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    <title>Transport Research International Documentation (TRID)</title>
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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>Transportation planning of milk 𝘪𝘯 𝘯𝘢𝘵𝘶𝘳𝘢 to supply the dairy industry in developing countries</title>
      <link>https://trid.trb.org/View/2703664</link>
      <description><![CDATA[In developing countries, dairy farms are scattered over extensive rural areas, with roads in precarious conditions, which impacts the product's final cost. The truck routes are currently planned by dividing the collecting area into smaller areas, without software's aid. This research proposes a strategy to plan the routes globally, considering the complete area to reduce the total transportation cost. A new metaheuristic algorithm and a mathematical model are proposed, based in the periodic vehicle routing problem with multi-trips, to plan the truck routes. Data from a Brazilian dairy farm were used to assess the strategy and apply the metaheuristic algorithm. Results showed that the routes planned by the proposed metaheuristic are shorter in distance than the routes done currently by the industry and that globally planning the routes may bring financial gains and improve the milk's quality by the reduction of travel time.]]></description>
      <pubDate>Thu, 27 Aug 2026 16:32:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2703664</guid>
    </item>
    <item>
      <title>Enhancing Logistics Efficiency: A Holistic Approach to Last and First-Mile Integration in Intermodal Transport</title>
      <link>https://trid.trb.org/View/2681698</link>
      <description><![CDATA[This study addresses a challenge faced by Multimodal Transport Operators (MTOs) in planning truck routes for containerized goods. By focusing on the integration of last-mile, first-mile, and road transport services, we propose an optimization approach to include in a decision support system of a MTO to enhance its logistics operations. Our method emphasizes scheduling and coordination of deliveries and pickups, aiming to minimize travel costs, reduce empty trips, and improve resource utilization. The approach consists of two main steps: assessing a comprehensive transport order list to identify compatible orders and generate services sets and employing a mathematical model to allocate specific transport tasks (services) to available vehicles. Preliminary tests were conducted using a series of carefully designed test cases to evaluate the approach’s performance and effectiveness. Key indicators were proposed too. The results indicate that the proposed optimization framework effectively enhances logistics efficiency and contributes to sustainable transport practices.]]></description>
      <pubDate>Mon, 27 Apr 2026 15:01:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2681698</guid>
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    <item>
      <title>Impact of Proposed Truck Routes in Vehicular Movement in Metro Manila</title>
      <link>https://trid.trb.org/View/2655526</link>
      <description><![CDATA[The study evaluated the effects of new truck routes on urban travel in Metro Manila, aiming to identify the optimal operating scenario for truck traffic with or without a truck ban while incorporating additional or alternative routes along key corridors. Initially, a shortest path analysis was conducted to identify potential new corridors for truck use. This was followed by a multi-class traffic assignment using the EMME transport planning software to model network traffic. The study simulated truck demand during off-peak hours when no truck ban was in effect, along with other vehicle types such as private cars and public transport. It assessed and compared link characteristics, including travel time, volume, average speed, vehicle-hours travelled, and vehicle-delay travelled. The findings indicated that opening new truck routes would enhance truck mobility in Metro Manila but would also reduce travel speed and increase travel time for other vehicles, particularly on affected routes.]]></description>
      <pubDate>Mon, 23 Mar 2026 15:20:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2655526</guid>
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    <item>
      <title>A Hybrid Tabu Search and Variable Neighborhood Descent Heuristic for the Load-dependent Vehicle Routing Problem with Time Windows</title>
      <link>https://trid.trb.org/View/2655493</link>
      <description><![CDATA[Logistics becomes more important to the global supply chain after COVID-19. To achieve net zero in 2050, companies realized that it is imperative to reduce the fuel consumption and carbon emissions. The traditional vehicle routing problem is to minimize total travel time or cost. Th fuel consumption and carbon emissions were not considered. As the fuel consumption or carbon emission is impacted by the truck load and their travel distance, the load dependent vehicle routing problem (LDVRP) becomes more important. The LDVRP with time windows (LDVRPTW) which is the topic of this research is related to Practical consideration. Due to the NP-hardness of the LDVRPTW, most researches developed metaheuristics to solve the problem. In this research, we propose a multi-start tabu search hybrid with variable neighborhood descent (VND) to improve the search space and computation efficiency. We test our algorithm with benchmark instance of LDVRP and Soloman VRPTW instances.]]></description>
      <pubDate>Mon, 23 Mar 2026 15:20:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2655493</guid>
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    <item>
      <title>Scenario Predict-Then-Optimize for Data-Driven Online Inventory Routing</title>
      <link>https://trid.trb.org/View/2616173</link>
      <description><![CDATA[The real-time joint optimization of inventory replenishment and vehicle routing is essential for cost-efficiently operating one-warehouse, multiple-retailer systems. This is complex because future demand predictions should capture (auto)correlation and lumpy retailer demand, and based upon such predictions, inventory replenishment and vehicle-routing decisions must be taken. Traditionally, such decisions are made by either making distributional assumptions or using machine-learning-based point forecasts. The former approach ignores nonstationary demand patterns, whereas the latter approach provides only a point forecast ignoring the inherent forecast error. Consequently, in practice, service levels often do not meet their targets, and truck fill rates fall short, harming the efficiency and sustainability of daily operations. We propose Scenario Predict-then-Optimize. This fully data-driven approach for online inventory routing consists of two subsequent steps at each real-time decision epoch. The scenario-predict step exploits neural networks—specifically multi-horizon quantile recurrent neural networks—to predict future demand quantiles, upon which we design a scenario sampling approach. The subsequent scenario-optimize step then solves a scenario-based two-stage stochastic programming approximation. Results show that our approach outperforms a classic sequential learning and (stochastic) optimization approach, distributional approaches, empirical sampling methods, residuals-based sample average approximation, and a state-of-the-art integrated learning and (stochastic) optimization approach. We show this on both synthetic data and large-scale real-life data from our industry partner. Our approach is appealing to practitioners. It is fast, does not rely on any distributional assumption, and does not face the burden of single-scenario forecasts. It also outperforms residuals-based scenario generation techniques. We show that it is robust for various demand and cost parameters, enhancing the efficiency and sustainability of daily inventory replenishment and truck-routing decisions. Finally, scenario Predict-then-Optimize is general and can be easily extended to account for other operational constraints, making it a useful tool in practice.]]></description>
      <pubDate>Mon, 02 Feb 2026 09:33:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2616173</guid>
    </item>
    <item>
      <title>User’s Guide to the Overweight Truck Route Analysis (OTRA) Program</title>
      <link>https://trid.trb.org/View/2582386</link>
      <description><![CDATA[The 75th and 76th Texas Legislatures passed bills allowing trucks with gross vehicle weights (GVWs) of up to 125,000 lb. to routinely use a route in south Texas along the Mexican border. This route proceeds from the Veterans International Bridge to the Port of Brownsville via US77, SH4, and SH48. The portion of the route along US77 is on a new concrete pavement and includes an elevated structure over half of its length. Most of the permitted truck route runs along SH4 and SH48 in Brownsville. Concerned about the effects of routine overweight truck traffic on its roadways, the Texas Department of Transportation (TxDOT) sponsored a research project with the Texas Transportation Institute (TTI) to characterize the effects of routine overweight truck traffic along SH4/48 and develop pavement design guidelines for roadways subjected to routine overweight trucks. A product of this project is the Overweight Truck Route Analysis (OTRA) program, which is described in this report. OTRA is a modification of the Program for Load-Zoning Analysis (PLZA) developed in an earlier TxDOT project. OTRA may be used to evaluate the structural adequacy of an existing route to carry routine overweight truck traffic over a specified design period. Additionally, it may be used to estimate the thickness of asphalt concrete overlay required to carry the expected number of truck axle loads over the design life based on a user-prescribed reliability level. The program incorporates a layered elastic pavement model for predicting the induced response under surface wheel loads. Both linear and nonlinear pavement materials may be modeled. The predicted horizontal strain at the bottom of the asphalt layer and the vertical strain at the top of the subgrade are used to determine the service life for a given pavement and loading condition. To combine the effects of different axle loads and axle configurations, Miner’s hypothesis of cumulative damage is used to predict pavement service life. Instructions on using OTRA are provided in this guide.]]></description>
      <pubDate>Sun, 30 Nov 2025 16:39:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2582386</guid>
    </item>
    <item>
      <title>Guidelines for Evaluating Routine Overweight Truck Routes</title>
      <link>https://trid.trb.org/View/2582257</link>
      <description><![CDATA[The impact of increasing overweight truck loads on Texas highways is a growing concern within the Texas Department of Transportation (TxDOT). Since pavement performance is significantly influenced by the magnitude and frequency of truck traffic loads, guidelines are needed for evaluating the capacity of existing highways to sustain routine overweight truck traffic over a specified performance period. The problem of overweight truck loads has been investigated in related TxDOT projects that led to the development of analysis procedures for evaluating superheavy load routes and load-zoning requirements. Researchers used results from these TxDOT projects to develop pavement evaluation guidelines for routine overweight truck routes that are presented in this report. In this project, researchers developed a two-stage framework that is based on using existing TxDOT capabilities for pavement evaluation, including nondestructive test methods and pavement analysis programs. Level I involves the use of pavement evaluation charts to identify the best possible route from among the alternatives considered and to determine what additional tests and analyses are needed for Level II. It is primarily intended as a screening tool to assist the engineer in identifying candidate overweight truck routes and potential problem areas. Level II involves the application of the Overweight Truck Route Analysis (OTRA) program to evaluate the structural adequacy of an existing route to carry routine overweight truck traffic over the specified performance period. Additionally, OTRA may be used to estimate the thickness of asphalt concrete overlay required to carry the expected number of truck axle loads over the specified design life based on a user-prescribed reliability level. This report presents guidelines on the application of the methodology to evaluate the suitability of using an existing route for routine overweight truck use.]]></description>
      <pubDate>Sat, 22 Nov 2025 17:17:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2582257</guid>
    </item>
    <item>
      <title>Parametric design of time-sensitive routing with recipient-dependent contributions</title>
      <link>https://trid.trb.org/View/2564184</link>
      <description><![CDATA[Last-mile delivery complexities intensify for perishable goods, which must maintain quality, mainly when transported on non-refrigerated vehicles. If recipients are unavailable, delivery failure may prolong the delivery of perishable goods, thus jeopardizing their integrity. This study proposes recipient-dependent last-mile delivery solutions for perishable goods with time-sensitive delivery routes where the recipients contribute to the process. The authors explore applications of Autonomous Vehicles (AVs) in recipient-dependent deliveries of perishable goods and compare traditional truck delivery with a proposed AV pickup policy and other multi-echelon routing policies. The authors propose a parametric design of the policies, characterizing each policy by a set of variables inspired by the network design literature. In this study, routes are regarded as length-constrained, which is essential for the time-sensitive delivery of perishable goods. The authors compare the optimal cost of policies in length-bounding (time-sensitive) with capacity-bounding routes. A detailed dominance space analysis highlights the optimal policy under various cost structures and shows that the status quo for truck delivery is dominated as the number of deliveries increases. Increasing hand-off costs also lead to the dominance of AV and hybrid policies over traditional truck delivery. The authors validate the proposed managerial insights through a case study of a Walmart location delivery service in Toronto, proving the applicability of the models. This research contributes to the strategic integration of AVs in last-mile delivery of perishable goods.]]></description>
      <pubDate>Thu, 21 Aug 2025 09:19:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2564184</guid>
    </item>
    <item>
      <title>Investigating how consumption values and place identity shape long-haul truck drivers’ intention to visit roadside rest areas</title>
      <link>https://trid.trb.org/View/2438218</link>
      <description><![CDATA[Transportation business organisations, policymakers, and government entities attempt to enhance the welfare of long-haul truck drivers (LHTDs) by promoting safe driving practices. LHTDs spend long hours on goods delivery journeys, and that causes various health and in-route safety issues for drivers. For improved LHTD health and safety, many developed nations introduced the concept of roadside rest areas (RRAs) for regulated, safe, and enhanced driving experience for LHTDs, whereas developing nations’ research on RRAs development is nascent. The RRAs offer a chance to reduce negative truck driving impacts and promote wellbeing among LHTDs, but why drivers will visit RRAs during their tracking journey is a question for policymakers. Hence, the present study tries to comprehend the significance of drivers’ visit consumption values in terms of functional, social, emotional, epistemic, conditional value, and place identity concerning visit intentions towards RRAs. In addition, the present work examines the influence of consumption values on word of mouth (WoM) for RRAs by LHTDs. The research problem is examined via the lens of the theory of consumption values. The study utilised 247 valid responses for model analysis, using the partial least squares-structural equation modelling (PLS-SEM) technique. The findings establish that consumption value is a formative construct that further positively influences place identity, intentions to visit RRAs, and WoM for RRAs by LHTDs. This implies that the intentions of LHTDs to visit relaxation sites are mostly influenced by the values they perceive from such places. This perception of consumption values also helps LHTDs develop place identity with RRAs and influences them to spread positive WoM about it. However, the results of the present study did not find the mediation role of place identity. This implies that Indian LHTDs will develop the intention to visit RRAs because of the consumption values rather than any attachment to the place. The findings of this paper will help transport policymakers develop adequate measures for developing rest areas on major transport routes to improve road safety and drivers’ wellbeing.]]></description>
      <pubDate>Thu, 17 Oct 2024 09:15:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/2438218</guid>
    </item>
    <item>
      <title>Rail and Terminal Analysis</title>
      <link>https://trid.trb.org/View/2413808</link>
      <description><![CDATA[In the comprehensive Indianapolis Regional Transportation and Development Study, all forms of transportation are considered. Among these, of course, is railway transportation. Railroad rights-of-way are "lineal" land use elements which provide channels for the flow of rail traffic. Associated with these lineal land use elements of the railroads are broader uses of land: railroad yards, shops, freight stations, passenger depots, truck-train ramp areas, etc. The operational flow of rail traffic and its relation to associated facilities is analyzed in this part of the Study. The rail portion of this work paper aims at depicting the present and future pattern of railroad operations in the Study Area, and gaging possible changes in railroad land use. Truck terminals also are analyzed in this section of the Study. The objective of the research is to depict the present and future locational distribution of truck terminals in the Study Area, and to estimate the impact of changes in terms of truck routes, land use, and traffic.]]></description>
      <pubDate>Mon, 12 Aug 2024 17:03:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2413808</guid>
    </item>
    <item>
      <title>Study of Operational Characteristics of HAZMAT Vehicles Based on GPS Data</title>
      <link>https://trid.trb.org/View/2203471</link>
      <description><![CDATA[The transportation of hazardous materials (HAZMAT) is a growing problem worldwide due to increasing transportation volumes. In this paper, the problems of risk assessment methods and the characteristics of hazmat vehicles are discussed. This research is based on a case study in Shenzhen, China. The operation data is acquired with global positioning system (GPS) data acquisition technology. The characteristics of hazmat transportation are obtained through analyzing the vehicle operation characteristics changing by time and space, and the relationship between the hazmat transportation route and urban transportation system. With the conclusions above, the paper gives some advice to support the Government's decision-making about the assessment of the transport risk for hazmat and the planning of the hazmat transportation.]]></description>
      <pubDate>Mon, 17 Jun 2024 14:45:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2203471</guid>
    </item>
    <item>
      <title>Planning the fuel distribution in urban centres using multi-compartment trucks under restrictions on the delivery period and the model of the truck</title>
      <link>https://trid.trb.org/View/2321496</link>
      <description><![CDATA[Distribution companies need to improve their distribution strategies to meet the increasing fuel stations demand, respecting restrictions on the delivery period and on the truck's model, both defined by fuel stations and/or local authorities. Currently, these companies plan one trip per truck and divide the whole distribution region in several small regions, on the other hand, our paper proposes to plan several trips per truck and consider one global distribution region. A mathematical model and an algorithm integrating in the fuel replenishment problem characteristics as: multi-compartment, multi-trip, site dependent, time windows and heterogeneous fleet are proposed. Tests were done using data from a Brazilian fuel distribution company. Results showed that planning considering a global distribution region brought financial gains for the company. The access constraint highly impacted the transport costs.]]></description>
      <pubDate>Wed, 21 Feb 2024 16:47:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2321496</guid>
    </item>
    <item>
      <title>Considering the Joint Distribution of Trucks and Drones under the Influence of COVID-19</title>
      <link>https://trid.trb.org/View/2237697</link>
      <description><![CDATA[Under the influence of COVID-19, some residents have been sealed at home. To reduce the risk of cross-infection when distributing materials, how to safely and effectively has become a major problem. Considering that the application technology of drones in the field of logistics has become more and more mature in recent years, the joint distribution mode of trucks and drones has been proposed. According to the characteristics of the problem, a three-stage solution algorithm is designed; the first stage is to set the safety distance threshold between the demand point and the truck stop and use the K-means algorithm to find the truck stop site; the second stage is to design an improved ALNS (Adaptive Large Neighborhood Search) algorithm to optimize the drone path and compare the results with the optimizer Gurobi; and the third stage is to optimize the truck route. All three phases are implemented using python programming, and the second stage of the core is studied and evaluated, which shows that the ALNS algorithm solves faster and with better results than Gurobi.]]></description>
      <pubDate>Tue, 06 Feb 2024 15:05:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2237697</guid>
    </item>
    <item>
      <title>Optimisation of the electric truck route for milk collection problem: a real case study</title>
      <link>https://trid.trb.org/View/2144239</link>
      <description><![CDATA[This study dwells upon the electric milk collection problem. The problem extends electric vehicle routing problem with time windows, considering multi-depot, multi-product, split deliveries, multi-compartment, fast chargers and fleet composition. The problem aims to create an effective routing decision-making system for electric trucks in transporting milk of different quality from producers (or milk collection points) in different locations to the factories. The objective of the electric milk collection problem is to minimize the sum of the total energy costs of electric trucks. The authors develop an adaptive general variable neighborhood search algorithm that involves several procedures having been tailored to handle specific features of the problem. The authors perform extensive computational experiments on real-life data to investigate the performance of the heuristics and offer particular insight. The results indicate that the authors' algorithm successfully solves small- and large-scale instances in terms of solution quality and computational time. The authors' results also quantify the benefits of using fast charger types and fleet composition on the total energy costs.]]></description>
      <pubDate>Wed, 31 May 2023 10:58:09 GMT</pubDate>
      <guid>https://trid.trb.org/View/2144239</guid>
    </item>
    <item>
      <title>Asymmetric Arc Routing by Coordinating a Truck and Multiple Drones</title>
      <link>https://trid.trb.org/View/2012658</link>
      <description><![CDATA[Unmanned Aerial Vehicles, commonly known as drones, have been widely used in transmission line inspection and traffic patrolling due to their flexibility and environmental adaptability. To take advantage of drones and overcome their limited endurance, the patrolling tasks are parallelized by concurrently dispatching the drones from a truck which travels on the road network to the nearby task arc. The road network considered in previous research is undirected; however, in reality, the road network usually contains unidirectional arcs, i.e., the road network is asymmetric. Hence, the authors propose an asymmetric coordinated vehicle-drones arc routing mode for traffic patrolling. In this mode, a truck travelling on an asymmetric road network with multiple drones needs to patrol multiple task arcs, and the drones can be launched and recovered at certain nodes on the truck route, making it possible for drones and the truck to patrol the task in parallel. The total patrol time is the objective function that needs to be minimized given the time limit constraints of drones. The whole problem can be considered as an asymmetric arc routing problem of coordinating a truck and multiple drones. To solve this problem, a large-scale neighborhood search with simulated annealing algorithm (LNS-SA) is proposed. Finally, extensive computation experiments and a real case are carried out. The experimental results show the efficiency of the proposed algorithm. Moreover, a detailed sensitivity analysis is performed on several drone-parameters of interest.]]></description>
      <pubDate>Wed, 21 Sep 2022 09:28:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2012658</guid>
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