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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>Flight Frequency, Schedule Differentiation, and Route Revenue: Evidence for Competition and Scheduling Policy</title>
      <link>https://trid.trb.org/View/2752704</link>
      <description><![CDATA[This study examines the joint effects of flight frequency and schedule differentiation on passenger number, airfare, and revenue in the Australian domestic airline market. It distinguishes between two dimensions of scheduling strategies: between-carrier schedule differentiation and within-carrier schedule dispersion. The monthly panel dataset employed comprises 349 carrier-route-specific cross-sectional units, covering 102 oligopolistic domestic routes (51 airport pairs) in Australia for the 2015M1∼2025M6 period (excluding the COVID period 2020M1∼2021M10). The results show that flight frequency has a consistently positive and significant effect on passenger number and revenue. Between-carrier schedule differentiation is found to increase carrier-route-specific passenger demand and support higher airfare, suggesting that temporal differentiation across competitors reduces direct competition and enhances pricing power. In contrast, more clustered within-carrier scheduling increases passenger attraction by improving service convenience, although its effect on airfare is generally insignificant. To summarise, the empirical findings indicate that a strategy combining higher frequency, greater differentiation relative to competitors, and more concentrated within-carrier scheduling is most conducive to route revenue generation.]]></description>
      <pubDate>Tue, 01 Sep 2026 14:02:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/2752704</guid>
    </item>
    <item>
      <title>Enhancing the intelligence of loading dock booking systems</title>
      <link>https://trid.trb.org/View/2731056</link>
      <description><![CDATA[Loading docks are critical bottlenecks in urban distribution systems, significantly impacting transport costs due to delays and inefficient time slot utilisation. Intelligent scheduling systems, utilising Artificial Intelligence (AI) based models, offer promising solutions for optimising resource allocation, minimising conflicts, and enhancing efficiency. Current loading dock booking systems, while advanced, often fail to deliver expected time savings due to non-adherence to scheduled slots, leading to inefficiencies. This paper describes how AI models were developed based on large-scale historical booking data. Models were developed to predict classifications of no-shows, arrival times, and departure times. A comparison of their performance is also presented.]]></description>
      <pubDate>Thu, 27 Aug 2026 13:46:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2731056</guid>
    </item>
    <item>
      <title>Optimising airport ground handling operations considering multi-skilled resources</title>
      <link>https://trid.trb.org/View/2724949</link>
      <description><![CDATA[Efficient airport ground handling operations are critical to ensuring timely aircraft handling and maintaining overall airport performance. These operations face tight schedules, resource constraints, and disturbances that can cause delays. Multi-skilled resources enhance flexibility by enabling multiple operations, reducing idle time and improving resilience to disturbances. Tactical optimization of ground handling operations is investigated, focusing on allocating and scheduling multi-skilled resources. A new optimization model is proposed to tackle the problem of allocating multi-skilled resources to interdependent ground handling operations under temporal, spatial, and disturbance-related constraints. To address the computational challenges of solving real-world problem instances, a heuristic-based approach is developed, as these instances prove too complex for traditional linear optimization methods. The proposed approach is tested using a case study based on a full day of operations at Düsseldorf Airport, applied to a simplified layout representation, showing its ability to minimize delays and improve workforce efficiency. The results show significant advantages of incorporating multi-skilled resources, including increased flexibility and reduced delays. For example, with 600 resources and 60% disturbed operations, the higher-skilled resource set reduced total delay by 26% compared with the lower-skilled set. The heuristic solved all tested instances in less than one second, whereas exact optimization required up to 24,097 s and became intractable for larger instances. These findings demonstrate that tactical scheduling with multi-skilled resources can improve both operational resilience and computational tractability. The results further show that the proposed approach extends existing ground handling planning methods by enabling proactive tactical scheduling under realistic operational constraints.]]></description>
      <pubDate>Fri, 21 Aug 2026 14:01:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2724949</guid>
    </item>
    <item>
      <title>Measuring the Destination Access and Equity Impacts of Public Transit Service Changes: A Case Study of Title VI Policy at MBTA</title>
      <link>https://trid.trb.org/View/2762072</link>
      <description><![CDATA[Under Title VI of the U.S. Civil Rights Act, public transportation agencies that receive federal funding in the U.S. are obligated to ensure that changes to transit service do not disproportionately burden populations of color or low-income households. However, existing Title VI policies only require that agencies analyze the social equity impacts of large-scale, often pre-planned changes, such as new or extended routes. The equity impacts of the often smaller-scale service changes to multiple routes that transit agencies make on a regular basis in response to operating conditions have attracted limited attention in the research literature. Using a case study of eight service changes implemented by the Massachusetts Bay Transportation Authority between December, 2022, and December, 2024, this study demonstrates that regular service changes can sometimes have substantial and racially disparate impacts on job access, in some cases even exceeding the impacts of the “major” projects that are covered by existing Title VI policies. The results also suggest that “quantity-of-service” measures, such as revenue vehicle hours, which current Title VI policies suggest using to define “major” changes and disparate impacts, are flawed indicators of the user benefits and burdens of transit service changes, with measures of destination accessibility representing a more robust alternative.]]></description>
      <pubDate>Thu, 20 Aug 2026 09:54:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2762072</guid>
    </item>
    <item>
      <title>Impact of vehicle scheduling and strategic transition planning on zero-emission bus systems</title>
      <link>https://trid.trb.org/View/2714789</link>
      <description><![CDATA[This paper presents a holistic framework for the transition from diesel to electric bus networks, crucial for meeting EU regulations targeting 100% zero-emission urban buses by 2035. The authors employ a two-phase solution framework: in phase 1, the authors solve the Charging Location and Electric Vehicle Scheduling Problem to generate vehicle schedules that are feasible for electric operation; in phase 2, these schedules serve as input to a multi-period transition planning model that minimizes the total cost of ownership while determining fleet replacement and charging infrastructure deployment. The experiments show that schedules obtained from solving the integrated charging location and vehicle scheduling problem significantly outperform traditional methods, resulting in lower total cost of ownership. Additionally, transition plans reduce local emissions by up to 85% compared to a diesel-only scenario. The authors find that vehicle rotations with long distances and sufficient idle time are prioritized for electrification, enabling earlier emission reductions and cost savings. This highlights the importance of adopting vehicle scheduling tailored for electric buses, rather than relying on legacy diesel schedules.]]></description>
      <pubDate>Fri, 14 Aug 2026 15:04:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2714789</guid>
    </item>
    <item>
      <title>An Hour-Ahead EV Charging Scheduling Strategy Considering Heterogeneous Demands Via an Online Reservation System</title>
      <link>https://trid.trb.org/View/2717777</link>
      <description><![CDATA[With the increasing penetration of electric vehicles (EVs) into the transportation sector, the efficient scheduling of charging demands has emerged as a critical challenge. Traditional day-ahead methods depend heavily on accurate predictions of charging demand, which are often impractical due to the dynamic and uncertain nature of EV charging behavior. To tackle this issue, this study develops a smart hour-ahead EV charging scheduling strategy (HCSS) that leverages real-time demand information to dynamically manage and control the charging behavior. An online reservation system (ORS) considering the heterogeneous demands is introduced to enable EV users to submit charging requests in advance, while allowing the system to optimize charging schedules in real time. The objective of the ORS-based HCSS is to minimize peak load within a charging zone to ensure grid stability and secure operation. A tailored simulated annealing (SA) approach is proposed to solve the hour-ahead scheduling problem, where a depth-first search (DFS)-based initial solution construction method and a set of customized neighborhood solution search operators are specifically designed to enhance the exploration and exploitation capabilities of the SA approach. Numerical experiments on multiple problem sizes are conducted to assess the effectiveness of the proposed HCSS and SA-based solution approach. Extensive sensitivity analysis is also performed to explore the impact of several key factors on the hour-ahead scheduling performance.]]></description>
      <pubDate>Thu, 30 Jul 2026 10:09:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/2717777</guid>
    </item>
    <item>
      <title>An Enhanced Ship-Speed Prediction Model with Stacking Ensemble Learning</title>
      <link>https://trid.trb.org/View/2717771</link>
      <description><![CDATA[In the field of maritime transportation, precise accurate prediction of ship-speed is paramount for route planning, ship scheduling, and navigational safety. However, the difficulty of the prediction lies in the fact that ship-speed is influenced by numerous complex and variable factors, and traditional speed prediction methods often struggle to attain the desired accuracy in the presence of complex maritime environments and ship characteristics. To address this challenge, this paper aims to develop a robust ship-speed prediction model capable of effectively capturing complex data relationships and improving prediction accuracy and stability. A stacking ensemble learning model is proposed, integrating extra trees (ET), random forest (RF), categorical boosting (CatBoost), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and histogram gradient boosting (HGBT) as base learners, with support vector machine (SVM) as the meta-learner to leverage diverse models' strengths. A standardized workflow for data cleaning, multi-source data fusion, and feature engineering is established. Additionally, the SHapley Additive exPlanation (SHAP) is introduced for model interpretability. Experiments with historical trajectory data from five ships and meteorological-oceanographic data show that the stacking model outperforms single models in prediction accuracy and stability. SHAP analysis reveals that ship course and wave height are key influencing factors, with their impact varying across different navigation scenarios. The proposed model enhances operational efficiency, safety, and decision-making in the maritime industry by providing reliable speed predictions and interpretable insights.]]></description>
      <pubDate>Thu, 30 Jul 2026 10:09:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/2717771</guid>
    </item>
    <item>
      <title>Research on the Preparation of the Freight Train Operation Plan Considering Transportation Demand Delivery Time Limits</title>
      <link>https://trid.trb.org/View/2717685</link>
      <description><![CDATA[To enhance the market competitiveness of railway freight transportation, the current organization mode must be optimized to incorporate the high timeliness demands of high value-added freight products. Through the optimized preparation of the freight train operation plan, transportation services that ensure the delivery timeliness can be provided for goods in the market. This helps attract more freight, improve operational efficiency, and enhance the performance of railway freight enterprises. The essence of the problem in 'the preparation of the freight train operation plan considering transportation demand and delivery time limits' is a type of dynamic service network design (DSND) problem with time windows. This paper first designs closed interval time windows and semi-closed time windows to address different transportation demands. To more accurately describe the transportation process of transportation demands in the spatio-temporal network, transit arcs and waiting arcs are introduced in the design of the arc-based model, while the DFS algorithm is employed to determine all feasible paths in the design of the route-based model. Using the Shenyang-Dalian Railway as a case study, several instances of different sizes are designed for the case. The Gurobi solver is then applied to solve the two modeling approaches separately, followed by an analysis of the solution process and results. The results show that both models can accurately solve the problem within the computational time constraints for instances with different sizes. In terms of solution quality, the two modeling approaches achieve the same objective function value. In terms of computational efficiency, the arc-based DSND model is more advantageous.]]></description>
      <pubDate>Tue, 28 Jul 2026 11:07:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2717685</guid>
    </item>
    <item>
      <title>Organization of Transport Services in Grain Logistics Using Various Types of Transport in Wartime Conditions</title>
      <link>https://trid.trb.org/View/2581928</link>
      <description><![CDATA[Russia’s military intervention in Ukraine has disrupted the logistics chains of grain exports. Nibulon’s traditional system of transshipment of grain from road to river transport was disrupted due to problems with water transportation on the Dnipro River. This necessitated exploring the possibility of using alternative modes of transportation, including road and rail, to restore the grain terminals. On the basis of scientific methods, ways to improve the interaction of different types of transport during grain transshipment at the terminal are substantiated. A methodology for determining the optimal weight of products transferred from the terminal to the mainline transport is proposed in order to increase the efficiency of logistics operations in the current conditions. A quantitative method for estimating the average daily load on the terminal has been developed, which allows forecasting transportation needs and optimizing traffic schedules. Practical significance includes improving the planning of the terminal, increasing its throughput, reducing transportation costs, and increasing the efficiency of grain exports. The optimal weight of grain for transfer to railroad transportation was calculated. The results can be used to model the operation of other terminals and develop strategies for the development of grain logistics in Ukraine and other countries with developed agricultural sectors.]]></description>
      <pubDate>Mon, 27 Jul 2026 11:16:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2581928</guid>
    </item>
    <item>
      <title>The effects of split sleep on sleepiness on performance: A train simulator study</title>
      <link>https://trid.trb.org/View/2717921</link>
      <description><![CDATA[Fatigue and sleepiness are critical safety risks in train operations, particularly among train drivers. One contributing factor is poor sleep quality, which may result from split or fragmented sleep patterns due to operational schedules and personal time-use constraints. This study aimed to investigate the effects of split sleep on fatigue and sleepiness during simulated train operations. Fifteen male participants completed a 2.5-hour train-driving simulation under three sleep conditions: split sleep (05:00 a.m. – 10:00 a.m. and 00:00p.m. – 3:00p.m.), consolidated daytime sleep (05:00 a.m. – 01:00p.m.), and baseline nighttime sleep (09:00p.m. – 05:00 a.m.). Sleepiness was assessed using ocular measures (blink duration, blink frequency, and microsleep per minute), subjective video rating, EEG parameters (alpha and theta wave power), and the Karolinska Sleepiness Scale (KSS). Results showed that the split sleep condition led to a substantial increase in fatigue indicators, with ocular and facial measures increasing by 15–70% compared to baseline. The consolidated sleep condition produced moderate increases (9–31%). No significant differences were observed in EEG parameters, and subjective KSS scores showed only marginal changes. These findings indicate that, despite an equivalent total sleep duration, split sleep patterns significantly impair alertness. The results highlight the need for careful scheduling and the provision of appropriate rest facilities in operational settings to mitigate fatigue-related risks.]]></description>
      <pubDate>Tue, 21 Jul 2026 09:49:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/2717921</guid>
    </item>
    <item>
      <title>Association between scheduling attributes and unsafe behaviors of bus drivers: Considering unobserved heterogeneity and temporal instability</title>
      <link>https://trid.trb.org/View/2721807</link>
      <description><![CDATA[Mitigating unsafe driving behaviors among public transport drivers is vital for enhancing operational safety. Existing studies have mainly focused on identifying unsafe behaviors, whereas the role of organizational and managerial attributes, particularly shift scheduling, remains insufficiently understood. Using one year of real-world operational data from a bus company in China, this study examines how driver characteristics, scheduling attributes (work profiles and route properties), and environmental conditions are associated with unsafe driving behaviors and how these associations vary across quarters. Unsafe behaviors were classified via incidence rate ratios (IRRs) into three types: operational safety violations, impaired driving states, and regulatory non-compliance. To jointly capture fixed marginal effects, unobserved heterogeneity, and quarterly instability, a Random Parameters Logit Model with Heterogeneity in Means and Variances (RPLMV) is developed and combined with global and local temporal stability tests. This approach identifies significant fixed effect associations, two random parameters—annual safety violations ≥ 2 and age > 50 years—and five variables associated with mean and variance heterogeneity. The results reveal pronounced quarterly variability and complex association patterns arising from the interaction between scheduling conditions, subgroup sensitivities, and operating environments. These findings support an integrated management framework combines baseline controls with targeted interventions, operationalized through scheduling, route optimization, and quarter-specific safety interventions. This study contributes methodological innovation by embedding heterogeneity modeling within a temporal stability framework and offers practical guidance for precision risk management and targeted dispatch in public transport systems.]]></description>
      <pubDate>Tue, 21 Jul 2026 09:49:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/2721807</guid>
    </item>
    <item>
      <title>Integrated Electric Bus and Charger Scheduling with Time-Continuous Optimization and Efficient Combinational Algorithms</title>
      <link>https://trid.trb.org/View/2717500</link>
      <description><![CDATA[Battery electric buses (BEBs) are increasingly adopted in urban transit systems due to their significant environmental benefits. However, their limited driving range and prolonged charging times introduce significant operational challenges. To address these issues, this study investigates an integrated bus and charger scheduling problem through a time-continuous optimization framework. The problem incorporates key operational considerations, including time-of-use electricity pricing, partial charging strategy, nonlinear battery charging profile, stochastic trip durations and energy consumption, and a limited number of chargers. A mixed-integer model is formulated on a specially constructed two-layer trip-charging integrated scheduling network to minimize the total system cost. To effectively solve the problem, a two-stage solution approach is developed for the charger scheduling subproblem, which employs a minimum-cost-maximum-flow-based algorithm to determine the charging durations under TOU pricing, followed by a deficit-function-based heuristic to optimize charging start times. Building on this, two solution frameworks – branch-and-bound (BB) and adaptive large neighborhood search (ALNS) – are developed to solve the integrated scheduling problem. Computational experiments demonstrate that the BB approach consistently outperforms the commercial solver Gurobi, yielding superior solutions within similar or shorter computational times. Meanwhile, the ALNS delivers high-quality solutions with significantly lower computational effort, making it well-suited for large-scale real-world applications.]]></description>
      <pubDate>Wed, 15 Jul 2026 16:27:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2717500</guid>
    </item>
    <item>
      <title>AOI-Aware Joint Scheduling and Power Control for Multi-Platoon Vehicular Networks Via Multi-Agent Reinforcement Learning</title>
      <link>https://trid.trb.org/View/2717484</link>
      <description><![CDATA[In the realm of the Internet of Vehicles (IoV), the concept of grouping autonomous vehicles into platoons stands out as a promising driving scenario. A platoon comprises interconnected vehicles, with the foremost vehicle designated as the Platoon Leader (PL), while each of those trailing behind is a Platoon Member (PM). In such contexts, information freshness quantified using the Age of Information (AoI) critically ensures road traffic safety. This paper explores the joint packet transmission scheduling and power allocation problem with the objective of minimizing AoI in multi-platoon vehicular networks; these latter exhibiting high dynamics incurring notable uncertainty and complexity. To alleviate this optimization problem's complexity a decentralized partially observable Markov Decision Process (Dec-POMDP) formulation is adopted. Then, an AoI-aware joint scheduling and power control scheme based on Multi-Agent Twin Delayed Deep Deterministic policy gradient (MATD3) algorithm is proposed. In addition, in order to improve the efficiency of the MATD3's learning phase, the algorithm has been augmented with Priority Experience Replay (PER). Simulation results show that this approach outperforms the baseline MATD3 method by 17.3% in terms of the achieved mean AoI.]]></description>
      <pubDate>Wed, 15 Jul 2026 16:27:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2717484</guid>
    </item>
    <item>
      <title>Modeling and Optimization of a Share-a-Ride Problem with Flexible Pick-up and Drop-Off Points</title>
      <link>https://trid.trb.org/View/2717483</link>
      <description><![CDATA[A share-a-ride problem (SARP), which integrates the transportation of both passengers and parcels by the ride-hailing platforms such as Uber and Lyft, has drawn considerable attention. This work introduces a novel share-a-ride problem with flexible pick-up and drop-off points (SARP-FUO) with the objectives of maximizing the total revenue of the ride-hailing platforms and minimizing the total travel distance of vehicles. A mixed integer programming model is developed to formulate SARP-FUO. Then, a knowledge-based multi-objective brain storm optimization algorithm (KM-BSO) is proposed to solve it. Two knowledge-based local search operators are specifically designed to enhance the exploration capability of KM-BSO for identifying potential nondominated solutions. The first operator employs a dynamic programming algorithm to readjust pickup and drop-off points, while the second modifies vehicle routes based on four derived properties. Extensive experiments are conducted to compare KM-BSO with nondominated sorting genetic algorithm II, multi-objective evolutionary algorithm based on decomposition, multi-objective artificial bee colony algorithm, and a mathematical programming solver CPLEX. The results and statistical analysis demonstrate the superiority of the proposed approach in solving the studied problem. Finally, a sensitivity analysis is performed with and without flexible pick-up and drop-off points, demonstrating the advantages of the proposed model in developing intelligent public transportation systems.]]></description>
      <pubDate>Wed, 15 Jul 2026 16:27:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2717483</guid>
    </item>
    <item>
      <title>Energy consumption and makespan by considering set up and transportation time: A hybrid AHO-MARR technique</title>
      <link>https://trid.trb.org/View/2706053</link>
      <description><![CDATA[This manuscript presents a hybrid approach to the job shop scheduling problem (JSP) with sequence-dependent set-up and transportation times, utilizing the AHO-MARR technique. The method aims to minimize both makespan and overall energy consumption in energy-efficient manufacturing environments. By integrating the Archerfish Hunting Optimizer (AHO) and the Median-Average Round Robin (MARR) scheduling algorithm, the approach considers processing time, idle time, sequence-dependent set-up time, and transportation time to improve production efficiency. AHO calculates total processing time and energy for each job, while MARR ensures practical task distribution across machines. The method effectively reduces setup time and energy usage, achieving an energy consumption of 730 kW/min. Comparative analysis shows that the AHO-MARR technique outperforms existing methods such as the Salp Swarm Algorithm (SSA), Wild Horse Optimizer (WHO), and Heap-Based Optimizer (HBO) in terms of energy efficiency and makespan reduction.]]></description>
      <pubDate>Tue, 23 Jun 2026 16:59:44 GMT</pubDate>
      <guid>https://trid.trb.org/View/2706053</guid>
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