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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>
    <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>Multi-voyage vessel routing and scheduling under nonlinear metocean-induced speed variation: A bidirectional A* and genetic-algorithm-embedded label-correcting framework</title>
      <link>https://trid.trb.org/View/2721525</link>
      <description><![CDATA[Vessel sailing speed at sea is affected nonlinearly by heading, winds, and ocean currents. Consequently, the geometrically shortest path between two locations is not necessarily the fastest one, and distance-based travel times can misrepresent operational feasibility. These effects become more challenging in coordinated multi-vessel planning, where routing decisions must also satisfy limited endurance with port-only replenishment, multi-voyage organization, and task time windows. Motivated by this setting, this paper studies a meteorology-aware multi-voyage routing and scheduling problem for a homogeneous fleet with intermediate port replenishment. We formulate the problem as a mixed-integer linear programming model that jointly represents task selection, vessel assignment, visit sequencing, voyage partitioning, and replenishment insertion. To solve it efficiently, we develop a hybrid framework that combines a meteorology-aware bidirectional A* routing module with a genetic algorithm embedding a label-correcting evaluator (GA–LC). The bidirectional A* routing module computes meteorology-aware point-to-point sailing paths, while GA–LC determines task selection, vessel assignment, visit sequencing, voyage partitioning, and replenishment decisions to construct feasible multi-voyage fleet schedules. Computational results show that meteorology-aware routing changes both route geometry and sailing time, and that ignoring meteorology causes about 10% loss in planned task execution, along with increased waiting time and longer voyage durations under realistic conditions.]]></description>
      <pubDate>Mon, 27 Jul 2026 09:46:44 GMT</pubDate>
      <guid>https://trid.trb.org/View/2721525</guid>
    </item>
    <item>
      <title>Electric vehicle charging optimization with coordinated mobile and fixed chargers</title>
      <link>https://trid.trb.org/View/2605014</link>
      <description><![CDATA[Mobile Chargers (MCs) enhance the flexibility and convenience of Electric Vehicle (EV) charging by enabling spatial–temporal power transfer, yet their effectiveness depends on optimal recharging strategies. This study addresses the EV charging problem using both fixed chargers (FCs) and MCs, considering the recharging of MCs at FC sites. A mixed-integer programming model is developed to integrate the operation of FCs and MCs, taking into account several practical considerations, including the time constraints of EV charging requests, the limited capacity of FCs, and the need to recharge MCs between serving EVs. A two-layer adaptive large neighborhood search algorithm is designed with problem-tailored removal and insertion operators. Computational experiment with instances constructed using real world charging data demonstrate the effectiveness of the proposed algorithm and the tailored operators. Managerial insights into operation efficiency are also provided, especially concerning the battery size of MCs and geographic distributions of FCs.]]></description>
      <pubDate>Wed, 17 Jun 2026 16:14:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/2605014</guid>
    </item>
    <item>
      <title>Optimization of urban logistics with multi-modal systems: A comprehensive study of the airship-vehicle routing problem</title>
      <link>https://trid.trb.org/View/2604735</link>
      <description><![CDATA[This paper presents a novel approach to the Airship-Vehicle Routing Problem (AVRP) with drone integration, aiming to optimize the delivery logistics in urban and rural environments. The model leverages a hybrid fleet comprising airships, ground vehicles, and drones, each with distinct operational capabilities and constraints. The airships serve as mobile hubs, deploying drones for last-mile deliveries, while ground vehicles handle bulk transportation from depots to intermediate nodes. The proposed Mixed-Integer Linear Programming (MILP) model minimizes the total cost by considering the travel distances, operational costs, and delivery times for each vehicle type. The constraints ensure feasible routes for ground and aerial vehicles, adherence to capacity limits, and efficient synchronization between drones and their respective airships. Computational experiments on realistic datasets demonstrate the model’s efficacy in reducing delivery costs and improving service levels compared to traditional routing methods. This study highlights the potential of integrating advanced transportation modes to enhance logistics operations, offering significant benefits for industries reliant on timely and cost-effective deliveries.]]></description>
      <pubDate>Wed, 17 Jun 2026 16:14:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/2604735</guid>
    </item>
    <item>
      <title>Model-based variable speed limit control on wireless charging lanes: Formulation and algorithm</title>
      <link>https://trid.trb.org/View/2618117</link>
      <description><![CDATA[This paper addresses the variable speed limit (VSL) control problem on wireless charging lanes (WCLs). We first introduce a predictive model to describe the evolution of both traffic flow and the state of charge of electric vehicles, considering the impact of VSL control. The model is formulated as a piecewise affine system through various linearization techniques. Subsequently, we propose a control model that accounts for both traffic and charging efficiencies. By employing a hybrid model predictive control approach, the control problem at each stage is cast as a mixed-integer linear programming (MILP) problem. To expedite the MILP problem, we propose an innovative learning-based algorithm, termed Learning from K-nearest Neighbors mode sequences (LKNMS). The algorithm identifies and eliminates predicted inactive system states (each represented by a binary variable) by leveraging historical solutions of the binary configuration. It thereby significantly reduces the size of the resulting MILP problem. We conduct a series of numerical examples on a 10.7 km WCL to test the proposed control model and algorithm. Our simulation results reveal that VSL control can significantly affect the charging efficiency of WCLs, particularly under light traffic conditions. Moreover, an inherent conflict between traffic efficiency and charging efficiency consistently arises on WCLs. The proposed algorithm significantly reduces the computational time of the MILP problem from 46 % of the 60s control cycle to 5∼12%, without compromising closed-loop performance, which implies strong potential for real-time implementation. We further test the proposed algorithm on a 26.75 km WCL to confirm its robust scalability to large-scale networks.]]></description>
      <pubDate>Wed, 17 Jun 2026 16:14:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2618117</guid>
    </item>
    <item>
      <title>Equivalent ellipsoid method for efficient prediction of viscous pressure resistance of ship</title>
      <link>https://trid.trb.org/View/2702700</link>
      <description><![CDATA[In the rapid evaluation of ship resistance, while frictional and wave-making resistances possess independent, forward prediction methods, viscous pressure resistance (R[subscript pv]) typically requires implicit coupled solving (e.g., CFD) or backward extraction from known data (e.g., the 1+k method). This reliance hinders the efficient prediction of total resistance. Therefore, based on the physical mechanism of R[subscript pv], this research proposes the Equivalent Ellipsoid Method (EEM) as a direct and independent prediction approach. EEM constructs a geometric mapping framework between the streamlined afterbody geometry and an equivalent ellipsoid. This enables the direct utilization of existing ellipsoid resistance databases to predict R[subscript pv], thereby enriching the classical 1+k method. The proposed method is implemented on an underwater streamlined vehicle (SUBOFF) and two conventional surface vessels (KCS, KVLCC2). It is observed that the mean relative error (MRE) of the simple case (SUBOFF) is around 4%. The calculated R[subscript pv] of KCS and KVLCC2 predominantly fall within the range established by existing studies, with the |MRE| being 5.98% and 7.04% respectively. Satisfactory agreement observed in these applicable test cases offers validation of the EEM for conventional streamlined hulls. By combining existing independent methods for frictional and wave-making resistances with the proposed EEM, a complete rapid evaluation framework for total hull resistance can now be established for conventional streamlined vessels. This contributes to vessel speed prediction, preliminary hull form design, and ship dynamic modeling.]]></description>
      <pubDate>Thu, 04 Jun 2026 11:56:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2702700</guid>
    </item>
    <item>
      <title>Scheduling electric vehicles by simulated annealing with recombination through ILP</title>
      <link>https://trid.trb.org/View/2685623</link>
      <description><![CDATA[In this paper, we consider the electric vehicle scheduling problem (e-VSP): a set of trips corresponding to a given timetable have to be driven by a set of electric buses with limited capacity. This problem, like many other planning problems, boils down to assigning to each bus a subset of the trips with the obvious side constraint that the selected subset can be feasibly driven by this single bus; such a feasible subset is called a vehicle schedule. If we know all possible vehicle schedules, we can select the best set of these by solving an integer linear program (ILP). This idea has inspired many researchers to the heuristic of finding a decent subset of all vehicle schedules using the technique of column generation and then solve the ILP. For the e-VSP, this approach leads to good solutions, but there is still room for improvement. Instead of using column generation, we apply simulated annealing to find the subset of vehicle schedules that we use as input for the ILP. For the e-VSP, this leads to better solutions. Moreover, this approach, which we call simulated annealing with recombination through ILP, is generally applicable and has as a clear advantage that we do not have to solve the pricing problem, because this approach increases the application possibilities and takes far less time.]]></description>
      <pubDate>Wed, 20 May 2026 10:20:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685623</guid>
    </item>
    <item>
      <title>A platoon formation algorithm for intersections with blue phase control in mixed traffic</title>
      <link>https://trid.trb.org/View/2616215</link>
      <description><![CDATA[ncreasing attention is being paid to intersection signal control with cooperative platoons. Assuming platoons being formed, such platoons cannot only improve the intersection capacity but also minimize the number of control units, especially when dedicated connected and automated vehicle (CAV) lanes are considered. However, the platoon formation process is often neglected, especially for lane-changing and overtaking maneuvers in mixed traffic. This may jeopardize the potential of signal control with platoons. This article proposes a platoon formation algorithm that computes the optimal lane, platoon sequence, and speed profiles of CAVs under the requirement of the central traffic controller. The algorithm is designed for mixed traffic conditions and hence the performance of human-driven vehicles is also considered. A mixed integer linear program model is formulated to minimize the deviation from the desired platoon configuration and the disturbance to overall traffic under any arbitrary initial condition. Numerical experiments are designed to test the effectiveness and the computational performance of the proposed algorithm. Results show that CAVs with signal control can form platoons with rational motion. Besides, the platoon penetration significantly affects platooning feasibility, while the platoon length does not. This suggests that CAVs can form long platoons at intersections to improve traffic throughput.]]></description>
      <pubDate>Thu, 07 May 2026 11:02:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2616215</guid>
    </item>
    <item>
      <title>A two-stage linear programming model for efficient optimization of public transport network design</title>
      <link>https://trid.trb.org/View/2682073</link>
      <description><![CDATA[Public transport is essential for sustainable mobility, and optimized network design is crucial for making it competitive with other modes. While several network optimization methods exist, their practical use is limited due to assumptions, simplifications, and limited scope. This paper proposes a two-stage sequential linear programming approach for the transit network design and frequency setting problem that significantly cuts down computation times, enabling the solution of more comprehensive models. The first stage routes passengers along shortest paths, selects infrastructure to operate, and sets minimum aggregated frequencies. The second stage determines line designs and sets frequencies to minimize waiting times and transfers for passengers. We also introduce symmetry-breaking constraints to further speed up the optimization process. We evaluate the model performance using Mandl’s network by comparing it with a simultaneous optimization model in multiple scenarios (balancing operator and passenger objectives). Results show that the sequential optimization model runs over 180 times faster for operator-focused scenarios, while passenger-focused scenarios do not yet achieve the predicted computational efficiency. The sequential optimization model shows losses of 0.3-7.0-% optimality compared to the simultaneous optimization model. Overall, the model shows potential in significantly reducing the complexity of the transit network design problem.]]></description>
      <pubDate>Mon, 27 Apr 2026 15:01:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2682073</guid>
    </item>
    <item>
      <title>Integrated optimization of timetabling and electric multiple unit (EMU) circulation on a new connecting line to the high-speed railway (HSR) network</title>
      <link>https://trid.trb.org/View/2654569</link>
      <description><![CDATA[This study optimizes train timetables and electric multiple unit (EMU) circulation plans for the newly built high-speed railway (HSR) connecting line, addressing the challenge of integrating new infrastructure into existing networks without disrupting current operations. We propose an integer linear programming model that joins timetabling and EMU circulation planning by generating candidate train sets (extended and cross-line trains) and incorporating flow, time, and spatial demand constraints. Validated on a real-world connecting line in the Yangtze River Delta, China, the model reduces EMU usage by 2.17% and total connection time by 5.02% compared to manual planning while fully meeting travel demands. Sensitivity analyses highlight dynamic trade-offs between demand fluctuations, station capacities, and resource efficiency. The framework provides a scalable tool for the cost-effective commissioning of new lines.]]></description>
      <pubDate>Tue, 21 Apr 2026 14:30:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2654569</guid>
    </item>
    <item>
      <title>BMG-Q: Localized Bipartite Match Graph Attention Q-Learning for Ride-Pooling Order Dispatch</title>
      <link>https://trid.trb.org/View/2617726</link>
      <description><![CDATA[This paper introduces Localized Bipartite Match Graph Attention Q-Learning (BMG-Q), a novel Multi-Agent Reinforcement Learning (MARL) algorithm framework tailored for ride-pooling order dispatch. BMG-Q advances ride-pooling decision-making process with the localized bipartite match graph underlying the Markov Decision Process, enabling the development of novel Graph Attention Double Deep Q Network (GATDDQN) as the MARL backbone to capture the dynamic interactions among ride-pooling vehicles in fleet. Our approach enriches the state information for each agent with GATDDQN by leveraging a localized bipartite interdependence graph and enables a centralized global coordinator to optimize order matching and agent behavior using Integer Linear Programming (ILP). Enhanced by gradient clipping and localized graph sampling, our GATDDQN improves scalability and robustness. Furthermore, the inclusion of a posterior score function in the ILP captures the online exploration-exploitation trade-off and reduces the potential overestimation bias of agents, thereby elevating the quality of the derived solutions. Through extensive experiments and validation, BMG-Q has demonstrated superior performance in both training and operations for thousands of vehicle agents, outperforming benchmark reinforcement learning frameworks by around 10% in accumulative rewards and showing a significant reduction in overestimation bias by over 50%. Additionally, it maintains robustness amidst task variations and fleet size changes, establishing BMG-Q as an effective, scalable, and robust framework for advancing ride-pooling order dispatch operations.]]></description>
      <pubDate>Tue, 24 Mar 2026 16:23:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2617726</guid>
    </item>
    <item>
      <title>A static mixed bike repositioning problem with both man-powered bikes and e-bikes</title>
      <link>https://trid.trb.org/View/2594376</link>
      <description><![CDATA[The rapid expansion of bike-sharing systems has significantly enhanced travel convenience and the sustainability of urban development. In addition to man-powered bikes, transport solutions are further enriched with the introduction of electric bicycles (e-bikes) to the existing bike-sharing system to cater to diverse needs. However, managing such a bike-sharing system with both man-powered bikes and e-bikes presents a complex challenge, consisting of addressing the imbalance between bike supply, dock supply, and user demand, as well as recharging low-battery e-bikes. No studies have so far designed efficient rebalancing and recharging schemes to tackle the challenge. Therefore, this study presents a novel static bike repositioning problem that considers both man-powered bikes and e-bikes while allowing low-battery e-bikes to recharge at charging stations. A mixed-integer linear programming model is developed to minimize the total penalty cost due to the deviation from the target inventory of man-powered bikes and e-bikes at each station and the total fixed cost, and the total travel cost of repositioning vehicles within the time budget. Numerical experiments are conducted using a commercial solver on small instances to show the effects of different percentages (or numbers) of usable and low-battery e-bikes initially in the system. An efficient hybrid genetic search framework is also designed to provide good solutions to the problem under large instances. This algorithmic framework is developed based on a Hybrid Genetic Search with Advanced Diversity Control and can incorporate one of the two proposed loading strategies, namely, exact and rebalancing-first-collection-second methods. Computational experiments based on real data validate the performance of the proposed hybrid algorithm with the two loading strategies.]]></description>
      <pubDate>Wed, 11 Mar 2026 14:44:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2594376</guid>
    </item>
    <item>
      <title>Multi-objective optimization for transportation mode selection: A case study in logistics</title>
      <link>https://trid.trb.org/View/2666723</link>
      <description><![CDATA[Selection of transportation mode (air, sea, road etc.) for sourcing goods is a critical decision in supply chain management because it directly affects cost efficiency, service flexibility and reliability. Freight cost and delivery lead time are two major decision criteria that dictates the selection process. Traditional methods often fail to account for the dynamic nature of the decision criteria in constrained logistics infrastructure. To address the challenges is logistics decision making, this study proposed a data driven multi-objective optimization (MOO) framework using Mixed-Integer Linear Programming (MILP) to minimize freight cost and lead time simultaneously. The study also developed a weighted sum approach to optimize the selection process. MATLAB intlinprog solver was utilized to solve the optimization problem and find the best route and sensitivity analysis was performed to check the model robustness and response to changes in the weight of decision criteria. A case study of 504 KG yarn shipment from India to Bangladesh was employed to validate the model effectiveness. The results highlighted that the road is the best option that minimizes freight cost and lead time for most of the cases for a given freight charges and lead time in this shipment route. The adoption of this model ensured tangible benefits for the company in terms of freight cost reduction. This optimization model could be an effective tool for flexible decision support system in logistics transportation system.]]></description>
      <pubDate>Tue, 17 Feb 2026 13:11:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2666723</guid>
    </item>
    <item>
      <title>Optimizing on-site green hydrogen consumption using heavy-duty hydrogen fuel cell electric vehicles</title>
      <link>https://trid.trb.org/View/2652804</link>
      <description><![CDATA[Transitioning to zero-emission heavy-duty freight vehicles, such as fuel cell electric vehicles (FCEVs), could contribute to mitigating the environmental footprint in the freight sector. However, sustainable and economical production, transportation, and storage of hydrogen fuels remain challenging. The goal of this study is to leverage the flexibility of logistics delivery to maximize the utilization of green hydrogen while balancing the freight operational costs. To achieve this goal, we develop a mixed integer programming (MIP) model that optimizes the planning, routing/scheduling, refueling, and on-site “green” hydrogen production to support the operations of FCEVs. The model strategically determines the locations and sizes of hydrogen refueling stations (HRSs) and the corresponding vehicle routing/refueling plans to minimize total costs, while utilizing solar-powered on-site hydrogen generation in freight operations. Our model was initially tested using the Solomon benchmark and later implemented in a real-world case study of freight distribution systems in Florida to evaluate its robustness and efficiency. Computational performance between exact methods and a customized Adaptive Large Neighborhood Search (ALNS) metaheuristic algorithm are also compared. Extensive sensitivity analyses were conducted on operational and environmental parameters to generate numerical insights. We found that strategic trade-offs in routing the FCEV fleet and placing HRSs, coupled with optimized solar hydrogen production, substantially reduce operational costs and enhance sustainability.]]></description>
      <pubDate>Mon, 02 Feb 2026 09:32:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2652804</guid>
    </item>
    <item>
      <title>Joint optimization of flood water routing and congestion-aware evacuation scheduling</title>
      <link>https://trid.trb.org/View/2652803</link>
      <description><![CDATA[Urban flood emergencies pose significant risks to human safety and infrastructure operability, particularly in smart cities with interdependent systems. This study proposes an integrated optimization model for coordinating water and transportation networks during flood evacuations. The model simultaneously determines optimal reservoir discharge rates and dynamic vehicular evacuation schedules to maximize the number of evacuees within the limited warning time. Water flow is modeled using the Muskingum-Cunge flood-routing method to simulate flood propagation through a river-reservoir system, while traffic flow is captured via the Cell Transmission Model, which accounts for congestion dynamics and road capacities. The problem is formulated as a nonlinear program and solved through a linear relaxation using generalized Benders decomposition. A case study of the Town of High River, Canada, illustrates the model’s practical utility. Results show that the integrated strategy extends warning times, reduces congestion, and lowers the number of individuals exposed to flood risks compared to uncoordinated approaches. By enabling real-time, infrastructure-aware evacuation planning, the proposed framework offers a scalable decision-support tool for emergency managers. This work contributes to the growing body of research on the management of city infrastructures under disruption and supports the development of resilient and coordinated evacuation strategies in smart urban environments.]]></description>
      <pubDate>Mon, 02 Feb 2026 09:32:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2652803</guid>
    </item>
    <item>
      <title>Column generation-based matheuristic for pickup and delivery problem with two-dimensional loading</title>
      <link>https://trid.trb.org/View/2611564</link>
      <description><![CDATA[The pickup and delivery problem (PDP) is a well-known NP-complete problem that aims to minimize route length while satisfying all customer requests. While PDP has traditionally considered one-dimensional loading constraints such as weight, this study addresses more realistic loading constraints by incorporating rectangular items. This study restricted item rotation and reloading by assuming that all the items are too heavy to move. A column generation-based matheuristic is proposed to construct the routing sequence. This is followed by a packing algorithm that includes a mixed-integer linear programming model, a constraint programming model, and an open space-based packing heuristics to verify whether the items can be packed based on the route. Finally, logic-based Benders decomposition was applied to refine the routing sequence. When tested against benchmark instances and compared with a state-of-the-art algorithm, the proposed algorithm performed better.]]></description>
      <pubDate>Thu, 15 Jan 2026 09:11:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2611564</guid>
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