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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
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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>
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      <title>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Electric or fuel? Green routing optimization with time uncertainty in fourth-party logistics</title>
      <link>https://trid.trb.org/View/2702209</link>
      <description><![CDATA[This paper investigates a green fourth-party logistics (4PL) routing optimization problem, incorporating transportation time uncertainty and the strategic choice between fuel vehicles (FVs) and electric vehicles (EVs) to reduce carbon emissions. To ensure timely delivery under time uncertainty, a mixed-integer chance-constrained programming (MICCP) model is developed. Given limited historical data, the MICCP model is reformulated through sample average approximation method and a Sample Mean-based Heuristic (SMH) algorithm is further proposed, which works as a data-driven method for potentially broader chance constrained programming with left-hand uncertainty. Numerical study validates the effectiveness of proposed models and algorithm, and offers managerial insights on balancing electric and fuel vehicle usage. We find that (1) stricter carbon tax policies do not necessarily motivate 4PLs to adopt EVs for emission reduction; (2) increasing switching time between transport providers will weaken the pooling advantages of 4PL mode, but encouraging the adoption of EVs.]]></description>
      <pubDate>Wed, 20 May 2026 09:10:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2702209</guid>
    </item>
    <item>
      <title>Macroscopic Flow Control of Connected and Automated Vehicles at Signalized Intersections</title>
      <link>https://trid.trb.org/View/2646689</link>
      <description><![CDATA[To fully leverage connected automated vehicle (CAV) technology for improving traffic flow at signalized intersections, this paper addresses the scalability limitations of traditional microscopic control methods. We propose a macroscopic connected automated flow control (CAFC) framework based on the cell transmission model (CTM), which formulates the vehicle sorting problem as a computationally efficient Mixed-Integer Quadratically Constrained Program (MIQCP). Numerical experiments, comparing our CAFC strategy against a traditional dedicated-lane benchmark, demonstrate a throughput improvement of approximately 63%. The framework also shows strong robustness in dynamic scenarios with mismatched traffic demand and signal timings, consistently outperforming a stronger, demand-responsive baseline. The results indicate that macroscopic flow control offers a scalable and highly effective alternative to microscopic methods for real-time traffic management in pure CAV environments.]]></description>
      <pubDate>Mon, 18 May 2026 16:36:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2646689</guid>
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    <item>
      <title>Integrated Container Slot Allocation and Automated Stacking Crane Scheduling in Automated Container Terminals with Limited Buffers</title>
      <link>https://trid.trb.org/View/2703736</link>
      <description><![CDATA[This paper investigates the integrated scheduling of automated stacking cranes (ASCs) and container slot allocation in automated container terminals (ACTs) with limited buffer capacity. A hybrid stacking strategy based on time windows is proposed, and a bi-objective mixed-integer programming model is developed, considering automated guided vehicles and truck buffer capacities, ASC safety distances, and handshake area operations. An enhanced non-dominated sorting genetic algorithm II with tabu search (NSGA-II-TS) is designed, with parameters optimized via sensitivity analysis. Experiment results show that comparisons with an exact mixed-integer linear programming solver validate the solution quality of the proposed approach, and that the proposed algorithm significantly outperforms benchmark heuristic methods in generating high-quality Pareto-optimal solutions. Case studies reveal that dynamically adjusting handshake area locations and setting buffer capacity to six units effectively balance container flow and operational costs. The proposed approach is also validated against two alternative scheduling strategies, demonstrating superior effectiveness. This research provides new strategies and a robust method for improving the operational efficiency of ACTs under buffer constraints.]]></description>
      <pubDate>Sat, 16 May 2026 12:15:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2703736</guid>
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    <item>
      <title>New Models and Solutions to Vehicle Routing with Cardinality and Distance Constraints</title>
      <link>https://trid.trb.org/View/2703788</link>
      <description><![CDATA[Many emerging transportation and logistics operations are constrained by both the maximum distance a vehicle can travel and the number of customers it can serve before requiring replenishment, recharging, or maintenance. These operational realities motivate the need for new routing optimization models that explicitly integrate distance and cardinality constraints. This project proposes the first comprehensive study of a novel Black-and-White Vehicle Routing Problem (BWVRP), where customer nodes and replenishment nodes are jointly routed across a fleet of vehicles, with replenishment nodes allowed to be visited multiple times. The project will develop new mixed-integer linear programming models and exact branch-and-cut methods to obtain optimal solutions for small and medium-sized instances. To address large-scale instances, efficient heuristic and metaheuristic algorithms will be designed and implemented. In addition to methodological advances, the project will develop a data-driven optimization decision-support tool integrating models, algorithms, and user-friendly interface. 
]]></description>
      <pubDate>Sat, 16 May 2026 11:45:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2703788</guid>
    </item>
    <item>
      <title>Route-Constrained Optimization Models for Electric School Bus Allocation and Stop Sequencing</title>
      <link>https://trid.trb.org/View/2701299</link>
      <description><![CDATA[This study underscores the growing need to transition from conventional school buses powered by internal combustion engines to electric school buses (ESBs). We propose mixed integer programming (MIP)-based approaches to optimize the allocation of ESBs to predetermined routes while determining their travel paths and bus stop sequences. The problem formulation incorporates heterogeneous ESBs with mixed student loading. Essential factors such as bus seating capacity, battery capacity, energy consumption cost, and procurement cost are considered to determine optimal bus selection and allocation strategies that minimize total expenses. To address this challenge, we introduced two methodologies: a MIP-based optimization model and a two-stage hybrid model that integrates optimization with heuristic approaches. We evaluate the efficiency of the proposed approaches through nineteen computational scenarios and compare their performance. The results indicate that the hybrid model significantly improves computational efficiency for large-scale problem instances, demonstrating its potential as a scalable tool for strategic planning.]]></description>
      <pubDate>Fri, 15 May 2026 09:18:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2701299</guid>
    </item>
    <item>
      <title>Optimization of electrified railway operations: Energy efficiency, travel time, and regenerative energy utilization</title>
      <link>https://trid.trb.org/View/2672092</link>
      <description><![CDATA[Electrified railway systems are a cornerstone of sustainable transportation due to their energy efficiency and low emissions. Enhancing energy management during train operations by minimizing power consumption and travel time while maximizing the utilization of regenerative braking energy is essential for improving both operability and environmental performance. This study formulates a multi-objective optimization problem as a Mixed-Integer Nonlinear Programming (MINLP) model aimed at achieving these objectives concurrently. Numerical analysis based on different electrified railway case studies demonstrates the effectiveness of the proposed approach, revealing substantial reductions in energy consumption and travel time, along with improved use of regenerated energy. The optimized solution achieved a regeneration percentage of 17.98%, which is significantly higher compared to all other case studies. The results highlight the potential for broad implementation of the framework in urban rail systems to support more sustainable and efficient railway operations.]]></description>
      <pubDate>Wed, 13 May 2026 09:33:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2672092</guid>
    </item>
    <item>
      <title>Evaluation of reserved loading areas for last mile delivery</title>
      <link>https://trid.trb.org/View/2668521</link>
      <description><![CDATA[The increasing volume of parcels leads to challenges due to unauthorized parking especially in cities. This paper considers the concept of loading areas which are the only allowed parking spaces for delivery services. Loading areas are installed by cities and include one or several parking spaces. Via a smartphone app these parking spaces can be reserved and opened (bollards) by companies or drivers. We introduce a model to collaboratively assign parking times in loading areas. The model is investigated in a comprehensive computational study and compared with the state-of-the-art, where delivery services may stop in unauthorized positions, and loading areas without collaborative reservation with a first-come-first-serve scheme. Our results show that the concept can be a useful solution for the trade-off between safety (no unauthorized parking) and tour duration. Tour durations can be improved substantially by collaborative planning, an increased number of loading areas and parking spaces, and high delivery speeds between loading areas and customers.]]></description>
      <pubDate>Mon, 11 May 2026 08:50:46 GMT</pubDate>
      <guid>https://trid.trb.org/View/2668521</guid>
    </item>
    <item>
      <title>Enhancing Efficiency and Fairness: Rolling Stock Scheduling with Virtual Coupling and Short-Turning Strategies</title>
      <link>https://trid.trb.org/View/2646850</link>
      <description><![CDATA[In this paper, focusing on an urban rail transit line, we propose a novel operational mode that combines the short-turning strategy and flexible train composition with the technology of virtual coupling, in order to effectively match transportation capacity with passenger demand in both time and space dimensions, while ensuring fairness in service provision. Using a spatio-temporal representation, we formulate a Mixed Integer Linear Programming model that generates the rolling stock schedule with virtual coupling and short-turning strategies, with the goal of optimizing the operational cost, passenger service quality, and fairness. Considering the complexity of the model in addressing large-scale problems, we develop three types of exact algorithms, that is, Branch-and-Benders-cut (BBC), Section-based decomposition (SBD), and Branch-and-Section-Add (BSA), on the basis of the mathematical properties of the proposed model. To validate the proposed approaches, a series of experiments are conducted by using the real operation data from Xi’an metro Line 3. Experimental results demonstrate that our designed algorithms are able to solve the large-scale instance effectively, outperforming the commercial solver. Moreover, our presented strategy also exhibits superior performance in comparison with the currently used operation scheme on this experimental metro line. © 2025 INFORMS.]]></description>
      <pubDate>Wed, 22 Apr 2026 16:15:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2646850</guid>
    </item>
    <item>
      <title>Integrated routing optimization of pilotage and tugging services</title>
      <link>https://trid.trb.org/View/2656086</link>
      <description><![CDATA[Seaports are important connections between inland and maritime transportation. During the vessels’ entering/leaving ports, the pilotage service is necessary to mitigate risk, especially for large vessels and congested ports. In the pilotage process, pilots are transported by pilot boats to board the vessels and provide guidance until the vessels’ arriving/leaving the berths, and tugboats are in charge of providing horsepower for vessels to move safely near the port. In this paper, a joint optimization problem considering the pilotage and tugging services is studied. Realistic constraints, including the multi-waypoints of tugboats, required service time windows of vessels, different types of tugboats and pilots. A mixed integer programming model is introduced, and small-size instances are solved by CPLEX solver. To solve large-scale instances, an adaptive large neighborhood search algorithm with linear programming models (ALNS-LP) together with a tailored feasibility check procedure and cost evaluation process is proposed. Extensive computational experiments are conducted to verify efficiency and effectiveness of the algorithm and obtain some managerial insights for port operators.]]></description>
      <pubDate>Tue, 21 Apr 2026 14:30:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2656086</guid>
    </item>
    <item>
      <title>Assessing a Sustainable Aviation Fuel Supply Chain from Winter Canola and Its Carbon Intensity Considering Feedstock Yield Variations</title>
      <link>https://trid.trb.org/View/2685583</link>
      <description><![CDATA[Replacing conventional aviation fuel (CAF) with sustainable aviation fuel (SAF) has been suggested as a vital means to decarbonize the aviation industry. We considered winter canola a feedstock for SAF production through the hydro-processed esters and fatty acids pathway in the Southeast United States. We incorporated feedstock yield variations in a stochastic mixed-integer linear programming model to optimize the SAF supply chain. Results suggest the potential SAF production for Nashville International Airport (BNA), from an existing oil extraction mill and 0.19 million hectares (ha) of winter canola cultivation, range from 129 million liters yearly (MLY) to 246 MLY with a 90 % likelihood. With additional investment and expanded winter canola cultivation to 0.61 million ha, the expected SAF supply to BNA could increase to 348 MLY. An additional 212 MLY can be supplied to Memphis International Airport. After considering the co-product revenues, the expected breakeven cost for SAF is $1.1 per liter. The SAF's carbon intensity falls into the range from 31 g of carbon dioxide equivalent per megajoule of fuel (g CO₂e MJ−1) to 43 g CO₂e MJ−1, with 90 % probability, which is at least 50 % lower than CAF.]]></description>
      <pubDate>Mon, 13 Apr 2026 16:48:09 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685583</guid>
    </item>
    <item>
      <title>Spatial Optimization of the Sustainable Aviation Fuel Supply Chains from Forest Residues via Fast Pyrolysis/Hydrotreatment Considering Feedstock Ash Content Variability</title>
      <link>https://trid.trb.org/View/2685506</link>
      <description><![CDATA[Policymakers and the aviation industry are working to decarbonize commercial flights by replacing conventional jet fuel with sustainable aviation fuel (SAF). Logging residues have been identified as a valuable resource for SAF production. However, the quality of the feedstock, particularly the ash content, can adversely affect bio-oil yield and SAF production using the fast pyrolysis/hydrotreatment process, and potentially its supply chain optimization. Previous research often assumes fixed biofuel yields and neglects the variability in feedstock quality when optimizing the supply chain. Thus, this study seeks to address this gap in the literature by employing a two-stage mixed-integer linear programming (MILP) model to investigate the influence of varying ash content in logging residues on the potential maximum supply quantity (MSQ) and net revenue (NR) of SAF production. Two scenarios were conducted in the Southeastern United States (US): one assuming constant ash content and the other accounting for heterogeneous ash content in logging residues. Results indicate that ignoring ash variability in the feedstock could lead to overestimation of SAF MSQ and NR by 14.15 % and 18.27 %, respectively, in the study area. Additionally, higher ash content leads to lower bio-oil yields, resulting in fewer refineries and reduced capacity. The study emphasizes the need for best management practices to mitigate soil contamination during feedstock processing and improve the resilience of the biomass-based SAF supply chain. Furthermore, it is crucial to effectively manage the mechanisms of mineral uptake and their integration into the structure of lignocellulosic materials.]]></description>
      <pubDate>Mon, 13 Apr 2026 16:48:09 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685506</guid>
    </item>
    <item>
      <title>BAP and Time-variant QCSP Modeling and Optimization</title>
      <link>https://trid.trb.org/View/2687570</link>
      <description><![CDATA[Enhanced data sharing between shipping companies and ports fosters seamless cooperation and more efficient use of terminal resources. This study addresses the integrated Berth Allocation Problem (BAP) and Time-variant Quay Crane Scheduling Problem (QCSP) in container terminals, aiming to optimize vessel berthing order, timing, and dynamic quay crane assignments. A Mixed Integer Linear Programming (MILP) model is introduced alongside a novel solution framework that combines Adaptive Large Neighborhood Search (ALNS) with Reinforcement Learning (RL). The integrated ALNS-RL framework sequentially updates ship sequences and quay crane schedules, enabling efficient convergence to high-quality solutions within limited iterations. Numerical experiments demonstrate that: 1) The proposed method yields superior solutions, reducing port stay time by 7.61% and quay crane idle rate by 13.48%; 2) The ratio of terminal berths to quay cranes significantly impacts operational efficiency, with an optimal allocation ratio identified; 3) The approach effectively handles instances from small to large scale, including problems with 30 berths and 90 vessels. Furthermore, the ALNS-RL framework outperforms both standard ALNS and a Hybrid Genetic Algorithm (HGA), achieving a minimal average deviation of 0.5% from the best solution—significantly better than ALNS (2.64%) and HGA (4.07%). While computationally efficient for small problems, the method demonstrates particular strength in large-scale instances, establishing its robustness for complex, real-world terminal scheduling applications.]]></description>
      <pubDate>Wed, 08 Apr 2026 13:55:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/2687570</guid>
    </item>
    <item>
      <title>Optimizing order bundling and dispatching in online food delivery for enhanced delivery efficiency</title>
      <link>https://trid.trb.org/View/2652354</link>
      <description><![CDATA[The online food delivery industry is undergoing a rapid global expansion, making it an easily accessible service for a growing number of consumers. With a simple swipe on a smartphone, customers can conveniently order food from a wide range of restaurants through online food delivery platforms such as Uber Eats, Grubhub, Meituan, and Eleme. The core functionality of these platforms is their algorithmic approach to order dispatching, which is the focus of our study. The objective is to optimize the courier-order matching process, thereby minimizing delivery time and distance, enhancing the efficiency and effectiveness of the service. In contrast to the conventional approach of matching a single order to each courier, our study explores a one-to-many courier-order matching process, which we term a concurrent order dispatching process optimizing order bundling, courier matching and route planning jointly. Our study proposes a comprehensive framework that intricately models the concurrent order dispatching process in great detail. Specifically, we construct a mixed-integer programming model and develop a hybrid heuristic algorithm to address the issue in an efficient manner. We introduce a novel order-bundling closeness measurement value to strategically dispatch multiple orders concurrently to each single courier during a designated decision time window. To assess the model’s and algorithm’s efficacy, we conducted experiments on both small-scale synthetic instances and large-scale real cases, utilizing data from a prominent online food delivery platform in China. The computational results demonstrate that our proposed approach yields solutions that are very close to the optimum in small-scale cases, and achieves significant improvements in terms of average delay time reduction and average distance savings in large-scale cases. In particular, our approach can save the average distance per order by 1.8 km and reduce the average delay time per order from 35 min to 10 min, in comparison to existing policies. We seek to ensure that a significant portion of orders are delivered on time, even with a limited number of couriers.]]></description>
      <pubDate>Tue, 31 Mar 2026 16:35:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/2652354</guid>
    </item>
    <item>
      <title>Multi-period yard template planning in container terminals based on dual-service storage strategy</title>
      <link>https://trid.trb.org/View/2594572</link>
      <description><![CDATA[As the bottleneck in the container port transformed from the seaside to the yard area, the optimizing for yard space allocation has become increasingly essential for increasing production and operational efficiency. However, challenges such as resource limitations and the heterogeneous multi-period of arriving services make it difficult for traditional fixed template-based strategy to meet the dynamic needs of ports. This paper proposes a flexible yard template based on dual-service storage strategy, it allows the assignment of two services to a single sub-block, catering to the storage needs of vessels with different arrival services. Based on the flexible template, a mixed-integer programming model with the objective of optimizing container transportation cost and yard space utilization for yard space allocation is established. To address the NP-hard nature of the problem, a tabu search-based greedy random adaptive search algorithm is designed. A case study and experiments with different scales are conducted to validate the effectiveness of the model and method, and the impact of relevant parameters and scenarios on berth and yard space allocation is also analyzed. The findings demonstrate that the proposed dual-service storage strategy can significantly enhance yard space utilization and reduce operational cost.]]></description>
      <pubDate>Wed, 11 Mar 2026 14:44:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2594572</guid>
    </item>
    <item>
      <title>Collaborative Optimization of Ship-Drone-Helicopter Operations for Maritime Emergency Response</title>
      <link>https://trid.trb.org/View/2634137</link>
      <description><![CDATA[Maritime economic growth has led to more frequent major marine accidents, highlighting the urgent need for efficient emergency response. However, most existing studies rely on ship-based scheduling, which often causes delays and high operational costs. To address these limitations, this study introduces a collaborative ship-drone-helicopter dispatch strategy aimed at improving the efficiency and responsiveness of maritime emergency operations. A mixed-integer programming (MIP) model is formulated to operationalize the proposed strategy, incorporating key operational factors such as environmental uncertainty affecting equipment mobility, the drift of demand points, and navigation-restricted areas that make certain areas accessible only by aerial platforms. An Improved Adaptive Large Neighborhood Search (IALNS) algorithm is subsequently proposed to efficiently solve the model, incorporating six local search operators pairs designed to enhance the algorithm’s ability to handle complex constraints. Extensive computational experiments across instances of varying scales demonstrate that the proposed algorithm delivers both high solution quality and computational efficiency. Moreover, a parameter sensitivity analysis offers managerial insights, and a case study based on an oil spill explosion in the South China Sea is conducted to visualize the optimized dispatch routes, further demonstrating the practical value and application potential of the proposed method.]]></description>
      <pubDate>Tue, 10 Mar 2026 09:55:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2634137</guid>
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