Railway Environment Research Articles

Narrow-Band Radio Propagation Prediction Based on a Highly Accurate Three-Dimensional Railway Environment Model

In this paper, radio propagation predictions were performed by a self-developed ray tracing simulator based on highly accurate three-dimensional (3D) environment models obtained by oblique aerial photography (OAP). In order to support ray tracing computations, the highly complex environment models were managed by three steps, including model separation, electromagnetic parameter assignment, and model lightweighting. Simulations and real-world experiments were conducted to test the proposed method. The results show that the root mean square errors (RMSE) in the predicted received signal strength curves were achieved 4 dB with comparably low computational complexity in the selected railway scenarios.

A half-wave electromagnetic energy-harvesting tie towards safe and intelligent rail transportation

This paper presents the design, modeling, and experimental testing of a novel railroad energy harvesting tie for improving rail safety and connectivity and bringing intelligence to the railroad. The system is intended for applications that require trackside power in remote locations and tunnels, where electrical power for wayside safety equipment, wireless communication systems, and health monitoring systems is needed but difficult to supply. Designed to have nearly the same dimensions as a conventional railroad tie, the proposed energy harvesting tie can be installed in the same manner as a standard tie on a track. Two rotary electromagnetic energy harvesters are embedded and shielded inside the energy harvesting tie, and the integrated system is capable of generating electricity from the vertical track movement due to passing wheels, in a robust configuration that is suitable for the railroad environment. The integration of the harvesters into a composite tie that has the same appearance of other ties makes less variable to theft and vandalism. A novel ball-screw type mechanical motion half-wave rectification mechanism is adopted in the proposed design to harvest the kinetic energy of the track during its downward motion and rebound upward without any resistance from the harvesters. A simulation study is performed to better understand the system and predict the performance, based on a nonlinear model. Experiments are subsequently carried out in a load frame on a half-tie prototype with both sinusoidal and field-recorded track displacements to measure the amount of power that can be harvested under ideal conditions and actual field conditions. The test results, which agree well with the simulations, indicate a maximum of 78.1% mechanical efficiency is achieved under harmonic excitations. Field-recorded tie displacements yield 16.1–44.5 W of average power depending on the electrical loads, sufficient for powering sensor suite and trackside electronics that could improve track monitoring and safety.

Intervening to prevent suicide at railway locations: findings from a qualitative study with front-line staff and rail commuters

For every suicide on the British railway network, at least six potential attempts are interrupted by front-line staff or rail commuters. However, the factors that maximise or hinder the likelihood and effectiveness of such interventions are poorly understood. The aim of the current study was to shed light on the experience of intervening to prevent a suicide at a railway location, including how and why people intervene, and their feelings and reflections in the aftermath. In-depth interviews were carried out with rail commuters (n = 11) and front-line railway staff (n = 10) who had intervened to stop a suicide by train. Data were analysed thematically. Participants had intervened to prevent suicide in several ways, both from afar (e.g. by calling a member of staff) and more directly (verbally or non-verbally), in some cases with no prior training or experience in suicide prevention, and often as a 'quick, gut reaction' given the limited time to intervene. In more 'reasoned' interventions, poor confidence and concerns around safety were the greatest barriers to action. Although often privy to their final outcome, most participants reflected positively on their intervention/s, stressing the importance of training and teamwork, as well as small talk and non-judgemental listening. Suicides in railway environments can present bystanders with little time to intervene. Potential interveners should therefore be resourced as best as possible through clear infrastructure help/emergency points, visibility of station staff and training for gatekeepers.

QoS-Driven Resource Optimization for Intelligent Fog Radio Access Network: A Dynamic Power Allocation Perspective

The fog radio access network (Fog-RAN) has been considered a promising wireless access architecture to help shorten the communication delay and relieve the large data delivery burden over the backhaul links. However, limited by conventional inflexible communication design, Fog-RAN cannot be used in some complex communication scenarios. In this study, we focus on investigating a more intelligent Fog-RAN to assist the communication in a high-speed railway environment. Due to the train’s continuously moving, the communication should be designed intelligently to adapt to channel variation. Specifically, we dynamically optimize the power allocation in the remote radio heads (RRHs) to minimize the total network power cost considering multiple quality-of-service (QoS) requirements and channel variation. The impact of caching on the power allocation is considered. The dynamic power optimization is analyzed to obtain a closed-form solution in certain cases. The inherent tradeoff among the total network cost, delay and delivery content size is further discussed. To evaluate the performance of the proposed dynamic power allocation, we present an invariant power allocation counterpart as a performance comparison benchmark. The result of our simulation reveals that dynamic power allocation can significantly outperform the invariant power allocation scheme, especially with a random caching strategy or limited caching resources at the RRHs.

Visualization of Railway Transportation Engineering Management Using BIM Technology under the Application of Internet of Things Edge Computing

In the past, railway line planning usually required engineers to design based on their own experience after a series of field visits, leading to heavy workload and low efficiency. Moreover, operation and maintenance management is more complicated due to an abundance of railway station equipment. Based on the above problems, this paper first puts forward the railway transportation line planning and design method based on Building Information Modeling (BIM) technology. Besides, LocaSpace Viewer realizes the three-dimensional (3D) visual scene modeling of the railway environment to improve the efficiency of railway line planning and design. Secondly, the railway station’s visual operation and maintenance management system is constructed via BIM Technology. Besides, the Internet of Things (IoT) is combined with edge computing and deep learning technology to build a 3D model of station equipment, collect data in real time, and analyze data efficiently. Finally, the design effect of the model, the performance of the visual management system, and the test results of network transmission delay are displayed and analyzed. The results show that BIM can construct the 3D visualization model with high fidelity for the railway environment. This model can get a reasonable line planning scheme and analyze its feasibility, provide a reliable basis for engineers to plan railway transportation lines, and improve design efficiency. In addition, the GPU occupation rate, CPU occupation rate, and memory occupation rate of the operation and maintenance management system in different operating environments are within the standard range; when multiple clients access the system, the system data access delay is 100% less than 8 ms, which has good performance. Furthermore, the performance of the IoT transmission data real-time scheduling model and the edge computing optimization algorithm applied to this system is better than other popular methods, which can significantly improve the operation efficiency of the system. This study aims to enhance the efficiency of railway transportation line planning and station operation and maintenance management with the help of digital technologies.

Lightweight convolutional neural network for real-time 3D object detection in road and railway environments

For smart mobility, and autonomous vehicles (AV), it is necessary to have a very precise perception of the environment to guarantee reliable decision-making, and to be able to extend the results obtained for the road sector to other areas such as rail. To this end, we introduce a new single-stage monocular real-time 3D object detection convolutional neural network (CNN) based on YOLOv5, dedicated to smart mobility applications for both road and rail environments. To perform the 3D parameter regression, we replace YOLOv5’s anchor boxes with our hybrid anchor boxes. Our method is available in different model sizes such as YOLOv5: small, medium, and large. The new model that we propose is optimized for real-time embedded constraints (lightweight, speed, and accuracy) that takes advantage of the improvement brought by split attention (SA) convolutions called small split attention model (Small-SA). To validate our CNN model, we also introduce a new virtual dataset for both road and rail environments by leveraging the video game Grand Theft Auto V (GTAV). We provide extensive results of our different models on both KITTI and our own GTAV datasets. Through our results, we show that our method is the fastest available 3D object detection with accuracy results close to state-of-the-art methods on the KITTI road dataset. We further demonstrate that the pre-training process on our GTAV virtual dataset improves the accuracy on real datasets such as KITTI, thus allowing our method to obtain an even greater accuracy than state-of-the-art approaches with 16.16% 3D average precision on hard car detection with inference time of 11.1 ms/image on an RTX 3080 GPU.

A Rotational Vibration Energy Harvester for Near-Zero-Energy Applications in Railway Environment

A Rotational Vibration Energy Harvester for Near-Zero-Energy Applications in Railway Environment

Railway Wireless Communications Channel Characterization

Railway applications are in continuous evolution with the aim of offering a more efficient, sustainable, and safer transportation system for the users. Generally, these applications are constantly exchanging information between the systems onboard the train and the trackside through a wireless communication. Nowadays, Global System for Mobile communications-Railway (GSM-R) is the technology used by European Train Control System (ETCS), but it is becoming obsolete. Therefore, alternatives for this technology have to be found for the different railway applications. Its natural evolution is to move forward with the latest technology deployed: Long-Term Evolution (LTE), which the Public Land Mobile Networks (PLMN) have already deployed. Therefore, testing the performance of this communication technology in the railway environment could be useful to assess its suitability and reduce the cost of railway network dedicated deployment. In order to do that, a methodology to characterize the communication environment is proposed. The main goal is to measure geolocated impairments of any communication channel in a railway environment being able to determine its behavior of the different communication technologies and find out possible coverage issues. Moreover, it could help in the selection of suitable communication technology for railway. This paper presents a brief description of the communication for railways and its QoS parameters for performance measuring. Afterward, the testing methodology is described, and then, the communication channel measurement campaign on a real track in Spain where the railway environment is variable is presented (tunnels, rural/urban area…). Finally, the measurements and results on this real track in Spain are shown. The results provide suitability of the 4G technologies based on the delay requirements for the implementation of ETCS over it.

Passenger overall comfort in high-speed railway environments based on EEG: Assessment and degradation mechanism

The overall comfort of train passenger is influenced by many environmental factors such as vibration, noise and pressure. However, the couple effect of these influencing factors causes the difficulty in evaluating the overall comfort. This study revealed the potential comfort degradation mechanisms in high-speed railway environments and proposed a machine learning evaluation model to assess passenger comfort. Here, the subjective overall comfort ratings and the electroencephalography (EEG) of twenty passengers were collected in the field tests. Compared with passengers who were in a state of comfort, the brain areas (BA6/13/20/24/31/40/47) of passengers who felt uncomfortable were all significantly activated in the beta band. Based on the neural signature above, three related human reactions when passengers feel uncomfortable were recognized, including perceiving the environment, inducing negative emotions, and finally producing body movement intention. To assess the overall comfort of train passengers, six kinds of features extracted from EEG signals were used to train an evaluation model based on the LightGBM algorithm. This work offers a neurological explanation for the mechanisms of degradation of overall comfort and provides a novel and effective method to assess it.

Surface-Integrated Electric Field Sensor for the Detection of High-Voltage Power Lines.

When a drone is used for inspection of facilities, there are often cases in which high-voltage power lines interfere, resulting in the drone being caught or falling. To prevent this type of incident, drones must be capable of detecting high-voltage power lines. Typically, a strong electric field is formed around the high-voltage lines. To detect the electric fields around high-voltage lines, this study proposes an electric field sensor that may be integrated within the body of a drone. In a laboratory environment, a voltage of 25 kV was applied to an overhead line, and the induced voltage in the proposed sensor was measured at various electric field intensities. Over an electric field range of 0.5 to 10.1 kV/m, a voltage of 0 to 0.77 V was measured with each proposed sensor. In addition, the electric field and the voltage induced in the sensor were measured in a real-world railway environment with overhead lines. Under these conditions, the proposed sensor has the compensated value of 4.5 when the measured electric field was 4.05 kV/m. Therefore, the proposed sensor may be applied in drones to measure large electric fields and to detect the presence of high-voltage lines in its vicinity.

Deep learning based virtual point tracking for real-time target-less dynamic displacement measurement in railway applications

In the application of computer-vision-based displacement measurement, an optical target is usually required to prove the reference. If the optical target cannot be attached to the measuring objective, edge detection and template matching are the most common approaches in target-less photogrammetry. However, their performance significantly relies on parameter settings. This becomes problematic in dynamic scenes where complicated background texture exists and varies over time. We propose virtual point tracking for real-time target-less dynamic displacement measurement, incorporating deep learning techniques and domain knowledge to tackle this issue. Our approach consists of three steps: 1) automatic calibration for detection of region of interest; 2) virtual point detection for each video frame using deep convolutional neural network; 3) domain-knowledge based rule engine for point tracking in adjacent frames. The proposed approach can be executed on an edge computer in a real-time manner (i.e. over 30 frames per second). We demonstrate our approach for a railway application, where the lateral displacement of the wheel on the rail is measured during operation. The numerical experiments have been performed to evaluate our approach’s performance and latency in a harsh railway environment with dynamic complex backgrounds. We make our code and data available at https://github.com/quickhdsdc/Point-Tracking-for-Displacement-Measurement-in-Railway-Applications.

Soft-Boundary Label Relaxation with class placement constraints for semantic segmentation of the railway environment

In this paper, we focus on the challenging task of the semantic segmentation of train front-view images. Managing trackside facilities can be done by using detailed and precise information about the surrounding railway environment. Semantic segmentation enables us to understand the 2D environment, but there is no adequate large-scale dataset available for training a CNN for this purpose. Some attempts have been made to generate pseudo-data from unlabeled sequential frames to compensate for the lack of volume in training data, but the moving speed of trains makes it difficult to apply them directly. We aim to solve this problem by proposing the Soft Boundary Label Relaxation (Soft-BLR) method, which considers label boundaries extending over multiple pixels to cope with more severely distorted pseudo-data and to better train the CNN in the initial training stage. Furthermore, we modify the loss function to penalize inference results based on the distance from the label boundary to solve the misalignment problems of border pixels. Through experimental evaluation, we report that the proposed method outperforms previous methods on not only the semantic segmentation of challenging railway images, but also that of general street-view images.

Real-time object detection technology in railway operations

Development of new technology to enhance train operability, in particular during manual driving by real-time object detection on track, is one of the rising trends in the railway industry. The function of object detection can provide train operators with reminder alerts whenever there is an object detected close to a train, e.g. a defined distance from the train. In this paper, a two-stage vision-based method is proposed to achieve this goal. At first, the Targets Generation Stage focuses on extracting all potential targets by identifying the centre points of targets. Meanwhile, the Targets Reconfirmation Stage is further adopted to re-analyse the potential targets from the previous stage to filter out incorrect potential targets in the output. The experiment and evaluation result shows that the proposed method achieved an Average Precision (AP) of 0.876 and 0.526 respectively under typical scenario sub-groups and extreme scenario sub-groups of the data set collected from a real railway environment at the methodological level. Furthermore, at the application level, high performance with the False Alarm Rate (FAR) of 0.01% and Missed Detection Rate (MDR) of 0.94%, which is capable of practical application, was achieved during the operation in the Tsuen Wan Line (TWL) in Hong Kong.

Gathering Energy of the Stray Currents in Electrified Railways Environment for Power Supply

The paper presents a new, unconventional energy harvesting (EH) method for supplying low-power devices on electrified railway lines that utilises stray currents and the non-zero potential of the rails to the ground. The EH device gathers the energy and stores it in batteries. It could even work in extremely unfavourable weather conditions and could be easily placed in almost any location. The presented real-life data show that the average available power is less than 250 mW and the average useful power is about 100 mW. This is enough to supply ultra-low power microcontrollers, which only occasionally use energy-consuming modules to perform measurements or communicate. The disadvantage of the EH method is the introduction of resistance between the rail and the earth, which increases stray currents and could increase the electrochemical corrosion of the rail. To reduce the impact of this resistance, a method for balancing the flowing charge is proposed. After balancing, the average of the flowing current is zero and electrochemical corrosion should be reduced. The proposed charge balancing algorithms could reduce the unbalanced charge to nearly zero at the expense of energy gathering efficiency, which decreases by 20–40%.

SOCIAL BEHAVIOR OF EARLY CHILDREN IN THE NUSUKAN RAILWAY ENVIRONMENT, SURAKARTA

Children's social development is how early childhood interacts with peers, adults, and the broader community to adapt. Parents and the environments are figures or models closest to children and become role models for children. This study aimed to determine children's social behavior and the role of the environment on the social behavior of children aged 5-6 years in the Nusukan Surakarta railway environment. This type of research uses descriptive qualitative methods—sampling using a purposive sampling technique. The data analysis technique used the Milles and Huberman model with three stages: data reduction, presentation, and conclusion. The results show that children's social behavior in the Nusukan railway area tends to be active, but some behaviors still need guidance so that children have good social behavior. It is inseparable from family environmental factors, both parents, siblings, peers, or other adults through their environment. Child development is strongly influenced by treatment or parental guidance for children in recognizing various aspects of social life or norms of social life. Social behavior is closely related to children's behavior in adjusting to the rules of society in the surrounding environment. Children obtain social behavior through maturity and learning opportunities from various stimuli provided by their environment.

Effects of solid friction modifier on friction and rolling contact fatigue damage of wheel-rail surfaces

In railway network, friction is an important factor to consider in terms of the service behaviors of wheel-rail system. The objective of this study was to investigate the effect of a solid friction modifier (FM) in a railway environment. This was achieved by studying the friction, wear, and rolling contact fatigue (RCF) damage on the wheel-rail materials at different slip ratios. The results showed that when a solid FM was applied, the friction coefficient decreased. After the solid FM was separated from the wheel-rail interface, the friction coefficient gradually increased to its original level. With the application of the solid FM, the wear rates of the wheel-rail decreased. In addition, the thickness and hardness of the plastic deformation layers of the wheel-rail materials were reduced. The worn surfaces of the wheel-rail were dominated by pits and RCF cracks. Without the FM, RCF cracks ranged from 84 to 120 µm, and subsurface cracks were generated. However, with the FM, RCF cracks ranged from 17 to 97 µm and no subsurface cracks were generated. These findings indicate possible methods of improving the performance of railway rolling stock by managing friction, and reducing wear and permanent RCF damage affecting both the wheels and rails.

Correlation Analysis of External Environment Risk Factors for High-Speed Railway Derailment Based on Unstructured Data

In railway operation, unsafe events such as faults may occur, and a large number of unsafe event records are generated in the process of unsafe events’ recording and reporting. Unsafe events have been described in unstructured natural language, which often has inconsistent structure and complex sources, involving multiple railway specialties, with multisource, heterogeneous, and unstructured characteristics. In practical application, the efficiency of processing is extremely low, leading to potentially unsafe management utilization. Based on the data on unsafe events, this paper utilizes big data processing technology, conducts association rules mining and association degree analysis, extracts the word segmentation, and obtains the feature vector of unsafe fault event data. At the same time, the unsafe event data analysis model is constructed in combination with regular expression and pattern matching technology. This paper establishes the matching model of high-speed railway derailment-based external environment risk factors and applies it to the occurrence of unsafe events. This model could be utilized to analyze and excavate the link between external environment risk factors and the occurrence of unsafe events and carry out the automatic extraction of characteristic information such as risk possibility and consequence severity; hence, it has potential for identifying, with enhanced accuracy, high-risk factors that may lead to high-speed railway derailment. Based on this study, we could make full use of the unsafe event data, forecast the risk trend, and discover the law of high-speed railway derailment. This study introduces a viable approach to analyzing the unsafe event data, forecasting risk trend, and conceptualizing high-speed railway derailment. It could also enforce the accurate quantification of high-speed railway safety situation, refine the risk index and conduct in-depth analysis combined with the model, and effectively support the digitalization and intellectualization of high-speed railway operation safety.

SMART on-board multi-sensor obstacle detection system for improvement of rail transport safety

This paper presents an on-board multi-sensor system which is able to detect obstacles and estimate their distances in railway scenes in different illumination conditions. The system was developed within the H2020 Shift2Rail project SMART (Smart Automation of Rail Transport) and aims at increasing the safety of rail transport by detecting obstacles on the rail tracks ahead of a moving train in order to reduce the number of collisions. The system hardware consists of cameras of different types integrated into a specially designed housing, mounted on the front of the train. Multiple vision sensors complement each other in order to handle different illumination and environmental conditions. The system software uses a novel machine learning-based method that is suited to a particular challenge of railway operations, the need for long-range obstacle detection and distance estimation. The development of this method used a long-range railway dataset, which was specifically generated for this project. Evaluation results of reliable obstacle detection in various environmental conditions using the SMART RGB camera in day light illumination conditions and using the SMART Night Vision sensor in poor (night) illumination conditions are presented. The results demonstrate both the potential of the on-board SMART obstacle detection system in the operational railway environment and the benefit of the use of different cameras to be more independent of light and environmental conditions.

Automatic Point Cloud Semantic Segmentation of Complex Railway Environments

The growing development of data digitalisation methods has increased their demand and applications in the transportation infrastructure field. Currently, mobile mapping systems (MMSs) are one of the most popular technologies for the acquisition of infrastructure data, with three-dimensional (3D) point clouds as their main product. In this work, a heuristic-based workflow for semantic segmentation of complex railway environments is presented, in which their most relevant elements are classified, namely, rails, masts, wiring, droppers, traffic lights, and signals. This method takes advantage of existing methodologies in the field for point cloud processing and segmentation, taking into account the geometry and spatial context of each classified element in the railway environment. This method is applied to a 90-kilometre-long railway lane and validated against a manual reference on random sections of the case study data. The results are presented and discussed at the object level, differentiating the type of the element. The indicators F1 scores obtained for each element are superior to 85%, being higher than 99% in rails, the most significant element of the infrastructure. These metrics showcase the quality of the algorithm, which proves that this method is efficient for the classification of long and variable railway sections, and for the assisted labelling of point cloud data for future applications based on training supervised learning models.

‘“The whistle blows, and we are whisked into a tunnel”: Railways and the Environment in Illawarra, 1850s–1915’

Historians are becoming alert to the large role of railways in environmental history. To date, many studies in Australasia focus on a specific industry, such as timber. It is now worth turning to the distinctive local or regional effects of railways beyond a single industry or commodity, so to better understand the links between technology, environment, and place. Illawarra presents a valuable case study. The environmental history of the first decades of rail transport exposes how Wollongong and its region industrialised and the ways in which this process affected everything from primary producers to the sounds of daily life. This article takes in the 1850s through to the start of World War I (WWI), a period when rail transport grew from being the adjunct of a few coal mines into an essential common carrier. It progresses through a series of themes that show the economic, social, and cultural attributes that shaped and were shaped by the railway environment. It begins with the railway as a carrier: the extent to which trains fulfilled their intended role to transport Illawarra’s natural resources to Sydney and other markets. It then moves on to the railway as a consumer, putting the local environment to work for its benefit and requiring materials made from resources of distant lands. Railways did more than carry or consume resources; they created their own environment and provided new perspectives on nature. Trains brought people closer to nature, carried them into new—and dangerous—environments in tunnels, and transformed the sonic landscape. Rail travel differed significantly to horseback or sea voyages in capacity and speed, and by WWI it was enmeshed in Illawarra’s environment.