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    <title>Transport Research International Documentation (TRID)</title>
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    <language>en-us</language>
    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
    <image>
      <title>Transport Research International Documentation (TRID)</title>
      <url>https://trid.trb.org/Images/PageHeader-wTitle.jpg</url>
      <link>https://trid.trb.org/</link>
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    <item>
      <title>Evolution of isolated heaving defects for vertical rail displacements: a multi-disciplinary study</title>
      <link>https://trid.trb.org/View/2655762</link>
      <description><![CDATA[Track heaving defects are likely caused by multiple complex factors, including irregular superstructure displacement and frost-induced expansion of subgrades and subsoils. These defects can significantly disrupt normal train operations. Early detection and trend prediction of such defects are essential for implementing effective predictive maintenance, enabling maintenance prioritization, and reducing both costs and operational disruptions. However, rail geometry profiles often exhibit non-stationary and complex characteristics, making the identification of isolated defects challenging. While most current studies focus on evolution of defect magnitude, the impact of defect length on train–track interaction remains largely unexplored. Furthermore, understanding the natural degradation process of isolated defects is complicated by maintenance activities that disrupt the development of track defects. To address these challenges, a multi-disciplinary approach was employed, incorporating signal processing techniques, multibody dynamics simulations, and geotechnical site investigations to identify, characterize, and analyse the effects of defect evolution on the train-track system. A 50-km section of measurement train data, collected over a five-year period, was analysed to examine the impact of both defect amplitude and length evolution on train performance, including operational risks and passenger comfort. Statistical analysis of the location, magnitude, and length of isolated heaving defects, along with their evolution patterns, was conducted. The findings were synthesized into an evolution diagram of train performance based on changes in defect profiles and applied to real-world railway data.]]></description>
      <pubDate>Thu, 09 Apr 2026 10:08:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/2655762</guid>
    </item>
    <item>
      <title>Traction enhancement evaluation of Magnetite and Alumina</title>
      <link>https://trid.trb.org/View/2655758</link>
      <description><![CDATA[This study explores the application of traction-enhancing materials for railroad applications using the VT – FRA Roller Rig. Two oxides are investigated for their effectiveness in increasing traction without causing added wear. An ethanol-based slurry of Magnetite and Alumina is used to apply the powdered oxides onto the roller (rail) surface. The tests are performed with 5000 N wheel load and three discrete creepage conditions. The dynamic tests are performed with a cylindrical wheel to maintain the primary focus on the oxides and neglect any effect due to the wheel taper at large contact angles. Ten cubic centimetres of the Magnetite/Alumina and ethanol slurry are applied to the roller surface, and the alcohol is allowed to evaporate, leaving behind the dried oxide on the roller surface. The results indicate that both materials enhanced forces in the transient traction region. On average, Magnetite increases the traction rate by 90% and Alumina by 190%, compared with the dry condition. The findings suggest that the small particle sizes of Magnetite and Alumina contribute to traction enhancement, with Alumina being more than twice as effective in increasing traction.]]></description>
      <pubDate>Wed, 08 Apr 2026 13:57:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2655758</guid>
    </item>
    <item>
      <title>Optimization and validation of a thermal simulation model for tread braking using an innovative small-scale experimental rig</title>
      <link>https://trid.trb.org/View/2655754</link>
      <description><![CDATA[This study presents the development and calibration of a simulation model for analysing thermal behaviour during tread braking, using an innovative small-scale experimental rig called ‘4-contact machine.’ This small-scale rig replicates the simultaneous interaction between the wheel, brake blocks, and rails, offering a unique capability to simulate real-world braking conditions in a controlled environment. The model was calibrated with experimental data from a 300-s test, achieving close agreement between simulated and measured temperature distributions. Key thermal parameters, including thermal resistances and contact conductance between components, were optimized and validated using tests of different durations (150 s, 450 s, and 650 s). Heat partitioning between the wheel, brake block, and rail samples was consistent across tests of various durations, aligning with values reported in the literature. The model provides a reliable framework for simulating the thermal effects of tread braking, offering valuable insights for improving railway brake system design. Future work will explore the scalability of these results for full-scale applications.]]></description>
      <pubDate>Wed, 08 Apr 2026 13:57:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2655754</guid>
    </item>
    <item>
      <title>Unsupervised Representation Learning for Monitoring Rail Infrastructures With High-Frequency Moving Vibration Sensors</title>
      <link>https://trid.trb.org/View/2591339</link>
      <description><![CDATA[Nowadays, rolling stock can be equipped with high-frequency vibration sensors to continuously monitor rail infrastructures and detect defects. These moving sensors measure at high speeds and sampling frequencies, generating a massive amount of data that covers each track position with very short signal durations. These data contain a variety of dynamic and transient responses that vary significantly along the track and are affected by noise. This leads to a large amount of unlabeled and noisy data, complicating the extraction of dynamic responses for effective anomaly detection. To address these challenges, this paper proposes an unsupervised representation learning methodology to automatically capture and extract characteristic features of dynamic responses that reflect the conditions of rail infrastructures. The unsupervised nature allows exploratory analysis of high-frequency vibration signals when prior knowledge or reference information about infrastructure conditions is unavailable or very limited. A collaborative optimization process that synchronizes empirical mode decomposition (EMD) with a convolutional autoencoder (CAE) is presented. The EMD level is tuned to remove noise while preserving effective vibration responses. The CAE is trained using demodulated signals that are considered normal to generate representations that ensure reconstruction quality and differentiate between normal and abnormal conditions. Furthermore, a Gaussian mixture model is used to showcase the effectiveness of the learned representations for rail infrastructures. Applied to validated axle box acceleration data for rail defect detection and train-borne laser Doppler vibrometer data for rail fastener monitoring, our method outperforms other variants of autoencoder-based models and the wavelet-based CAE in accurately identifying the conditions. It achieves an average improvement of 16% with the axle box acceleration data and 21% with the laser Doppler vibrometer data.]]></description>
      <pubDate>Tue, 31 Mar 2026 16:34:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2591339</guid>
    </item>
    <item>
      <title>Comparison of wheel/rail contact modelling in prediction of wheel tread wear under changeable friction conditions</title>
      <link>https://trid.trb.org/View/2646997</link>
      <description><![CDATA[Wheel/rail interaction modelling is pronouncedly critical in characterising the frictional contact behaviours which immediately causes vehicle-track coupled vibrations and wheel/rail surface wear damage. Kalker’s complete theory that programmed as CONTACT has been widely used to examine wheel/rail contact modelling, which nevertheless, is in the form of static aspect in most existing studies. The main motivation of this study is to compare the on-line application of the Hertzian, non-Hertzian and CONTACT models in vehicle-track interactions and long-term wheel tread wear evolution with presence of the changeable friction conditions. The numerical results point out that the non-Hertzian models are capable of higher precision in addressing the wheel/rail frictional contact compared to the Hertzian model under changeable friction conditions. However, the Hertzian model is more efficient than the non-Hertzian models when applied in vehicle-track dynamics calculations. The difference of wear depths between the Hertzian and non-Hertzian models is not significant (within 0.1 mm after 12 iterations). Therefore, the Hertzian model can be regarded as a compromise when solving wheel tread wear development. This study can help to provide a certain reference in determining wheel/rail contact model when addressing wheel tread wear predictions.]]></description>
      <pubDate>Tue, 24 Mar 2026 09:10:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2646997</guid>
    </item>
    <item>
      <title>A Review of Computer Vision for Railways</title>
      <link>https://trid.trb.org/View/2591228</link>
      <description><![CDATA[Modern railways continue to strive for remote and automated methods to improve the visual inspection procedures for their assets. In some cases, these inspections provide new information that could not previously be collected, while in other cases they help them improve upon the quality control, safety, time and costs associated with manual inspection. As such, computer vision continues to find applications for visually inspecting the track, earthworks, tunnels, overhead line equipment and rolling stock. Considering the recent pace of computer vision related developments, this paper seeks to review the state of the art of the field for railways. First, the hardware and data requirements are discussed, focusing on the unique challenges associated with operating optical equipment in a railway environment, such as contamination, power sources and lighting. This also discusses the most common mounting arrangements for camera hardware, including rolling-stock, satellites and way-side cameras. Next, image processing algorithms are discussed, comparing classical approaches and more modern artificial intelligence approaches, for example You Only Look Once (YOLO) and Region-Based Convolutional Neural Network (R-CNN). Then the most common applications for computer vision in the rail industry are analysed. First the track is studied considering computer vision analysis for the detection of different types of rail surface defects on plain line and turnouts, fastener defects, concrete track slab cracking and ballast particle characterisation. Next, the overhead line equipment is considered with applications related to detecting contact loss between pantograph and contact wire, stagger behaviour and defective catenary components. This is followed by discussion of other applications such as rail tunnelling subsidence, tunnel inspection, level crossings, trespass and on-track safety hazards. Finally, opportunities for future research are discussed such as hyperspectral imaging and generative AI, along with possible frontier technologies such as quantum computing.]]></description>
      <pubDate>Mon, 23 Mar 2026 17:14:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2591228</guid>
    </item>
    <item>
      <title>Research on wheel–rail impact dynamics due to combined rail weld irregularities and polygonal wheel effects considering 3D contact geometry</title>
      <link>https://trid.trb.org/View/2646993</link>
      <description><![CDATA[The geometric irregularities of rail welds and polygonal wheels are common defects observed in high-speed railways, leading to the generation of high-frequency impact forces and vibrations in the wheel–rail contact system. This research establishes a novel vehicle-track coupled dynamic model that incorporates a 3D wheel–rail contact model using meshing grid and conjugate gradient methods to study the high-magnitude wheel–rail impact forces caused by rail weld irregularities and polygonal wheel combinations. The primary objective of this study is to develop a precise 3D contact model that incorporates the actual geometry of wheel and rail surface irregularities into the calculation of vehicle-track dynamic interactions. The study evaluates the effects of 3D rail weld irregularities and polygonal wheels on wheel–rail dynamic interaction by comparing results with those obtained from a conventional vehicle-track coupled model that considers a 2D contact model. The results show that the lengths and depths of polygonal wheels and rail weld irregularities, as well as vehicle speeds, significantly increase the wheel/rail dynamic impact forces. The results also indicate that the widening of irregularities not only significantly affects the wheel–rail contact force but also has an important influence on the contact stiffness.]]></description>
      <pubDate>Fri, 20 Mar 2026 14:47:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2646993</guid>
    </item>
    <item>
      <title>Independent versus collaborative double-checking for errors on a simulated rail control task</title>
      <link>https://trid.trb.org/View/2643948</link>
      <description><![CDATA[Double-checking is a safety practice performed by workers across high-risk industries.We aimed to examine the effectiveness of two types of double-checking (independent versus collaborative) for the detection of errors. We also examined the effect of two classes of checking tasks (matching versus critical analysis and assimilation) and interruptions on error detection. A total of 198 participants completed a 32-min rail control simulation. The primary objective for participants was to identify misrouted trains. Participants worked in pairs and performed tasks that involved matching versus critical analysis and assimilation, with interruptions occurring during the tasks. Independent double-checking was associated with greater response accuracy for identifying misrouted trains compared with collaborative double-checking. Response accuracy was also greater when participants engaged in matching compared to critical analysis and assimilation. Interruptions were not associated with task performance. Our findings suggest that independent double-checking may be superior to collaborative double-checking for the detection of errors.]]></description>
      <pubDate>Wed, 04 Mar 2026 09:16:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643948</guid>
    </item>
    <item>
      <title>Improving railway safety and reliability during earthquakes: Rail-mounted sensors exploiting rails as seismic waveguides</title>
      <link>https://trid.trb.org/View/2636753</link>
      <description><![CDATA[Railway systems are vulnerable to earthquake-induced deformation and derailment, requiring rapid and reliable protective action. Existing Railway Earthquake Early Warning Systems (REEWS) rely on seismometers outside the superstructure, limiting network density, warning lead times, and robustness against false or missed alarms. This study introduces and field-tests a prototype REEWS that exploits steel rails as seismic waveguides by embedding low-noise, low-cost MEMS accelerometers directly onto the track. Using the aftershock sequence of the Mw 6.1 Balıkesir–Sındırgı earthquake (Türkiye, 2025), three prototype stations recorded 39 events over one week. Results show 100 % detection probability for Mw ≥ 3.5 within 45 km, a conservative threshold well below magnitudes known to threaten railway integrity. Compared with national broadband stations, rail-mounted sensors provided up to 18 s earlier warnings (average 3 s), though small delays (≤ 5 s) occurred depending on source characteristics. High-resolution delay statistics and event-centered ΔV (speed-reduction target) coverage mapping further demonstrate that the rail-mounted system achieves median detection delays near 0 s and compact, predictable warning rings sufficient for meaningful train deceleration. Importantly, rail response amplitudes were not linearly correlated with earthquake magnitude, underscoring the need for a rail-specific methodology that reduces false alarms while enhancing reliability and safety. The approach is cost-efficient, leveraging sensors already used for wheel–track monitoring to enable dense, scalable networks at a fraction of conventional costs. These findings establish rail-mounted sensing as a viable foundation for next-generation REEWS.]]></description>
      <pubDate>Wed, 25 Feb 2026 16:28:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/2636753</guid>
    </item>
    <item>
      <title>Experimental investigation of wheel rail adhesion under leaf contamination conditions</title>
      <link>https://trid.trb.org/View/2613686</link>
      <description><![CDATA[In order to investigate the adhesion degradation caused by leaves and the adhesion improvement behaviour under wheel sliding conditions, a novel circumferential wheel/ring based rolling contact test rig was used in this paper. The leaves were meticulously distributed across the ring-shaped rail surface to closely mimic real-world track conditions, facilitating adhesion testing under scenarios involving leaf pulp, green leaves, soaked fallen leaves, and different species of leaves. The results indicate that leaves can induce adhesion values as low as 0.05, with no significant discrepancy observed in the low adhesion values caused by different leaf species. In addition, the study shows that the low adhesion surface formed by leaves is not static, the sliding action of the wheels can effectively remove the black substance layer formed by leaf debris on the rail surface, with the cleaning effect becoming more pronounced with increasing speed and axle load. Finally, based on the experimental results, this paper proposes characteristic parameters for the Polach wheel-rail adhesion formula under leaf contamination conditions, and develops an empirical formula for adhesion improvement.]]></description>
      <pubDate>Mon, 26 Jan 2026 14:44:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/2613686</guid>
    </item>
    <item>
      <title>Study on the optimization of solid particle additives in top-of-rail friction modifiers based on a twin-disc testing apparatus</title>
      <link>https://trid.trb.org/View/2613682</link>
      <description><![CDATA[Top-of-rail friction modifiers are used to manage the friction on the top-of-rail and help alleviate corrugation, reduce noise, decrease material wear, etc. In this paper, five series of FM samples were prepared and tested using a twin-disc testing apparatus to optimize the solid particle parameters in the FM, aiming to achieve an intermediate adhesion level and a positive creep curve at the wheel-rail interface. The roles of every composition were explored and further the mass content of solid particles, the mass content ratio of lubrication to modifying particles, and the hardness and size of modifying particles were optimized. The possible influence mechanism of FM third body layer shear strength on the wheel-rail adhesion behaviour was discussed based on the Coulomb-Mohr theory. The FM sample containing 76.31 wt% of water, 2.77 wt% of carboxymethyl cellulose (CMC), 10.46 wt% of resin, 3.04 wt% of graphite particles, and 9.13 wt% of kaolin particles can reduce adhesion coefficient to 0.129 and generate an obvious positive creep curve in the wheel-rail interface.]]></description>
      <pubDate>Mon, 26 Jan 2026 14:44:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/2613682</guid>
    </item>
    <item>
      <title>RF Energy Harvesting for Safe Monitoring of Rail Condition on Autonomous Trains</title>
      <link>https://trid.trb.org/View/2651954</link>
      <description><![CDATA[This paper presents a safe sensing scheme and radio frequency energy harvesting system (RFEHS) to monitor rail conditions for autonomous trains. Conventional sensing schemes expose trains on railroads to danger because trains must pass the rail to know its condition. The proposed sensing scheme gets rid of the danger and reduces the complexity of sensing processes and train systems because trains do not have to communicate directly with sensors. In this sensing scheme, the RFEHS receives dual polarization (DP) signal, which is all of the base station's polarizations so that the sensor operation gets more frequent. The harvesting capability of DP and linear polarization (LP) from a real base station is compared. To prove the feasibility of the proposed sensing scheme, the RF energy harvesting was conducted from a base station on the rail track. As a result, rail temperature data were obtained using DP-RFEHS in the proposed sensing scheme, which has low-complexity and is a safer solution for autonomous trains.]]></description>
      <pubDate>Mon, 26 Jan 2026 08:41:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/2651954</guid>
    </item>
    <item>
      <title>Evaluation of the effect of rail pad stiffness and wheel polygonization on the dynamic behavior of corrugated railway tracks</title>
      <link>https://trid.trb.org/View/2610805</link>
      <description><![CDATA[Oscillatory damage or irregularities on the rails and wheels of railway systems significantly affect the system’s performance. Mitigating these defects is a task that concerns operators worldwide. In this work, a methodology is proposed to study the effect of pad stiffness and low-order wheel polygonization on the dynamic behavior of a corrugated railway track. Corrugation was measured using an accelerometer mounted on a grinding vehicle, and it was modeled as a wave-type excitation on the rail. Displacement probe-based methodology was used in wheel eccentricity test and the polygonization was considered by incorporating the local radius at every degree of rotation for the studied wheels. The methodology considers different static stiffness values of the pads, and after several evaluations, the most suitable pad was selected to mitigate the effects of the oscillatory defects. The performance of the selected pad was verified under different track stiffness values (measured using the video gauge method), this pad with a stiffness of 179 kN/mm presented the best performance for most speed scenarios with an increase of 71% when corrugation and wheel polygonization were considered for the simulation conditions (small curve of radius 304 m, corrugation wavelength of 45 mm, speed of 80 km/h). Finally, anomalous behaviors related to system resonance between the track and vehicle were analyzed. The evaluation showed that a pad stiffness of 60 kN/mm causes a resonance increasing 513% the accelerations when wave defects are present.]]></description>
      <pubDate>Fri, 09 Jan 2026 16:59:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/2610805</guid>
    </item>
    <item>
      <title>Optimization and field evaluation of a detachable thermal insulation fabric for rail temperature mitigation</title>
      <link>https://trid.trb.org/View/2616650</link>
      <description><![CDATA[Rail temperature control is a critical factor in ensuring the safety and reliability of railway systems, especially under extreme summer heat conditions. Conventional countermeasures such as water spraying systems and solar-reflective coatings have demonstrated limited applicability due to high installation and maintenance costs, short duration of cooling effects, and practical constraints in field deployment. As a novel solution, this study introduces and validates a detachable thermal insulation fabric designed for steel rails, consisting of a heat-reflective fluoropolymer-coated glass fiber fabric and embedded magnets for easy installation and reuse. Through controlled laboratory tests, the optimal coating thickness (100 μm) and effective application area (rail web only) were determined. Field application over two summer seasons on an actual railway track demonstrated an average temperature reduction of 4–5℃ and a maximum of 9.7℃, particularly effective during peak solar radiation periods. Compared to conventional coatings, the fabric exhibited similar or superior thermal mitigation performance, with significantly improved reusability and maintenance convenience. A one-year outdoor exposure test confirmed minimal performance degradation (∼3 %) primarily due to UV-induced discoloration. This study is the first to provide a comprehensive and quantitative validation of detachable thermal insulation fabric for rail applications. The results suggest that this fabric-based approach offers a practical, durable, and scalable alternative to existing methods, with broader applicability to urban infrastructure, noise barriers, and steel bridge decks under thermal stress.]]></description>
      <pubDate>Mon, 29 Dec 2025 09:34:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2616650</guid>
    </item>
    <item>
      <title>A machine learning based voting regression method for adhesion estimation in wheel-rail contact</title>
      <link>https://trid.trb.org/View/2604151</link>
      <description><![CDATA[The majority of control methodologies for railway vehicles depend on adhesion data to attain optimal traction. Therefore, researchers have been actively investigating practical and feasible approaches to ascertain precise adhesion data with the objective of optimising the efficiency of railway vehicle operations. While several methods are accessible for indirectly measuring or estimating adhesion, there remains a need for more accurate and expedient estimation techniques. Therefore, in this study, we propose a novel machine learning-based voting regression (VR) model that can efficiently estimate the adhesion between wheel and rail. The proposed VR model is constructed using weighted averages of individual models such as Histogram-based gradient boosted trees (HistGBT), random forest (RF), and linear regression (LR). It is demonstrated by various real-world measurements that the proposed method achieves a high score of 0.922 R² for the estimation of the adhesion coefficient and outperforms the benchmark model-based Polach's method.]]></description>
      <pubDate>Fri, 05 Dec 2025 14:12:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2604151</guid>
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