This study solves the train timetable rescheduling (TTR) problem from a brand-new perspective. We assume that train traffic controllers take three main actions, i.e., adjusting dwelling times, running times, and train orders, to reschedule the timetable in real-time dispatching. To raise the interpretability of rescheduling models, we propose a graph neural network (GNN) approach to map the train timetable data into evolution graphs that fit the paradigm of train operation processes. Based on graphs, two experiments from node and edge perspectives were investigated based on train operation data, i.e., (1) node experiment: train dwelling times and running times are predicted; and (2) edge experiment: an algorithm based on evolution graph, called overtaking identification algorithm (OIA), is proposed to identify train overtaking based on the consequences of the node experiment. Timetable rescheduling solutions are obtained by integrating the GNN, OIA, and train operation constraints. Experimental results show that the proposed approach has a satisfactory predictive performance. Timetable rescheduling cases under diverse delay scenarios are examined, showing that the proposed method is superior to other three standard rule-based benchmarks regarding train delays of the disturbed train groups under the given scenarios. Additionally, the model exhibits high efficiency in the three timetable rescheduling scenarios, demonstrating the model’s applicability in real-time train dispatching.

The growing demand for sustainable transport has led to increasing interest in developing rail transit networks for both intra-city and inter-city travel. However, train-induced vibrations may cause significant negative environmental impacts on nearby buildings, sensitive equipment, and residents, thereby garnering considerable attention from researchers and engineers. An efficient prediction model is essential for assessing train-induced vibrations and for designing appropriate vibration mitigation measures. The complex dynamics of the train, track, infrastructure, soils, and buildings, along with their interactions, make the modeling of train-induced environmental vibrations a challenging task. This paper provides a comprehensive review of the current state-of-the-art methods for modeling train-induced vibrations from surface and underground railway traffic. It begins by addressing wave propagation in natural soils, followed by an in-depth examination of analytical, numerical, and empirical approaches for predicting train-induced vibrations in the ground and buildings. Finally, this paper identifies unresolved issues in the field and outlines areas that require further investigation.

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.

Ventilation is one of the most effective ways to improve the air quality in trains. Top exhaust and bottom exhaust are two commonly used modes. The study hopes to switch the exhaust mode to adapt to the indoor requirements of different scenes without changing the layout of the end pipe. In the study, the airflow characteristics, energy consumption, thermal comfort and air quality in the compartment are evaluated based on computational fluid dynamics. The results show that the energy consumption decreases with the increase of the top exhaust air volume in summer conditions, while the opposite is true in winter conditions. In terms of thermal comfort, combining top and bottom exhaust can effectively improve air speed index, temperature difference index and air diffusion performance index. In addition, the draft rate index and percent dissatisfied index are less than 10% and 3%, respectively, which meet the requirements of ISO 7730 standard. In terms of air quality, the average pollutant concentration inside the vehicle decreased, but the longitudinal penetration capacity of the pollutant has increased. The research results can provide some suggestions and help for the ventilation design of high-speed trains.

This research investigates the aerodynamic flow behavior and noise contribution of various cavity configurations designed to reduce aerodynamic noise in a simplified DSA 350 SEK pantograph model, scaled to 1/10. The cavities are classified into dual-shape and single-shape designs, with four distinct models (concave–convex, convex–concave, convex, and concave) analyzed in three sizes. A base cavity with a sloped edge at θ = 80° serves as a reference for comparison. Computational fluid dynamics (CFD) simulations are performed to evaluate flow characteristics, followed by the Ffowcs Williams and Hawkings (FW–H) aeroacoustic analogy is applied to estimate far-field sound pressure levels (SPLs). The results demonstrate that the convex-edged cavity improves aerodynamic performance by reducing the root-mean-square (RMS) drag and lift coefficients from 0.026 to 0.023 and from −0.06 to −0.038, respectively, and lowering the mean drag and lift coefficients from 0.23 to 0.18 and from −1.3 to −0.85, relative to the base cavity, thereby mitigating both steady and unsteady aerodynamic forces. Noise predictions, obtained from receivers positioned 2.5 m away in the scaled model at a train speed of 300 km/h, show reductions in noise levels from 81.9 to 77.3 dB at the top receiver and from 68.4 to 63.1 dB at the side receiver. Incorporating the pantograph into the optimal and base cavity designs reveals further aerodynamic improvements, with the optimal cavity reducing the pantograph’s aerodynamic noise by 2.7 dB(A) in total sound power. Sound pressure levels decrease by 2.3 dB(A) at the top receiver and 1.8 dB(A) at the side receiver compared to the base cavity.

To investigate the evolution of fatigue crack growth (FCG) resistance in bogie frame materials after long-term service in high-speed trains, this study systematically evaluates the fatigue fracture behavior of key structural regions before and after aging, using full-scale frame fatigue tests, multiaxial FCG experiments, and numerical simulations. A finite element model was established based on strain measurements from full-scale fatigue tests to determine the equivalent crack loading. FCG experiments were then conducted on the as-welded (AW) and base metal (BM) regions before and after service, and digital image correlation was applied to obtain surface displacement fields for calculating the stress intensity factors. The results indicate that service significantly reduces the crack growth resistance of the frame materials, with a maximum remaining useful life reduction of 70.54% in the AW region and 22.31% in the BM region. Correspondingly, the strain response at the crack tip increases significantly after service, reaching more than twice the original value in the AW region and 1.44 times in the BM region, indicating a reduction in crack growth resistance. Microscopic fracture surface analysis reveals that post-service materials exhibit more secondary cracks, unstable crack paths, and blurred fatigue striations, confirming the detrimental effect of service-induced damage on fatigue performance.

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.

In this research, a robust and efficient damage detection methodology for identifying defects in railway catenary systems is presented. An encoder-decoder architecture supplemented by residual analysis is employed for this purpose. A novel signal segmentation strategy based on the structural features of catenaries is introduced, coupled with a quasi-Welch method designed to mitigate edge effects. The potential impact of GPS inaccuracies on detection precision is examined. Additionally, a comprehensive analysis of various normalization techniques and their significant effects on defect identification outcomes is conducted. Two primary types of defects are considered: hard points in the contact wire and periodic short-wavelength irregularities (PSWI) of the contact wire, with variations in train speeds and defect magnitudes. A defect detection criterion has been developed, facilitating rapid and automatic identification of catenary defects. This integrated approach enables effective detection of defects and accurate determination of their location and can overcome the limitations of previous approaches, such as the requirement for high sampling frequency. This work not only advances the methodology for catenary inspection but also contributes to enhancing the safety and reliability of railway operations. The innovation of this work lies in the integration of the reconstruction capabilities of the encoder-decoder architecture with a residual-based defect detection method. This synergy allows the respective features of each to complement the other effectively.

This paper presents a comprehensive framework for wide-area modeling and harmonic resonance assessment in the AC single-phase Swedish electric railway power system (ERPS). The methodology integrates the modeling of key elements of the catenary system, such as synchronous generators, transmission lines, transformers, and filters, while addressing the dynamic behavior of rolling stocks and inherent system uncertainties. Study cases, including Monte Carlo simulations, are developed to evaluate probabilistic scenarios and impedance variations across the network using nodal admittance modeling and frequency scanning. Key contributions include a method to model moving loads, a comprehensive approach to harmonic resonance analysis based on meshed grid characteristics of the ERPS, and an uncertainty assessment framework that highlights insights for system planning and mitigation actions. Discussion outlines future research directions for improved ERPS harmonic resonance studies.

As critical component of heavy-haul railway (HHR) bridge, the interlayer is designed to transfer vehicle load and dissipate residual oscillation. The increasing transport volumes and axle loads can degrade interlayer condition, thereby threatening the operation safety of HHR bridge. This paper proposes an innovative drive-by inspection methodology combining vertical axle box acceleration with a hybrid filtering approach for rapid interlayer damage detection in multi-span HHR bridges. The developed framework introduces a Hilbert-transform-based indicator, instantaneous amplitude quartic index (IAQI), to enhance damage localization accuracy. The hybrid filtering methodology integrates two components: (1) bandpass filtering targeting sleeper-passing frequency components to suppress track irregularity effects and enhance the interlayer damage detection efficiency; (2) a statistical diagnostic tool to diagnose whether the abnormal signal from the sleeper-related driving component of axle box acceleration is interlayer damage in multi-span HHR bridges. The feasibility of the proposed method is validated by numerical analyses and a field test of an 18-span HHR bridge. The analyses results indicate that the proposed method has good effectiveness and efficiency in detecting interlayer damage, even combined with beam damage, irregularity and noise. This research offers a new strategy to enhance the inspection efficiency and ensure the operation safety of HHR bridges.