consequently, the proposed multi-target traversal method provides significant theoretical support for enhancing the intelligence of multiple USVs.]]></description>
      <pubDate>Mon, 27 Apr 2026 14:57:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2661524</guid>
    </item>
    <item>
      <title>Mooring optimization for net-cage group system based on NSGA-III multi-objective genetic algorithm</title>
      <link>https://trid.trb.org/View/2661519</link>
      <description><![CDATA[A well-established mooring optimization procedure is proposed for the anchor lines of net-cage groups. The equivalent mesh method, Morison equation and lumped mass method are used to establish the numerical model of the net-cage, and the accuracy is verified with experimental results, the maximum error of 4% in current and 9.4 % in regular waves are got. The NSGA-Ⅲ multi-objective genetic algorithm is combined with the numerical simulations, aiming to minimize the mooring tension, horizontal displacement, and mooring cost of the cage group. Meanwhile, the optimum anchor points and line parameters are obtained. To improve the efficiency, the relationship of the maximum tension between a single cage and the cage group is derivated and regarded as a constraint condition for the optimization. The spatial intersection screening method is introduced to simplify the result screening process and quickly get the optimal solution under multiple working conditions, the error of maximum displacement and tension between two models are 2% and 4.6% respectively. Finally, optimization design to the anchor lines of a 2 × 2 layout cage group in 50m water depth is performed under different current, regular waves and irregular waves, and the results in time domain are compared comprehensively. It is found option 4 can effectively reduce the maximum mooring tension, cage displacement and cost, it is determined as the final option, then the practical applicability is illustrated.]]></description>
      <pubDate>Mon, 27 Apr 2026 14:57:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2661519</guid>
    </item>
    <item>
      <title>Tramper Allocation Planning by Genetic Algorithm for and after the Pull Type Transportation Term after a Mega-Earthquake</title>
      <link>https://trid.trb.org/View/2675531</link>
      <description><![CDATA[After a massive earthquake, there is a high likelihood of a severe shortage of land transportation capacity for a certain period. This shortage could significantly hinder the maintenance of social order and the early recovery and reconstruction efforts. As a mitigation measure, preemptive consideration of utilizing maritime transportation is highly valuable. This study assumes the Nankai Trough earthquake as a case of a massive earthquake and examines the transportation of disaster-related cargo. The target term is the pull-type transportation period, the recovery period and the reconstruction period. Given the severe post-disaster transportation constraints, a web-based reservation system is assumed for operating ferries and RORO ships as tramper vessels. To optimize vessel allocation under these conditions, a planning method was developed by integrating a genetic algorithm with a transportation simulator. Linkage Learning Genetic Algorithm (LLGA), which organizes chromosomes in a circular structure, was introduced. Various modifications were also added to better suit maritime transport. As a result, a feasible and practically implementable tramper allocation method was obtained.]]></description>
      <pubDate>Mon, 27 Apr 2026 14:55:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2675531</guid>
    </item>
    <item>
      <title>Cooperative path following control of USV-UAVs with genetic algorithm extended state observer</title>
      <link>https://trid.trb.org/View/2660798</link>
      <description><![CDATA[This work investigates an innovative decentralized formation control method for underactuated surface vessel (USV) and underactuated aerial vehicles (UAVs) to achieve path following in the presence of external disturbances. The design of the USV-UAVs cooperative path following controller is divided mainly into two parts. The first part is the guidance system,which employs a line of sight (LOS) based on a switching mechanism for selecting the next way point to steer the USV in path following, thereby mitigating overshooting at turning points. For UAVs, the virtual attitude angles are designed to guide UAVs in tracking the USV’s trajectory. The second part is the control system, which uses sliding mode to design a controller to enable the USV-UAVs to track the reference path. An extended state observer,the genetic algorithm-based extended state observer (GAESO), is proposed to estimate unmodeled dynamics and external disturbances. The stability proof of both the USVs and UAVs is presented using the Lyapunov theorem. Finally, the simulation tests are carried out to validate the proposed system and control methods in the presence of environmental disturbance.]]></description>
      <pubDate>Thu, 23 Apr 2026 09:12:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2660798</guid>
    </item>
    <item>
      <title>Emergency Material Scheduling in Hub-and-Spoke Networks Considering Resilience</title>
      <link>https://trid.trb.org/View/2694545</link>
      <description><![CDATA[Emergency supply distribution networks face significant resilience challenges during large-scale disasters because of hub congestion and demand–supply mismatches. This study addresses this issue by proposing and comparing two congestion management strategies for hybrid hub-and-spoke rail–road intermodal networks: a waiting versus path redistribution strategy using backup hub mechanisms. A multi-objective optimization model was constructed to maximize network resilience, minimize transportation time, and reduce costs. Resilience was measured by the demand gap weighted by demand urgency. A rolling horizon optimization framework is established to address the temporal dynamics of disaster relief operations. A Q-learning-enhanced Non-dominated Sorting Genetic Algorithm II (NSGA-II) algorithm is developed to solve the optimization problem, constructing a 16-dimensional state space by integrating objective function values and population diversity metrics for intelligent local search. Using severely affected areas from the Wenchuan earthquake as a case study, the experimental results demonstrate that the improved algorithm reduces average objective function values by 17.75%, 49.82%, and 19.92%, respectively, compared with the standard NSGA-II. Incorporating demand urgency factors reduces the material shortage index by 53.41%, better reflecting humanitarian priorities. By comparing the average function values across Periods 1–6, the first four periods are suitable for the path reallocation strategy, while the subsequent two periods should adopt the “continue waiting” strategy. The study provides actionable insights for emergency managers in optimal strategy selection in disaster relief operations.]]></description>
      <pubDate>Thu, 23 Apr 2026 09:10:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/2694545</guid>
    </item>
    <item>
      <title>A hybrid genetic search for the inventory routing problem</title>
      <link>https://trid.trb.org/View/2656108</link>
      <description><![CDATA[The Inventory Routing Problem (IRP), an essential component of supply chain management, involves efficiently managing deliveries from a depot to clients using a fleet of vehicles. Recognized in a recent international challenge, the IRP requires innovative approaches. This paper presents a Hybrid Genetic Search (HGS) algorithm, with a distinctive crossover operation tailored for IRP and a fast way to calculate optimal inventory levels using network flows on an auxiliary graph. Our method integrates HGS with the Network Simplex IRP decoder, combined with a refined solution constructor, efficient IRP and Capacitated Vehicle Routing Problem local searches, and several components that make the hybrid framework effective for the IRP. We provide an extensive computational evaluation, showing that our algorithm outperforms 22 recent methods from the literature, providing 137 new best-known solutions for a classical instance set and 180 new best-known solutions for a recent larger instance set. Moreover, according to the rules of the recent international challenge, our method would rank first.]]></description>
      <pubDate>Tue, 21 Apr 2026 14:30:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2656108</guid>
    </item>
    <item>
      <title>Enhancing Ant Colony Optimization with Genetic Algorithm and 3-Opt for Multiple Drone Spraying Path Planning in Precision Agriculture</title>
      <link>https://trid.trb.org/View/2686143</link>
      <description><![CDATA[Efficient and environmentally responsible pesticide application is a major challenge in precision agriculture. Excessive pesticide use in conventional farming increases costs, harms the environment, and poses health risks. Recent advancements in unmanned aerial vehicles (UAVs) or drones have enabled targeted spraying, yet optimizing multiple-drone route planning and task allocation remains complex due to dynamic field conditions and limited drone capacity. To address this gap, this study proposes a hybrid optimization approach that integrates Ant Colony Optimization (ACO), Genetic Algorithm (GA), and 3Opt to generate efficient flight routes for multiple sprayer drones based on plant health levels. In this framework, ACO assigns drones to target points, GA automatically tunes key ACO parameters, and 3Opt enhances route efficiency through local optimization. Experimental results show that GA effectively automates the tuning of four key ACO parameters and that drone capacity significantly affects route length. The integration of GA, ACO and 3Opt further reduces total route length, achieving up to 13.6% improvement in efficiency compared to traditional ACO. These findings demonstrate the potential of the proposed method to enhance route efficiency, reduce energy consumption, shorter mission completion time and offers a practical solution for improving the performance and sustainability of multiple-drone spraying operations.]]></description>
      <pubDate>Wed, 15 Apr 2026 10:30:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2686143</guid>
    </item>
    <item>
      <title>A Hybrid Approach for Pavement Maintenance Management of an Italian Motorway</title>
      <link>https://trid.trb.org/View/2165761</link>
      <description><![CDATA[Transportation officials are faced every day with competing investment demands, and they must distribute limited resources in the presence of uncertainty or in the absence of complete and accurate information. Therefore, they must have access to valid, efficient, and robust asset management tools, which can support the optimization of maintenance interventions and support the policies at all decisional levels. Within this context, emerging technologies, such as artificial intelligence techniques, provide efficient alternatives to the traditional mathematical approaches. In this paper a hybrid approach for optimal management of road pavement maintenance for a pluri-annual planning period is proposed. The proposal of the procedure has been the optimal management of the rehabilitation interventions of pavement friction. In particular, a Sideway Force Coefficient prediction model has been created through an appropriate neural network to characterize the pavement conditions. Defined by this model it was possible to solve the optimization problem. A genetic algorithm was used to tackle the combinatorial nature of the network-level maintenance programming. The procedure was applied to a motorway infrastructure belonging to the motorway network of Eastern Sicily (Italy).]]></description>
      <pubDate>Sun, 29 Mar 2026 17:20:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2165761</guid>
    </item>
    <item>
      <title>Optimal Design of Bridge and Roof Truss Systems Using Multi-Objective Genetic Algorithms</title>
      <link>https://trid.trb.org/View/2165771</link>
      <description><![CDATA[A computational design method is proposed to assist structural engineers in designing optimal bridge and roof truss systems. The results presented show that the proposed design method is capable of increasing both the efficiency of the computational process and the optimality of the truss designs evolved. The method explores a diverse range of truss topologies and geometries, while also optimizing the member sizes. A multi-objective genetic algorithm is used to perform shape optimization in order to satisfy the conflicting objectives of minimizing volume, minimizing deflections, and maximizing stress. Instead of handling stress and deflection objectives as constraints, these design criteria are stated as additional objectives to be optimized. The concept of Pareto-optimality is used to determine a ranking of current design alternatives that guides the search process toward optimal solutions. To reduce the design time required, the proposed computational method is implemented on parallel computers. Truss designs obtained using the proposed method are compared to designs obtained by other researchers on a benchmark bridge truss design problem in order to evaluate the performance of the proposed method. The truss designs identified provide more optimal truss designs than reported in other research efforts, while still providing comparable search efficiency. The proposed method was also applied to identify near-optimal designs for a new roof truss design problem.]]></description>
      <pubDate>Sun, 29 Mar 2026 17:20:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2165771</guid>
    </item>
    <item>
      <title>Coupled dynamics of electric vehicle with a novel solution of equilibrium under regenerative braking</title>
      <link>https://trid.trb.org/View/2666891</link>
      <description><![CDATA[In this paper, to focus on the vehicle handling stability under regenerative braking, a nonlinear high-dimensional vehicle dynamics model considering longitudinal and lateral coupling and load transfer was established. By conducting the experiments to obtain the regenerative braking torque characteristics, fitting expressions are introduced into the dynamics model of the vehicle to analyze the coupled dynamic behavior and stability variations under different maneuvers during regenerative braking. A genetic algorithm-based method is proposed to solve stable stem of the high-dimensional vehicle systems. The results indicate that the vehicle loses stability under both conditions, once unstable the vehicle has difficulty restoring stability under braking force. For stable region of initial states, the phase trajectory rapidly converges to a stable stem. The length of stable stems could serve as a reference indicator for single-pedal regenerative braking controller design. Meanwhile, the initial conditions should close to the stable stems, ensuring that the vehicle can quickly restore stability under perturbation, to improving the robustness of vehicle handling under braking conditions.]]></description>
      <pubDate>Wed, 25 Mar 2026 11:44:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2666891</guid>
    </item>
    <item>
      <title>AdaNGA: Adaptive neuronal genetic algorithms for unmanned surface vehicle dynamic path planning in dynamic environment</title>
      <link>https://trid.trb.org/View/2682792</link>
      <description><![CDATA[Dynamic path planning for unmanned surface vehicles (USVs) in dynamic water environments remains a significant challenge due to the dynamic changing obstacles and conditions. To address the challenges, this study introduces AdaNGA (an Adaptive Neuronal Genetic Algorithm) that enhances navigation by integrating neural networks with an adaptive genetic algorithm framework. The main novelty lies in a dynamic fitness function that changes based on obstacle type (static or dynamic), enabling real-time adaptation and efficient path optimization. This study implemented a 3D simulation using Unity 3D platform, incorporating real-time sensor inputs, real-world parameters (wavy water surface, water drag, air drag, buoyancy force, water current, and wind), physics parameters (inertia and thruster delay) and tested on the real-world map-based simulation with dynamic water environment (the Dammam Corniche beach area, Dammam City, Saudi Arabia). The proposed method AdaNGA demonstrates superior convergence speed and reduced travel time compared other basic metaheuristic algorithms (simulated annealing, particle swarm optimization, and basic genetic algorithms) and baseline hybrid algorithms (neural network-genetic algorithms and modified neuronal genetic algorithms). The results validate its effectiveness in both static and dynamic scenarios, offering a robust and intelligent approach to USV navigation in complex environments.]]></description>
      <pubDate>Wed, 25 Mar 2026 11:44:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2682792</guid>
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