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    <title>Transport Research International Documentation (TRID)</title>
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    <atom:link href="https://trid.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
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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>
      <url>https://trid.trb.org/Images/PageHeader-wTitle.jpg</url>
      <link>https://trid.trb.org/</link>
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    <item>
      <title>Determining the Algorithm of Transportation of Highly Concentrated Coal-Water Fuel to Consumption Points of the Transport Network</title>
      <link>https://trid.trb.org/View/2579772</link>
      <description><![CDATA[In order to improve the environmental conditions and reduce the cost of coal-water fuel, its production is based on the use of all types of coal, as well as coal refuse. The use of coal-water fuel in thermal power generation allows reducing emissions of nitrogen oxides, sulfur, and carbon monoxide into the atmosphere. The paper provides a rational way of using different modes of transport for transporting highly concentrated coal-water fuel depending on the freight volume and the transportation distance. The optimal order of fuel transportation to the consumption points on the route of the selected hydrotransport network has been determined using Prim’s algorithm and the method of branches and boundaries. The shortest distances between the points included in one route have been determined using the minimum spanning tree algorithm. The algorithm for choosing the optimal option for transporting highly concentrated coal-water fuel is developed according to two options: with one point of fuel production and with two points of production. The optimization process has been carried out taking into account the reduction of the total loss of the coal-water fuel flow during its transportation between the departure point and all consumption points in turn along the common pipeline reducing the diameters of pipes in the network sections. When using the author's method, the topology of the transport network of distribution among the consumption points of coal-water fuel is optimized and the total length of the selected transport network is determined. The economic benefits of the proposed research are determined.]]></description>
      <pubDate>Mon, 27 Jul 2026 11:16:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2579772</guid>
    </item>
    <item>
      <title>Solving the Fuzzy Traveling Salesman Problem Using Genetic Algorithm with Clustering by Ward’s Method</title>
      <link>https://trid.trb.org/View/2579754</link>
      <description><![CDATA[The article describes a method for solving the fuzzy traveling salesman problem on a given transportation network based on a two-stage approach. In the fuzzy traveling salesman problem, the task is to find the shortest route with a fuzzy specified travel time between individual cities of the network. The travel time along the network is given in the form of fuzzy trapezoid numbers. A method for converting fuzzy numbers into a special form is proposed, and operations on such numbers are considered. The route is formed in two stages: at the first stage, the cities of the network are grouped into clusters, after which the optimal solution to the traveling salesman problem is found for each cluster using a genetic algorithm. A modification of the genetic algorithm scheme is used based on improving the mutation operation and increasing the diversity of populations. Two variants of the cluster formation topology with a limited number of available paths and a fully connected topology are considered. Numerous experiments are conducted, the results of which confirm the constructiveness of using the preliminary clustering methodology for a large number of cities in the network. Pre-clustering improves the overall route duration in the traveling salesman problem compared to solutions obtained by using a genetic algorithm without using clusters.]]></description>
      <pubDate>Mon, 27 Jul 2026 11:16:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2579754</guid>
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    <item>
      <title>Path finding under uncertainty: Considering both reliability and unreliability of travel time distributions</title>
      <link>https://trid.trb.org/View/2686716</link>
      <description><![CDATA[In transportation networks, travel times are stochastic and often exhibit a skewed distribution with a long right tail, which can lead to significant delays. In this paper, we present an α-reliable mean-excess shortest path model, where mean-excess travel time is interpreted as the conditional value-at-risk (CVaR) of the travel time distribution, for identifying optimal paths in stochastic networks. This model addresses both the reliability aspect–minimizing the travel time budget that ensures a certain level of on-time arrival–and the unreliability aspect–managing potential worst-case travel times beyond that budget. Formulated as a mixed-integer nonlinear programming (MINLP) problem with complex coupling of random link travel time distributions, the model is reformulated into a mixed-integer linear programming (MILP) problem based on link travel time samples for tractability. We develop a tailored Benders Decomposition (BD) method, which selectively uses worst-case samples to generate optimality cuts, reducing the computational burden caused by large sample sizes. An accelerated variant, BD with multiple cuts, further improves convergence. Numerical experiments on small, medium, and large-scale networks demonstrate the path-finding model’s effectiveness, the solution procedure’s efficiency, and its scalability for real-world applications like in-vehicle route guidance systems.]]></description>
      <pubDate>Thu, 09 Jul 2026 13:31:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/2686716</guid>
    </item>
    <item>
      <title>A Hybrid Heuristic Algorithm for Optimizing the Optimal Landing Time Window Problem in Intelligent Air Transportation Systems</title>
      <link>https://trid.trb.org/View/2617913</link>
      <description><![CDATA[The Aircraft Landing Problem (ALP) involves optimizing the scheduling of flight arrivals and departures while simultaneously managing airport resources to maximize flight utilization, minimize delays, reduce costs, and enhance overall operational efficiency. However, existing algorithms for solving ALP often face challenges in converging optimally and effectively handling penalty values associated with timing violations. To address these issues, this paper proposes a novel hybrid heuristic algorithm, AGWOA, designed to enhance convergence rates and effectively manage penalties in ALP. AGWOA integrates Artificial Bee Colony (ABC) and Genetic Algorithm (GA) techniques into the Whale Optimization Algorithm (WOA), leveraging their complementary strengths to strengthen global search efficiency and refine local constraint handling. This integration accelerates convergence and significantly mitigates penalty costs. Moreover, AGWOA incorporates an adaptive coefficient to facilitate improved convergence, along with a Fast Convergence Update Mechanism (FCUM) to guide the algorithm toward optimal solutions more efficiently. Experimental results conducted on public datasets demonstrate that AGWOA outperforms existing advanced algorithms in both convergence speed and penalty minimization. Specifically, AGWOA achieves a 4.05% decrease in penalty costs compared to baseline algorithms. These results underscore AGWOA’s effectiveness in overcoming the challenge of slow convergence and its competitive advantage over other methods in optimizing ALP. The proposed algorithm offers a promising solution for real-world ALP applications, significantly enhancing airline operational efficiency and optimizing resource management.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2617913</guid>
    </item>
    <item>
      <title>Research on Traffic Dynamic Shortest Path Allocation Model Based on Triangular Fuzzy Number Weight and K-Means Algorithm</title>
      <link>https://trid.trb.org/View/2617876</link>
      <description><![CDATA[Aiming at the traffic dynamic shortest path allocation problem with triangular fuzzy numbers as attribute values, a traffic dynamic shortest path allocation model method based on triangular fuzzy number weights is proposed. The weighted distance optimization model of triangular fuzzy number, ideal solution and negative ideal solution is established. The weight value of each attribute is obtained by solving and optimizing. The similarity difference between attribute information is determined longitudinally by using the idea of deviation maximization, and the evaluation uncertainty of each scheme under different attributes is described horizontally by entropy value. The attribute weights based on reliability are obtained by synthesizing the similarity difference index and the uncertainty index. Based on triangular fuzzy weights. A subset of vertex set V, R, is selected as the representative vertex, and the shortest path distance between all vertex pairs in R is calculated. At the same time, considering the spatial correlation of road traffic flow and the dynamic change process of traffic flow in each section of the road network, the time is discretized, and the abrupt point of road traffic state is taken as the node pair, and the road traffic flow is dynamically loaded. The prediction model of road section travel time under occasional congestion is established. Experiments show that when the error upper limit is small, the retrieval results of the approximation algorithm are relatively accurate. Then, an example analysis of traffic dynamic path allocation is provided, which shows the effectiveness and feasibility of this method in traffic dynamic shortest path allocation.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/2617876</guid>
    </item>
    <item>
      <title>A Structure-Aware Lane Graph Transformer Model for Vehicle Trajectory Prediction</title>
      <link>https://trid.trb.org/View/2672985</link>
      <description><![CDATA[Accurate prediction of future trajectories for surrounding vehicles is vital for the safe operation of autonomous vehicles. This study proposes a Lane Graph Transformer (LGT) model with structure-aware capabilities. Its key contribution lies in encoding the map topology structure into the attention mechanism. To address variations in lane information from different directions, four relative positional encoding (RPE) matrices are introduced to capture the local details of the map topology structure. Additionally, two shortest path distance (SPD) matrices are employed to capture distance information between two accessible lanes. The prediction results of the Argoverse 2 dataset indicate that the proposed LGT model can decrease the minimum final displacement error (minFDE6) metric by 60.73% compared to the nearest neighbor model and reduce the b-minFDE6 by 2.65% compared to the baseline LaneGCN model. Furthermore, ablation experiments demonstrated that the consideration of map topology structure led to a 4.24% drop in the b-minFDE6 metric, validating the effectiveness of this model. Our code is publicly available at: https://github.com/dongcaiyin/LGT2024.]]></description>
      <pubDate>Wed, 24 Jun 2026 13:22:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/2672985</guid>
    </item>
    <item>
      <title>Towards automated Physical Internet system: Simulations of two privacy-protecting routing protocols</title>
      <link>https://trid.trb.org/View/2618113</link>
      <description><![CDATA[The purpose of this paper is to address the trust issue that leads to reluctance to share data within the logistics sector. This paper leverages the latest logistics paradigm concept Physical Internet (PI), and introduces two decentralised routing protocols for PI, focusing on their performance and impact on privacy by minimising data sharing. We use Agent-Based Modelling (ABM) and Monte Carlo (MC) simulations to evaluate the effectiveness of the protocols in optimising route quality, monetary costs and external costs in a realistic business setup on the Belgian scale. In addition, a sensitivity analysis was performed to assess the impact of response delays in a logistics network. Our research demonstrates the possibility of sharing less data without compromising the optimality of routes. We find that at our problem scale, trucks are the preferred mode when only considering monetary costs. Our findings also illustrate the significant impact of response delays and the handling capacity of intermodal hubs on the efficiency of route planning and the need for automation to improve PI systems’ reliability. We further suggest that trust issues should become one of the primary focuses for the current stage of PI research.]]></description>
      <pubDate>Wed, 17 Jun 2026 16:14:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2618113</guid>
    </item>
    <item>
      <title>Arctic route planning under ice uncertainty: A risk-averse stochastic shortest path problem</title>
      <link>https://trid.trb.org/View/2618103</link>
      <description><![CDATA[As global warming leads to reduced ice coverage, Arctic shipping routes are becoming increasingly accessible. These routes offer significant reductions in shipping time and may provide both economic and environmental advantages through lower fuel consumption and reduced emissions. The primary challenge in route planning comes from unpredictable ice conditions, which affect transit time. In this paper, we formulate a risk-averse stochastic shortest path problem for conservatively estimating transit times for Arctic shipping. This model estimates a risk-averse transit time for a ship prior to departure, considering the uncertainties in ice conditions. It minimises a weighted sum of expected and Conditional Value at Risk (CVaR) transit times, subject to a given risk-aversion level. We evaluate our model using projected ice data for the Northeast and Northwest Passages. Our conservative findings indicate that as sea ice retreats, shipping routes move northward, resulting in shorter CVaR transit times. While greater risk aversion can extend expected transit times by up to two days, this increase is relatively minor compared to the corresponding gain in reliability.]]></description>
      <pubDate>Wed, 17 Jun 2026 16:14:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2618103</guid>
    </item>
    <item>
      <title>Sunlight Algorithm: A Novel Shortest Path Planning Technique for Unmanned Surface Vehicles With Enhanced Search Efficiency</title>
      <link>https://trid.trb.org/View/2658902</link>
      <description><![CDATA[This paper investigates the shortest path planning of unmanned surface vehicles (USV) in complicated marine environments. A novel path planning method called “sunlight algorithm” is first presented by mimicking the radiation of the sun on the surface. The essence of this algorithm follows from the fact that the shortest path always occurs at the corners of obstacles. Different from the RRT* (Rapidly-exploring Random Tree Star) algorithm, which is characterized by sparse and random sampling, the proposed method only samples the tangent points of the sunlight on the obstacle edges. By iteratively treating the tangent point as a new sun, the proposed scheme can search the shortest path more efficiently. The implementation of this algorithm has four notable features: 1) a point filtering mechanism through the father-son relationship in the open set eliminates the insignificant sampling; 2) the setting of forward distances endows the planned route with the ability to fit the curve geometric shape of the obstacle; 3) the heuristic function gives the tuning between optimality and rapidity, and the probabilistic selection brings in the random exploration with robustness; 4) the bidirectional back-end processing procedure makes the further improvement on the initially planned paths. By using OpenCV simulations, it is proved that the proposed scheme outperforms the others in searching the shortest path. The open source codes are available at https://github.com/dengyingjie1993/Dengyingjie-algorithm-for-IEEE-TIS]]></description>
      <pubDate>Thu, 28 May 2026 17:09:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2658902</guid>
    </item>
    <item>
      <title>Energy Consumption Optimization for Cellular-Connected Multi-UAV Pickup and Delivery System</title>
      <link>https://trid.trb.org/View/2658793</link>
      <description><![CDATA[In this paper, a cellular-connected uncrewed aerial vehicles (UAVs) pickup and delivery system is studied in which the multiple UAVs are served by ground base station (GBS). These UAVs commence operations from the hangar, systematically execute a sequence of pickups and deliveries before returning, all while maintaining a continuous and reliable communication link with the GBS throughout their missions. Owing to the limited onboard energy resources of UAVs and the influence of task sequence and payload characteristics on UAV’s energy consumption, this study focuses on minimizing energy usage through the optimization of task sequences and flight trajectories. Firstly, a radio map of the operational area is constructed to identify flight zones that ensure reliable communication, an improved Dijkstra algorithm is then proposed to compute the shortest viable path between any two access points that satisfy reliable communication criteria. Furthermore, a chromosome structure tailored for a hybrid genetic algorithm (HGA) is devised to address the complexities of multi-UAV task allocation. With the aid of a developed distance matrix, the HGA is utilized to determine the optimal delivery sequence, thereby achieving the objectives of minimizing the maximum energy consumption (MME) and minimizing the sum energy consumption (MSE) respectively. Finally, simulation analyses demonstrate that the proposed MME and MSE optimization strategies enable approximately a 50% reduction in peak flight energy consumption and around a 40% decrease in total energy consumption, compared to multi-UAV pickup and delivery systems without energy optimization.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2658793</guid>
    </item>
    <item>
      <title>An Effective Iterated Search for the Profitable Tour Problem With Simultaneous Pickup and Delivery</title>
      <link>https://trid.trb.org/View/2658775</link>
      <description><![CDATA[The Profitable Tour Problem with Simultaneous Pickup and Delivery (PTPSPD) is a practical and challenging variant of the Vehicle Routing Problem with Simultaneous Pickup and Delivery (VRPSPD), in which not all customers need to be served. This reflects real-world scenarios where fleet size is often limited and incurs significant costs. This study addresses the PTPSPD under realistic constraints and proposes an effective iterated local search algorithm that integrates complementary components to balance exploration and exploitation. A cluster-first, route-second heuristic is employed to generate high-quality initial solutions. The search process is intensified via a randomized variable neighborhood descent strategy. To enhance global exploration, a combination of random perturbation mechanisms and adaptive threshold acceptance criteria is employed. A dynamic tabu list further promotes diversification and prevents premature convergence. The proposed method is evaluated on a benchmark set of 117 PTPSPD instances involving 50 to 199 customers. Results highlight the effectiveness and robustness of the approach, establishing new best-known lower bounds for 64 instances. Additionally, the algorithm is tested on classical VRPSPD instances from the literature, further confirming its ability to consistently provide high-quality solutions within reasonable computational time.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2658775</guid>
    </item>
    <item>
      <title>Multi-Objective Optimization for Multimodal Multi-Objective Multi-Point Shortest Path Problem Considering Unforeseeable Road Eventualities</title>
      <link>https://trid.trb.org/View/2561902</link>
      <description><![CDATA[Multi-objective multi-point shortest path planning problems are commonly encountered in real-world applications. Numerous path planning algorithms have been proposed to accommodate different model assumptions. However, most existing algorithms can only identify a subset of the Pareto optimal paths and overlook equivalent Pareto optimal paths. Relying solely on a subset of Pareto optimal solutions is insufficient to effectively respond to unforeseeable road eventualities in the real-world traffic environment. In this paper, multi-objective multi-point shortest path planning problem is modeled as a multimodal multi-objective optimization problem with necessary points constrains. A multimodal multi-objective evolutionary algorithm using constraint dominance principle-based path comparison strategy and path similarity-based multimodal solutions selection strategy is proposed to address this problem. The proposed constraint dominance principle-based path comparison strategy can effectively navigate through large infeasible regions by relaxing necessary point constraints, thereby obtaining a true constrained Pareto front. The proposed path similarity-based multimodal solutions selection strategy can effectively balance the distribution of solutions in the decision space, thereby preserving multiple equivalent optimal solutions. The proposed algorithm is compared with five state-of-the-art path planning algorithms from the benchmark test suite derived from the 2021 IEEE CEC path planning competition, where city maps are adapted from real transportation networks in Chinese cities, in our experiments. The exceptional performance is demonstrated through thirty independent runs, yielding experimental results that showcase the superiority of the proposed algorithm on the test problem set. This superior performance highlights the potential for designing more resilient path planners suitable for scenarios affected by unpredictable road eventualities.]]></description>
      <pubDate>Mon, 23 Mar 2026 17:14:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2561902</guid>
    </item>
    <item>
      <title>A Two-Stage Intelligent Optimization Method for Highway Alignment</title>
      <link>https://trid.trb.org/View/2613307</link>
      <description><![CDATA[As the core of highway construction, highway alignment design has a significant impact on the entire life cycle of the road. However, the conventional method is not only time-consuming and labor-intensive but also fails to guarantee an optimal solution. To address the issue, a two-stage highway alignment intelligent optimization method is proposed. In the first stage, the improved Lazy Theta* is proposed and utilized to determine an initial alignment by losing grid accuracy. In the second stage, combining the initial alignment with a Gaussian Mixture Model, a probabilistic roadmap (PRM) is obtained using high grid accuracy, enhancing the quality of highway alignment. Subsequently, the improved Lazy Theta* is reused to search final optimal highway alignment in PRM. Finally, the effectiveness of the proposed method is verified through a real-world case. The results demonstrate that the proposed method can explore and optimize highway alignment within minutes and reduce construction costs compared to the manual design, satisfying design specifications.]]></description>
      <pubDate>Fri, 20 Mar 2026 14:10:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2613307</guid>
    </item>
    <item>
      <title>A Graph Transformer Model with Shortest Path Information for Developing Data-Driven Traffic Assignment Solutions</title>
      <link>https://trid.trb.org/View/2656971</link>
      <description><![CDATA[Recently, data-driven traffic assignment solutions have been developed using different graph-based neural network architectures. Although analytical and simulation-based solutions rely on the shortest paths to find optimal routes between nodes in the network, shortest path information is not included in data-driven solutions. In this paper, the authors develop a novel graph transformer model that uses shortest path information as edge features to generate data-driven traffic assignment solutions. The model utilizes a dynamic attention module to effectively capture the impact of links that are present in shortest paths between node pairs and enhances the model’s ability to learn the varying importance of different links in the network. The authors run numerical experiments on two networks (Sioux Falls and Eastern-Massachusetts) and show that adding shortest path information outperforms state-of-the-art neural network models in predicting link flows (9.65% and 3.92% improvement of RMSE for Sioux Falls and Eastern-Massachusetts networks, respectively compared to a graph transformer model without shortest path information). The authors also develop a transfer learning method and test it using a model trained on Sioux Falls network to predict link flows of Eastern-Massachusetts network and find that shortest path information enhances the generalization capability of the model to different networks. Finally, the authors present an analysis of whether the predicted flows are close to equilibrium flows. Thus, by improving prediction accuracy and generalization capability, the developed approach based on graph transformer and shortest path information enhances the current solutions of data-driven traffic assignment problems.]]></description>
      <pubDate>Tue, 17 Feb 2026 10:30:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2656971</guid>
    </item>
    <item>
      <title>Ride-sharing matching and route planning based on adjustable passenger pick-up and drop-off points</title>
      <link>https://trid.trb.org/View/2618862</link>
      <description><![CDATA[This paper addresses the joint ride-sharing matching and route planning problem, in which passenger pick-up and drop-off points are adjustable to reduce vehicle detours. We first formulate the problem as a mixed-integer linear programming (MILP) model and demonstrate its NP-hardness. The model incorporates practical constraints, such as passenger travel-time windows, and simultaneously optimizes ride-sharing matches, pick-up and drop-off points, and vehicle routes to minimize total system costs (including vehicle activation costs, vehicle travel costs, passenger travel-time window penalties, and passenger time cost). Given the inefficiency of commercial solvers in solving the MILP model and the need for efficient computation in real-world cases, we introduce the concept of “spatial-temporal order similarity” to decompose large-order demands into data clusters for parallel computing. For each data cluster, we develop a two-stage memory search-dynamic programming algorithm. In the first stage, an improved memory search algorithm is proposed to address ride-sharing matching and route planning with fixed pick-up and drop-off points. In the second stage, a specialized dynamic programming algorithm is developed to select the optimized pick-up and drop-off points for ride-sharing. A feedback mechanism is introduced between the two stages, whereby if the second stage fails to reduce total system costs, it will return to the first stage to re-optimize ride-sharing matching and route planning. We apply the MILP model and algorithms within the areas of the Beijing Fifth Ring Road. Experimental results demonstrate a 51.10 % reduction in vehicle travel distance and a 1124.53 kg decrease in CO₂ emissions compared to conventional ride-hailing services. Furthermore, the MILP model reduces total platform operating costs by 2.08 % compared to fixed pick-up/drop-off systems, underscoring its economic and environmental advantages.]]></description>
      <pubDate>Thu, 12 Feb 2026 08:51:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2618862</guid>
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