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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>National Police Fleet Standards: First Edition</title>
      <link>https://trid.trb.org/View/2667388</link>
      <description><![CDATA[This item is one of a set of four standards drawn up by the Transportation Research Laboratory (TRL) for the United Kingdom (UK) National Police Chiefs’ Council (NPCC). The standards combine the best practices from police and operators of commercial vehicle fleets, creating a total approach to the life cycles of police vehicles. The standards accomplish several aims: Assemblage of best practices from police forces across the UK; Creation of a consistent national standard for fleet management; National minimum requirements and the encouragement of excellence; Empowerment and support for fleet managers; and a safe and effective police vehicle fleet. The standards reflect the unique characteristics and challenges of police fleets in the UK and will support adoption of new vehicle technologies as they emerge. The four standards cover the full life cycle of police vehicle fleets in the UK: Acquisition, conversion, maintenance, and disposal.]]></description>
      <pubDate>Mon, 11 May 2026 08:50:46 GMT</pubDate>
      <guid>https://trid.trb.org/View/2667388</guid>
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    <item>
      <title>Linear spectral vibration and multi-modal vibration mitigation of pipeline systems using a multi-stable nonlinear energy sink</title>
      <link>https://trid.trb.org/View/2668771</link>
      <description><![CDATA[This study focuses on the linear spectral vibration and multi-modal vibration mitigation of pipeline systems by using a single multi-stable nonlinear energy sink (MNES) which is critical for ship acoustic stealth. Methodologically, the finite element method is employed to construct a dynamic model of the pipeline system, subsequently analyzing an analysis of the system’s natural characteristics. Furthermore, an improved MNES configuration is proposed, the working mechanism of which achieves adaptive absorption of the broadband vibrations through potential well transitions, with its integration into the pipeline-MNES coupled system elaborated. To assess the MNES’s wideband vibration mitigation capability for the pipeline system, the genetic algorithm (GA) is employed to optimize the vibration-reduction parameters of MNES. Simulations have revealed that under fixed three-frequency base excitation, the suppressions of the MNES can reach 86.2 %, 81.7 %, and 80.6 % for the vibration transmission rate responses, the rates are 90.5 %, 87.3 %, and 98.9 % for the acceleration responses, the rates stand at 82.4 %, 81.7 %, and 80.5 % for the displacement responses, at 24 Hz, 48 Hz, and 120 Hz. Under sweep three-frequency base excitation, the MNES’s vibration suppressions for the vibration transmission rate responses are 82.7 %, 83.1 %, and 80.3 %, 82.4 %, 83.4 %, and 80.2 % for the acceleration responses, and 82.7 %, 83.3 %, and 80.4 % for the displacement responses, at 24 Hz, 48 Hz, and 120 Hz. A set of experiments are conducted to validate the reliability and engineering applicability. The findings are that under fixed three-frequency base excitation, the MNES achieves acceleration response suppressions of 88.3 %, 87.7 %, and 86.2 % at 24 Hz, 48 Hz, and 120 Hz. Under sweep single-frequency base excitation, a three-mode resonant vibration excitation, the suppressions for acceleration responses at 24 Hz, 45 Hz, and 107 Hz are 86.4 %, 84.7 %, and 83.5 %. Test results confirm that MNES exhibits robust broadband vibration damping performance for both linear spectral vibration and multi-modal vibration of pipeline systems.]]></description>
      <pubDate>Mon, 11 May 2026 08:50:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/2668771</guid>
    </item>
    <item>
      <title>A novel method for subway wheelset tread defect detection with improved self-attention and loss function</title>
      <link>https://trid.trb.org/View/2669735</link>
      <description><![CDATA[Wheelsets are crucial components of subway locomotives, and defects on their tread surfaces pose significant safety risks. This study presents an enhanced defect detection algorithm based on the YOLOv5 model, specifically designed to identify tread defects in subway wheelsets to meet the demands of intelligent maintenance. To improve the detection of small targets, we incorporate a multi-head self-attention module, which enhances the model’s ability to capture long-range dependencies within global feature maps. Additionally, a weighted bidirectional feature pyramid network is adopted to achieve balanced multi-scale feature fusion, enabling efficient cross-scale integration. To overcome limited labeled data and annotation inaccuracies, we propose a novel loss function (W-MPDIoU) to accelerate model convergence. Experimental results demonstrate that our enhanced model achieves 99.1% average precision—a 4.29% improvement over the original YOLOv5. With reduced parameters and a detection speed of 15 ms per image, the proposed solution enables real-time tread defect detection in subway systems, significantly improving safety and operational efficiency.]]></description>
      <pubDate>Thu, 07 May 2026 09:20:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2669735</guid>
    </item>
    <item>
      <title>A comprehensive data-driven approach to estimate track longitudinal level from inertial measurements</title>
      <link>https://trid.trb.org/View/2669745</link>
      <description><![CDATA[Infrastructure managers rely on diagnostic trains that periodically measure track geometry and vehicle accelerations to ensure the safety of the railway network. Their runs are scheduled depending on the line priority, in order to safely monitor the evolution of track defects. However, sudden and unpredictable defect growth may happen and be missed between successive runs. Therefore, condition monitoring systems have been installed on in-service vehicles. In fact, these trains run every day along the same line, so they can provide additional information useful for maintenance practices. When trains run along conventional lines, their speed significantly changes depending on the line characteristics, and vehicle accelerations strongly depend on speed. Therefore, monitoring systems that rely on vehicle accelerations should carefully take this effect into account. In this paper, a methodology to estimate the track longitudinal level using bogie accelerations from an in-service vehicle is presented. The recorded accelerations were double-integrated to account for the speed variation, and a model-based strategy was adopted to reduce the filtering action of the primary suspension. Data were recorded during a two-year monitoring campaign along an Italian railway line. The methodology allowed for the estimation of the longitudinal level along specific track sections, considering statistical measures like the peak value. A maximum error of 1 mm was found between the estimated values and those measured by the diagnostic train (considering a defect with magnitude of 7.5 mm). Therefore, the results showed that it is possible to estimate the peak longitudinal level between the two rails using one single vertical accelerometer installed on the bogie of an in-service vehicle. The results of this research may be used to support the current maintenance strategy with daily estimations of track longitudinal level. It should be noted that specific attention was given only to this type of track geometry parameter, since it often drives maintenance operations. In the future, the possibility to extend the methodology to the estimation of different type of defects, like cross-level and twist, could be considered.]]></description>
      <pubDate>Thu, 07 May 2026 09:20:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2669745</guid>
    </item>
    <item>
      <title>Recent advances in wayside condition monitoring for railways: a comprehensive review</title>
      <link>https://trid.trb.org/View/2669744</link>
      <description><![CDATA[The demand for faster, longer, and safer railway networks has intensified the need for robust condition monitoring systems, particularly those capable of detecting rolling stock defects such as wheel flats, bearing faults, hunting movement, overloading, and unbalanced loads. Effective monitoring and timely maintenance are essential to mitigate these issues, enhancing both reliability and safety in railway operations. This paper presents a comprehensive review of wayside condition monitoring (WCM) systems. The study begins with a structured overview of WCM architectures, followed by a bibliometric analysis that highlights recent research trends in machine learning applications for railway condition monitoring. A detailed classification of rolling stock defects is provided to establish the foundation for condition assessment. The review then presents an extensive survey of commercial wayside monitoring systems currently in use, including hot axle box detectors, wheel impact load detectors, acoustic detection systems, and weigh-in-motion technologies. The various sensor types integrated into these systems are also described in detail. To bridge the gap between raw data collection and actionable insights, the paper includes a dedicated section on knowledge extraction from WCM systems. This section outlines key approaches for fault detection, diagnosis, and classification using advanced data processing methods, including signal processing and machine learning techniques. Finally, the paper identifies critical challenges such as data quality, real-time processing constraints, infrastructure limitations, and the need for generalizable models. It also discusses research gaps and suggests future directions, including the integration of edge computing, digital twin technology, and self-diagnostic capabilities to support the development of next-generation intelligent WCM systems.]]></description>
      <pubDate>Thu, 07 May 2026 09:20:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2669744</guid>
    </item>
    <item>
      <title>Thermal–environmental effects on degradation of railway ballast aggregates: a climate change perspective</title>
      <link>https://trid.trb.org/View/2669743</link>
      <description><![CDATA[Climate change represents one of the most significant challenges affecting the behavior of materials and infrastructures. This phenomenon can lead to excessive increases or decreases in temperature, heightened sandstorms, increased sulfate attacks and exacerbated freeze–thaw cycles. Among the infrastructures perennially exposed to climate change are railway tracks. In ballasted railway tracks, the ballast layer is affected by various factors such as freezing and thawing, extreme temperatures and attacks by various sulfate salts which can accelerate the degradation of ballast aggregates. Considering that most of the maintenance costs of railway tracks are associated with the deterioration of the ballast layer, the impact of climate change and environmental conditions on ballast durability is examined in the current study. To achieve this goal, a comprehensive experimental investigation was conducted through a series of laboratory tests. Specifically, the durability of ballast aggregates under various states, including environmental temperature changes ranging from − 20 °C to + 100 °C, freeze–thaw cycles, and sulfate attacks, was assessed by simulating these conditions in a laboratory environment. The durability properties of ballast in these conditions were evaluated using different indices such as Los Angeles abrasion, micro-Deval wear, crushing resistance, impact performance, and breakage potential. The results indicate that the durability performance under sulfate attacks, freeze–thaw cycles, and extreme cold and warm temperatures deteriorated by an average of 50%, 20%, 40%, and 35%, respectively. Interestingly, the obtained results also led to a series of insightful empirical formulations to estimate the ballast degradation indices accounting for the impacts of thermal and environmental conditions. These findings highlight a notable impact of thermal and environmental conditions on the durability of ballast aggregates.]]></description>
      <pubDate>Thu, 07 May 2026 09:20:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2669743</guid>
    </item>
    <item>
      <title>Simulating the effects of structural discontinuities on the long-term behavior of the ballasted railway track</title>
      <link>https://trid.trb.org/View/2669741</link>
      <description><![CDATA[Structural discontinuities in railway tracks have proven challenging from a maintenance perspective. These discontinuities can lead to uneven settlements, reducing serviceability of the railway network and increasing the track’s dynamic loading. To optimize the long-term performance of railway structures, it is essential to evaluate different design solutions under varying loading conditions to identify potential risk factors early. Consequently, this study proposes a novel computational model for simulating the dynamic long-term behavior of ballasted railway tracks. The proposed model enables computationally efficient simulation and provides an innovative mathematical framework for analyzing the mechanical behavior of structural discontinuities, allowing detailed consideration of substructure and subsoil properties, including their variations along the longitudinal direction of the track. Simulations were conducted to investigate the effects of bridge transition zones and rail defects on the short- and long-term behavior of the track for two vehicle types. In addition, extensive field measurement data were utilized for model verification. Based on simulations, the axle load appears to be the primary factor influencing the long-term performance of railway transition zones. However, for more localized defect types, the significance of driving speed and the unsprung mass of rolling stock becomes more pronounced. Overall, the findings highlight the nonlinear relationship between vehicle loading and structural deterioration, emphasizing its strong dependence on track properties.]]></description>
      <pubDate>Thu, 07 May 2026 09:20:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2669741</guid>
    </item>
    <item>
      <title>Modelling the adhesion enhancement at the wheel–rail interface: the role of surface roughness and plastic deformation during rail sanding operation</title>
      <link>https://trid.trb.org/View/2669736</link>
      <description><![CDATA[Efficient train operation relies on optimal traction at the wheel–rail interface, which can be compromised by factors such as water and/or contamination (e.g. leaf, sand, oil, and surface abrasion debris). This study introduces a finite element model to assess adhesion enhancement at the wheel–rail interface, with a focus on the impact of sand particles during the sanding process. Surface roughness is initially introduced to quantify its effect on adhesion, followed by the inclusion of rail plastic deformation. By integrating these two factors, the model provides a comprehensive framework for evaluating the complex mechanisms influencing adhesion at the wheel–rail interface, particularly in real-world train operations where surface conditions interact with contaminants such as sand fragments. This approach addresses the existing gap in understanding how rail surface condition and rail plastic deformation contribute to adhesion enhancement during the sanding process, offering new insights for optimising railway maintenance strategies.]]></description>
      <pubDate>Thu, 07 May 2026 09:20:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2669736</guid>
    </item>
    <item>
      <title>Autonomous Drone Prototype for Bridge Inspection and Maintenance</title>
      <link>https://trid.trb.org/View/2696137</link>
      <description><![CDATA[This report presents a comprehensive pipeline for the autonomous visual inspection of bridges using drones. It details the architecture of a vision-based autonomous flight stack designed for operation without GPS, alongside the design of the custom drone prototype. The system’s performance is validated through three flight tests, including deployments at two real-world bridge sites, demonstrating the pipeline’s capability to autonomously navigate complex GPS-denied environments and acquire high-quality inspection data.]]></description>
      <pubDate>Tue, 05 May 2026 10:19:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2696137</guid>
    </item>
    <item>
      <title>The Economic Impact of a Salisbury-to-Asheville Rail Corridor</title>
      <link>https://trid.trb.org/View/2696036</link>
      <description><![CDATA[Reestablishing passenger rail service between Salisbury and Asheville presents a significant opportunity to strengthen North Carolina’s economy and transportation network. By connecting major population centers—including the Charlotte metro, the Piedmont Triad, the Research Triangle, and the Blue Ridge region—the corridor would enhance mobility for residents, commuters, and visitors while providing a more sustainable and efficient alternative to highway travel. Communities along the route stand to benefit from increased tourism, greater visibility, and new economic activity, particularly in towns with cultural, recreational, and historic appeal. The economic benefits of the corridor are wide-ranging. Capital investments in the corridor will create a one-time stimulus for North Carolina’s economy through activities such as construction spending on rail infrastructure, station development, and related upgrades; the purchase of materials and equipment from local suppliers; engineering and design services provided by in-state firms; and temporary job creation in sectors like construction, architecture, and project management. It is estimated that capital investments will result in a total of 5,270 job-years, $360.5 million in employee earnings, $1.05 billion in economic output, and $33.6 million in local and state tax dollars (monetary estimates provided using 2025$).The corridor will also create sustained economic impacts resulting from ongoing operations and maintenance, new occurrences of visitor spending, and improved access to North Carolina’s labor pool. Altogether, the corridor is estimated to sustain 305 jobs, $19.9 million in employee earnings, $59.8 million in total economic output, and result in $1.8 million in local and state tax revenue on an annual basis.]]></description>
      <pubDate>Tue, 05 May 2026 10:18:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2696036</guid>
    </item>
    <item>
      <title>Caltrans Field Trials of the Intelligent Truck-Mounted Attenuator (ITMA)</title>
      <link>https://trid.trb.org/View/2696136</link>
      <description><![CDATA[Truck-mounted attenuator (TMA) operators are exposed to traffic-related hazards during Caltrans maintenance operations. To help reduce this exposure, the Advanced Highway Maintenance and Construction Technology Research Center (AHMCT), in partnership with Caltrans, conducted controlled public-road field trials of the Intelligent Truck-Mounted Attenuator (ITMA). The ITMA is a two-vehicle system in which a leader vehicle guides a semi- or fully intelligent follower vehicle equipped with a TMA. Building on prior closed-course evaluations, this project prepared the system for field evaluation through hardware upgrades, communications improvements, interface enhancements, and operator training. The ITMA was evaluated during striping, sweeping, and raised pavement marker operations in both semi-intelligent and fully intelligent modes. Field trials demonstrated consistent following behavior, predictable emergency stopping, and stable performance in varied roadway and GPS-challenged environments. Operator feedback indicated positive acceptance and increasing familiarity with system operation. The results support the continued evaluation of the ITMA and inform future decisions regarding its potential operational use.]]></description>
      <pubDate>Mon, 04 May 2026 11:19:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2696136</guid>
    </item>
    <item>
      <title>Adaptive turbofan engine health assessment for multi-component performance degradation coupling scenarios: A spatiotemporal attention graph convolutional network approach</title>
      <link>https://trid.trb.org/View/2664335</link>
      <description><![CDATA[Aircraft turbofan engines are essential components that directly influence flight safety through their operational health. The complex functional and structural coupling among internal components results in interconnected performance degradation, manifesting as distinct spatial patterns in sensor monitoring data. Operating under extreme conditions of high temperature, high pressure, and sustained high-speed rotation, these engines experience temporal variations in thermodynamic states and mechanical properties, creating temporal dependencies in the monitoring data. Traditional health assessment methods face significant challenges in processing comprehensive performance state information, as data perturbations can compromise both evaluation accuracy and stability, resulting in health trends that fail to reflect the engine's actual degradation state. To overcome these limitations, this study introduces a Spatiotemporal Attention Graph Convolutional Network (STAGCN) method, which combines spatiotemporal attention mechanisms with graph convolutions. This integration enables adaptive feature reduction and precise quantification of turbofan engine health states under complex, time-varying conditions and multi-component degradation coupling scenarios. The STAGCN utilizes attention mechanism's correlation-mining and focus capabilities to identify high-relevance regions of performance degradation within high-dimensional, long-sequence monitoring data, thereby enhancing assessment accuracy through adaptive extraction of dense degradation information. Furthermore, by leveraging deep graph convolutional networks' capacity to capture complex patterns in high-dimensional data, STAGCN effectively mines and represents engine performance degradation states from feature channels with high degradation density, improving feature extraction efficiency and significantly enhancing assessment accuracy and stability. The method's validation employs NASA's open-source turbofan engine dataset, incorporating multi-condition and multi-fault coupled simulation models. Experimental results demonstrate that STAGCN achieves RMSE reductions of 89.2%, 87.2%, and 81.2% compared to LSTM, GRU, and GCN, respectively. Additionally, relative robustness decreases by 76.0%, 74.1%, and 72.4%, while relative trendability decreases by 78.2%, 60.7%, and 79.7%.]]></description>
      <pubDate>Fri, 01 May 2026 14:31:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2664335</guid>
    </item>
    <item>
      <title>Understanding Human Error in Military Aviation Maintenance: The role of Performance Shaping Factors, Cognitive Workload and Error Orientation</title>
      <link>https://trid.trb.org/View/2664371</link>
      <description><![CDATA[Human error remains a major source of reliability and safety risk in aviation maintenance, particularly in military operations where task complexity and operational pressure are unavoidable. Despite continued advancements in technical reliability, the mechanisms through which working conditions and cognitive demands translate into maintenance error, remain insufficiently understood. In particular, the combined influence of systemic factors, cognitive workload, and individual differences has received limited empirical attention. This study examines the effect of Performance Shaping Factors (PSFs) on human error in military aviation maintenance, considering cognitive workload as a mediating mechanism and Error Orientation (EO) as a moderating factor. Survey data from 282 military aviation maintenance personnel were analyzed using structural equation modeling. The results show that adverse PSFs significantly increase both cognitive workload and the likelihood of maintenance error. Cognitive workload partially mediates this relationship, indicating that increased mental demand is a key pathway through which unfavorable system conditions degrade maintenance reliability. Error Orientation moderates both direct and indirect effects. Personnel with lower EO are more susceptible to workload-related error. These findings extend human reliability analysis by explaining when and why maintenance errors are most likely to occur. The results support integrated safety management strategies that combine system design improvements, workload control, and targeted personnel development to enhance reliability in high-risk aviation maintenance environments.]]></description>
      <pubDate>Fri, 01 May 2026 14:31:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2664371</guid>
    </item>
    <item>
      <title>Zone-Collaborative Integrated Framework for Probabilistic Flaw Tolerance Assessment of Aeroengine Structure</title>
      <link>https://trid.trb.org/View/2664365</link>
      <description><![CDATA[Traditional flaw tolerance assessment methods for aeroengine turbine blisks suffer from incomplete spatial coverage and inadequate uncertainty quantification, as hotspot-based approaches focus solely on predetermined high-stress regions while neglecting stochastic flaw distributions across structural surfaces. This study develops a Zone-Collaborative Integrated (ZCI) framework that systematically addresses these limitations through three integrated components: zone-probabilistic decomposition using improved Gaussian Mixture Models with K-means initialization for surface partitioning and Combined Sampling Method for flaw coordinate uncertainty quantification; Genetic Algorithm-enhanced Kriging (GA-Kriging) surrogate modeling integrated with series system theory for multi-zone reliability assessment; and systematic implementation algorithm enabling comprehensive spatial coverage with computational efficiency. Validation through notched plate and turbine blisk case studies demonstrate that GA-Kriging achieves 63.3% improvement in computational efficiency and 31.8%/26.7% enhancement in training/testing precision compared to conventional methods, with normalized RMSE below 0.02. The ZCI framework exhibits 94.95-98.70% accuracy relative to direct simulation while predicting 12-76% higher fatigue life than hotspot method at equivalent reliability levels (720 cycles for hotspot vs. 1518 cycles for two-zone ZCI at R=0.99 in Case 2). Sensitivity analysis reveals flaw geometry parameters dominate reliability outcomes (flaw radius: -1.75, flaw depth: -1.25), providing quantitative guidance for structural design optimization. The proposed framework transforms computationally prohibitive full-scale reliability problems into manageable zone-based assessments, offering a systematic approach for probabilistic flaw tolerance design of critical aerospace components.]]></description>
      <pubDate>Fri, 01 May 2026 14:31:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2664365</guid>
    </item>
    <item>
      <title>A Data-Driven System-Theoretic Bayesian Network Framework for Probabilistic Safety Assessment of Passenger Vessels</title>
      <link>https://trid.trb.org/View/2664341</link>
      <description><![CDATA[Passenger vessel operations present a high-consequence environment where a paradox has emerged: incident frequency is decreasing, yet catastrophic severity is not. This trend exposes the inadequacy of existing risk models, which are typically localized, and reliant on subjective expert elicitation. This study develops a robust, data-driven risk assessment framework by synergizing System-Theoretic Process Analysis (STPA) with a Bayesian Network (BN), grounded in a novel database of 235 official European accident reports. STPA defines the BN’s causal topology, ensuring theoretical coherence and mitigating the epistemic uncertainty and bias of conventional expert-led modeling. Sensitivity analysis reveals the probabilistic primacy of latent systemic precursors, identifying Structural Failure and Defective Maintenance as dominant risk control points. The analysis moves beyond simplistic attributions of “human error”, revealing how operational failures like COLREGs infringements are symptoms of distinct causal pathways dependent on vessel type and operational conditions. The resulting model is a quantitative instrument that identifies the most probable pathways to catastrophe, offering an objective foundation for transitioning from reactive compliance to proactive, data-driven safety governance.]]></description>
      <pubDate>Fri, 01 May 2026 14:31:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2664341</guid>
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