Safety Of Railway Transportation Research Articles

A novel linetype recognition algorithm based on track geometry detection data

Abstract The detection and recognition of track geometry are crucial for ensuring railway transportation safety and maintenance efficiency. Traditional linetype recognition algorithms struggle to effectively handle the complexities of variable track geometry and noise interference. To address these challenges, a novel linetype recognition algorithm (NLRA) is proposed, which thoroughly analyzes the characteristics of track curvature across straight lines, transition curves, and circular segments. By employing partial differential equations for data preprocessing, NLRA effectively filters out noise. Utilizing curve design parameters and integrating line anti-noise recognition with multi-segment line fitting techniques, the algorithm accurately identifies each line segment and incorporates a single-point recognition method for data gaps. Experimental results demonstrate that NLRA outperforms traditional algorithms in terms of real-time performance, recognition accuracy, anti-interference capability, and noise handling. Notably, NLRA enhances accuracy by at least 35.1% and recall by at least 17.4% on selected typical routes. This work underscores the algorithm's potential to provide efficient and accurate tools for track maintenance, facilitate advancements in track detection software, and significantly contribute to railway transportation safety.

SH guided wave excitation in rails for defect and stress monitoring

Monitoring rail defects and thermal stress is imperative in ensuring the safety of railway transportation. Guided-wave-based inspection techniques emerge as a promising solution for concurrently detecting both the defects and thermal stress in rails. However, the substantial multimodal and dispersive nature of guided waves in the rail poses limitations on the application and advancement of this technology. This study systematically investigates the propagation characteristics of the SH0-like wave (the lowest-order shear horizontal-like wave) in the rail and its effectiveness in defect and axial stress monitoring through finite element simulations and experiments. A method based on dual opposite polarization active elements, and the corresponding piezoelectric transducers are proposed to excite SH0-like wave in the rail web and rail foot. Results show that SH0-like wave can travel stably across a broad frequency range in the rail web and rail foot. Additionally, both the simulations and experiments confirm that the SH0-like wave in the rail web is suitable for monitoring axial stress, as its velocity changes linearly with axial stress variations. Furthermore, the ability of the SH0-like wave to detect cracks and hole defects in the rail web is experimentally demonstrated. Given that the SH0-like wave is quasi-nondispersive in a broad frequency range and an SH0-like wave with high SRN can be effectively generated using the proposed method, this research aims to offer a practical technical solution to overcome the challenges of multimodal and dispersive effects in the guided-wave-based rail inspection techniques. It also sets the stage for developing a comprehensive system capable of simultaneously monitoring the defects and thermal stress in rails.

RTINet: A Lightweight and High-Performance Railway Turnout Identification Network Based on Semantic Segmentation.

To lighten the workload of train drivers and enhance railway transportation safety, a novel and intelligent method for railway turnout identification is investigated based on semantic segmentation. More specifically, a railway turnout scene perception (RTSP) dataset is constructed and annotated manually in this paper, wherein the innovative concept of side rails is introduced as part of the labeling process. After that, based on the work of Deeplabv3+, combined with a lightweight design and an attention mechanism, a railway turnout identification network (RTINet) is proposed. Firstly, in consideration of the need for rapid response in the deployment of the identification model on high-speed trains, this paper selects the MobileNetV2 network, renowned for its suitability for lightweight deployment, as the backbone of the RTINet model. Secondly, to reduce the computational load of the model while ensuring accuracy, depth-separable convolutions are employed to replace the standard convolutions within the network architecture. Thirdly, the bottleneck attention module (BAM) is integrated into the model to enhance position and feature information perception, bolster the robustness and quality of the segmentation masks generated, and ensure that the outcomes are characterized by precision and reliability. Finally, to address the issue of foreground and background imbalance in turnout recognition, the Dice loss function is incorporated into the network training procedure. Both the quantitative and qualitative experimental results demonstrate that the proposed method is feasible for railway turnout identification, and it outperformed the compared baseline models. In particular, the RTINet was able to achieve a remarkable mIoU of 85.94%, coupled with an inference speed of 78 fps on the customized dataset. Furthermore, the effectiveness of each optimized component of the proposed RTINet is verified by an additional ablation study.

Selective neglect of viral risk: Tokyo’s commuter trains during the COVID-19 pandemic

This article asks why Tokyo’s crowded trains were largely absent from official discourses of viral risk in Japan during the COVID-19 pandemic. While public transport environments were often framed as spaces of contagion in other countries, Japanese public health discourses instead tended to emphasize the viral safety of urban railway transport. Drawing on an analysis of media and expert discourse, auto-ethnographic fieldwork, and repeat interviews with commuters, this article examines the processes through which Tokyo’s urban railway network was extracted from discourses of viral risk. It identifies four factors that contributed to this: (1) dominant definitions of risk spaces in public health messaging, (2) trains’ integral role in the operation of socio-economic life, (3) elusiveness of public transport in the epidemiological data, and (4) a focus on “unruly” passenger conduct. This analysis of the selective treatment of viral risks is tied to a critical engagement with theoretical distinctions between “essential” and “existential” mobilities developed in prior anthropological scholarship on the pandemic. Exploring the discursive framing of pandemic mobility practices on one of the world’s most extensive urban railway networks, the article thus contributes to the anthropological study of mobilities and transport in Japan and beyond.

High-frequency Resonance Suppression of Railway Traction Power Supply System Based on the Combination of Single-tuned and C-type Filters

Background: Railroad transportation in the actual operation process, there are also many dangerous accidents caused by resonance, which greatly affect the safety of railroad transportation. A comprehensive examination of the operational dynamics within the power branch of a traction substation is imperative for sustaining system equilibrium. Discrepancies between these facets pose a potential threat to the safety of railway transport. Thus, a meticulous analysis of highfrequency resonance characteristics and the formulation of effective suppression techniques become paramount. Objective: Harmonics from grid-side locomotive traction braking are scrutinized under different operational scenarios and different train runs. The aim is to develop an effective harmonic management strategy to normalize the THD and thus maintain the traction power supply system to become more stable. Methods: This study investigates the high-frequency harmonic resonance characteristics using a control variable approach. Harmonics and negative sequences generated during locomotive traction braking under different operating conditions are investigated by means of extensive analysis. These challenges require the implementation of a harmonic management method. This method uses a combination of two monotonic filters and a C filter in parallel. This method applies to different operating conditions and can dynamically adapt harmonics to changes in the number of trains, which is related to the actual dynamics of the traction power system closely. Results: A comparative evaluation of seven operating conditions shows that the filter in this patent exceeds the efficiency of existing methods. The filter reduces the fluctuations during spectral changes, and the voltage distortion rate decreases from the previous 2.33% to 0.55%, making it more adaptable and promising effective harmonic management in high voltage and high current situations. Conclusion: Through multiple simulation tests, a synergistic configuration involving the C-type filter and two single-tuned parallel connections under regenerative braking conditions emerges. This refined, patented filter design not only mitigates the impact of negative sequence during the filtration process but also substantially diminishes high harmonics and THD.

Validation of numerical method of unsteady Reynolds-averaged Navier–Stokes coupled with discrete phase model and optimization for the snow-resistance performance of bogies of a high-speed train

This paper presents an investigation aimed at enhancing the snow resistance of bogies in a three-car grouped alpine high-speed train (HST) that operates in a snowy region. The study employs a combination of the unsteady Reynolds-averaged Navier–Stokes model, coupled with the discrete phase model. This modeling approach has been validated through wind tunnel tests, including flow field tests, two-phase flow tests, and snow-ice tests. The study comprehensively compares the characteristics of the wind-snow flow, spatial snow concentration, and the distribution of snow on the train's underbody between two scenarios: the original train's underbody structure and the multifactor collaborative optimization scheme (MCOS). The results indicate that the MCOS case facilitates the escape of snow particles from the bogie cavities by weakening the upward and reverse flows. Furthermore, the MCOS case significantly reduces the concentration of snow particles around the heat-producing elements and decreases the accumulation of snow on both the lower and upper surfaces of the bogies. As a result, it reduces snow accumulation on the bogies by 50.6%, 56.4%, 60.8%, and 62.4% at train speeds of 200, 250, 300, and 350 km/h, respectively. In summary, this research provides valuable insights for improving the snow resistance of HSTs operating in snowy regions, with potential applications in enhancing railway transportation safety and efficiency.

Application of vibration signals in railway track diagnostics using a mobile railway platform

The article presents a comprehensive method for using vibration signals to diagnose railway tracks. The primary objective is to gather detailed information on track conditions through a passive experiment. This involves using mobile diagnostic tools and techniques to assess railway infrastructure. The article elaborates on the range of diagnostic activities conducted in accordance with detailed railway regulations and highlights the benefits and capabilities of mobile diagnostics in railway transport. The research includes mobile field measurements across the general railway manager’s network, employing vibration signals to detect and evaluate track conditions. The methodology section provides a thorough description of the mobile measurement rail platform, detailing the equipment used, the routes taken for measurements, and the processes of data acquisition and processing. The data obtained from these measurements is crucial for understanding the actual technical condition of the railway tracks. The method of obtaining and processing data is explained in relation to the real technical condition of the railway track. This involves using transducers with specific parameters and parametrically defined signal recording, along with dedicated analysis techniques in post-processing. Vibration signals serve as the primary carrier of information in this diagnostic method. The article details the step-by-step procedures for collecting and analyzing these signals to provide accurate assessments of track conditions. Based on the results from the mobile measurement rail platform, the article characterizes various areas of diagnostics where vibration signals are particularly effective for technical evaluation. These areas include identifying track defects, monitoring track surface and railway crossing and assessing the overall structural health of the railway infrastructure. The use of vibration signals offers a non-invasive and efficient means of track diagnostics, providing real-time data for maintenance and repair decisions. In conclusion, the article underscores the significance of mobile diagnostics in enhancing the safety and reliability of railway transport. By leveraging vibration signals and advanced data processing techniques, this method provides a framework for continuous monitoring and assessment of railway track conditions, ultimately contributing to improved maintenance strategies and operational efficiency.

Safety culture: from student to professional

Traffic safety culture, recently this definition is very often heard at conference calls of heads of services. What does this concept imply, how is it interconnected with safety management and why do these areas of work relate to the main tasks of the Russian Railways holding in the field of traffic safety and operation of railway transport? The article discusses the causes of traffic safety violations, which are associated with the influence of the human factor, violation of labor discipline, and improper performance of official duties. The main objective of the study is to find approaches to raising the level of safety culture among railway workers. Most often, when solving a certain task, it is necessary to go beyond it and consider issues that at fi rst glance do not relate to the essence of the problem, but the more you delve into the study, you come to conclusions that show the connection between themselves sometimes in the most unexpected and unpredictable way. How to improve the traffic safety culture among the company’s employees? Is it possible to correct employees whose professional experience has been assessed not for one year, but for decades? Is it possible to change the thinking of an employee whose professional experience has been assessed not for one year, but for decades? In my opinion, an employee who has been violating for many years will no longer be able to perform his job duties properly. This is a “professional” defect that cannot be corrected, because most often it was taught this way during an internship in production. Most cases could have been avoided because minor violations combined with certain factors are fatal.

Data-driven design of a derailment detection system for freight wagons

Railway transportation is widely known for its high safety level. Nevertheless, derailments still occur, typically causing large service disruptions and economic losses. For these reasons, identifying derailments is of paramount importance for improving the railway transportation safety. This paper presents the design and testing of a derailment detection system for freight trains. A derailment detection algorithm able to process and analyze data in real-time has been designed leveraging the knowledge gained from full-scale derailment tests on freight wagons. Moreover, a LoRa-based wireless communication system has been implemented to relay freight wagon derailment warnings to the locomotive. The effectiveness of the proposed derailment detection algorithm is proved through its deployment to a freight wagon used for common operations. In that, it demonstrated increased robustness against derailment false triggering compared to traditional mechanic-pneumatic detectors. Furthermore, the proposed wagon-locomotive wireless communication system is proven effective in transmitting information across various convoy compositions and freight wagon loading conditions during operational testing on the railway line.

Communication security analysis of fully electronic interlocking systems

Communication security in fully electronic interlocking systems is one of the key factors ensuring the safe and reliable operation of the entire system. EN50159 is an important standard in the European railway communication sector, aimed at ensuring the safety, reliability, and efficiency of railway transportation. According to the communication security standards of EN50159, there are six types of security risks in closed communication: data duplication, deletion, insertion, misordering, delay, and corruption. This paper analyzes and explains the aspects of communication security that need to be considered based on the safety communication standards of China's railway signaling system and the signal safety standards in EN50159, focusing primarily on communication security and reliability.

Analysis of wheel-rail contact under oil lubrication conditions

Abstract This investigation centers on the characteristics of the contact between the wheel and rail when lubricated with oil, with a consideration of the impact of surface roughness. Confronting significant challenges related to low adhesion, a thorough examination is undertaken to analyze the changes in film thickness under oil lubrication, investigating trends at varying vehicle speeds and axle loads. Employing detailed modeling based on micro-elastohydrodynamic theory, the distribution of oil film thickness under diverse operational conditions is determined. The findings indicate that, under oil lubrication, a film thickness in the range of several tens of micrometers is established, effectively insulating the surfaces of the wheel and rail, thereby reducing adhesion. With increasing vehicle speed, the thickness of the oil film also rises, whereas an elevation in axle load results in a marginal decrease in film thickness. This research contributes to a comprehensive comprehension of the wheel-rail contact mechanism in oil lubrication scenarios, providing theoretical backing to address concerns related to low adhesion. The implications are substantial for improving the safety and economic efficiency of efficient railway transportation.

Интеллектуальная система контроля бдительности и действий машиниста

Objective: the article considers an approach to the development of a system for monitoring the vigilance and actions of the driver using technical vision and neural network models. The main purpose of the research is to improve the safety of railway transport. Methods: data collection from various sources, annotation, data purification and normalization, neural network training based on video recordings of drivers’ faces in various states and data on their behavior. Neural network learning algorithms based on the architecture of convolutional neural networks, teaching methods with a teacher, mask segmentation methods and proportional resizing of the area of interest, mask segmentation methods for determining the contour of an object in an image, deep learning algorithms such as stochastic gradient descent and error back propagation. Results: a system has been developed that determines the emotional state of the driver based on a real-time video stream, detecting signs of fatigue or distraction, warning of possible dangerous situations. This approach will allow you to quickly respond to the risks that arise in the process of train control, which allows you to increase the level of train safety. Practical significance: a system for monitoring the vigilance and actions of the driver has been developed and can be implemented on locomotives or motor-car rolling stock for real monitoring and prevention of emergency situations.

Selected Aspects of Research on Railway Transport Safety

This article showcases the findings of research conducted at the Faculty of Transport, Electrotechnics, and Informatics of the Technological-Humanistic University in Radom. Initially, it delves into a statistical analysis of measurements procured during automatic ultrasonic testing. By integrating these measurements with distinct features of the diagnostic method and fusing differential coding with traditional lossless compression techniques (like Huffman and dictionary coding), a potent measurement compression algorithm was developed. This algorithm subsequently found application in railway track diagnostics. The primary objective of this article is to illustrate this algorithm's efficiency and benchmark its advantages against other compression techniques. Subsequently, the paper delineates the design of a system tailored for collating operational data and evaluating the reliability and safety of railway automation systems. This concept was actualized under the auspices of a project sponsored by the National Center for Research and Development, PBS3/A6/29/2015. A spotlight will be thrown on the software that acts as an integrative tool, assimilating data from various systems. In its final segments, the article discusses the initiative to harness open systems, premised on the PN-EN 50159:2011 standard, to bolster the safety standards of railway transportation. The first one resulted from the maximum train speed, the second one is the variety of rolling stock used on this line (compartmental and non-compartmental wagons). The part of research concerning collection of measurement material was performed within the framework of completed research work PBS3/A6/29/2015. As a result of conducted in-depth literature analysis as well as performed measurements and calculations, it allowed to develop a model and software for simulating system operation in real conditions. A prototype of this system, accompanied by select research outcomes from railway routes, will be laid out. Ultimately, the article seeks to emphasize the significance of diverse research endeavors that hold the potential to directly enhance the safety and operational efficiency of railway transport.

Electromagnetic Impact of Overhead High-Voltage Lines during Power Transmission on Buried Signaling Cables of the Traffic Control Systems in Modernized Railway Lines

The railway transport system is a key factor supporting industrialization in all aspects of human activity. However, in order not to lose its importance, it must meet the challenge of modern civilization. The safety, reliability, and efficiency of railway transport, to a large degree, depend on using highly integrated electronics, which are very sensitive to various disturbances generated in the electric traction system and train or coming from the environment. One of the sources of electromagnetic disturbances are high-voltage (HV) power lines running close to the railway infrastructure. The purpose was to assess the electromagnetic impact of overhead HV transmission lines on buried signaling cables of the railway traffic control system crossbreeding with them. The levels of voltage induced in the cable under steady state and the earth fault in the HV line at various soil resistivity were estimated. A software tool based on a hybrid numerical method that combines circuit theory and electromagnetic field theory was used for computations. It was found that very high voltages may be induced in the signaling cables during earth faults in the HV lines, which may lead to serious interference or damage to the equipment. The results provide useful knowledge for implementing modern railway traffic control systems and protection measures.

Railway accident causation prediction with improved transformer model based on lexical information and contextual relationships

The railway system is a prime example of a safety-critical system. Predicting the causes of railway accidents holds immense significance in enhancing railway transportation safety. Previous approaches to railway causation analysis have encountered huge challenges regarding data processing and analytical capabilities. To address this concern, this paper proposes an innovative deep model framework based on the Transformer architecture that utilizes historical data on railway equipment accidents to predict the causes behind such incidents. Firstly, this paper proposes the utilization of Convolutional Block Attention in the domain of text processing, serving as a lexical encoder to augment word semantics acquisition in accident texts. Subsequently, in order to address the deficiency of traditional Transformers that lack positional representation information, we propose incorporating a BiGRU (Bidirectional Gated Recurrent Unit) as a contextual positional information encoder to capture contextual positional information in railway accident data effectively. Finally, considering that accident data reports are discrete tabular data, this study suggests employing cue word techniques for preprocessing accident data to alleviate the model's learning burden. We applied the proposed model to the FRA (Federal Railroad Administration) dataset. The results demonstrate that our model surpasses the current state-of-the-art language models, exhibiting superior performance compared to the optimal model with a notable improvement of 3.56%, 0.42%, and 0.76% in Precision, Recall, and F1-score, respectively. Furthermore, our model accurately predicts accident categories prone to misjudgment even when trained on limited data, outperforming existing language models. The study findings will contribute to the prevention and management of railway accidents.

Clustering-Based Classification of Polygonal Wheels in a Railway Freight Vehicle Using a Wayside System

Polygonal wheels are one of the most common defects in train wheels, causing a reduction in comfort levels for passengers and a higher degradation of vehicle and track components. With the aim of contributing to the safety and reliability of railway transport, this paper presents the development of an innovative methodology for classifying polygonal wheels based on a wayside system. To achieve that, a numerical train-track interaction model was adopted to simulate the passage of a freight train over a virtual wayside monitoring system composed of a set of accelerometers installed on the rails. Then, the acquired acceleration time series was transformed to a frequency domain using a Fast Fourier transform (FFT), and on this data, damage-sensitive features were extracted. The features based on Principal Component Analysis (PCA) showed great sensitivity to the harmonic order, while the ones based on Continuous Wavelet Transform (CWT) model showed great sensitivity to the defect amplitude. One step further, all features are merged using the Mahalanobis distance in order to obtain a damage index strongly correlated with the polygonal defect. Finally, a cluster analysis allowed the automatic classification of polygonal wheels, according to the harmonic order (harmonic-based) and defect amplitude (amplitude-based). The proposed methodology demonstrated high efficiency in identifying different types of polygonal wheels using a minimum layout of two sensors.

Организационно-правовые направления совершенствования системы обеспечения антитеррористической защищённости объектов транспорта: приоритет современных информационных технологий

Authors validate the relevance of development for regulatory and other organizational measures, which ensure public safety in railway transport. Internet of Things and Big Data technologies are inspected in part of ensuring the security of transport facilities. Fragments of the Concept for the construction and development of narrow-band wireless communication networks “Internet of Things” in the territory of the Russian Federation are being studied. Directions for the use of modern information technologies are proposed. Authors summarize expert opinions on amendments to the legislation on transport security, on control and supervision in the field of transport security.

Determining Smart Phone Sensing and K-Means Clustering for Accurate and Timely Railway Track Joint Fault Diagnosis

The railway track joint is an important component that connects two sections of the rail and ensures a smooth and safe operation of trains. However, the joint is also a critical point of failure that can lead to train derailments and accidents. Therefore, accurate and timely detection of joint faults is crucial for ensuring the safety and reliability of railway transportation. In this paper, we propose a novel approach for railway track joint fault diagnosis using smart phone sensing and k-means clustering. Our approach utilizes the accelerometer sensor of a smart phone to measure the vibrations and movements of a specifically developed railway shoe stick that is employed on an actual railway track for the condition monitoring of the railway tracks. More than 60000 data values are collected and are then processed and analysed using k-means clustering, a popular unsupervised machine learning technique that groups similar data points together. The K means clustering in this study forms 3 clusters as a result. The 3 clusters after being validated on the track by virtue of visual inspection are determined to be acceleration values of the healthy track, track with higher joint gap than the standardized value and super-elevated railway track joint fault(s), respectively. In addition to its high accuracy and efficiency, our approach has several advantages over traditional methods, such as low cost, easy deployment, and high scalability. Moreover, the smart phone sensing technology can be easily integrated with existing train monitoring systems, making it a useful tool for real-time joint fault diagnosis and maintenance. Overall, this study demonstrates the potential of smart phone sensing and k-means clustering for railway track joint fault diagnosis and highlights the need for further research in this field.

Image segmentation method of rail head defects and area measurement of selected segments

The operation safety of railway transport, which is the most important economic and social factor, is largely determined by the technical condition of the rail track and measures to maintain the quality of its track management system. One of the system elements for ensuring the accident-free operation of the track is the technical diagnosis of rails using a method complex of non-destructive control of rails, such as acoustic (ultrasonic), magnetic, combined, etc., and monitoring of the track using methods of measuring the geometry of the rail track and its disturbances. When the wheel interacts with the rail, especially on high-speed and load-stressed sections, defects and damage inevitably occur in the rails. A rather large share of such defects are on the rolling surface of the rail head. Formed defects develop rapidly, which seriously complicates the safety of train traffic. Therefore, accurate and quick detection of defects on the rolling surface of the rail head is very important. However, it is quite difficult to detect defects on the rolling surface of the rail by the acoustic (ultrasound) method due to the violation of tight contact between the rolling surface of the rail head and the piezoelectric transducer. In this case, it is quite convenient to detect surface defects of the rail head using video control. The article provides a comparative analysis of segmentation methods. There has been presented the method of image segmentation of main rail defects based on general contour preparation and parallel-hierarchical (PH) transformation using their classification. The parallel-hierarchical transformation method allows to increase the segmentation accuracy of individual areas in the original image compared to similar ones. The algorithm of pyramidal generalized-contour preparation and the criterion system allows, by calculating the threshold for each level of the gray scale, to present the study of the image with the corresponding contour preparations at the segmentation level. Modeling of recursive generalized-contour preparation and PH transformation method for image segmentation problem of rail head defects shows that, compared to the segmentation method based on the increase of areas, the accuracy of image segmentation is better. A modified method of calculating the image contour area based on the coding of lines forming the boundaries of the black and white areas of the two-gradation image has been given.

Enhancing railway transportation safety with proactive maintenance strategies incorporating machine learning

Enhancing railway transportation safety with proactive maintenance strategies incorporating machine learning