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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>
    <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>Analysis and Calculation of the Turnback Capacity of Rail Transit Station</title>
      <link>https://trid.trb.org/View/2113905</link>
      <description><![CDATA[Turnback capability is one of the key factors restricting the line capacity of rail transit. By taking the typical turnback station rail transit as background, considering train turnback mode and stop time factors, an integer programming model with the minimum turnback time as the goal, train receiving-departure operation process and occupation rules of track section as the constraints was established, and the maximum turnback capability under different turnback modes was obtained. Finally, one pre-station turnback station and one post-station turnback station as cases were calculated to evaluate the turnback ability.]]></description>
      <pubDate>Fri, 24 Apr 2026 08:55:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2113905</guid>
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    <item>
      <title>Train Timetabling With Stop Planning and Passenger Distributing Integration Orientated by Railway Capacity and Passenger Service</title>
      <link>https://trid.trb.org/View/2591119</link>
      <description><![CDATA[In the process of railway operation planning, it is essential to take into account both railway capacity and origin to destination (OD) passenger demand. Stop plan plays a vital role in generating a train timetable with maximum railway capacity and ensuring high-quality service to transport passengers. Therefore, we are addressing the challenge of optimizing both the stop plan and timetable for a group of trains on a railway line, focusing on railway capacity estimation and passenger demand satisfaction. To provide realistic and precise passenger distribution, the preferences of different categories of passengers are given due regard. A classic time-space network describes the integrated problem, based on which a mathematical model is formulated to minimize train occupancy time on the high-speed railway line and maximize passenger kilometers at the same time. A decomposition approach based on Lagrangian relaxation (LR) is suggested to address the problem, which decomposes the integrated scheduling problem into two sub-problems: a train timetabling sub-problem, and a stop planning and passenger distributing sub-problem by dualizing constraints linking the two. A heuristic approach based on genetic algorithms is designed to obtain feasible solutions. The proposed model and approach are shown to generate good solutions efficiently. A series of real-world instances are conducted on the Beijing-Shanghai high-speed railway line in China, and the experimental outcomes show the benefits of optimizing the stop plan. Other related analyses are discussed by comparing results with different total number of stops, heterogeneous and homogeneous cases.]]></description>
      <pubDate>Fri, 20 Mar 2026 14:10:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2591119</guid>
    </item>
    <item>
      <title>Signal Wake Model (SWM)</title>
      <link>https://trid.trb.org/View/2675149</link>
      <description><![CDATA[The Signal Wake Model (SWM) is a detailed computer simulation model which allows analysis of the effect of a signal system on the movement of following trains. Specifically, the simulation moves a fleet of trains over a given signal plant as close together as the signal system allows. Minimum train headways are determined at each signal location. SWM is often used in conjunction with the Route Capacity Model (RCM) for analyzing the capacity of a CTC rail line. Specifically, RCM implements a simulation of train movements based on the headways provided to it by the SWM. This report outlines the capabilities of the Canadian National Railways (CN) Signal Wake Model (SWM) and describes the design of the model from a user perspective.]]></description>
      <pubDate>Sun, 15 Mar 2026 17:52:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/2675149</guid>
    </item>
    <item>
      <title>Route Capacity Model (RCM)</title>
      <link>https://trid.trb.org/View/2675148</link>
      <description><![CDATA[The Route Capacity Model (RCM) is a software tool for analyzing the capacity of a CTC rail line. Specifically, it implements a simulation of train movements that can be used to determine train delays under different plant, traffic and maintenance conditions. The RCM is often used in conjunction with the Signal Wake Model (SWM) that determines the minimum train headway input for the Route Capacity Model. This report outlines the capabilities of the Canadian National Railways (CN) Route Capacity Model (RCM) and describes the design of the model from a user perspective.]]></description>
      <pubDate>Sun, 15 Mar 2026 17:52:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/2675148</guid>
    </item>
    <item>
      <title>Railroad Line Capacity, Scheduling, and Dispatching Models: State-of-the-Art and Possible Extensions</title>
      <link>https://trid.trb.org/View/2666670</link>
      <description><![CDATA[The central thesis of this work is that the current set of models used in railroad capacity analysis, scheduling, and dispatching is such that there exists a gap between the large scale computer based models and sketch planning tools. The concepts of line capacity, train scheduling, and train dispatching are examined and placed in a conceptual framework known as Sequential Problem Solving. The current set of models is examined and evaluated, and it is found that there are no appropriate models for examining complex intersections or critical segments within a line. The system design for an event based simulation model to remedy this problem is presented, which uses alternative train priority schemes, thoroughly probabilistic event timing, and fleet meetable conditions. Conclusions and recommendations for future research are presented.]]></description>
      <pubDate>Sun, 08 Mar 2026 16:14:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/2666670</guid>
    </item>
    <item>
      <title>A methodology for the analysis of timetables’ resilience in railway rapid transit lines: The case of regular timetables</title>
      <link>https://trid.trb.org/View/2636776</link>
      <description><![CDATA[Disruptions in railway rapid transit systems produce negative effects affecting passengers’ daily movements and considerable costs due to abnormal operation. In this study, we propose a methodology for analyzing the effect of disruptions on regular timetables. Among the post-disruption alternatives that service operators can use to try to restore the normal functioning of transport services, we study the execution of small corrective actions on the timetable to guarantee its feasibility in terms of inter-service safety times at stations and the possibility of performing a timetable re-optimization. In the latter case, we propose an original mixed-integer non-linear optimization model to recover the timetable stability (or regularity) as soon as possible. We illustrate and compare the two strategies. Using timetable stability as a system performance indicator, we propose a measure of timetable resilience. Finally, we study the effect of extra train capacity on the resilience of the timetable.]]></description>
      <pubDate>Wed, 25 Feb 2026 16:28:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/2636776</guid>
    </item>
    <item>
      <title>Evaluation Study on the Matchability of Urban Railway Transportation Capacity and Volume</title>
      <link>https://trid.trb.org/View/2613071</link>
      <description><![CDATA[Focusing on transport capacity utilization and passenger service level of urban rail transit network, the study establishes a capacity-volume matching assessment system. The developed assessment method includes three spatial levels: stations, intervals, and line layers. The rail transit line network is then evaluated from these three spatial levels. A simple network of three subway lines in a city is used as a case study to verify the applicability of the constructed capacity matching assessment method.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2613071</guid>
    </item>
    <item>
      <title>Study on the Dynamic Preparation and Optimization of Multi-standard Rail Transit Train Operation Scheme</title>
      <link>https://trid.trb.org/View/2581360</link>
      <description><![CDATA[The development of the collaborative operation mode of multi-standard rail transit system formed by urban rail transit, municipal railway, intercity railway, and trunk railway has become the key to promote the integrated development of regional transportation. The core task of multi-standard regional rail transit coordination is to realize the dynamic preparation of train operation scheme under the background of different standard cooperative operations in the context of dynamic passenger flow demand. This paper with the system rail transit operation enterprise operation benefit maximization and passenger travel cost minimization as the target function, collaborative optimization of departure frequency, stop scheme elements, considering the capacity matching degree, train stops, minimum departure frequency, passenger flow demand and transportation capacity limit constraints, build multi-system coupling train operation scheme dynamic optimization model, and using the principle of genetic algorithm, the design algorithm to solve. The calculation results demonstrate that after optimizing the train operation scheme of the multi-standard rail transit system, the operational capacity provided by the train operation plans for lines L and S aligns perfectly with the transfer passenger flow at stations S1 and S3 and other rail transit systems, falling within the optimal operational capacity matching range. This validates the effectiveness of the model and algorithm. The research findings offer a decision-making approach for dynamically preparing train operation schemes in multi-system coupling scenarios.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/2581360</guid>
    </item>
    <item>
      <title>Coordinated Optimization Research of Multi-standard Rail Transit Train Operation Plans Based on Capacity Matching</title>
      <link>https://trid.trb.org/View/2581358</link>
      <description><![CDATA[With the continuous development of rail transit in China, the coordinated development of different standard rail transit systems has become an irreversible trend. The core of multi-standard rail transit transportation organization lies in devising train operation plans that achieve mutual coordination and capacity matching. Existing optimization research on train operation plans mainly focuses on optimizing from the perspectives of enterprises or passengers, with the research direction targeting single-standard train operation plans. Therefore, this study coordinates and optimizes multi-standard train operation plans based on demand and capacity matching, aiming to maximize comprehensive capacity matching, enhance economic benefits for operating departments, and minimize passenger travel costs. A coordinated optimization model for multi-standard rail transit train operation plans is established and solved using the ideal point method. Finally, using a certain region's multi-standard rail transit as an example, the optimal operation plan is determined. The results indicate that the deviation between the comprehensive capacity matching and optimal matching of the optimized train operation plan has been reduced by 28.1%, leading to improvements in economic benefits for operating departments and passenger travel costs.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/2581358</guid>
    </item>
    <item>
      <title>Determinants of irregular demand for regional rail passenger services – case study of High Tatras in Slovakia</title>
      <link>https://trid.trb.org/View/2590557</link>
      <description><![CDATA[The demand for public transport by tourists increases significantly in tourist-attractive destinations. This is in addition to regular passengers commuting to school and work. The level of irregular demand is influenced by several factors related to the characteristics of the day of the week, the period of the year, and the current weather. The main goal of the paper is to verify which factors most influence the irregular demand for transport in a tourist-attractive area to ensure operational planning of public passenger transport. Thanks to this, it is possible to ensure sufficient capacity and, at the same time, the efficiency of the operation of public passenger transport. The paper analyzes the main determinants of the irregular demand for regional public rail passenger transport in the High Tatras region of Slovakia. Multiple linear regressions were used to model the number of irregular passengers. The variables representing the day of the week, the attractiveness of the period, and the holiday were found to be the most significant. The variables describing the weather such as maximum daily temperature, precipitation, clouds, and wind had less influence. The obtained mathematical models for forecasting the irregular demand for public passenger transport can help optimize the timetable’s operational setting and the train sets’ size.]]></description>
      <pubDate>Thu, 19 Feb 2026 10:53:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2590557</guid>
    </item>
    <item>
      <title>Route-Based Intermediate and Terminal Station Capacity Models for High-Speed Rail</title>
      <link>https://trid.trb.org/View/2647051</link>
      <description><![CDATA[Stations serve as critical capacity bottlenecks within a high-speed rail (HSR) system, where the interaction among trains on different routes plays a pivotal role in determining overall capacity. Past studies have often neglected to delve into the nuanced influence of specific route combinations on station capacity. This research addresses this gap by focusing on intermediate and terminal (turn-back) stations as spatial reference points. We introduce route-based capacity models and simulation processes to assess station capacity, considering potential headways among adjacent trains. Our approach utilizes a hybrid process, seamlessly integrating analytical techniques with simulation methods. Initially, several trains with various route possibilities are generated by simulation, and their corresponding average headways are analytically computed. The proposed models and processes are validated using the Taiwan HSR network. The results conclusively demonstrate the efficacy of our approach in evaluating HSR station capacity, offering valuable insights for station capacity assessment and management during the planning, design, and operational phases.]]></description>
      <pubDate>Thu, 08 Jan 2026 10:29:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2647051</guid>
    </item>
    <item>
      <title>Optimization method for maximizing capacity utilization of high-speed railway train timetable based on node routing model</title>
      <link>https://trid.trb.org/View/2600597</link>
      <description><![CDATA[The increasing passenger demand on certain high-speed railway lines has resulted in strained capacity utilization, posing a challenge for railway operators to optimize train timetables to maximize capacity utilization. This paper investigates the impact of train path sequences on section carrying capacity, as well as how train paths utilize time-space resources of train timetables. The problem of scheduling train timetables is transformed into the problem of node routing, where nodes represent train paths, directed arcs depict the sequential connections between train paths. A train timetable optimization model based on node routing is constructed, with the objective of maximizing the number of trains and the constraint that the total arc weight does not exceed total amount of time-space resources on the train timetable. Then considering the characteristics of the model, this paper designs an optimization algorithm for train timetables, which includes modules for designing train overtaking combinations, quantifying time-space resources, optimizing train path sequences, and scheduling train timetables. The algorithm is tested on the Shanghai-Hangzhou High-speed Railway in China. The computational results prove that the optimization method based on node routing can help railway operators improve the capacity utilization level of high-speed railways. Further, we design a series of experiments to study the impact of train headway times, train running speed, and other factors on the section carrying capacity, reveal the impact mechanisms of these train timetable factors on section carrying capacity, and propose optimization strategies for high-speed train timetables to railway operators.]]></description>
      <pubDate>Mon, 22 Dec 2025 16:07:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2600597</guid>
    </item>
    <item>
      <title>A queueing-based approach for timetable-independent railway station performance analysis</title>
      <link>https://trid.trb.org/View/2593236</link>
      <description><![CDATA[Railway stations serve as critical nodes within railway networks, facilitating connections across diverse travel directions. Traditionally, the analytical performance analysis of railway stations has been divided into two distinct components: the examination of stopping tracks and the evaluation of route nodes, the locations within a station where switches determine the direction of travel. This study introduces an innovative Continuous-Time Markov Chain model that represents a comprehensive queueing system encompassing the entire railway station. By deriving timetable-independent performance indicators, this model provides a robust framework for assessing station performance. Consequently, it equips infrastructure operators with a holistic tool for infrastructure planning and evaluation.]]></description>
      <pubDate>Tue, 18 Nov 2025 11:04:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/2593236</guid>
    </item>
    <item>
      <title>A two-stage stochastic model for road-rail intermodal freight transportation under demand and capacity uncertainty</title>
      <link>https://trid.trb.org/View/2601331</link>
      <description><![CDATA[With the steady increase in global logistics and freight transport demand, the need for efficient and sustainable intermodal transport systems becomes increasingly important. This study addresses the optimization of container movement by intermodal transport with fixed train schedules. We emphasize the integration of road-rail intermodal transport amid uncertain demand and train (spot) capacities. A two-stage stochastic optimization model is developed to strategically manage the transportation of containers from multiple origins to designated intermodal hubs. By leveraging spot capacities at train stations and addressing uncertainties in demand and train capacity, the model integrates Conditional Value-at-Risk (CVaR) to balance cost efficiency and risk management, enabling robust decision-making under uncertainty. The model’s objectives encompass minimizing transportation costs, mitigating carbon emissions, and enhancing the reliability of containerized freight movement across the network. A comprehensive case study using real-world data demonstrates the practical applicability of the model, highlighting its effectiveness in reducing operational costs, minimizing environmental impacts, and providing actionable insights for stakeholders to navigate the trade-offs between expected costs and risk management in dynamic intermodal transport settings.]]></description>
      <pubDate>Thu, 06 Nov 2025 09:00:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2601331</guid>
    </item>
    <item>
      <title>Probabilistic prediction method for seismic response and post-earthquake capacity of regional high-speed railway bridges</title>
      <link>https://trid.trb.org/View/2594497</link>
      <description><![CDATA[The rapid and accurate assessment of post-earthquake operational performance and the reliable planning of train operation schemes for a regional high-speed railway bridge network are fundamental to cross-regional disaster relief and rescue operations. This study proposes a method for constructing a regional bridge structure sample library based on Latin Hypercube Sampling (LHS) and Gibbs Sampling. A predictive model is developed to evaluate the seismic response of a regional high-speed railway bridge network, incorporating inputs from multiple feature sources. The seismic response limits and operational thresholds corresponding to various post-earthquake performance states of the bridges are quantified. A probabilistic assessment method for determining the seismic response of the regional bridge network is derived. In addition, an assessment method for post-earthquake capacity accounting for multiple sources of uncertainty, is established. The study demonstrates that the proposed sample library construction method for high‑speed railway structures requires at least 5 chains, each containing 8,000 samples. In the case study, the developed multimodal neural network surrogate model with embedded uncertainties successfully envelopes the true responses. Earthquake magnitude and epicentral distance are identified as the dominant factors affecting post‑earthquake operational performance, while topographic effects become significant when seismic intensity exceeds 7 degrees. Furthermore, the selection of decision criteria exerts a greater influence on optimizing post‑earthquake traffic routing than the choice of decision objectives.]]></description>
      <pubDate>Wed, 29 Oct 2025 13:39:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2594497</guid>
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