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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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    <item>
      <title>Development of Train Operation Risk Assessment System Based on a Data-Driven Bayesian Network</title>
      <link>https://trid.trb.org/View/2772182</link>
      <description><![CDATA[Risks of severe train accidents vary along a railway corridor by track sections with different infrastructure, rolling stock, and operational characteristics because of the complicated nature of train driving environment. Therefore, this research develops a Train Operational Risk Assessment System (TORAS) that assists train drivers to recognize the level of risk for upcoming track sections to enhance their situational awareness. This research proposes a five-phase approach that integrates the Human Factors Analysis and Classification System (HFACS) with Bayesian networks (BN) to identify potential risk events through historical accident data and selected environmental and operational factors that may contribute to the occurrence of risk events. A BN constructed using historical Automatic Train Protection (ATP) data quantifies the relationships between various risk factors while calculating the probabilities of risk events. The results show that the new framework can predict track-section-specific train driving risks by using train operational and route information. TORAS can enhance the train drivers' situational awareness by informing them of the risk levels of different track sections in advance, which enables proper caution and proactive preparation, improving the overall safety of train operations by ensuring that drivers are well-informed and able to respond effectively to varying risk conditions.]]></description>
      <pubDate>Sat, 29 Aug 2026 19:33:44 GMT</pubDate>
      <guid>https://trid.trb.org/View/2772182</guid>
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    <item>
      <title>Railway safety under increasing speed: effect of cognitive ability on train drivers’ hazard perception</title>
      <link>https://trid.trb.org/View/2703971</link>
      <description><![CDATA[Increasing speeds in high-speed rail heighten safety concerns, particularly as foreign object detection technologies remain limited under adverse conditions. Train drivers, as the final safeguard, must accurately perceive and respond to hazards with reduced reaction margins. This study examines how four speed classes and three cognitive abilities—attention, reaction, and learning—affect hazard perception (HP) using a high-fidelity hazard perception test (HPT) developed in Unity 3D. Thirty participants completed the HPT, with performance assessed via response time and two signal detection theory indicators (sensitivity and response bias). Results show that higher speeds shorten HP response times, which seemingly indicates improved HP; however, their negative impact on sensitivity and response bias suggests reduced accuracy and cautiousness in the HP process. These findings highlight the importance of targeted cognitive training, advanced cognitive assistant systems, and adaptive cabin designs in mitigating speed-induced risks and improving safety in high-speed rail operations.]]></description>
      <pubDate>Thu, 27 Aug 2026 16:32:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2703971</guid>
    </item>
    <item>
      <title>FCDM-Net: A Feature-Calibrated Dynamic Masking Network Based on Pre-Trained Models for Few-Shot Catenary Component Anomaly Detection</title>
      <link>https://trid.trb.org/View/2767160</link>
      <description><![CDATA[Existing detection systems for electrified railway catenary components still face the following challenges. (1) The background of aerial images of the catenary support component is complex; (2) fault samples are difficult to obtain, and the significant feature shift between training and testing images results in a scarcity of negative samples available for training; and (3) the coexistence of microscopic texture defects and macroscopic logical anomalies in catenary components. To address these challenges, this paper proposes a novel feature-calibrated dynamic masking network (FCDM-Net) for the unified few-shot anomaly detection of catenary components. First, a lightweight residual adapter is integrated with a frozen visual encoder. Through a few-shot fine-tuning strategy, the general visual features are mapped to the specific manifold space of catenary components, thereby achieving the feature domain calibration. Second, we introduce a noise-resistant mechanism named heatmap-guided dynamic masking, which employs a coarse anomaly heatmap extracted from the adapted features as prior guidance to dynamically filter background redundancy in the initial mask and accurately extract the component body. On this basis, a dual-branch inference system is proposed to enable the collaborative detection of microscopic texture defects and macroscopic logical anomalies. Finally, experiments conducted on the real catenary component dataset collected by drones demonstrate that under a four-sample setting, the proposed method achieves image-level metrics (I-AUROC (area under the receiver operating characteristic curve)/average precision (AP)/F1) of 94.7/93.1/96.7, respectively.]]></description>
      <pubDate>Mon, 24 Aug 2026 08:46:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2767160</guid>
    </item>
    <item>
      <title>Development of an Evaluation Indicator System for the Predeparture Suitability Status of Railway Drivers</title>
      <link>https://trid.trb.org/View/2761588</link>
      <description><![CDATA[With the rapid expansion of high-speed railways, ensuring the operational safety of drivers has become a critical challenge for the sustainable development of the transportation sector. However, existing assessments of driver suitability focus mainly on long-term attributes, while systematic frameworks for evaluating drivers’ predeparture suitability status remain underdeveloped. To address this gap, this study screened and validated key indicators from practical contexts through grounded theory and semistructured interviews to construct a multilevel evaluation index system consisting of three dimensions, seven first-level indicators, and fourteen second-level indicators. A combined weighting method integrating the analytic hierarchy process (AHP) and the entropy method was employed to determine indicator weights, thereby balancing expert judgment with objective data. Regression analysis based on data from 123 railway drivers showed that both the composite suitability score and its three dimensions significantly predicted safety performance, with cognitive ability exerting the strongest effect. These findings validate the scientific robustness and practical utility of the proposed framework in diagnosing drivers’ readiness for duty. The study contributes to the literature by bridging the gap between long-term qualifications and real-time safety performance. It further provides railway enterprises and policymakers with a reliable tool to enhance fitness-for-duty screening and to strengthen prediction–intervention–prevention mechanisms in safety management.]]></description>
      <pubDate>Tue, 18 Aug 2026 14:11:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2761588</guid>
    </item>
    <item>
      <title>Understanding the railway driving activity to design HUD: recommendations and specificities for future light trains</title>
      <link>https://trid.trb.org/View/2698395</link>
      <description><![CDATA[With the emergence of new modes of rail transport, it is essential that an ergonomics and human factors study is carried out to ensure that the drivers’ needs are fully considered during this transition. Several studies have highlighted the benefits of HUD’s in transport domains, particularly in improving the visibility of key information and reducing the time that drivers need to look away from the train and its environment. However, the implementation of such interfaces is also a challenge, as technological solutions can be complex and must meet high safety standards. This article investigates the possibility of integrating head-up displays (HUD’s) into the design of driver’s cabs for new-generation trains, i.e. very light trains operating on small rural lines. The work is based on a dual approach: a review of the relevant literature and an in situ analysis of train driving activities, with the aim of understanding the requirements of train driving activity, identifying information needs and assessing how HUD’s could support drivers in their tasks. We firstly examine the advantages and disadvantages of HUD’s and highlight the potential contributions of HUD’s in a new generation train. We then analyse train driving activities to identify the main information requirements and examine the constraints imposed by existing railway standards on the integration of HUD’s. Finally, we propose design recommendations and preliminary visual interfaces adapted to the specific needs of driving very light trains.]]></description>
      <pubDate>Fri, 31 Jul 2026 09:23:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2698395</guid>
    </item>
    <item>
      <title>Grades of operational and tactical automation in rail domain for an artificial intelligence driving assistance system</title>
      <link>https://trid.trb.org/View/2698391</link>
      <description><![CDATA[This work proposes new additional levels of progressive driver assistance, extending the traditional Grades Of Automation (GoA) in order to allow both dedicated Operational and Tactical assistances. The second contribution is an Artificial Intelligence Driving Assistance System (AIDAS) that aims to restore the driver to their central role in the train driving activity, with the new Grades of Operational and Tactical Automation (GOTA) defined previously, taking into account human factors. The framework of Digital Co-Driver (DCD) is comprised of multiple monitoring and modules, each addressing a distinct issue arising from the augmented level of automation. The Driver State, Driving Performance and Environmental Monitoring provide indicators of global driver involvement to maintain a high level of performance in manual driving and to deal with system failures. The GOTA Selector Module then helps the driver to be optimally engaged while driving by adapting the GOTA to the specific needs. Finally, the Driver Companion Module learns about the driver’s preferences and needs to adapt the assistance and help the driver to improve their own driving skills.]]></description>
      <pubDate>Fri, 31 Jul 2026 09:23:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2698391</guid>
    </item>
    <item>
      <title>Towards management of vigilance in railway operations through drivers state feedback loop</title>
      <link>https://trid.trb.org/View/2698387</link>
      <description><![CDATA[Professional drivers face fatigue and decrease of vigilance over the long driving sessions paving their everyday life. This naturally occurring phenomenon is acknowledged and preventive measures, adapted to the vehicles and missions, are deployed around the world to limit the related risks. As technology opened the way to affordable probing of human bio-signals and activities, more active strategies are investigated such as sleepiness monitoring and alert systems. Such systems already existed in trains, although in a more primitive form, known as “dead-man switch”. As the limitations of this system in detecting actual vigilance decrements is known from practitioners, we took upon ourselves to explore the opportunities offered by the recent developments, under the strict security constraint that characterises railway operations. Going further than monitoring and alert, we consider the ideas of a bio-signal feedback loop and adaptive levels of automation to encourage a real cooperation between the driver and the system in managing fatigue and vigilance. This challenge is particularly significant in teleoperation, which emerges as a potential evolution of the railway activity where fatigue and vigilance are affected by information loss and increased reliance on visual information. Such cooperative work would pave the way for a new definition of what a train driver is, emphasizing its critical role of safeguarding the train and its passengers. This is especially important in a context of autonomous systems’ proliferation, putting the drivers’ position at risks.]]></description>
      <pubDate>Fri, 31 Jul 2026 09:23:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2698387</guid>
    </item>
    <item>
      <title>Visual behaviour of train drivers: a comparative analysis of ETCS and a lineside signalling system</title>
      <link>https://trid.trb.org/View/2698386</link>
      <description><![CDATA[Train drivers’ outside attention impacts the chance of acting in time to avoid potential collisions with people or other objects. This project aimed to study the effect of the in-cabin signalling system ETCS on driver attention. We specifically examined gaze behaviour in events of special relevance for train driving, such as unattended level-crossings, with and without speed changes. An eye-tracking study was conducted in a simulated environment with 40 experienced Swedish train drivers driving the same route equipped with the Swedish lineside signalling system ATC and ETCS, respectively. The results showed that ETCS generally affected drivers’ outside attention negatively. Compared to ATC, with ETCS significantly more glances were directed inside the cabin at the expense of outside glances. When assessing the gaze behaviour at unattended level-crossings with and without speed changes, approximately 20 percent less attention was spent towards the outside when approaching an unattended level-crossing with a speed change. We conclude that ETCS negatively affects outward attention and thus reduces the chances that the driver will detect a person or other object on the track and act in time to avoid a collision. As speed changes negatively affect outward attention, we recommend that speed changes should be avoided in critical parts of the track where the risk of collision is higher. The results should also have implications for the design of connected driver advisory systems, which provides the driver with real-time guidance on speed operation via a tablet or screen and thus, demand driver attention.]]></description>
      <pubDate>Fri, 31 Jul 2026 09:23:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2698386</guid>
    </item>
    <item>
      <title>Beyond object identification: how train drivers evaluate the risk of collision</title>
      <link>https://trid.trb.org/View/2698384</link>
      <description><![CDATA[When trains collide with obstacles, the consequences are often severe. To assess how artificial intelligence might contribute to avoiding collisions, we need to understand how train drivers do it. What aspects of a situation do they consider when evaluating the risk of collision? In the present study, we assumed that train drivers do not only identify potential obstacles but interpret what they see in order to anticipate how the situation might unfold. However, to date it is unclear how exactly this is accomplished. Therefore, we assessed which cues train drivers use and what inferences they make. To this end, image-based expert interviews were conducted with 33 train drivers. Participants saw images with potential obstacles, rated the risk of collision, and explained their evaluation. Moreover, they were asked how the situation would need to change to decrease or increase collision risk. From their verbal reports, we extracted concepts about the potential obstacles, contexts, or consequences, and assigned these concepts to various categories (e.g., people’s identity, location, movement, action, physical features, and mental states). The results revealed that although the majority of concepts referred to potential obstacles, train drivers also heavily relied on context factors, used different categories to reason about people and objects, and paid ample attention to people’s actions and mental states. They regularly drew relations between concepts to make further inferences. Our findings emphasise the need to understand train drivers’ risk evaluation processes when aiming to enhance the safety of both human and automatic train operation.]]></description>
      <pubDate>Fri, 31 Jul 2026 09:23:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2698384</guid>
    </item>
    <item>
      <title>Energy-Saving Optimization of Urban Rail Transit Operation Considering Onboard Energy Storage Devices</title>
      <link>https://trid.trb.org/View/2732171</link>
      <description><![CDATA[To address the energy-saving optimization problem of urban rail transit train operation, this paper proposes a collaborative optimization method for onboard energy storage device (OBESD) capacity configuration and train interstation operation strategies. Based on a train multi-interval operation model and a train energy flow model, a collaborative optimization framework is established with the objective of minimizing the total life-cycle cost, comprehensively considering both OBESD investment costs and train operational energy consumption costs. Constraints including multi-interval train operation, fixed interstation running times, OBESD charging/discharging power and capacity, and passenger riding comfort are incorporated into the model. With the number of OBESD modules and train speed profiles as decision variables, an energy-saving optimization model considering multi-interval train dynamics and time-varying energy flow is developed. To solve the model, a dual-layer optimization algorithm is proposed. The outer layer determines the optimal number of OBESD modules using a fixed-step search strategy, while the inner layer optimizes train operation strategies under a given storage configuration using a simulated annealing algorithm based on multiparameter speed combinations. This approach enables collaborative optimization between train operation strategies and onboard energy storage capacity. The proposed method is validated using real-world operational data from Guangzhou Metro Line 1. The results show that the proposed model can effectively reduce train net energy consumption while strictly satisfying interstation running time constraints, achieving an energy-saving rate of 13.90% for full-line operation. In addition, sensitivity analysis results indicate that the optimal OBESD configuration is significantly influenced by economic parameters such as electricity price, storage investment cost, and life-cycle years, highlighting the important role of economic conditions in practical engineering applications. The findings provide practical decision support for urban rail transit operators in configuring onboard energy storage systems to achieve cost-effective energy savings.]]></description>
      <pubDate>Mon, 27 Jul 2026 09:46:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2732171</guid>
    </item>
    <item>
      <title>Study on Vibration Superposition Induced by Dual-Line Subway Train Operation in Submerged Tunneling Sections</title>
      <link>https://trid.trb.org/View/2727483</link>
      <description><![CDATA[Accurately predicting the vibration response when a new tunnel passes beneath an existing double-track railway represents a major challenge in assessing the environmental impact of urban underground engineering. Traditional numerical simulations rely heavily on noisy field-measured signals and simplified single-source assumptions, leading to significant errors in complex multisource scenarios. To achieve refined vibration response simulation, this paper proposes a FLAC3D simulation method integrating Variational Mode Decomposition (VMD) signal denoising with multisource vibration superposition theory. The VMD algorithm (with optimized parameters K = 8, a = 2500) is applied to denoise field-measured vibration signals, achieving a correlation coefficient of 0.9861 between the denoised and original signals—a 23.7% improvement over conventional denoising methods. Subsequently, based on vibration superposition theory, a multisource input model accounting for propagation path attenuation is constructed. Finally, high-precision dynamic calculations are performed within FLAC3D. The reliability of the proposed method is validated by comparing results with field measurements, showing a reduction in peak acceleration prediction error from 18.6% (using un-denoised signals) to 6.8% (using the proposed method). The study analyzes the combined effects of source magnitude, source location, and propagation attenuation. Results indicate that the superposition generated by double-track operation is not a simple numerical summation; the measured superposition value at the center point C2 is 1.54 times the average of the two individual responses, indicating constructive interference. The strongest vibration superposition effect occurs at C2, with a limited influence range—only the tunnel crown experiences significant vibration superposition (superposition rate of 54.6% at roof monitoring point P1, decreasing to less than 10% at the invert). This research provides new computational tools and methods for vibration studies in underground transportation networks.]]></description>
      <pubDate>Mon, 27 Jul 2026 09:46:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2727483</guid>
    </item>
    <item>
      <title>Emergency Response Capability Assessment for Dispatching Personnel on Fully Automated Urban Rail Lines The Case of Zhengzhou Metro Line 5</title>
      <link>https://trid.trb.org/View/2727408</link>
      <description><![CDATA[Fully automated operation (FAO) concentrates the critical risks of urban rail transit within the operations control center (OCC), posing unprecedented demands on the emergency response capabilities of dispatching personnel. Existing research, predominantly focused on traditional operational models, has largely overlooked the quantitative assessment of human factors, such as dispatcher cognition and decision making in FAO systems. To address this research gap, this study establishes a human factors–based evaluation framework for FAO dispatcher emergency capabilities, encompassing five dimensions: resource planning, situational awareness, organizational management, information processing, and workload processing capability. An empirical analysis was conducted using the “7.20” extreme rainstorm disaster on the Zhengzhou Metro as a case study. The results indicated that the overall emergency response capability of dispatchers was at a moderate- to high risk level. Specifically, resource planning capability was identified as the most critical vulnerability, exhibiting the highest risk intensity and most significant systematic deficiencies. Furthermore, notable fluctuations and shortcomings were identified in situational awareness, organizational management, and information-processing capabilities. Workload processing capability, although exhibiting a moderate to high risk level, displayed the lowest entropy among all dimensions, indicating that workload-related vulnerabilities were uniformly and systematically experienced across roles and shifts. This study precisely identifies key human-factor vulnerabilities in FAO emergency response, providing a data-driven foundation for optimizing dispatcher training programs and enhancing the safety and resilience of urban rail transit systems.]]></description>
      <pubDate>Thu, 23 Jul 2026 16:11:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2727408</guid>
    </item>
    <item>
      <title>Integrated optimization of train stop planning, train timetabling, and maintenance planning for railway container system under passenger transport mode</title>
      <link>https://trid.trb.org/View/2686681</link>
      <description><![CDATA[The Railway Container System under Passenger Transport Mode (RCSPTM) is an organizational strategy that applies passenger train operating practices to container transport, aiming to enhance transport efficiency. Its efficient operation requires the integrated coordination of three key planning tasks: train stop planning, timetabling, and maintenance planning. As these plans are highly interdependent, sequential optimization often leads to suboptimal results. To address this challenge, this paper proposes an integrated optimization approach, formulated as a mixed-integer linear programming model that embeds stop decisions into the timetabling process and represents maintenance operations as a “virtual train” scheduled together with real trains. The model addresses three aspects of train operations: it allocates container demand across services, avoids scheduling conflicts among trains, and minimizes disruptions from maintenance activities. The effectiveness of the model is validated through computational experiments using the CPLEX solver on both small-scale cases and the Beijing-Shanghai railway corridor. Compared with pre-planned maintenance approaches, the integrated optimization method eliminates unnecessary stops and reduces computation time by 47.5%, demonstrating its computational and operational efficiency. More importantly, it shortens the system-wide committable transit period from 3 days to 2 days, thereby enabling guaranteed delivery within stricter time windows for time-sensitive shipments.]]></description>
      <pubDate>Thu, 09 Jul 2026 13:31:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/2686681</guid>
    </item>
    <item>
      <title>Data-Driven Sliding Mode Optimal Control for Multi-Power Unit High-Speed Train: A Composite Approach</title>
      <link>https://trid.trb.org/View/2617959</link>
      <description><![CDATA[In this study, a multiple-input multiple-output (MIMO) data-driven sliding mode optimal control scheme is investigated for a multi-power unit high-speed train (HST) automatic driving system under disturbances. First, an integral terminal sliding mode control (ITSMC) law based on the dynamic linearization method is introduced to achieve error convergence in finite time. Subsequently, a parameter update law and an adaptive extended state observer (AESO) are designed to estimate the control gain and total uncertainty, respectively, which solves the problem of traditional sliding mode control requiring a large switching gain to handle the disturbance. Third, an optimal control signal is obtained by predictive control to achieve a higher level of tracking error accuracy and quickly reach the quasi-sliding mode state, and the compound optimal control scheme is derived under the combined action of the ITSMC and predictive control. The scheme considers and compensates for the total uncertainty caused by the error feedback, parameter estimation error, and unknown nonlinearity. The main advantages of this scheme include the following: controller design process uses only system data, provides excellent model adaptability, and disturbance rejection ability. Upon providing a stability-proof analysis of the proposed method, the proposed composite control method was compared and tested on a CRH380A train simulation test bench equipped in the laboratory. The simulation results show that the speed tracking errors of each power unit of the HST under the proposed control scheme are within [ - 0.123 km·h ⁻¹, 0.144 km·h ⁻¹. The control forces and accelerations are within [$-$55 kN, 46 kN] and [-0.592 m·s ⁻², 0.512 m·s ⁻²] respectively, which meets the requirements of safe, stable, and efficient operation of the train.]]></description>
      <pubDate>Wed, 24 Jun 2026 13:22:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2617959</guid>
    </item>
    <item>
      <title>State of the art of railway onboard systems for driver-machine cooperation</title>
      <link>https://trid.trb.org/View/2712856</link>
      <description><![CDATA[This research presents a comprehensive review of existing literature on train driver assistance systems and associated technologies suitable for onboard railway implementation. The primary objective is to systematically identify recent technological advancements, analyze their development patterns and prevalence, and gain a deeper understanding of current research trends in railway onboard systems. To achieve this, articles published since 2015 were retrieved from the Scopus database, resulting in a selection of 179 relevant studies. Each article was manually reviewed to ensure a thorough evaluation of its objectives, methodologies, and reported impacts. The selected studies were categorized into five key domains: train condition monitoring, which focuses on predictive maintenance and system health diagnostics; environment monitoring, addressing external conditions such as weather and track status; object detection, involving obstacle identification and collision avoidance; driver monitoring, which examines human factors such as attention, fatigue, and cognitive state; and brake assistance systems, aimed at improving safety and operational efficiency. This structured classification enabled a clearer comparison of technological maturity and research emphasis across different areas. Furthermore, the review explores opportunities to enhance human-machine cooperation by linking these findings with the latest developments in railway driver advisory systems (R-DAS). Based on this synthesis, four promising future research directions are identified: adaptive trajectory optimization for energy-efficient and context-aware driving, cooperative R-DAS (CR-DAS) enabling collaborative decision-making between human drivers and automation, human-in-the-loop (HITL) shared control strategies to balance authority between operator and system, and robust remote operation with reliable authority transfer mechanisms. These directions highlight the potential for more intelligent, adaptive, and cooperative onboard systems in next-generation railways.]]></description>
      <pubDate>Tue, 23 Jun 2026 13:51:05 GMT</pubDate>
      <guid>https://trid.trb.org/View/2712856</guid>
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