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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>
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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>Solving dynamic crowdsourcing delivery problem considering flexible load capacity with deep reinforcement learning</title>
      <link>https://trid.trb.org/View/2706968</link>
      <description><![CDATA[The use of crowdsourced solutions, where occasional drivers provide their services through online crowdsourcing platforms (OCPs), is gaining significant attention in the industry. This study addresses a dynamic crowdsourcing delivery problem (DCDP) characterized by the real-time arrival of orders and crowdsourced vehicles. To mitigate future supply-demand mismatches and maximize the OCP’s total gain over the entire planning horizon, we propose a flexible load capacity strategy—the first to investigate dynamic capacity utilization under uncertainty. This strategy adjusts vehicle capacity constraints using a dynamic loading coefficient to strategically balance immediate utilization against future vehicle availability through penalty-based capacity adjustments. To operationalize this flexible strategy, we develop a co-designed learning-optimization framework: deep Q-learning enhanced hybrid adaptive large neighborhood search (DQLHA). The DQLHA incorporates an offline Transformer-deep neural network hybrid that learns optimal loading coefficients during training. To address the high-dimensional state space complexity, this study engineers a compact set of spatiotemporal features that aggregate the essential dynamics of DCDP from the raw system state. Additionally, the DQLHA incorporates an online runtime-efficient hybrid adaptive large neighborhood search algorithm. This component simultaneously optimizes crowdsourced vehicle-customer assignments and generates near-optimal routing solutions while embedding the loading coefficient as a soft constraint. Experimental results demonstrate that the proposed algorithm achieves an average improvement of 5.29% to 42.6% in total gain for the OCP compared to seven other comparative algorithms, showcasing its superior efficacy. Furthermore, our findings highlight the algorithm’s robustness across varying problem sizes, degrees of dynamism, temporal and spatial distributions, and decision-making frequencies. Moreover, we verify that the proposed framework can be effectively extended to dynamic crowdsourcing delivery scenarios with heterogeneous crowdsourced vehicles and multiple depots.]]></description>
      <pubDate>Thu, 27 Aug 2026 16:32:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/2706968</guid>
    </item>
    <item>
      <title>Passenger-freight shared mobility for decarbonizing urban-rural bus systems: fleet and capacity optimization</title>
      <link>https://trid.trb.org/View/2762214</link>
      <description><![CDATA[Urban-rural public transport faces a self-reinforcing cycle of declining ridership, service deterioration, and mounting deficits, while parallel diesel freight operations generate substantial carbon emissions along the same corridors. Passenger-freight shared mobility (PFSM) addresses both challenges by using idle bus capacity to co-transport parcels, yet its implementation must balance passenger service quality and freight efficiency. This study formulates a bilevel optimization model that jointly determines fleet composition, and dynamic capacity allocation, and vehicle scheduling while incorporating stochastic travel time effects of freight operations. An improved jellyfish search algorithm is developed to solve the resulting mixed-integer program. A case study of two urban–rural routes in Shanxi Province, China, shows that the optimized PFSM scheme turns a daily operating deficit of RMB 2462.16 into a profit of RMB 537.55, eliminates dedicated freight trucks, and reduces annual freight-related CO₂ emissions by 19.12 tons per route, demonstrating a viable pathway for decarbonizing rural transport.]]></description>
      <pubDate>Tue, 25 Aug 2026 16:23:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/2762214</guid>
    </item>
    <item>
      <title>A bilevel model and a column generation heuristic for the transit network design and frequency setting problem with capacity constraints</title>
      <link>https://trid.trb.org/View/2726678</link>
      <description><![CDATA[The Transit Network Design and Frequency Setting Problem (TNDFSP) is a fundamental component of urban public transport planning, involving the integrated optimization of route configurations and service frequencies. Despite its practical relevance, existing solution approaches are predominantly heuristic or metaheuristic in nature, often overlooking operational constraints such as vehicle capacity. This work addresses these limitations by proposing a mathematical programming-based solution method formulated within a column generation framework. The method explicitly incorporates vehicle capacity constraints into the master problem formulation, offering a more realistic representation of system dynamics. Computational experiments are conducted on a small synthetic instance to benchmark performance against optimal solutions, as well as on the well-known Mandl network for comparative validation. Additionally, the method is applied to a real-world case study involving the city of Rivera, Uruguay, demonstrating its practical applicability. Results highlight the potential of mathematical programming-based optimization techniques to close the gap between theoretical rigor and scalable, real-world transit network design.]]></description>
      <pubDate>Wed, 12 Aug 2026 15:14:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/2726678</guid>
    </item>
    <item>
      <title>Decision-making based on sensor data from rail and infrastructure : how information from rail vehicles and infrastructure can assist decisions on energy savings and capacity increases</title>
      <link>https://trid.trb.org/View/2752013</link>
      <description><![CDATA[Urban rail systems as foundational public transport for metropolitan areas play an increasingly important role due to their high capacity, high efficiency and low environmental impact. In Stockholm, the urban rail network is an indispensable component of the city's daily function, serving nearly 900,000 passengers on a daily basis. Aligning with its objective to become the world's most sustainable public transport provider, Stockholm's trains and buses have operated entirely on renewable energy sources since 2017. Despite the use of green electricity and the inherent efficiency of rail transport, the system's vast operational scale leads to substantial energy consumption. This demand is primarily attributed to two major areas: the traction systems that power the movement of the trains, and the auxiliary systems responsible for ensuring passenger comfort, with the Heating, Ventilation, and Air Conditioning (HVAC) units being the most significant component. Research has indicated that auxiliary power can account for as much as 30% of a train's total energy demand. This issue is particularly pronounced in high-latitude regions such as Stockholm. Concurrently, technological advancements have equipped modern rolling stocks, such as the commuter train X60 and the metro train C30, with an extensive array of sensors. These sensors are capable of recording and transmitting a continuous stream of operational data in real-time, including vehicle speed, power consumption, position, interior and exterior temperatures, and the status of various onboard systems, e.g., doors, air springs and bearings. The availability of this rich dataset creates an unprecedented opportunity to apply a data driven approach to deeply analyze the relationship between train operation and energy consumption, identify potential savings of energy and operational costs, and ultimately, support more informed and effective decision-making. It is within this context that the present project was established. The focus is on the urban rail transport system of the Stockholm region, with a specific emphasis on its commuter train (pendeltåg) and metro (tunnelbana) networks.]]></description>
      <pubDate>Fri, 07 Aug 2026 08:35:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2752013</guid>
    </item>
    <item>
      <title>Formulating the express railway network design for relieving in-vehicle crowding, incorporating a bi-level modeling approach</title>
      <link>https://trid.trb.org/View/2698417</link>
      <description><![CDATA[Crowding in vehicles is worse in concentrated urban areas. It is necessary to extend the public transit network, delivering travelers with a high level of service. This study aims to develop a design model of an express railway to improve mobility and relieve congestion on the existing urban rail transit network. A bi-level structure has been employed to solve the network design problem. The upper-level model is formulated to maximize the net-benefit of the entire rail network, whereas the lower-level model is formulated to grasp the effect of crowding on vehicles. A genetic algorithm is employed to solve the combinatorial optimization problem in a reasonable time. The results show that the developed model can find the optimal express railway by reflecting the relation of the trade-off between accessibility and mobility of the system. This study contributes to the establishment of the long-term plan for public transit networks in metropolitan areas.]]></description>
      <pubDate>Mon, 03 Aug 2026 09:23:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/2698417</guid>
    </item>
    <item>
      <title>Real-time on-board passenger comfort estimation in complex public transport networks with intersecting lines</title>
      <link>https://trid.trb.org/View/2679111</link>
      <description><![CDATA[Comfort on-board public transport vehicles is a critical metric of user experience and service performance. The quantification of this metric requires knowledge of the number of passengers on-board every time a vehicle arrives at or departs from a stop or station. Automatic Passenger Counting (APC) systems allow obtaining such knowledge in real-time, but the information is often incomplete due to system malfunctions, or, more commonly, a lack of the relevant equipment in some vehicles. This study develops an advanced method for passenger estimation that fills gaps in incomplete APC datasets, with computational performance allowing real-time application, and calculates comfort levels on-board public transport vehicles in complex networks where stations are served by multiple lines. The proposed method is tested on a case study considering the Helsinki commuter train network, comprising 6 service lines and 20 stations. The results indicate that the proposed framework can achieve comfort level estimations with high precision across the different cases evaluated. Furthermore, the study provides insight into the key practical question of the number of vehicles that need to be equipped with APC devices in order to obtain sufficiently accurate on-board passenger comfort estimates, and it is shown that it is possible to obtain these estimates even when only a small subset of the runs of any single day are performed by equipped vehicles. Finally, the proposed estimation approach is a valuable tool for operators to obtain a better understanding of daily mobility patterns, evaluate their services through quantifying user experience, and enhance their operations.]]></description>
      <pubDate>Wed, 24 Jun 2026 13:22:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/2679111</guid>
    </item>
    <item>
      <title>A Robust Method for Bus Scheduling and Passenger Flow Coordination Considering Arterial Signal Coordination Under Connected Environment</title>
      <link>https://trid.trb.org/View/2658846</link>
      <description><![CDATA[Urban public transportation is a complex and open system integral to urban mobility. Its operation is often disrupted by various random factors, necessitating robust scheduling solutions. This study develops a bus robust scheduling model based on mixed-integer linear programming to enhance system resilience. First, an arterial signal coordination model is proposed for mixed traffic environments, enabling autonomous public transport vehicles to traverse intersections without stopping. Second, a demand-deterministic bus scheduling model is constructed, integrating timetables, trajectories, and origin-destination transfer schemes to balance passenger waiting time fairness and efficiency. Third, to address stochastic passenger demand during actual operations, a robust bus scheduling model is developed by incorporating robust constraints. Numerical experiments demonstrate that the demand-deterministic model generates optimal scheduling schemes when passenger demand remains within bus capacity. However, when passenger demand exceeds capacity, the demand-deterministic model becomes infeasible. In such scenarios, the robust scheduling model produces feasible schemes, albeit with reduced optimization, and its robustness can be tuned by adjusting model parameters. Additionally, practical management insights are provided for real-world applications.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2658846</guid>
    </item>
    <item>
      <title>A Novel Approach for Monitoring Vessel Load Capacity Through Ship Dimension Extraction From Synthetic Aperture Radar Images</title>
      <link>https://trid.trb.org/View/2659027</link>
      <description><![CDATA[Vessels are regulated to operate within designated water areas based on their types, dimensions, load-carrying capacity, etc. However, despite regulations, some ship operators still attempt to evade them to reduce operational costs or obtain additional benefits. This is achieved through certificate forgery, vessel information concealing or unauthorized illegal behavior, which poses a significant threat to maritime traffic safety. Monitoring through only Automatic Identification System (AIS) data has limitations, as it relies on vessels actively submitting information, and is unable to track vessels intentionally shutting down AIS or those without AIS equipment. This paper proposes a method for intelligent waterway transportation supervision that utilizes Synthetic Aperture Radar (SAR) remote sensing technology to automatically detect vessel targets and predict their Deadweight Tonnage (DWT). Vessel targets are identified and their main dimensions are extracted through instance segmentation models based on SAR marine images. Subsequently, a multiple regression analysis establishes the relationship between vessel type, dimensions, and DWT, effectively predicting their load capacity. The experimental results show that the Mean Absolute Percentage Error (MAPE) for the three primary types of ships (i.e., container ship, bulk carrier, tanker) are 4.99%, 6.09%, and 4.20%, respectively. The proposed method serves as an auxiliary to laborious traditional manual methods, which can automatically detect vessels and their load capacity on a large scale and in batches, regardless of whether the ships are in port or not. It can be applied in scheduling vessel routes and channel layouts, serving as an additional reference to avoid inspection oversights, and significantly enhance maritime regulatory efficiency.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659027</guid>
    </item>
    <item>
      <title>Logistics vehicle routing optimisation with synchronised transfer</title>
      <link>https://trid.trb.org/View/2663014</link>
      <description><![CDATA[This paper presents a synchronized transfer strategy in which operators can schedule logistics vehicles among employees and arrange the transfer of commodities. The transfer time consists of the vehicle waiting time and commodity transition time. By adopting the strategy, logistics vehicles can visit fewer communities, resulting in savings in transportation cost under ensuring punctual delivery. An integer programming model based on a space-time-state network is proposed to describe the complex transfer process. To simplify the model, time window and vehicle capacity constraints are embedded into the network. The alternating direction method of multipliers (ADMM) is designed to solve the model. In the ADMM-based solution framework, the original model can be converted into a series of shortest-path searching subproblems and iteratively solved using a dynamic programming (DP) algorithm. Our computational results show that the proposed model and algorithm are efficient and competitive.]]></description>
      <pubDate>Thu, 14 May 2026 17:04:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2663014</guid>
    </item>
    <item>
      <title>Optimization of Seaplane Transport System in Greece with the Use of Conjoint Analysis</title>
      <link>https://trid.trb.org/View/2579517</link>
      <description><![CDATA[Research was conducted on optimizing the seaplane transportation system in Greece using the Choice-Base Conjoint Analysis method. Due to its geographical morphology and its level of touristic development, Greece is considered an ideal country for using seaplanes. A sample of 216 people were asked questions related to the utility of seaplanes, the quality of seaplane services, and seaplanes’ quality characteristics as set mainly by the Greek business world but also by their standards of use from foreign tourist countries in the field of touristic services. The results were the basis for designing scenarios simulated in market conditions. These scenarios were evaluated in comparison to each other regarding their quality characteristics and the scale of their appeal to the public regarding their contribution to a more effective use of seaplanes. It was found that the optimal seaplane transport system is as follows: The seaplane capacity consists of 18 persons, the frequency of the flights is three (3) times per day, no intermediate stop is interposed, the departure times are in the afternoon, and the cost of the ticket is equal to the cost of traveling by car. The attributes were found to have the following importance: (i) ticket cost, 45.13%; (ii) density of the flight, 21.76%; (iii) intermediate stops, 15.61%; (iv) capacity and size of the seaplane, 11.14%; and (v) departure times, 6.35%. This optimal system gathered 77.28% of respondents who prefer it over the base system, with a rate of 22.72%.]]></description>
      <pubDate>Mon, 27 Apr 2026 15:01:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2579517</guid>
    </item>
    <item>
      <title>Tactical Network Planning for Intermodal Barge Transportation Considering Varying Water Levels</title>
      <link>https://trid.trb.org/View/2682114</link>
      <description><![CDATA[Barge transportation provides a sustainable and cost-effective alternative to road transport, offering solutions to reduce congestion and lower emissions. However, increasing drought frequency and severity, driven by climate change, along with human interventions such as dam operations, dredging, and water withdrawals, cause fluctuations in water levels in rivers and canals that jeopardize its reliability and efficiency. These variations, including shallow water conditions, reduced water levels, and restricted navigable depths, directly impact barge operations by limiting transportation capacity. A barge’s load capacity depends not only on its physical characteristics but also on the available water level along its route. Insufficient or diminished water levels impose draught restrictions that constrain freight volume and weight, creating operational challenges that hinder carriers’ ability to meet shipper demands efficiently and maintain profitability. This study presents a tactical planning framework for consolidation-based barge transportation that explicitly models the relationship between water levels and barge load capacity. The framework integrates vessel characteristics and predicted water levels to optimize resource utilization and maximize expected carrier revenue while ensuring reliable demand fulfillment. Through computational experiments conducted using a commercial software, we analyze how varying water levels in rivers and canals impact profitability, operational efficiency, shipper satisfaction, and the service network structure.]]></description>
      <pubDate>Mon, 27 Apr 2026 15:01:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2682114</guid>
    </item>
    <item>
      <title>An enhanced approximate dynamic programming approach to on-demand ride-Pooling</title>
      <link>https://trid.trb.org/View/2643288</link>
      <description><![CDATA[Ride-pooling services have been growing in popularity, increasing the need for efficient and effective operations. The main goal of ride-pooling services is to maximise the number of passengers served while limiting wait and delay times. However, factors such as the timing and volume of passenger requests, pick-up and drop-off locations, available vehicle capacity, and the trajectory to fulfil multiple requests introduce high degrees of uncertainty, creating challenges for ride-pooling operators. This study aims to expand the current state-of-the-art Approximate Dynamic Programming (ADP) approach for ride-pooling services, introduce key extensions, and perform a comparative analysis with the Neural Approximate Dynamic Programming (NeurADP) approach to optimise the efficiency and effectiveness of these services. Specifically, we develop an ADP approach that incorporates three important problem specifications: (i) pick-up and drop-off deadlines, (ii) vehicle rebalancing, and (iii) allowing more than two passengers in a vehicle. We conduct a detailed numerical study with the New York City taxi-cab dataset and a dataset of taxi-cab requests collected in the city of Chicago. We also provide a sensitivity analysis on key model parameters such as wait and delay times, passenger group sizes, and vehicle capacity. Our comparative analysis highlights the strengths and limitations of both ADP and NeurADP methodologies. Network density and road directionality are found to significantly impact the performance. NeurADP is found to be more efficient in learning value function approximations for larger and more complex problem settings than the ADP approach. However, in less complex cases, ADP is shown to outperform NeurADP.]]></description>
      <pubDate>Wed, 22 Apr 2026 16:15:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643288</guid>
    </item>
    <item>
      <title>Railway Out-of-Gauge Cargo Transportation Route Selection Method Considering Gauge Modification</title>
      <link>https://trid.trb.org/View/2113873</link>
      <description><![CDATA[The size of railway out-of-gauge cargo is large, and the loading outline will exceed the railway gauges. In the actual transportation organization, how to choose a safe and economic transportation route is an important issue in the transportation of railway out-of-gauge cargo. The purpose of this paper is to solve the route selection problem of railway out-of-gauge cargo transportation, taking the safety distance and curve radius as constraints and railway capacity loss and transportation cost as objective functions to construct the route selection model considering gauge modification. And on this basis, a route search heuristic algorithm can be designed to solve it. The case verification shows that the most economical route for railway out-of-gauge cargo transportation can be derived based on the model and algorithm.]]></description>
      <pubDate>Wed, 15 Apr 2026 08:31:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2113873</guid>
    </item>
    <item>
      <title>Capacity Vehicle Routing Problem with Time Windows: Simulation Tool for Footprint Network Design</title>
      <link>https://trid.trb.org/View/2579235</link>
      <description><![CDATA[The paper focuses on a decision support system designed for logistic experts and aimed at addressing Vehicle Routing Problem that includes multi-vehicles and multi-depot with time constraints and considers the capacities of vehicles and logistic nodes too. The paper proposes a novel solution featuring a three-layer architecture and a system able to simulate the behavior of the network. Therefore, the paper proposes a tool to assess the impact of changes in volumes and capacities on overall delivery times. The integration of information about sorting nodes, delivery nodes, travel distances, and daily item demands is crucial for simulating accurate arrival times at each destination point. Computational experiments are depicted for validating the model and showing its effectiveness and its application.]]></description>
      <pubDate>Tue, 31 Mar 2026 16:34:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2579235</guid>
    </item>
    <item>
      <title>Optimizing Route Selection in Liner Shipping Networks: Linear Programming Approach</title>
      <link>https://trid.trb.org/View/2674210</link>
      <description><![CDATA[The global container ship handling volume has reached approximately 1.7 billion TEU, increasing its importance. This increase is making it challenging to respond using conventional human-based methods. Therefore, this study aims to develop a novel mathematical optimization model with the objective of minimizing transportation costs. By establishing a systematic container transportation network, we will lay a crucial foundation for future developments. We analyzed the data from the perspectives of transport volume, efficiency, and cost. The results showed that increasing ship capacity increases transport volume, but the rate of increase gradually slows down. By deploying ships of different capacities on each route, transport efficiency can be improved. While utilizing short-distance routes contributes to cost reduction, using long-distance routes leads to increased costs.]]></description>
      <pubDate>Mon, 23 Mar 2026 15:21:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2674210</guid>
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