<?xml version="1.0" encoding="utf-8"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>TRB Publications Index</title><link>http://pubsindex.trb.org/</link><atom:link href="http://pubsindex.trb.org/common/TRIS Suite/feeds/rss.aspx?s=PHNlYXJjaD48cGFyYW1zPjxwYXJhbSBuYW1lPSJzdWJqZWN0aWQiIHZhbHVlPSIxNzc5IiAvPjxwYXJhbSBuYW1lPSJsb2NhdGlvbiIgdmFsdWU9IjIiIC8%2BPHBhcmFtIG5hbWU9InN1YmplY3Rsb2dpYyIgdmFsdWU9Im9yIiAvPjxwYXJhbSBuYW1lPSJ0ZXJtc2xvZ2ljIiB2YWx1ZT0ib3IiIC8%2BPC9wYXJhbXM%2BPGZpbHRlcnMgLz48cmFuZ2VzIC8%2BPHNvcnRzPjxzb3J0IGZpZWxkPSJwdWJsaXNoZWQiIG9yZGVyPSJkZXNjIiAvPjwvc29ydHM%2BPHBlcnNpc3RzPjxwZXJzaXN0IG5hbWU9InJhbmdldHlwZSIgdmFsdWU9InB1Ymxpc2hlZGRhdGUiIC8%2BPC9wZXJzaXN0cz48L3NlYXJjaD4%3D" rel="self" type="application/rss+xml" /><description></description><language>en-us</language><copyright>Copyright © 2015. 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>TRB Publications Index</title><url>http://pubsindex.trb.org/Images/PageHeader-wTitle.png</url><link>http://pubsindex.trb.org/</link></image><item><title>Slotted Schedules for Corridors with Mixed Freight and Passenger Operations</title><link>http://pubsindex.trb.org/view/2724650</link><description><![CDATA[Slotted scheduling is a planning and operational approach that defines specific time–space slots throughout the day for different classes of trains—typically categorized by speed, acceleration, stopping patterns, and service type—to enable trains to operate without conflicts. As transit agencies seek to expand service hours and improve bidirectional, all-day service, particularly in response to more dispersed post-pandemic travel demand, new planning and coordination approaches are needed. Slotted scheduling offers a potential framework for improving the efficiency, reliability, and transparency of shared rail operations.  TCRP Research Results Digest 124: Slotted Schedules for Corridors with Mixed Freight and Passenger Operations, produced by TRB’s Transit Cooperative Research Program, documents current practices, evaluates technical and institutional challenges, and identifies planning and modeling tools that can help public agencies and railroads achieve more coordinated and mutually beneficial operations. The digest summarizes one of 12 small research tasks conducted to support the development of new content for the Transit Capacity and Quality of Service Manual, Fourth Edition.]]></description><pubDate>Mon, 06 Jul 2026 15:58:55 GMT</pubDate><guid>http://pubsindex.trb.org/view/2724650</guid></item><item><title>Through-Running Regional Rail</title><link>http://pubsindex.trb.org/view/2724649</link><description><![CDATA[Interest is growing in transforming traditional commuter rail systems into regional rail systems with more transit-like characteristics. In contrast to traditional peak-period, peak-direction commuter rail service, a through-running regional rail system provides bidirectional, all-day, relatively frequent service through an urban core that can serve multiple trip purposes.  TCRP Research Results Digest 123: Through-Running Regional Rail, produced by TRB’s Transit Cooperative Research Program, provides insights into how a through running regional rail system’s capacity is affected by its physical and operational characteristics, service reliability, tolerance for train delays, and other factors. The digest summarizes one of 12 small research tasks conducted to support the development of new content for the Transit Capacity and Quality of Service Manual, 4th Edition.]]></description><pubDate>Mon, 06 Jul 2026 15:58:55 GMT</pubDate><guid>http://pubsindex.trb.org/view/2724649</guid></item><item><title>Enhancing Wind Field Prediction and Reconstruction around Windbreak Walls along High-Speed Railway by Advanced Neural Network Architectures: Accuracy and Stability Assessment</title><link>http://pubsindex.trb.org/view/2717202</link><description><![CDATA[Accurate prediction of wind fields around high-speed railway (HSR) infrastructure is critical for operational safety and energy efficiency. This study evaluates neural network approaches for predicting wind fields around HSR windbreak walls, focusing on transformer models. Field measurements were conducted using 15 anemometer masts arranged inside and outside windbreak walls on the Lanzhou–Xinjiang railway. We compared multiple deep learning architectures (multilayer perceptron, long-short-term memory, temporal convolutional network and transformer) for predicting interior wind conditions based on exterior measurements. The key findings are summarized as follows: (1) prediction accuracy improved substantially with longer historical contexts (10–60 timesteps); (2) significant spatial variability exists in wind predictability across measurement locations; (3) feature importance analysis identified critical measurement points, enabling cost-effective maintenance strategies and optimized sensor deployment; and (4) sequence mean filling performed best among the three tested strategies for handling missing data, maintaining positive predictive power even with substantial sensor loss. Among these models, the transformer model achieved the best overall performance (𝘙² = 0.9665 at 𝘛 = 60), with its advantage becoming most pronounced at longer historical contexts. These findings have important implications for railway safety and wind energy applications, enabling more efficient monitoring networks and robust forecasting systems. The demonstrated effectiveness of transformer models represent a significant advancement in applying attention-based architectures to infrastructure monitoring challenges.]]></description><pubDate>Wed, 24 Jun 2026 10:29:07 GMT</pubDate><guid>http://pubsindex.trb.org/view/2717202</guid></item><item><title>Application and Parameter Matching of a Novel Pipe-Roof-Concrete Slab Method in Metro Stations</title><link>http://pubsindex.trb.org/view/2714509</link><description><![CDATA[The pipe-roof method is an increasingly popular pre-construction support system for underground space development. However, conventional pipe-roof systems suffer from low bearing capacity and complex construction procedures, limiting their application in ultra-shallow-buried large-span metro stations. To address these limitations, this study proposes an innovative adaptation termed the pipe-roof-concrete slab (PRCS) method. Based on the Shenyang Metro Line 3 Heping South Street Station in China, a three-dimensional finite difference model was developed through refined modeling and validated against field measurements. The results reveal that surface settlement follows a quadratic function relationship with pipe-roof stiffness and roof slab modulus, and a linear relationship with slab spacing and thickness. Increasing pipe-roof stiffness from 0.5EI to 2.5EI reduces maximum settlement by 41.40%, while increasing slab thickness from 400 mm to 1,000 mm reduces settlement by 35.95%. A multiple linear regression model was established to quantify the matching relationships between parameters, achieving an adjusted R2 of 0.886. These quantitative relationships provide a theoretical basis for understanding the parameter interactions and optimizing the structural design of PRCS systems. Based on these findings, practical guidance is provided for the rational selection of structural parameters to meet different settlement control standards in similar underground engineering projects.]]></description><pubDate>Thu, 18 Jun 2026 11:10:43 GMT</pubDate><guid>http://pubsindex.trb.org/view/2714509</guid></item><item><title>Resilience-Oriented Line Planning for Multimodal Rail Transit Network Considering Uncertain Passenger Service Choice Behavior</title><link>http://pubsindex.trb.org/view/2711991</link><description><![CDATA[The train line planning problem (LPP) determines passenger travel path accessibility by optimizing train routes and stop plans. This study considers the uncertainty of passenger service choice behavior and the partial periodic operation pattern in the multimodal rail transit network (MRTN), defines system resilience as the network’s ability to resist interference, and constructs a resilience-oriented LPP model with constraints for passenger assignment that account for uncertain service choice behavior. A customized iterative solution procedure is designed to solve this model. In each iteration, a passenger assignment algorithm that integrates an available travel path search method is developed to determine passenger travel paths, and an improved adaptive large neighborhood search (IALNS) algorithm with a network decomposition strategy is designed to solve the LPP. The proposed approach is examined on Shanghai MRTN, with analysis of influences of travel path resilience requirement, uncertainty in passenger service choice behavior, and partial periodic operation strategy. The results indicate that incorporating path resilience into LPP can enhance network resilience with limited operational cost increases, accounting for passenger uncertain service choice behavior can more accurately match the transport capacity with passenger demand, and the partial periodic pattern can balance the regularity and flexibility of the line plan. Furthermore, the IALNS algorithm outperforms Gurobi on large-scale cases, and the proposed approach can well balance operational costs, generalized passenger travel time, and network resilience. Case study findings provide insights for rail operators in line planning.]]></description><pubDate>Thu, 11 Jun 2026 09:16:26 GMT</pubDate><guid>http://pubsindex.trb.org/view/2711991</guid></item><item><title>Classification of Bolt Corrosion Levels Combining Deep Learning and Multi-Feature Segmentation</title><link>http://pubsindex.trb.org/view/2712017</link><description><![CDATA[Many bolts are installed in subway tunnels, making manual inspection prohibitively costly, and deep learning models face difficulties in segmenting extremely small corroded regions, which results in low detection efficiency. To address these challenges, this study proposes a corrosion grade classification algorithm for subway tunnel bolts based on deep learning and multi-feature segmentation, which directly outputs the corrosion grade of each bolt to enhance maintenance efficiency. First, the YOLOv8 framework is improved using multi-scale channel group shuffle convolution (MSCGSC) and focal loss (FL) to develop the YOLO-MF (MSCGSC + FL) model for preliminary detection of corroded bolts. Second, the VGG16 network is employed as the backbone of U-Net, and channel shuffle is applied after the encoder–decoder concatenation to eliminate background noise of bolts using the VGG + channel shuffle (VCS)-Net model. Finally, the fusion of segmentation features, spatial features, and clustering features enables the accurate segmentation and grading of tiny corroded areas. Experiment results demonstrate that YOLO-MF and VCS-Net achieve higher accuracy in corroded-bolt detection and background noise removal. Compared with other segmentation approaches, the multi-feature fusion segmentation method improves the intersection over union by 0.1623. The corrosion grade results are directly printed on the images, facilitating maintenance operations, reducing the workload of tunnel maintenance personnel, and improving tunnel maintenance efficiency.]]></description><pubDate>Wed, 10 Jun 2026 09:06:00 GMT</pubDate><guid>http://pubsindex.trb.org/view/2712017</guid></item><item><title>Enhancing Rail Obstacle Detection Systems: Optimizing Accuracy in Adverse Weather Conditions</title><link>http://pubsindex.trb.org/view/2711639</link><description><![CDATA[Railway safety is paramount, especially with the increasing reliance on rail transport and the potential for catastrophic consequences from train colliding with obstacles. This paper introduces a novel obstacle detection methodology using Convolutional Neural Networks (CNNs) to enhance detection accuracy, particularly for diverse and unforeseen obstacles, including wildlife intrusion, under challenging environmental conditions. We employ the state-of-the-art (You Only Look Once) YOLOv11-Seg algorithm for simultaneous rail segmentation and obstacle detection, defining a critical safety margin around the tracks. A key contribution of this work is a novel synthetic image generation algorithm designed to address the critical scarcity of real-world obstacle data, particularly for rare and unpredictable hazards such as animals and uncharacterized debris. This algorithm strategically places various obstacles, extracted from diverse sources, at random locations on the rail or within the safety margin. Crucially, it incorporates diverse and realistic environmental conditions, such as train vibrations, rain, snow, dust, fog, and varying light intensities to augment the training data and improve the model’s robustness against these highly transient events. Experimental results demonstrate the effectiveness of the YOLOv11-Seg network, trained on our synthetically augmented data set, in accurately performing both segmentation and obstacle detection in a single step, paving the way for improved railway safety systems.]]></description><pubDate>Fri, 05 Jun 2026 11:27:53 GMT</pubDate><guid>http://pubsindex.trb.org/view/2711639</guid></item><item><title>Design of Rail Variable Cross Section Grinding Profile Based on NURBS Surface</title><link>http://pubsindex.trb.org/view/2705421</link><description><![CDATA[To eliminate the abnormal wear and shaking caused by the standard grinding method in curved rails, and improve the matching performance and passing ability, a Non-Uniform Rational B-Spline (NURBS) -based design optimization method is proposed in this paper, which is for the variable cross section grinding profile design of curved rails. Compared with the standard rail cross section profile, the effective wheel–rail contact length of the optimized curved section increases. The increases in the wheel–rail contact density of the gently curved section, the gently rounded point, and the rounded curved section are 161.08%, 68.57%, and 271.60%, respectively. Therefore, the wheel–rail contact relationship and the wheel–rail matching performance are significantly improved. The abnormal wear problem caused by the wheel–rail matching performance is reduced. In addition, compared with the standard grinding profile, the maximum reductions in the derailment coefficient, rate of wheel load reduction, wheel–rail transverse force, wheel–rail vertical force, wheel offset angle, and maximum wear index of the optimized designed profile in the curved section are 9.23%, 14.72%, 11.39%, 9.76%, 6.03%, and 12.03%, respectively. Therefore, the dynamic performance is significantly improved. Furthermore, the trains’ curve-passing ability improved, and the shaking-car phenomenon was suppressed. Finally, when the optimized rail grinding profile is adopted, the reduction in the rail grinding removal amount for the transition curve section, slow circular point, and circular curve section reaches 43.41%, 43.39%, and 61.94%, respectively. Therefore, the lifespan of the rail in the curve section is prolonged.]]></description><pubDate>Tue, 26 May 2026 09:44:22 GMT</pubDate><guid>http://pubsindex.trb.org/view/2705421</guid></item><item><title>Investigating Nonmotorist Crash Exposure at Highway–Rail Grade Crossings Using Artificial Intelligence-Based Object Detection and Generalized Linear Count Models</title><link>http://pubsindex.trb.org/view/2703807</link><description><![CDATA[A critical aspect of crash prediction models for highway–rail grade crossings (HRGCs) is crash exposure, which is a measure of train and highway traffic. Although data on motor vehicle traffic (e.g., annual average daily traffic) and train traffic at HRGCs are invariably available, nonmotorist traffic data at HRGCs are not readily available. Current Federal Railroad Administration and other HRGC crash models focus on train and motor vehicle traffic, overlooking nonmotorized traffic. Therefore, there is a need to gather nonmotorized traffic data to improve HRGC crash prediction models. To address this gap, nonmotorist traffic video data were recorded in this study at various urban and suburban HRGCs in Nebraska, followed by the application of an artificial intelligence-based You Only Look Once (version 8) algorithm for automated nonmotorist traffic volume detection. Data on HRGC characteristics, including surrounding area population density and land use, were collected to create a comprehensive HRGC safety database for nonmotorists. Three negative binomial models were estimated to analyze pedestrian, bicyclist, and combined nonmotorist exposure in relation to daily volumes, utilizing physical, dynamic, and temporal characteristics of HRGCs. Results indicated that sidewalks, greater visibility, and cloudy weather conditions were associated with increased nonmotorist traffic volume. Conversely, higher vehicular traffic levels, wet road conditions, low population density, and more traffic lanes correlated with lower nonmotorist traffic. This study established an initial framework for nonmotorist traffic monitoring and identified key environmental and technical challenges in automated detection at HRGCs; based on these findings, recommendations for addressing technical limitations were provided for future research.]]></description><pubDate>Tue, 19 May 2026 09:02:16 GMT</pubDate><guid>http://pubsindex.trb.org/view/2703807</guid></item><item><title>Assessing Railway Track Embodied Carbon: Life Cycle Inventory Literature Review</title><link>http://pubsindex.trb.org/view/2701139</link><description><![CDATA[Efforts to reduce carbon emissions have intensified across the transportation sector, yet life cycle inventory (LCI) data describing the global warming potential (GWP) of railway infrastructure remain limited in the U.S. context. Existing literature is dominated by non-U.S. studies and often aggregates track infrastructure GWP with other civil structures, limiting transparency and applicability. This study synthesized LCI data from 116 case studies reported across 53 sources and isolated at-grade railway track infrastructure. Foreground and background inventory data were compiled for track components, construction and maintenance equipment, material transportation, and end-of-life (EOL) treatment. Case studies were harmonized to a common track-kilometer-per-year functional unit to enable cross-comparison of track GWP. More than 75% of case studies originate from Europe, with concrete-tie and slab-track systems accounting for 74% of all cases, while wooden-tie tracks are underrepresented (10%). EOL treatment data are reported in only 10 studies, and maintenance-cycles are included in approximately half of the sources. The Ecoinvent database is used in 26 studies, while only four incorporate U.S. LCI datasets. Material-level inventories show substantial variability in GWP factors, particularly for steel (0.4–5.7 kg CO₂e/kg), whereas aggregates exhibit the lowest impacts (∼10 g CO₂e/kg). Fuel emissions from maintenance-of-way equipment and material transportation are regarded minor contributors (2%–10% of track GWP). After normalization, overall track GWP converges to a narrow range (21.6–25.7 t CO₂e/km/year) across track systems, indicating that system boundaries and background data choices influence results more than track type. The review also highlighted a critical need for U.S.-specific LCI datasets and environmental product declarations.]]></description><pubDate>Tue, 12 May 2026 16:57:36 GMT</pubDate><guid>http://pubsindex.trb.org/view/2701139</guid></item><item><title>Effect of Sand Grading and Proportion on the Performance of Cement Asphalt Mortar for High-Speed Rail Slab Track Systems</title><link>http://pubsindex.trb.org/view/2701133</link><description><![CDATA[Sand makes up about 40% of cement asphalt mortar (CAM) used in non-ballast tracks for high-speed rail infrastructure, playing a crucial role on the performance of CAM. This study investigates the influence of sand gradation (quantified using fineness modulus [FM]), and sand-to-cementitious component ratio (S/CC) on key CAM parameters, including flow time, working time, material separation, compressive strength, elastic modulus, and shrinkage. Multiple gradations and S/CC ratios were evaluated to establish combinations that achieve the required flow time (16–28 s), a minimum 30 min working time, minimize material separation, and ensure desired mechanical performance. Results indicate that coarser gradations improve workability but increase material separation, whereas finer gradations reduce separation but prolong flow time. An FM of 1.6, with particles passing 1.18 mm sieve ensure desired flow time and homogeneity of CAM. An S/CC ratio of 2 maintained good workability, reduced shrinkage and deformation, and improved compressive strength and modulus. These findings offer insights for CAM mix design particularly with reference to sand gradation and proportion selection.]]></description><pubDate>Tue, 12 May 2026 16:57:36 GMT</pubDate><guid>http://pubsindex.trb.org/view/2701133</guid></item><item><title>BallastAttN: Occlusion-Robust 3D Railway Ballast Characterization using Data Synthesis and Deep Learning</title><link>http://pubsindex.trb.org/view/2701226</link><description><![CDATA[Accurate characterization of railway ballast is critical for track safety and maintenance; however, traditional field sampling/sieving or two-dimensional images captured are often labor-intensive and limited for a representative analysis. Three-dimensional (3D) point cloud analysis may offer a more comprehensive approach; the dense packing and heavy occlusion of ballast particles restrict image segmentation. This study introduces a novel deep learning pipeline designed for robust 3D railway ballast characterization, BallastAttN. Its core contributions include a comprehensive synthetic training data set from high-fidelity 3D scans of new and degraded ballast particles, an enhanced point cloud segmentation model upgraded with edge-aware voxelization and curriculum learning, and the novel BallastAttN partial point cloud completion model architected to reconstruct complete particle shapes from the highly incomplete views typical of field conditions. The proposed pipeline was comprehensively validated using controlled laboratory experiments with isolated and clustered configurations of real ballast particles in new and degraded conditions. The results show that BallastAttN consistently outperforms the baseline completion framework that utilizes an encoder–decoder architecture mechanism built on attention mechanisms across commonly used size and morphological properties. The performance gap widened substantially in clustered scenarios that are close to the field ballast appearance, demonstrating the model’s enhanced ability to handle occlusion. The predictions were precise in differentiating between new and degraded ballast based on morphological properties, such as 3D sphericity, the Flat and Elongated Ratio, and the Angularity Index. This study establishes a practical framework for automated ballast inspection, for example, with the use of an innovative ballast scanning vehicle developed, paving the way for more efficient and reliable railway ballast maintenance.]]></description><pubDate>Mon, 11 May 2026 12:24:46 GMT</pubDate><guid>http://pubsindex.trb.org/view/2701226</guid></item><item><title>Multistage Physics-Informed Signal Processing Framework for In-Motion Detection of Track Stiffness Irregularities Using Onboard Vibration Sensors</title><link>http://pubsindex.trb.org/view/2698381</link><description><![CDATA[With nearly 140,000 mi of track, railroads are central to North America’s transportation infrastructure, carrying over 40% of freight by ton-miles and serving millions of passengers annually. Maintaining the structural integrity of the track is essential for operational safety and economic efficiency. Integrity is compromised by stiffness changes caused by ballast degradation, subgrade settlement, aging ties, temperature-induced stresses, and repeated loading. Proactive track health monitoring systems are needed to detect such changes continuously under dynamic train loads. This paper presents a multistage physics-informed framework for the in-motion detection of track stiffness irregularities (TSIs), serving as a proxy for identifying potential defects. The proposed system uses onboard vibration measurements, processed through advanced signal processing techniques. The system consists of three modules, for data acquisition, change detection, and change classification. It operates on an edge-computing platform, allowing real-time processing, and achieves over 95% data compression. The change-detection module, emphasized in this paper, combines wavelet packet analysis, variational mode decomposition, and the Hilbert transform to extract instantaneous energy features from vertical acceleration signals. These features act as sensitive indicators of track stiffness variation. The method was validated through both simulation and offline field data. Simulations captured a wide range of TSIs, including abrupt changes, gradual transitions, and localized weak zones. Field validation confirmed the model’s ability to consistently detect recurring irregularity patterns along the track, without requiring any assumptions about their physical origin. These results demonstrate the robustness, scalability, and real-world applicability of the proposed approach for continuous rail infrastructure monitoring.]]></description><pubDate>Tue, 05 May 2026 10:16:53 GMT</pubDate><guid>http://pubsindex.trb.org/view/2698381</guid></item><item><title>Risk Control of the Automatic Train Supervision System in Rail Transit Systems: Under a Van der Pol Equation-Based Framework</title><link>http://pubsindex.trb.org/view/2697864</link><description><![CDATA[Automatic train supervision (ATS) systems are a core safety component in metro operations. Its redundant design results in extremely scarce failure data, rendering traditional data-driven risk analysis ineffective. Consequently, existing studies often substitute reliability analysis for risk analysis. To overcome the limitations of static and vague reliability methods, this study employs the van der Pol equation to dynamically quantify inherent risk oscillations in ATS systems, providing managers with actionable control measures. Our paper begins by analyzing ATS risk characteristics and examining the feasibility of using the van der Pol equation to model risk state changes. Then, we establish a risk state equation derived from this framework and analyze the system’s risk dynamics. Finally, to control risk, we integrate a risk control function into the equation. A case study of Beijing Metro Line 2 demonstrates the method’s applicability. The proposed methodology enables accurate risk state judgment, potential risk prediction, and precise control implementation. By applying differential equation theory, it reduces reliance on historical data or expert knowledge while addressing inaccuracies from missing critical data. This work establishes a novel framework for system risk control and offers practical guidance for operators.]]></description><pubDate>Sat, 02 May 2026 15:47:06 GMT</pubDate><guid>http://pubsindex.trb.org/view/2697864</guid></item><item><title>Assessing the Structural Performance of Bolted Rail Joints Employing Various Fishplate Models via Finite Element Analysis</title><link>http://pubsindex.trb.org/view/2697861</link><description><![CDATA[In railway tracks, fishplates are attached to each side of two rail ends and secured with four bolts, providing what is known as a bolted rail joint (BRJ). This rail joint is involved in complex interactions between multiple components under wheel loads, leading to stress and deformation of each component, potentially resulting in failures of the railway track. In this study, the different roles of selected fishplate models in the structural performance of a BRJ under static load are investigated using finite element analysis with ABAQUS CAE. Three fishplate models are examined: a thin cross-section, a thick cross-section, and a modified design. The first two models are currently used in rail transportation, while the novel modified version is designed to enhance the structural performance of BRJs. Preliminary results indicate that using the modified fishplate significantly reduces stress on the upper rail fillet and fishplate. Additionally, vertical displacement in both the rail and fishplate is diminished. These improvements are expected to increase the service life and reliability of BRJs, thereby contributing to safer and more cost-effective railway operations.]]></description><pubDate>Sat, 02 May 2026 15:47:05 GMT</pubDate><guid>http://pubsindex.trb.org/view/2697861</guid></item></channel></rss>