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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>Railway Ground Truth and Digital Map Based on GNSS and Multi-sensor Big-Data Acquisition</title>
      <link>https://trid.trb.org/View/2671802</link>
      <description><![CDATA[Satellite-based localization solutions are expected to boost railway digitalization and in particular, they will enhance evolution and efficiency of railway signaling systems. The development of multi-sensor solutions is ongoing, but some gaps remain. This paper addresses two of them: the need for innovative high accuracy and precision Ground Truth and Digital Maps, essential elements of a EGNSS train positioning system and a V&V environment. These two objectives are focused in the RAILGAP EU project. For each of these tools, this paper presents the main high-level requirements and the selected architectural design exploiting specific data fusion algorithms. The novelty of the EGNSS multi-sensor solution proposed is that it does not require to install or modify any equipment on the track. It is based on datasets acquired through commercial runs in Italy and Spain, leveraging on regular train trips in different operational scenarios and time.]]></description>
      <pubDate>Tue, 30 Jun 2026 16:05:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2671802</guid>
    </item>
    <item>
      <title>Recommendations and Roadmaps Towards Intelligent Railways</title>
      <link>https://trid.trb.org/View/2671801</link>
      <description><![CDATA[This paper provides an overview of the main results achieved within the Horizon 2020 Shift2Rail project named RAILS (Roadmaps for Artificial Intelligence Integration in the Rail Sector). The RAILS roadmapping process provided state-of-the-art, taxonomy, future research directions, and recommendations in three macro areas: Railway Safety and Automation, Predictive Maintenance and Defect Detection, and Traffic Planning and Management. RAILS findings shed light on the potential of intelligent technologies and provided essential guidelines for integrating machine learning into next-generation smart railways.]]></description>
      <pubDate>Tue, 30 Jun 2026 16:05:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2671801</guid>
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    <item>
      <title>Train Dispatcher in the Cloud – Digitalising Track Warrant Control for Safe Train Operations in Structurally Transforming Areas</title>
      <link>https://trid.trb.org/View/2671794</link>
      <description><![CDATA[To mitigate the adverse effects of climate change, greenhouse gas emissions need to be minimised. The FlexiDug project investigates sustainable transport perspectives for structurally transforming areas where coal mining phases out. This work presents a safe, economical, and extendable approach on reusing industrial railways for passenger transport. We have digitalised the Zugleitbetrieb, a mode of operation that requires no trackside equipment. Our Train Dispatcher in the Cloud (German Zugleiter in der Cloud, henceforth ZLiC) has been developed ontology- and model-based. It also takes a railway network model as input, e.g., for the generic interlocking logic. Speech to text, naturallanguage understanding, and text to speech recreate the established speech interface towards conductors. A state machine ensures the prescribed voice procedure. Custom voice-activated recording allows using COTS radio devices. ZLiC has been evaluated successfully in simulations and field tests. We plan further improvements, evaluations, and a risk assessment.]]></description>
      <pubDate>Tue, 30 Jun 2026 16:05:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2671794</guid>
    </item>
    <item>
      <title>A Real Time Decision-Support Tool for Traffic Management</title>
      <link>https://trid.trb.org/View/2671787</link>
      <description><![CDATA[SNCF Voyageurs/TGV-Intercités operates over 800 highspeed trains (TGVs) per day in France. The system can be highly tense during peaks with up to 13 trains per hour on the same track section. Thus, minor delays can affect operations, especially for long-distance trains. Supervision of the system and real-time rescheduling are difficult tasks given the complexity of the rail network and the cohabitation of different train services. In this work, we present a real-time decision-support tool providing estimations on the arrival time of trains at each station and at destination and helping comparisons between rescheduling choices. The estimations of future delays have a focus on explainability, with information on the causes of the delay and on the possibility to recover the delay. The operators can use the tool to test different rescheduling choices and see the impact of each scenario on the traffic, helping them deciding the actions to take to minimize delays. The predictions of arrival times of trains are based on a macroscopic discrete event simulator, coupling the theoretical timetabling with real-time information on the trains’ positions. Arrival times are displayed on the interface of the developed web application, where the user can interact with the simulation by adding information and comparing different disruption management scenarios. The tool has been tested with success in an operational environment. The operators gave positive feedback on the tool, underlying its capacity to give them more insight on the expected delay evaluations and on potential conflicts. The information displayed was judged relevant and reliable. This is confirmed by our analysis of the quality of the simulation.]]></description>
      <pubDate>Tue, 30 Jun 2026 16:05:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2671787</guid>
    </item>
    <item>
      <title>Managing European Railway Traffic Management System Project: Challenges and Lessons Learnt from Denmark</title>
      <link>https://trid.trb.org/View/2671844</link>
      <description><![CDATA[The European Rail Traffic Management System (ERTMS) represents a critical infrastructure project dedicated to the digitalisation of railway transportation services within the European Union. Nevertheless, the ERTMS deployment has experienced a gradual pace, falling behind the EU ERTMS implementation schedule. Under this premise, this study investigates the schedule performance of ERTMS projects across the EU region, and the prevailing project management practices employed in managing the ERTMS project through a qualitative case study of the Danish ERTMS deployment. The findings reveal that ERTMS projects are inherently more susceptible to risks than their initial estimates, with the actual schedule exceeding initial estimation by 1.75. The qualitative analysis has identified fourteen prominent causes contributing to project delays and eleven distinct solutions to mitigate these underlying challenges. The study reveals that addressing specific challenges requires a combination of solutions across three levels of project management: technical, strategic, and institutional.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2671844</guid>
    </item>
    <item>
      <title>Cooperation Between a Human Traffic Manager and an AI Assistant for an Improved Railway Infrastructure Resilience</title>
      <link>https://trid.trb.org/View/2671838</link>
      <description><![CDATA[This article deals with railway traffic management, which includes tasks such as traffic planning, resource allocation, service adaptation and passenger information. Operators monitor the real-time movement of trains, passengers and resources, mitigate unexpected events and ensure safety. Human operators currently perform these complex tasks using their expertise. However, technical aspects of railways and concurrent disturbances increase cognitive load and biases, affecting traffic management and passenger satisfaction. To address these challenges, we propose an AI-based railway traffic manager assistant that combines Machine Learning (ML) and Human-Machine Interaction (HMI) to support operators in their daily tasks and assists them to make decisions when facing critical situations. This article outlines the design approach and introduces the initial assistant version. User-centred evaluation yields preliminary results from limited-scale experiments.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2671838</guid>
    </item>
    <item>
      <title>An Image-Based System to Improve Freight Capillaries Circulations</title>
      <link>https://trid.trb.org/View/2671826</link>
      <description><![CDATA[To date, many freight capillaries are limited or degraded. They are called “Voie Unique à Trafic Restreint » (VUTR). There is no security engagement there, which restrict traffic to one train on the track. To increase circulation, the freight line is separated into sections. For a train in section S, sections S-1 and S+1 must remain empty. To confirm the wholeness of the convoy in S, drivers must confirm the presence of rear red lights by checking visually. The procedure is time-consuming and does not bring as many improvements as expected. This paper presents a system to improve divided VUTR. On each side of the subsections, visual sensors are installed. They bring remote surveillance, easily understandable by a human agent. The video stream starts recording when the train arrives and stops when it leaves. The system also has an option to plan data acquisition on specific times, for example theoretical timetables. Data are sent on a central server, accessible to control centers, drivers, and station’s agents. This first use of this application is a human monitoring. In case of system failure, the drivers would go back to their original missions. The second use is an algorithmic processing of the data. Image processing and machine learning are considered. In both cases, the detection of red lights in section S allows to say “section S-1 is free and safe”.Note: no SIL is attributed to this system.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2671826</guid>
    </item>
    <item>
      <title>An Adaptive Graph Reinforcement Learning Method for Scalable Multi-Train Cooperative Control</title>
      <link>https://trid.trb.org/View/2646682</link>
      <description><![CDATA[Multi-Train Optimal Control (MTOC) addresses the cooperative control problem of multi-trains running on railway tracks through centralized or distributed controllers. However, two critical challenges emerge in solving MTOC problems: (1) the dynamic system dimensionality caused by time-varying train numbers during station arrivals and departures and (2) the strong inter-train command correlations in dense traffic scenarios. These complexities lead to computational challenges when scaling to extended railway networks with growing train populations, rendering conventional rule-based methods ineffective. To address these challenges, we propose Graph Attention Soft Actor-Critic (GASAC), a novel graph reinforcement learning algorithm integrating two core components: (1) A graph attention network (GAT) for efficient information aggregation from high-dimensional train observations, and (2) A Soft Actor-Critic (SAC) architecture serving as the centralized decision-maker. The GAT module performs dimensionality reduction through feature attention mechanisms, effectively supporting the SAC module in deriving optimal control policies. Comparative evaluations against multi-agent deep reinforcement learning baselines demonstrate that GASAC successfully synthesizes distributed train information to generate control commands, ensuring collision-free and on-time operations. Further sensitivity analysis shows the adaptability of the algorithm to different parameters.]]></description>
      <pubDate>Mon, 18 May 2026 16:36:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2646682</guid>
    </item>
    <item>
      <title>The Operational Resilience Evaluation Method as a Tool for Decision-Making during Traffic Disruptions in the Railway System</title>
      <link>https://trid.trb.org/View/2646843</link>
      <description><![CDATA[Basically, node connections and arc capacity issues are taken into account for resilience evaluation. Then, resilience investigation is mainly limited to catastrophic events with focus on the system layer. Nevertheless, from the operation point of view it is not enough to keep the correct node connection of the system but also to keep the appropriate process schedules. Thus, it is important to go beside the classical network (system) resilience and to develop the concept of operational resilience. In the typical resilience analysis, the main function necessary for resilience evaluation is the performance or functionality in time. Normally it is defined by one criterion, for example available railway lines, or number of trains, or hardly ever also punctuality. Therefore, the 1st aim of this article is to propose a multi properties functionality function, that takes into account operation process parameters like punctuality, delay probability, number of launched trains, and correctly assigned resources. 2nd, the article shows a tree stage fuzzy model to calculate the performance function using the incoherent process parameters. The multidimensional character of the functionality function is well covered by the proposed 3 stage fuzzy model. It makes it possible to put together different measures, and to calculate in an effective way the synthetic functionality/performance value. The model is in detail described as well as its developed including theoretical works, operational data analysis, as well as the experience of experts. The model description is followed by a railway case study, where scenarios elaborated by Experts are evaluated and compared, looking for the best one in terms of resilience. A resilient solution will be that one with the smallest performance/functionality loss in time. Basing on the case it can be concluded that the method is a step forward in resilience research. It has also a high practical potential due to simplification of very complex prediction issues. For example, possible further lack of crews or vehicles is represented as negative influence on the functionality function, without the need to make in short decision time complicated and not maybe incomplete.]]></description>
      <pubDate>Mon, 27 Apr 2026 14:59:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2646843</guid>
    </item>
    <item>
      <title>Computer Simulation in Real Time Rail Traffic Control</title>
      <link>https://trid.trb.org/View/2691560</link>
      <description><![CDATA[This thesis examines the use of computer-based dispatcher assist systems for real-time rail traffic control on heavily used single-track rail lines. It focuses on the problem of planning train meets and passes so that total delay is minimized while supporting dispatcher decision-making. The study evaluates methods for predicting point-to-point train running times and analyzes operating data to measure the variability of actual train performance. The results show that train running times are highly variable, and that this uncertainty must be incorporated directly into dispatching logic. Building on this finding, the thesis develops local and global optimization procedures for meet planning, including methods that account for train priorities, running times, acceleration penalties, and uncertainty in future arrivals. A branching procedure is also presented to search alternative meet-pass strategies and identify minimum-delay solutions under uncertain conditions. The thesis concludes that computerized dispatcher assist can improve rail line efficiency when it provides accurate, flexible, and interactive support rather than rigid automatic control, and it includes FORTRAN code implementing the proposed algorithms in the appendix.]]></description>
      <pubDate>Sun, 26 Apr 2026 17:38:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2691560</guid>
    </item>
    <item>
      <title>Modeling of train-induced environmental vibrations from railway traffic: a state-of-the-art review</title>
      <link>https://trid.trb.org/View/2658021</link>
      <description><![CDATA[The growing demand for sustainable transport has led to increasing interest in developing rail transit networks for both intra-city and inter-city travel. However, train-induced vibrations may cause significant negative environmental impacts on nearby buildings, sensitive equipment, and residents, thereby garnering considerable attention from researchers and engineers. An efficient prediction model is essential for assessing train-induced vibrations and for designing appropriate vibration mitigation measures. The complex dynamics of the train, track, infrastructure, soils, and buildings, along with their interactions, make the modeling of train-induced environmental vibrations a challenging task. This paper provides a comprehensive review of the current state-of-the-art methods for modeling train-induced vibrations from surface and underground railway traffic. It begins by addressing wave propagation in natural soils, followed by an in-depth examination of analytical, numerical, and empirical approaches for predicting train-induced vibrations in the ground and buildings. Finally, this paper identifies unresolved issues in the field and outlines areas that require further investigation.]]></description>
      <pubDate>Wed, 15 Apr 2026 08:31:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2658021</guid>
    </item>
    <item>
      <title>Spatiotemporal Changes in Traffic: A Comparison of Several Dimension-Reduction Methods Using a Railway Network Including Weather and Socioeconomic Variables</title>
      <link>https://trid.trb.org/View/2646100</link>
      <description><![CDATA[The main aim of this paper is to compare dimensionality reduction methods for analyzing two databases: a database regarding traffic (DBT), and a database regarding the network environment (DBE), e.g., socioeconomic factors, pollution, weather, or accidents/incidents. In many cases, only one database is considered, whereas this paper suggests an analysis procedure where DBT is analyzed first for visualizing and interpreting complex traffic patterns, and DBE is investigated as extra data to explain these patterns. For DBT, the space unit is the origin–destination (OD) pair, and the time unit is a time window. Data are organized through a large table where the rows correspond to the time windows and the columns to OD pairs. From this large frequency table, several levels of time summarization are possible for showing data, like minutes, hours, or days; the same is true for space. The procedure uses the singular value decomposition principle with correspondence analysis (CA), taxicab correspondence analysis (TCA), and principal component analysis (PCA) as comparison methods. The comparison using an actual data set indicated that CA shows more interesting results. Results with DBT and DBE are compared with those based on the hierarchical clustering (HC) principle. For a second aim, this paper explores real-time applicability by testing the methods’ ability to incorporate new time windows dynamically.]]></description>
      <pubDate>Fri, 20 Mar 2026 14:47:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2646100</guid>
    </item>
    <item>
      <title>A refined ground-borne noise prediction methodology for railway traffic in tunnels in bedrock</title>
      <link>https://trid.trb.org/View/2666562</link>
      <description><![CDATA[The expansion of railway networks has significantly improved transportation efficiency but has also led to increased noise and vibration, particularly affecting residential areas near underground infrastructure. Ground-borne noise from rail traffic in tunnels can impact human health, structural integrity, and overall environmental quality. To support effective mitigation and infrastructure planning, accurate and reliable prediction models are needed. This study developed a model and methodology for predicting ground-borne noise from railway traffic in tunnels, tailored for Swedish conditions with high-quality bedrock. Existing models are often proprietary, with limited data available for Swedish bedrock conditions, and the handling of uncertainties is often insufficiently explained. This work addresses these gaps by developing a structured, multi-stage model to support the Swedish Transport Administration projects. The methodology consists of three stages, location, planning, and construction, each adapted to the level of data available. The model is valid up to 1~kHz and incorporates a source term along with correction terms for train speed, distance attenuation, ground-to-building coupling, vibration transmission through structures, and room acoustics. Statistical uncertainty is included for each term, ensuring robust predictions. The model is based on field measurements from the Gårda tunnel in Gothenburg and the Åsa tunnel in Varberg. To enhance understanding, numerical simulations were also conducted to investigate the effects of cracked bedrock zones and tunnel structures on vibration propagation. The simulations showed that the cracked zone causes frequency-dependent attenuation beyond the zone and amplification on the source side under idealized conditions. Tunnel structures were found to reduce vibration levels above the tunnel and introduce fluctuations at higher frequencies. Additional field tests were conducted in a tunnel under construction using both shaker and hydraulic hammer excitations to further refine the model by assessing vibration transfer to nearby buildings. While these tests allowed for a comparison between excitation sources, no significant vibration was detected at the house level.??As a result, a methodology and detailed prediction model is proposed for ground-borne noise assessment in Swedish Transport Administration projects.]]></description>
      <pubDate>Thu, 05 Feb 2026 08:33:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2666562</guid>
    </item>
    <item>
      <title>Capacity modeling and shift optimization for train dispatchers (CAPMO-Train)</title>
      <link>https://trid.trb.org/View/2666521</link>
      <description><![CDATA[In the CAPMO-Train project, we aimed to illuminated the possibilities of automated, optimized train-dispatcher shifts that take all legal and operational restrictions into account and integrate train-dispatcher workload. To this end, we developed an optimization frame-work for shift scheduling. We exemplified our framework with results for Malmo¨ dispatching center, but the framework itself is flexible and can be applied for other dispatching centers. We derived the number of train movements in a dispatching area during a time period as an approximation for the objective task load (which is correlated to the subjective dispatcher workload) and inferred an upper bound for this approximation based on discussion with operational experts. Together with legal and operational requirements for train-dispatcher shifts, this task-load measure build the basis for the optimization framework.]]></description>
      <pubDate>Thu, 05 Feb 2026 08:33:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2666521</guid>
    </item>
    <item>
      <title>Evaluating the effects of an improved timetable compression model for railway capacity calculations</title>
      <link>https://trid.trb.org/View/2666488</link>
      <description><![CDATA[The interest in rail transportation is growing, but building new infrastructure is expensive, it is therefore important to make the most efficient use of the resources we already have. Measuring the railway network's capacity and utilisation is one important part of the work to guide more sustainable transportation by rail. To make these calculations useful, we must know what the results represent and understand the implication of the chosen method. This licentiate thesis explore how various improvements of a model can make results more realistic and reflect reality in an accurate way. The method used is timetable compression, a way of estimating how much capacity of a line segment or station is utilised by a defined timetable. The work has been focused on identifying which details are necessary to include to model activities such as simultaneous entry at crossing stations on single-tracks and stations. In Paper I, the resolution of the input data is improved to capture and study the effects of crossings at crossing stations, both with and without simultaneous entry, on single-track lines. The results are compared to two other established methods: The Swedish Transport Administration's model for line capacity calculations and the UIC 406 compression method. The findings show the importance of including crossing stations in calculations due to their effect on capacity, and that the additional time for crossings depends on whether a crossing actually takes place and whether simultaneous entry is possible. Paper II investigates how the improved data and additional features to the model such as including connections of turnarounds and alternative routes, can make capacity calculations for stations more realistic compared to the previous model. The study highlights that station and timetable design influences capacity utilisation and that the implemented extensions affect the results.]]></description>
      <pubDate>Thu, 05 Feb 2026 08:32:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2666488</guid>
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