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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>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Development of K-nearest neighbours model for diagnosing vehicle automatic transmission failures</title>
      <link>https://trid.trb.org/View/2677542</link>
      <description><![CDATA[This study investigates the operational conditions of automobiles in Vietnam and the standard automatic transmission (AT) failures that occur during their operation. Various types of vehicle automatic transmission failures, alongside normal operating conditions, are simulated in Simulation-X. The research involves data pre-processing and exploratory data analysis to identify appropriate models for classification. A comprehensive review of machine learning classification algorithms and hyperparameter tuning uses simulation datasets. The KNN model was trained and evaluated, achieving 92.2% accuracy on the test dataset. Permutation importance was evaluated using the open-source library scikit-learn. Potential improvements of the model classifiers are discussed, and recommendations are provided based on the findings. The results demonstrate that the proposed approach can effectively classify AT failures, supporting the development of software modules for real-time technical state supervision and the design of a test bench for assessing AT reliability.]]></description>
      <pubDate>Wed, 13 May 2026 17:01:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2677542</guid>
    </item>
    <item>
      <title>A fault diagnosis method for ship motors based on a multi-source data three-level fusion strategy</title>
      <link>https://trid.trb.org/View/2697318</link>
      <description><![CDATA[Three-phase asynchronous motors are widely used in various applications, such as ship propulsion and auxiliary power systems. Accurate monitoring of their operating conditions is of vital importance for ensuring the safety of ship navigation. However, most of the existing methods are limited to single-level fusion strategies, which makes it difficult to explore the signal features in multi-source data fully. For this reason, a diagnostic method based on a multi-source data three-level fusion strategy (MDTFS) is proposed. Secondly, different types of features are extracted using graph convolutional networks and improved convolutional neural networks. The features are fused, and an attention mechanism is applied to redistribute the weights of each channel to enhance key features. Finally, a decision-level fusion scheme based on entropy weighting is designed to diminish the influence of bad diagnostic channels and improve the effectiveness of the final decision. The experimental results show that MDTFS can effectively diagnose various types of motor faults, with a maximum accuracy of 98.12%. Thanks to the excellent diagnostic performance and maintenance support capabilities demonstrated by MDTFS in marine three-phase asynchronous motors, it is expected to improve the diagnostic accuracy of marine motors and enhance vessel safety in future practical applications.]]></description>
      <pubDate>Thu, 30 Apr 2026 16:39:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/2697318</guid>
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    <item>
      <title>Structural Heart Disease in Aircrew</title>
      <link>https://trid.trb.org/View/2685713</link>
      <description><![CDATA[Heart muscle diseases are challenging for the aeromedical examiner due to their heterogeneous nature and widely varying natural history. The diagnosis of heart muscle disease is likely to have a significant impact on flying privileges, with the risk assessment requiring careful testing and careful follow up. Consideration must also be given to the treatments used in each individual, as these may also pose an aeromedical risk. In athletic aircrew, differentiating athletic adaptation from a cardiomyopathy is essential to decide if flying restrictions are necessary. This article provides insights into the appropriate investigation of aircrew with suspected or proven heart muscle disease, to assist with licensing decisions. Four case vignettes are presented (myocarditis, hypertrophic cardiomyopathy, dilated cardiomyopathy, and athletic heart) to give a broad overview of the commonest areas of heart muscle disease seen in aircrew. The relevant features of evaluation, treatment, and aeromedical relevance are provided in a brief discussion of each case. This paper presents the most current recommendations for assessing aircrew with heart muscle disease and cardiomyopathy with data derived from current aeromedical and clinical literature, as well as the expert consensus of the NATO Working Groups on Occupational Cardiology (HFM WG 251, 316)]]></description>
      <pubDate>Mon, 20 Apr 2026 09:23:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685713</guid>
    </item>
    <item>
      <title>Neurophysiological Measures to Detect Spatial Disorientation</title>
      <link>https://trid.trb.org/View/2685715</link>
      <description><![CDATA[Maintaining spatial orientation in flight remains a critical aspect of aviation safety. Monitoring an aviator’s neurophysiological patterns may provide insight and opportunity to mitigate loss of spatial orientation. However, few studies have examined the utility of such measures. The purpose of the current study was to conduct a scoping review to document the research activities that have examined neurophysiological measures in relation to spatial orientation. Four databases were searched for literature using neurophysiological measures in studies assessing disorientation. The initial search yielded 110,135 articles. After removing duplicates and articles not meeting criteria, nine articles were reviewed. One of the nine articles used an aviation-relevant task. From the nine articles, evidence suggested roles of the parietal and frontal lobes maintaining orientation. Regarding the aviation-relevant task, the frontal lobe was supported for its involvement in the experience of unidentified spatial disorientation. Across all, the frontal lobe was consistently implicated (support from six studies) for its role in orientation. However, the studies differed in neurophysiological measures and outcomes evaluated. Electroencephalography emerged as a potential candidate for detecting disorientation, with six studies using it as the neurophysiological measurement device. Although the literature is limited on aviation-relevant tasks, there is strong support for activation patterns in the parietal and frontal lobes for orientation. This provides a starting point for experimental studies to further capture what patterns can be detected from neurophysiology when disorientation is experienced. Further research on aviation applications and using consistent measures is needed to further develop this area of research.]]></description>
      <pubDate>Mon, 20 Apr 2026 09:23:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685715</guid>
    </item>
    <item>
      <title>Coronary Artery Disease Detection and Disposition in Aircrew</title>
      <link>https://trid.trb.org/View/2685714</link>
      <description><![CDATA[Acute coronary events continue to represent a threat to aviation safety and mission completion and comprise a significant cause of loss of medical certification in both military and civilian aircrew. In the age range of active aircrew, coronary events often present as a plaque rupture event with acute incapacitation as the initial manifestation. The identification of asymptomatic aircrew with a high risk for an acute coronary event remains a major challenge to aviation medical practitioners. For aircrew who have had a coronary event or are have significant atherosclerosis, the challenge is to guide the appropriate evaluations to allow a risk assessment for consideration for continuing flight duties. Using a series of four case studies, this article will explore the evaluation, treatment, and proper aeromedical disposition of coronary artery disease (CAD). Cases will include CAD screening, asymptomatic non-obstructive CAD, asymptomatic obstructive/ischemic CAD, and symptomatic CAD with myocardial infarction. This paper presents the current benchmark for assessing aircrew for occult coronary disease and for assessment and disposition of aircrew with known coronary disease with data derived from current aeromedical and clinical literature, as well as the expert consensus of the North Atlantic Treaty Organization Working Group on Occupational Cardiology (HFM WG 251, 316).]]></description>
      <pubDate>Mon, 20 Apr 2026 09:23:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685714</guid>
    </item>
    <item>
      <title>Eye-tracking and visual processing tests for assessing driving ability in individuals with dementia and mild cognitive impairment: A pilot study</title>
      <link>https://trid.trb.org/View/2681652</link>
      <description><![CDATA[Changes in visual processing have been found to be affected in the early stages of dementia, potentially limiting driving ability. This pilot study investigated the sensitivity and specificity of eye-tracking, visual processing, and dementia screening tests in evaluating driving abilities among older drivers with and without cognitive impairment. Twenty-three participants aged 65+ years (n = 10 with cognitive impairment, 13 healthy controls) underwent dementia screening assessments including Mini Mental State Examination (MMSE) and Hopkins Verbal Learning Test (HVLT), a Visual Sensitivity Test (VST) and eye-tracking tasks (pro-saccade, anti-saccade, prospective eye movements) and compared these against a computerized driving-related hazard perception test (HPT) and self-report driving measures. Correlation analyses and ROC curves were used to explore relationships among the outcome measures. Drivers with cognitive impairment did not report different subjective driving performance, but had significantly lower HPT scores, with most scoring below the Driving and Vehicle Licensing Agency (DVLA) requirement for licensure. Eye-tracking data (n = 19) showed that drivers with cognitive impairment exhibited greater prosaccade latency variability. Antisaccade latency and prospective eye movement tests both correlated with self-reported in-vehicle task performance. The VST and HVLT tests strongly correlated with HPT scores and were highly predictive of scoring below the HPT DVLA cut-off scores. The VST and HVLT demonstrated high sensitivity and specificity for screening poor hazard perception performance in older drivers with cognitive impairment. Impaired eye movements correlated with self-reported difficulties in operating in-vehicle tasks, but not with HPT performance. Further research is needed to verify these findings in on-road assessments and with a larger sample size.]]></description>
      <pubDate>Wed, 08 Apr 2026 13:40:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2681652</guid>
    </item>
    <item>
      <title>Consensus Control and Sensor Fault Diagnosis of Heterogeneous Intelligent Connected Vehicles With Time-Varying Parameters and Random Noises Under Zero-Trust Architecture</title>
      <link>https://trid.trb.org/View/2674309</link>
      <description><![CDATA[This paper explores the problem of consistency control and sensor fault diagnosis for a class of intelligent connected vehicles with heterogeneous, time-varying nonlinear characteristics and random noise under a zero-trust architecture. Firstly, the vehicle platooning problem under zero-trust was analyzed in detail, and the mathematical model of the entire system was obtained. The generalized time-varying nonlinear intelligent connected vehicle platoon system without sensor faults was constructed using extended state vectors. Subsequently, based on the robust filter method and drawing lessons from the dedicated observer scheme, a novel robust filter is designed to reduce residual errors and improve the accuracy of state and sensor fault estimation. Using the estimation results of the filters for extended systems, a distributed consistency control protocol is designed for the intelligent connected vehicle platoon system, which calculates the gain coefficient of each intelligent connected vehicle to achieve consistency control through system dimension extension and linear matrix inequalities. In addition, drawing on the framework of the dedicated observer scheme, a sensor fault detection and isolation method was designed for the intelligent connected vehicle platoon system based on the zero-trust architecture. Finally, the effectiveness and efficiency of the developed methods are demonstrated through simulations and experimental examples.]]></description>
      <pubDate>Wed, 25 Mar 2026 11:44:57 GMT</pubDate>
      <guid>https://trid.trb.org/View/2674309</guid>
    </item>
    <item>
      <title>Analytical Control of the Mass of Injected Fuel into Diesel Engine Cylinder</title>
      <link>https://trid.trb.org/View/2676066</link>
      <description><![CDATA[In real operating conditions, the engine indicator diagram is the most common source of data for analyzing its current technical condition. Despite this, it is not possible to determine all diagnostic parameters directly from it, for example, the mass of fuel injected into the cylinder. To determine the fuel supply, it is possible to use the calculation of heat release in the cylinder or by solving a system of differential equations describing the working process in the cylinder. However, in those cases, the approximate value of the average temperature of the cylinder walls is unknown, as well as which of the empirical formulas for calculating the heat transfer from the gases to the cylinder walls should be used. Therefore, an additional method for calculating the mass of fuel injected into the cylinder was developed, in which the actual working process, under certain assumptions, was represented by a calculated cycle with isochoric and isobaric sections of fuel combustion. As a result, two systems of algebraic equations were compiled, the solutions of which can be used to find the mass of injected fuel. During the research, the process of solving equations was modernized, which allowed for a significant increase in accuracy.]]></description>
      <pubDate>Wed, 25 Mar 2026 11:44:57 GMT</pubDate>
      <guid>https://trid.trb.org/View/2676066</guid>
    </item>
    <item>
      <title>Multi-Grouping-Compatible Frame-Level Unsupervised Fault Diagnosis and Localization for Electric Vehicle Battery Packs in Realistic Conditions</title>
      <link>https://trid.trb.org/View/2679160</link>
      <description><![CDATA[Implementing real-world safety monitoring and fault diagnosis for lithium-ion batteries in electric vehicles is crucial. In this work, we design a fault diagnosis method based on an Attention-Gated Recurrent Unit (GRU)-Variational Autoencoder (VAE)-StatFusion neural network. This method not only identifies faults in batteries used in real-world applications but also meets the requirements for frame-level diagnosis, compatibility with different pack groupings, and fault cell localization. We constructed 15-dimensional online features of electrical and thermal characteristics to map battery safety. By combining the probabilistic distribution of the network's latent variables and unsupervised reconstruction loss, we design a comprehensive diagnostic index that can be output in real-time. Additionally, we quantify the contribution of each cell to the fault at abnormal moments, enabling online fault localization. Through fault cases such as electrolyte leakage, connection anomalies, excessive aging, and internal short circuits, the algorithm demonstrates effective fault diagnosis and localization for typical safety issues. Furthermore, we test the algorithm's Receiver Operating Characteristic (ROC) performance within 500 realistic vehicles. Under the same data conditions, compared to previous diagnostic networks, our proposed method showed a 42.1%–58.3% improvement in true positive rate (TPR) within the false positive rate (FPR) range of [0, 0.3]. Overall, this paper achieves a more accurate and practical battery fault diagnosis method under more refined application requirements, promoting the safety of battery applications.]]></description>
      <pubDate>Wed, 25 Mar 2026 11:44:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2679160</guid>
    </item>
    <item>
      <title>The relationship between electroencephalographic measures and driving performance in older adults: A scoping review</title>
      <link>https://trid.trb.org/View/2663681</link>
      <description><![CDATA[With the number of older adult drivers on the road increasing, more older adults are experiencing age-related changes in cognitive functions necessary for driving. Previous research suggests electroencephalography (EEG) may be a useful methodology for assessing these changes, given its high temporal resolution. However, the relationship between specific EEG markers and components of driving performance in older adults is currently unknown. The aim of this scoping review is to examine the current state of knowledge on EEG measures and driving performance in older adults. This scoping review was conducted in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). Papers were eligible for inclusion if they examined a) the relationship between EEG and driving performance measures or b) EEG and driving performance measures simultaneously, in older adults aged 55 years and older. A total of 468 papers were identified, and six papers were included in the final analysis. Results indicate frequency band analyses and event-related potentials are the most commonly used EEG measures to assess changes in driving performance. However, there is considerable variability between the current studies, in terms of the sample sizes, experimental design and the variables of interest. Considerable methodological heterogeneity and the lack of experimental data using cognitive paradigms for EEG, limits the ability to draw conclusions on the relationship between neurocognitive changes and driving performance in older adults. Directions for future research are discussed.]]></description>
      <pubDate>Wed, 25 Feb 2026 08:53:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/2663681</guid>
    </item>
    <item>
      <title>Automated diagnosis of bridge expansion joint defects using voiceprint features and deep learning</title>
      <link>https://trid.trb.org/View/2664175</link>
      <description><![CDATA[Bridge Expansion Joints (BEJs) are crucial for bridge safety, yet their acoustic signals are complex and easily disturbed by traffic noise, limiting traditional identification accuracy. To address this, an intelligent monitoring system based on voiceprint features and deep learning is developed. Its key contributions include: (1) a cloud-edge collaborative voiceprint monitoring device that integrates audio sampling, embedded processing, cloud server and wireless transmission, enabling long-term data collection and remote diagnosis under noisy environments; (2) the use of first- and second-order differential Mel Frequency Cepstral Coefficients (MFCC) for feature extraction, improving discriminability; and (3) the Hybrid Attention Fusion Network (HAFNet), built on a pre-trained convolutional backbone with multi-scale attention, achieving high-precision recognition of typical BEJ faults, with testing accuracies of 97.99% and 99.00% for two vehicle types. Field experiments demonstrate the system's stability, reliability, and feasibility for real-time BEJ monitoring.]]></description>
      <pubDate>Fri, 20 Feb 2026 09:02:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/2664175</guid>
    </item>
    <item>
      <title>Iris time-frequency map visual feature-based cluster matching: A universal domain adaptation method for propulsion shafting fault diagnosis</title>
      <link>https://trid.trb.org/View/2664969</link>
      <description><![CDATA[The propulsion shafting is a vital component of ship power systems. Timely and accurate fault diagnosis is essential for ensuring navigational safety. Domain adaptation techniques have been widely applied in intelligent fault diagnosis. However, most existing methods overlook the critical impact of input representation quality on diagnostic performance and are confined to specific domain adaptation scenarios. In practical engineering, the label space relationships between domains are often unavailable, limiting the applicability of these methods. To address these issues, this study proposes a universal domain adaptation (UniDA) method, termed the source domain category anchor-guided cluster matching network. Specifically, the network utilizes iris time-frequency maps as input, which enhances the readability of the information. A similarity criterion is formulated to cluster features of the same type, subsequently matching them to the corresponding category anchors. Moreover, an inter-class representation decoupling constraint is designed to shape a more globally discriminative feature space. Further, a distance-based detection strategy is proposed to build reliable decision boundaries between common and private categories. Experimental results on the propulsion shafting dataset validate the effectiveness of the proposed method in handling diagnostic tasks involving domain and category shifts, outperforming other state-of-the-art methods. Additionally, visualization via gradient-weighted class activation mapping indicates that the network's decision-making is grounded in physically meaningful evidence, revealing the complementarity between interpretability and transferability.]]></description>
      <pubDate>Mon, 09 Feb 2026 08:42:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2664969</guid>
    </item>
    <item>
      <title>Quantitative diagnosis of hydrogen crossover in fuel cell: based on the hydrogen concentration cell</title>
      <link>https://trid.trb.org/View/2652277</link>
      <description><![CDATA[Timely and efficient hydrogen crossover diagnosis is crucial for improving the performance, durability, and safety of proton exchange membrane fuel cell (PEMFC). However, most existing diagnostic methods are complicated to operate, time-consuming, costly, and necessitate complex data processing. There are almost no studies that can achieve hydrogen crossover diagnosis in seconds without requiring additional equipment other than conventional fuel cell testing bench. To address this gap, a novel quantitative diagnostic method of hydrogen crossover based on the hydrogen concentration cell is proposed. Specifically, the essence of the open circuit voltage (OCV) arising from the hydrogen concentration cell formed by hydrogen crossover phenomenon is thoroughly investigated via Nernst equation, to propose this novel quantitative diagnostic method. This novel quantitative diagnostic method requires only three parameters: OCV, cathode flow rates, and fuel cell temperature, to rapidly achieve quantitative diagnosis of hydrogen crossover current. After conducting 228 trials, the proposed novel quantitative diagnostic method demonstrated a maximum relative error and a mean absolute percentage error (MAPE) of 4.89 % and 2.54 %, when compared to the validated and reliable potential step method (PSM), fully verifying its repeatability and accuracy. This novel method can achieve quantitative diagnosis of hydrogen crossover in seconds on any conventional fuel cell testing bench, with high cost-effective and simple data processing. It will provide a powerful tool for quality control, optimization design, periodic diagnosis, and aging assessment of fuel cell.]]></description>
      <pubDate>Tue, 27 Jan 2026 15:18:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2652277</guid>
    </item>
    <item>
      <title>IoT-Enabled Real-Time Energy Consumption Anomaly Detection and Diagnosis for Automotive Paint Drying System</title>
      <link>https://trid.trb.org/View/2648342</link>
      <description><![CDATA[The energy-intensive automotive industry requires sophisticated energy management systems to improve energy efficiency. In automotive workshops, paint drying systems are a significant energy consumer, necessitating real-time monitoring and control to minimize energy waste and potentially prevent system malfunctions. Thus, this study proposed a novel real-time energy consumption anomaly detection and diagnosis methodology (eAnoD) for automotive paint drying systems to enhance their energy efficiency and operational safety. Specifically, an architecture combining a temporal convolutional network and graph attention network (TCN-GAT) was devised to extract spatiotemporal features from multidomain data, including energy consumption, equipment parameters, production states, and environmental conditions. A hybrid neural network combining a backpropagation neural network (BPNN) and variational autoencoder (VAE) was constructed to enable the prompt identification of energy consumption deviations. Furthermore, an anomaly grading method integrating combination weighting and cloud modeling techniques was developed to evaluate anomaly severity, facilitating targeted maintenance and proactive risk prevention. A real-world case study was conducted in a new-energy vehicle factory to validate the effectiveness and practicality of the proposed methodology and demonstrate its potential for energy saving and risk mitigation in automotive manufacturing. This study is expected to serve as a reference for practical implementation and generate new ideas for academic exploration.]]></description>
      <pubDate>Mon, 26 Jan 2026 08:41:46 GMT</pubDate>
      <guid>https://trid.trb.org/View/2648342</guid>
    </item>
    <item>
      <title>Multi-fault diagnosis strategy based on sliding mode enhanced carrier decision of the aging IPMSM for the electric rail transit</title>
      <link>https://trid.trb.org/View/2647880</link>
      <description><![CDATA[The interior permanent magnet synchronous motor (IPMSM) is the core of the traction drive system (TDS) in electric rail transit. Its aging faults can lead to reduced train power, wheelset-rail oscillations, and even trigger secondary failures that affect passenger safety. Existing diagnostic strategies often require additional observers, complex signal injection, or large volumes of fault data, which either reduce the reliability during train operation or make deployment challenging within the safety-oriented framework of train operations. This paper proposes a multi-fault diagnosis strategy (MFDS) based on sliding mode enhanced carrier decision (SMECD). The SMECD integrates existing onboard control signals of electric rail transit as fault carriers, eliminating the need for additional hardware and ensuring reliable onboard deployment capabilities. In addition, an aging fault injection model (AFIM) is proposed, which integrates diverse aging faults. The AFIM can replicate the aging faults of the IPMSM caused by frequent switching of different operating conditions in the TDS, ensuring the application validation of the MFDS. The experimental results show that the MFDS can accurately locate different aging faults, thereby providing predictive maintenance guidance for rail transit operations. Compared to traditional diagnostic strategies, the MFDS does not require additional observers or sensors to monitor the aging parameters of the IPMSM in the TDS, offering a lightweight and easily deployable diagnostic strategy for the entire lifecycle of train operations, thus enhancing the safety and reliability of electric rail transit.]]></description>
      <pubDate>Mon, 26 Jan 2026 08:41:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2647880</guid>
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