Search

Since foreign object intrusion is a cause of train accidents, railway image classification neural network is established to recognize any input railway images for intelligence control. With the cutting-edge technology and increasing data repositories emanating from Internet of Things (IoT) for driving system, Federated Learning (FL) has emerged as a prominent solution for training machine learning model in the context of distributed devices. Currently, federated learning can balance efficiency and security through differential privacy methods. In recent years, a noise adding mechanism in local differential privacy has provided a strict privacy guarantee for federated learning. However, two problems arise in the rail-driving system. On one hand, an attacker can still extract sensitive information from summation of data disturbed by zero mean noise generated by the existed noise adding mechanism. In this case, privacy concerns limit the publication of data in the IoT for driving system. On the other hand, a large variance of the noise makes the estimated average of the parameters of the model biased, which has an impact on the process of developing and refining Artificial Intelligence (AI) systems. To address these issues, we propose a federated learning framework of nonzero mean noise addition mechanism in differential privacy of deep networks based on fully homomorphic encryption and greedy average block Kaczmarz method, which is viewed as an improved AI application of foreign object detection in rail-driving system. Instead of zero mean noise, the nonzero mean noise to original data is determined locally according to the correlation between the local and global data distributions by a novel interior product calculation. As the means are different among the clients and unpredictable for attackers, the variance of the noise can be small while eliminating undue influence brought by a large variance of the zero mean noise in AI applications. Using fully homomorphic encryption and greedy average block Kaczmarz method, a denoising weighting aggregation strategy is derived without decryption of the mean to guarantee the privacy of individual device, while getting the real parameters on server. Moreover, the weight is generated completely randomly for safety. Meanwhile, the weight is associated with the heterogeneity of local data distribution across parties, balancing the trade-off between privacy loss and model performance. Experiments show that the proposed method provides a higher level of accuracy than the existed federated learning algorithms using the data collected by a number of devices, while it guarantees a better privacy during communications in driving system for multi-party machine learning tasks. [ABSTRACT FROM AUTHOR]

Derailment is the sense of being "off‐course" in life. But what could this mean for adolescents, who are often establishing identity and self‐direction for the first time? We examined the structure and correlates of the Derailment Scale and its short form, the Derailment Scale‐6 (DS‐6), among middle‐to‐late adolescents (N = 452). Both scales exhibited unidimensionality, but the DS‐6 demonstrated superior fit and correlated with cross‐sectional distress markers (e.g., greater depression, lower life satisfaction, strained sense of purpose). Breaking from adult‐based research, we failed to find evidence that derailment related to adolescent identity exploration and commitment. In extending assessment of derailment to adolescence, this study invites exploration of this experience during a time characterized by substantial transition and the emergence of stable self‐views. [ABSTRACT FROM AUTHOR]

A semi-floating cable-stayed bridge can swing longitudinally to absorb seismic energy in an earthquake for the purpose of reducing structural response. However, the large transverse (lateral) seismic response of such large-span cable-stayed bridges must be controlled to avoid severe bridge damage and ensure train safety. This study investigates the dynamic response and associated damping mechanism of a coupled high-speed rail (HSR) vehicle and cable-stayed bridge system subjected to various ground motions. A comprehensive vehicle–track–bridge interaction system is first established. Then, the dynamics of a semi-floating cable-stayed bridge-HSR vehicle system equipped with magnetorheological bearings (MRBs) and fluid viscous dampers (FVDs) is examined with various inputs of near-fault (NF) pulse-type, NF non-pulse-type, and far-field (FF) ground motions. To effectively mitigate the bridge internal force response and enhance train running safety, the transverse and longitudinal FVDs need to be concurrently utilized along with MRBs. This research presents a novel mitigation approach for simultaneously reducing the transverse vibrations of cable-stayed bridge and the derailment risk of running train. [ABSTRACT FROM AUTHOR]

The economic, environmental and safety consequences of fouling are the key reasons for industrial and research interest in heat exchanger fouling and cleaning. The implication of fouling in crude oil refineries has changed in the recent decades due to better monitoring tools, use of fouling mitigation devices, optimization of the use of antifoulants, optimization of process conditions, better blending of crudes, better design of heat exchangers and selection of exchanger tube materials. This brings a need for the economic impact of fouling in crude refinery preheat train to be revisited. The estimation of the cost of fouling in crude preheat train is discussed via updating the assumptions previously used in literature. The updated fouling cost estimate for the crude preheat train in the US refining sector is likely to be in the range of 1–1.2 B US$ per year for 2019. It is shown that the cost of fouling needs to be associated with a relevant refining profit or a crude refining margin as it is possible for the refinery to processes crudes with higher fouling problems after compensating the fouling cost with the price of the crude. [ABSTRACT FROM AUTHOR]

This research examines the seismic hazard impact on railway infrastructure along the U.S. West Coast (Washington, Oregon and California), using machine learning to explore how measures of seismic hazard such as fault density, earthquake frequency, and ground shaking relate to railway infrastructure accidents. By comparing linear and non-linear models, it finds non-linear approaches superior, particularly noting that higher fault densities and stronger peak ground shaking correlate with increased infrastructure accident rates. Shallow earthquakes with magnitudes of 3.5 or greater and hypocentral depths <20 km also exhibit a pronounced correlation with the incidence of railway infrastructure accidents The study extends to financial impact analysis through Net Present Value and Monte Carlo Simulation, and evaluates damage costs from 2000–2023 to guide financial planning and risk management strategies. It highlights the crucial role of advanced financial tools in optimizing maintenance and long-term planning that could result in better preparedness in high seismic hazard regions and emphasizes the need for robust risk management strategies in enhancing railway operational safety that considers the local and regional tectonic and seismic activity and local ground shaking intensity. [ABSTRACT FROM AUTHOR]

This paper presents a method for simulating multi-rigid-body collisions to investigate the performance of oblique collisions and motion after derailment in freight trains. Oblique collisions between trains and obstacles can pose serious safety risks, and lateral displacement of the wheels and wheel rise are identified as root causes of derailment. The study demonstrates that an anti-derailment device can significantly improve the safety zone of the derailment boundary, expanding it to twice its original size, thereby protecting the train from derailment in case of frontal oblique collisions. When the train collides with a 15-ton obstacle at an angle of 15° at 40 km/h, under the action of the anti-derailment device, the derailment coefficient decreased from 3.61 to 0.93 and the wheel rise decreased from 72.7 mm to 27 mm. Passing ability tests and derailment tests of the K6 bogie equipped with the device have been conducted to confirm that the device does not affect the normal operation of the freight train and evaluate the restraining effect of the device on the bogie during derailment. [ABSTRACT FROM AUTHOR]

In this paper, the lateral interaction between a rail vehicle and the track is investigated during cases of an occurrence of dynamic gauge widening. This is composed of the nominal track gauge, wear of the rail profiles and track widening due to wheel-rail forces on sections of curved track. A multibody simulation model is used to investigate the various influencing parameters. This model not only analyses the single rail's lateral displacement but also tilting. Furthermore, the simulation of rail pads with adjustable stiffnesses is enabled. The resulting different contact points between the wheel and rail are represented, affecting the wheel-rail contact forces. The results reveal that changes in contact geometry caused by rail tilting depend strongly on the specific wheel and rail profiles. This is illustrated by comparing a new rail profile with a heavily worn rail profile. Particular attention is also paid to the distinction between leading and trailing wheelsets. Finally, the model is validated using measured wheel-rail forces and rail head movement, as measured on the track. A better understanding of dynamic track widening can improve the running behaviour of the wheelset on small radius curves, help protect against derailment and reduce wear on the wheel and rail. [ABSTRACT FROM AUTHOR]

Objectives: Because of high frequency of extreme weather, railway subgrade disaster shows a trend of increasing, great harmfulness and is hard to detect in advance. Even a small-size subgrade disaster may cause railway paralysis, which prevents the development of transportation. Many incidents illustrate that the earliest subgrade disaster always appeared in areas where no case ever reported before. It means that traditional monitoring methods are hard to detect hidden danger area. Thus, a promising method is ur1,2,3gent to be proposed for meeting the monitoring requirement for railway safety. Methods: First, the charac1,2,3teristics of subgrade disaster are summarized under different disaster-pregnant environments, and the advan1,2,3tages of various monitoring methods are discussed to find a collaborative applications method for early iden1,2,3tification of subgrade disaster. The potential hazards that may cause damage to subgrade structure and the deterioration degree of subgrade are considered as the main monitoring object of subgrade service status, in1,2,3cluding subgrade deformation monitoring, structural health monitoring, geological hazard monitoring along the railway, track irregularity, and external environmental monitoring. Second, an investigative approach based on the integration of space-air-train-ground muti-source techniques is proposed to detect the geohazards and monitor the service status of railway subgrade. It means that collaborative application of different moni1,2,3toring methods, cooperative analysis of different scales and resolutions data and collaboration of between various railway departments are required. Two monitoring schemes for subgrade disasters and service sta1,2,3tus of subgrade are proposed based on the integration of multi-source and multi-scale monitoring technique. Finally, the development direction of subgrade service status monitoring is discussed. Results and Conclusions: This monitoring system has been applied in the identification of subgrade service status in the Shang1,2,3hai-Nanjing high speed railway, which can quickly investigate the location of the disasters and the deteriora1,2,3tion degree of subgrade [ABSTRACT FROM AUTHOR]

The derailment of a high‐speed train is a complex and uncertain dynamic process, especially under running conditions where the derailment index lacks comprehensive experimental validation. This research focuses on elucidating the mechanism of an earthquake‐induced train derailment and validating the structural response‐based spectrum intensity derailment index. To achieve this, a multi‐array shaking table system was utilized to test the safety of a running train and to physically replicate train derailment process under severe earthquake impact. We investigated the vibration characteristics and derailment progression of trains operating at different speeds, exposed to earthquakes of varying frequencies and intensities. In stationary derailments, significant wheelset lift was observed due to lateral rolling vibrations. However, in running test cases, there was no separation between the wheels and rails. Instead, the wheels underwent a long period of climbing and descending on the rails, similar to damped single‐degree‐of‐freedom oscillations. As the wheel climbed the rail and reached a critical potential energy point, the wheel flange of the wheel could potentially fall due to gravity or come off the rail due to external disturbances. The critical potential energy could be represented by the spectrum intensity threshold, and the prediction results aligned well with the derailment test results for both stationary and running trains. Furthermore, the method of determining train derailment based on structural responses was consistent with the test results. The spectrum intensity index shows strong positive correlations with other wheel‐force‐related indicators, albeit adopting a conservative perspective, reinforcing its efficacy in assessing train safety. [ABSTRACT FROM AUTHOR]

A derailment detection algorithm for railway freight cars based on micro inertial measurement units was designed to address the complex issue of the disassembly and assembly of derailment braking devices. Firstly, a horizontal attitude measurement model for freight cars was established, and attitude measurement algorithms based on gyroscopes and accelerometers were introduced. Subsequently, a high-precision attitude measurement algorithm based on variational Bayesian Kalman filtering was proposed, which used acceleration information as the observation data to correct attitude errors. In order to improve the accuracy of derailment detection, a dual-model instantaneous attitude difference measurement technique was further proposed. In order to verify the effectiveness of the algorithm, offline data from simulation experiments and in-vehicle experiments were used to validate the proposed algorithm. The results showed that the proposed algorithm can effectively improve the measurement accuracy of horizontal attitude changes, reducing the error by 89% compared to pure inertial attitude calculation, laying a technical foundation for improving the accuracy of derailment detection. [ABSTRACT FROM AUTHOR]

Adequate vertical track support is essential for safe and efficient railway operations. Insufficient support leads to distorted track geometry, increased dynamic loads, component stress, poor ride quality, rolling stock damage, and derailment risks. Current inspection practices focus on assessing the condition of the track components and geometry, rather than the root causes of degradation. To improve this condition, this study presents the use of a methodology that utilizes an autonomous vertical track deflection measurement system mounted on a loaded rail car (36 tonnes/axle) to support track maintenance decisions in a heavy haul railroad located in southeast Brazil. The system continuously measured substructure stiffness along the railway line. Over one year, data were collected from over 8000 km of track. The study highlighted seasonal effects on track degradation over time, identifying areas with significant deflections and high deflection rates, which contribute to issues such as differential settlement and reduced lifespan of track components. Additionally, the study revealed seasonal effects, with deflections peaking during wet weather and decreasing during dry cycles. A method to classify weak track areas was developed, facilitating monitoring and enabling more effective maintenance planning, contributing to the reduction of overall track maintenance costs and enhancing safety and operational efficiency. [ABSTRACT FROM AUTHOR]

Computational fluid dynamics (CFD) is a numerical tool often used to predict anticipated observations using only the physics involved by numerically solving the conservation equations for energy, momentum, and continuity. These governing equations have been around for more than one hundred years, but only limited analytical solutions exist for specific geometries and conditions. CFD provides a numerical solution to these governing equations, and several commercial software and shareware versions exist that provide numerical solutions for customized geometries requiring solutions. Often, experiments are cost prohibitive and/or time consuming, or cannot even be performed, such as the explosion of a chemical plant, downwind air concentrations and the impact on residents and animals, contamination in a river from a point source loading following a train derailment, etc. A modern solution to these problems is the use of CFD to digitally evaluate the output for a given scenario. This paper discusses the use of CFD at Corteva and offers a flavor of the types of problems that can be solved in agricultural manufacturing for pesticides and environmental scenarios in which pesticides are used. Only a handful of examples are provided, but there is a near semi-infinite number of future possibilities to consider. [ABSTRACT FROM AUTHOR]

In order to study the effect of the velocity pulse on the dynamic response of the train-bridge system of the high-speed railway simple supported beam bridge, the velocity pulse is simulated by the trigonometric function method and superimposed with the far-field earthquake without pulse to synthesize the pulse with different pulse types, pulse periods and pulse peaks. A 10 × 32m typical high-speed railway simple supported beam bridge is considered an case illustrating study. Then, the dynamic response of train-track-bridge coupling system is calculated by train-track-bridge seismic analysis software TTBSAS. Afterwards, the influence of pulse near-field earthquakes parameters and vertical components on dynamic response of train-bridge system and the safety of the train on the bridge are discussed in detail.The new derailment evaluation index is adopted to evaluate driving safety under earthquakes. The train safety control of simply supported beam bridge under the action of near-field earthquake is studied. The results show that the impact of pulse ground motion on the dynamic response of the train-track-bridge coupling system is significantly higher than that of no-pulse ground motion, especially the impact on the bridge and rail subsystem is more significant than that of train subsystem. Under the excitation of ground motion intensity of 0.05g ∼ 0.15g, the safe speed threshold of pulse near-field ground motion is smaller than that of far-field ground motion. When the ground motion intensity is 0.20g ∼ 0.30g, the safe speed threshold of pulse near-field ground motion and far field ground motion is 200km/h. So, the pulse near-field earthquake poses a greater threat to the safety of the train on the bridge than the far-field earthquake. Therefore, the influence of pulse near-field earthquakes should be considered in the seismic design. The research results of this paper can provide theoretitcal support for the design of a high-speed railway bridge in the near-field area. [ABSTRACT FROM AUTHOR]

With the rapid development of urbanization and underground transportation, as well as the frequent occurrence of extreme weather conditions such as extreme rainfall, flooding disasters for rail transit are becoming severe, and need to be urgently clarified in terms of the mechanism causing them. In this study, a comprehensive model for water damage at the entrance to a rail transit station is proposed, emphasizing the entire process of extreme weather–surface ponding–underground intrusion. The model is validated by the inundation process of Line 5 of the Zhengzhou Metro during the "7.20" event and further applied to Wanqingsha Station of Guangzhou Metro Line 18 in China to determine the surrounding water depth, distribution, total water inflow volume, and water damage time under different rainfall intensities, rain patterns and protection scenarios. It was found that when rainfall reaches the level of a 1-in-2000-years event, the surface water begins to invade the internal rail transit system through the rail transit entrances. When facing extreme rainfall akin to the "7.20" event in Zhengzhou, the rail transit system in Wanqingsha Station meets a heightened risk of water damage, resulting in significantly deeper water levels compared to 1-in-5000-year rainfall event in Guangzhou and exceeds the height of the subway entrances. Analysis of the water intrusion process reveals that, as rainfall intensity escalates, the total inflow water volume into the rail transit system increases while escape time diminishes. Moreover, under identical rainfall intensity, pre-type rainfall yields the highest total water inflow, whereas mid-type rainfall exhibits the shortest escape time. Enhancing the protection conditions can markedly attenuate surface water intrusion into the subterranean rail transit system, thereby enhancing the evacuation time for individuals within the system. [ABSTRACT FROM AUTHOR]

The rail rolling stock undercarriage condition monitoring is proposed by using an automated measuring system, located on the railway track and measuring the specified parameters of the wheels directly, while the train is moving. Regular undercarriage condition monitoring reduces the costs of preventive maintenance of rolling stock without compromising the traffic safety. An algorithm has been developed for the operation of a special software package for visualizing and assessing monitoring data on the condition of the undercarriage of rail rolling stock. The software package consists of separate software modules that can be used independently of each other. It is possible to make short- or long-term predictions of the behavior of any of the monitored parameters using an proposed automated measuring system. [ABSTRACT FROM AUTHOR]

As an emergent developmental construct, derailment – an individual difference hallmarked by a temporal discordance of the self – has been positively associated with depressive features, though less is known about the role of stress in this relationship. The aim of the present study was to explore the interaction between derailment and subjective stress on depressed mood. 265 adults recruited through Prolific – a crowd sourcing platform were first tasked with completing baseline measures, then were randomly assigned to a low stress or high stress condition. We utilized an adapted version of the Online Trier Stress Test to manipulate stress levels. Next, depressed mood and post-test perceived stress were measured. We analyzed data through STATA using a two-tailed independent samples t -test and multiple linear regression. Findings suggested the experimental manipulation increased subjective stress within the high stress condition compared to the low stress condition (M diff = 36.14, SE = 4.4, t = 8.21, p <.001). Further, we found the interaction between derailment and stress conditions was associated with increased depressed mood (β = −0.11, SE = 0.05, p =.038, R 2 = 0.5). Notably, we found significant between-condition differences in depressed mood for those who endorsed either low (d = 0.22, SE = 0.07, p =.003) or moderate levels of derailment (d = 0.11, SE = 0.05, p =.033), but not for those who endorsed high levels of derailment (d = 0.01, SE = 0.07, p =.92). This study utilized self-report measures, which may be susceptible to biased responses. Derailment may play a role in the persistence of depressed mood regardless of varying contexts of stress. • We focus on the role of derailment in the relationship between levels of subjective stress and depressed mood. • Contrary to our hypothesis, derailment negatively moderated this relationship. • We found significant differences in depressed mood at different levels of derailment only within the low stress condition. • The present study is the first to experimentally explore the impact of derailment and subjective stress on depressed mood. [ABSTRACT FROM AUTHOR]

The article focuses on the tragic train wreck near Esmond Station, Arizona, on January 28, 1903, involving the westbound Sunset Limited and the eastbound Crescent City Express trains. Topics include the collision's causes, the intense fire that resulted from the crash, the heroic efforts of passengers and crew to rescue survivors, and the challenges faced during the recovery and investigation of the incident.

The safety of the high-speed train traveling through the stationary thunderstorm downburst wind was studied. First, a thunderstorm wind test device was used to simulate the stationary thunderstorm downburst wind. Based on the rigid model pressure measurement tests, the aerodynamic forces of the train traveling along different paths through the stationary thunderstorm downburst wind were measured. The influence of the radial distance of the crossing path on the aerodynamic force coefficients of the train was investigated. On this basis, an unsteady aerodynamic model of the high-speed train crossing through the stationary thunderstorm downburst wind was established, and dynamic response analysis was carried out using the SIMPACK multibody dynamics simulation software to explore further the safety of the high-speed train crossing through the stationary thunderstorm downburst wind. The research showed that the stationary thunderstorm downburst wind field has significant spatial variation characteristics compared with the atmospheric boundary layer wind field. When the train passes through the thunderstorm downburst wind, the radial wind speed and wind yaw angle experienced by the train constantly change, and the change curve shows a symmetrical distribution. The aerodynamic force of the train will undergo sudden loading and unloading processes, and the lateral force coefficient of the train on different paths shows a “pulse-type” variation. Moreover, the lateral force coefficient increases with the increase of wind yaw angle. Under the influence of the thunderstorm downburst wind, the variation trend of the aerodynamic force coefficients of the train is consistent with that under crosswind. However, there are significant differences in the numerical values. Therefore, it is impossible to simply use the formula for calculating the aerodynamic force coefficients of the train under crosswinds to predict the aerodynamic force coefficients of the train under the thunderstorm downburst wind. While passing through the thunderstorm downburst wind, the overturning coefficient index plays a decisive role in the safety of train operations. Train rollover is the main form of train safety accidents, while derailment accidents are not easy. The numerical results obtained in this study are significant for evaluating the operational safety while moving trains traversing the stationary thunderstorm downburst wind. [ABSTRACT FROM AUTHOR]

Hazards threaten railway safety by their potential to trigger railway accidents, resulting in significant costs and impacting the public's willingness to use railways. Whilst many prior works investigate railway hazards, few offer a holistic view of hazards across jurisdictions and time because the large number of primary sources make synthesising such learnings time consuming and potentially incomplete. The conceptual framework HazardMap is developed to overcome this gap, employing open-sourced Natural Language Processing topic modelling for the automated analysis of textual data from Rail Accident Investigation Branch (RAIB), Australian Transport Safety Bureau (ATSB), National Transportation Safety Board (NTSB) and Transportation Safety Board of Canada (TSB) railway accident reports. The topic modelling depicts the relationships between hazards, railway accidents and investigator recommendations and is further extended and integrated with the existing risk theory and epidemiological accident models. The results allow the different aspects of each hazard to be listed along with the potential combinations of hazards that could trigger railway accidents. Better understanding of the aspects of individual hazards and the relationships between hazards and previous accidents can inform more effective hazard mitigation policies including technical or regulatory interventions. A case study of the risk at level crossings is provided to illustrate how HazardMap works with real-world data. This demonstrates a high degree of coverage within the existing risk management system, indicating the capability to better inform policymaking for managing risks. The primary contributions of the framework proposed are to enable a large amount of knowledge accumulated to be summarised for an intuitive policymaking process, and to allow other railway investigators to leverage lessons learnt across jurisdictions and time with limited human intervention. Future research could apply the technique to road, aviation or maritime accidents. • A framework HazardMap is developed for mapping hazards in the railway system. • A case study of the risk at level crossing is implemented. • Opportunities for practitioners in learning across jurisdiction and time. • Enabling knowledge accumulated to be summarised for policymaking process. [ABSTRACT FROM AUTHOR]

A crisis that occurs in a company has the potential to damage the company's reputation. However, if the crisis can be handled properly, then the crisis will not harm the company. The purpose of this research is to find out the crisis communication steps and efforts made by the Public Relations of Daop 2 Bandung in dealing with the crisis situation caused by the train accident. Researchers use a descriptive analysis approach because it is considered capable of describing something clearly and each part can be studied one by one, so that the facts of a case can be revealed transparently. Data collection techniques in the form of interviews, in this study the data source consists of primary data and secondary data. Primary data was obtained from interviews with Mr. Ayep Hanapi as Public Relations Manager of PT KAI Daop 2 Bandung. The results showed that PT KAI Public Relations has made several efforts to maintain the company's image, namely: First, pre-crisis PT KAI applies the zero accident principle by increasing employee competence and discipline as well as maintaining facilities and infrastructure. Second, PT KAI's crisis team collaborates with stakeholders, provides solutions, is responsive in serving the media by preparing accurate data and fact information. Third, post-crisis to improve the image by providing compensation to employees and accident victims and conducting evaluations to improve train safety and security in the future. [ABSTRACT FROM AUTHOR]

Road-railroad level crossing accidents pose serious safety risks to road users, and their significant increase requires more research efforts to propose substitute solutions. Such a solution must consider the impact of intersection geometry, user perception, traffic characteristics, driver behavior, environment, and seasonal variations on accidents. This study explores the considerable number of such accidents and develops a predictive model using all the factors that influence them. For these objectives, data were collected from databases maintained by the zonal head office of the East Central Railway (ECR) in India. Data included 175 level crossings that experienced at least one accident between 2006 and 2021 in the ECR region. This study presents two accident prediction models using logistic regression and ANN for the predominant factors of accidents in the ECR zone of Indian railways. The accuracy of fatal accident prediction was 96% for logistic regression and 98% for ANN. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the tremendous increase in the number of vehicles, the dense traffic created can lead to accidents and fatalities. In a traffic system, the costs for accidents are immeasurable. Numerous studies have been carried out to predict the cost of fatal accidents but have provided the actual values. Therefore, in this study, a monkey-based modular neural system (MbMNS) is developed to identify accident cost. The accident cases and cost data were collected and preprocessed to remove the noise, and the required features were extracted using the spider monkey function. Based on the extracted features, the accidents and the costs were identified. For rail engineering, this will support evaluating the number of railroad crossing accidents with different time intervals. The impact of every accident was also measured with different cost analysis constraints, including insurance, medical, and legal and administrative costs. Therefore, the present study contributes to the field by collecting and organizing the present railroad level crossing accident data from crossing inventory dashboards. Then, the introduction of a novel MbMNS for the cost analysis is the primary contribution of this study to further enrich the railroad level crossing protection system. The third contribution is the tuning of the prediction layer of a modular neural network to the desired level to achieve the highest predictive exactness score. Hence, the designed MbMNS was tested in the Python environment, and the results were validated with regard to recall, accuracy, F-measure, precision, and error values; a comparative analysis was also conducted to confirm the improvement. The novel MbMNS recorded high accuracy of 96.29% for accident and cost analysis, which is better than that reported for other traditional methods. [ABSTRACT FROM AUTHOR]

Policy action for sustainability transformation faces inherent and ever-present sources of conflict, pushback, and resistance (i.e., discord). However, conceptual frameworks and policy prescriptions for sustainability transformations often reflect an undue image of accord. This involves simplified assumptions about consensus, steering, friction, discreteness, and additiveness of policy action, conferring an unrealistic view of the potential to deliberately realize transformation. Instead, negotiating discord through continuously finding partial political settlements among divided actors needs to become a key focus of policy action for sustainability transformations. Doing so can help to navigate deeply political settings through imperfect but workable steps that loosen deadlock, generate momentum for further policy action, and avoid complete derailment of transformation agendas when discord arises. [ABSTRACT FROM AUTHOR]

The rail industry in Australia screens workers for probable obstructive sleep apnea (OSA) due to known safety risks. However, existing criteria to trigger screening only identify a small proportion of workers with OSA. The current study sought to examine the relationship between OSA risk and rail incidents in real-world data from Australian train drivers, and conducted a proof of concept analysis to determine whether more conservative screening criteria are justified. Health assessment (2016–2018) and subsequent rail incident data (2016–2020) were collected from two passenger rail service providers. Predictors included OSA status (confirmed no OSA with a sleep study, controlled OSA, unknown OSA [no recorded sleep assessment data] and confirmed OSA with no indication of treatment); OSA risk according to the current Standard, and OSA risk according to more conservative clinical markers (BMI threshold and cardiometabolic burden). Coded rail safety incidents involving the train driver were included. Data were analysed using zero-inflated negative binomial models to account for over-dispersion with high 0 counts, and rail safety incidents are reported using Incidence Risk Ratios (IRRs). A total of 751 train drivers, typically middle-aged, overweight to obese and mostly men, were included in analyses. There were 43 (5.7%) drivers with confirmed OSA, 62 (8.2%) with controlled OSA, 13 (1.7%) with confirmed no OSA and 633 (84.4%) drivers with unknown OSA. Of the 633 train drivers with unknown OSA status, 21 (3.3%) met 'at risk' criteria for OSA according to the Standard, and incidents were 61% greater (IRR: 1.61, 95% Confidence Interval (CI) 1.02–2.56) in the years following their health assessment compared to drivers who did not meet 'at risk' criteria. A more conservative OSA risk status using lower BMI threshold and cardiometabolic burden identified an additional 30 'at risk' train drivers who had 46% greater incidents compared to drivers who did not meet risk criteria (IRR (95% CI) 1.46 (1.00–2.13)). Our more conservative OSA risk criteria identified more workers, with greater prospective incidents. These findings suggest that existing validated tools could be considered in future iterations of the Standard in order to more sensitively screen for OSA. [ABSTRACT FROM AUTHOR]

Railway transportation has experienced significant growth worldwide, offering numerous benefits to society. Most railway accidents are caused by wheelset faults so it's significant to monitor wheelset conditions. Therefore, we need to collect wheelset images, repaint them, extract laser stripe centerlines, construct 3D contour, and measure their geometric parameters to judge the wheelset's conditions. Deep learning can fulfill the tasks satisfyingly because it's adaptable, robust, and generalize compared with traditional methods. To train the deep learning models effectively, we need rich and high-quality wheelset datasets. So far, there are no applicable public train wheelset datasets available, which greatly hinders the research on train wheelsets. Thus we construct a publicly available Wheelset Laser Image Dataset (WLI-Set). WLI-Set consists of four sub-datasets, Original, Inpainting, Segmentation, and Centerline. The dataset contains abundant annotated multiline laser stripe images that can facilitate the research on train wheelsets effectively. [ABSTRACT FROM AUTHOR]

Automated railway security systems prevent train collisions with trackside obstructions that cause accidents in high-speed railways. Rail safety is being improved and accident rates reduced through continuous research. A rapid advancement in deep learning has promoted new possibilities for research in this field. In this work, a novel deep learning-based FOD-YOLO net is proposed for detecting the fasteners faults and objects in the railway tracks. There are two basic components in the deep learning-based YOLOv8: the backbone and the head. YOLOv8 utilizes an improved version of the CSPDarknet53 network for detecting objects on the railway track. The head of YOLOv8 consists of EfficientNet with various convolutional layers with squeeze and excitation blocks for detecting any defect in the track fasteners. These layers are liable for detecting the objectness scores, bounding boxes and class probabilities structured with fully connected layers for the objects and faults in tracks. Based on the results from the Yolo network, the alert message is sent to the loco pilot to avoid accidents using fuzzy logic. The experimental fallouts of proposed FOD-YOLO net achieve higher accuracy and yields better evaluation results with 98.14% accuracy, 98.84% precision and 95.94% recall. From the experimental results, the FOD-YOLO net improves the overall accuracy range by 5.44%, 4.72%, 0.73%, and 13.18% better than Fast RCNN, YOLOv5s-VF, YOLO-GD, and 2D-SSA + Deep network respectively. [ABSTRACT FROM AUTHOR]

Automated railway security systems prevent train collisions with trackside obstructions that cause accidents in high-speed railways. Rail safety is being improved and accident rates reduced through continuous research. A rapid advancement in deep learning has promoted new possibilities for research in this field. In this work, a novel deep learning-based FOD-YOLO net is proposed for detecting the fasteners faults and objects in the railway tracks. There are two basic components in the deep learning-based YOLOv8: the backbone and the head. YOLOv8 utilizes an improved version of the CSPDarknet53 network for detecting objects on the railway track. The head of YOLOv8 consists of EfficientNet with various convolutional layers with squeeze and excitation blocks for detecting any defect in the track fasteners. These layers are liable for detecting the objectness scores, bounding boxes and class probabilities structured with fully connected layers for the objects and faults in tracks. Based on the results from the Yolo network, the alert message is sent to the loco pilot to avoid accidents using fuzzy logic. The experimental fallouts of proposed FOD-YOLO net achieve higher accuracy and yields better evaluation results with 98.14% accuracy, 98.84% precision and 95.94% recall. From the experimental results, the FOD-YOLO net improves the overall accuracy range by 5.44%, 4.72%, 0.73%, and 13.18% better than Fast RCNN, YOLOv5s-VF, YOLO-GD, and 2D-SSA + Deep network respectively. [ABSTRACT FROM AUTHOR]

In recent years, railway safety accidents have repeatedly occurred. Any omission in the process of management or operation can easily have very serious consequences. This study aimed to examine the causes and transmission mechanisms of safety risks in railway engineering departments. First, the multi-objective particle swarm optimization algorithm was employed to determine the key risk factors, allowing for indicator screening that was in line with the requirements of practical applications. Then, Bayesian networks were used, and their structure was optimized to analyze the propagation diagnosis and probability of key risk indicators, obtaining the causal logic chain that produces accidents and, from that, the four aspects (human, machine, environment, management) of the corresponding prevention of risk recommendations. Finally, in this article, it is shown that combining the indicators and Bayesian networks can improve the accuracy of risk prediction and provide more accurate results than using existing research and, hence, it can fill the gap in research on railway safety risks in risk transmission mechanisms. [ABSTRACT FROM AUTHOR]

A train accident is defined as "Collision, Derailment, or any other event involving the operation of n-track equipment". To estimate the pattern of injuries in Railway Accidents and to estimate the Cause of death and Manner of death in Railway Accidents. The present study is conducted in Department of Forensic Medicine, Government Stanley Medical College from January 2020 to December 2020 in all the cases of Railway Accidents subjected to postmortem examination. During the study period, 61 cases were subjected for autopsy, the most common age group is 20 to 40 years. The most commonly injured organ is Brain followed by Liver and most of them died within 12 to 24 hours prior to post mortem examination. The railway fatalities were more of accidental in nature, less frequently suicidal and rarely homicidal. Internal organs commonly involved Brain followed by spinal cord, liver etc., It is also found that injuries to the upper half of the body are more common when compared to that of lower half of the body. The most common cause of death was hemorrhage shock from Multiple injuries, followed by Head injury, Thoracic injury, and Abdominal injury. [ABSTRACT FROM AUTHOR]

Workforce shortages during the COVID-19 pandemic and the recent threat of railroad strike in the United States have generated greater public awareness of how freight flow disruptions can harm society. Unlike highways, trains often do not have the ability to take alternative routes that directly connect major metropolitan areas; hence railroad networks are vulnerable to disruptions like accidents. This study developed a data mining workflow to rank commodity movements that are at the highest risk of disruption from railroad accidents and other types of regional disasters that can affect railroad operations. A key finding is that five U.S. metropolitan areas are at least five times more likely than others to experience a railroad accident. Those five areas account for more than 40% of the monetary value in alcoholic beverages, raw meats, gasoline, plastic-based products, and rubber-based products moved by rail. Hence, any disruption in those five areas can lead to widespread shortages of those commodities. The implication is that decision makers should focus risk mitigation and resiliency strategies in those five metropolitan areas and on the top commodity categories at risk. [ABSTRACT FROM AUTHOR]

The rail pad damping is a critical parameter to evaluate its vibration reduction capacity, which has essential research significance. The mesh–type high damping rail pad (MTHDRP) is proposed for the first time in this paper, and the differences in damping performance between MTHDRP and traditional rail pads (PTRP and GRP) are compared and studied. It is concluded that the MTHDRP has the highest damping coefficient than any other rail pads. The dynamics model calculation has discovered that the increase of the rail pad damping coefficient can not only effectively decrease the vibration acceleration, wheel–rail force, and derailment coefficient, but also is beneficial for both vehicle safety and stability and track structure protection. The field test shows that the acceleration of the rail, clip, iron pad, and track bed in the MTHDRP laying section is significantly lower than that in the PTRP laying section, and the rail surface roughness improves significantly after the rail pad is replaced with the MTHDRP for half a year. [ABSTRACT FROM AUTHOR]

This study investigates the thermal buckling in railway infrastructures, which can cause derailments and safety risks. The research analyses the influence of various parameters on thermal buckling, focusing on misalignment and ballast resistance. Using a finite element model, curved and tangent tracks are simulated, considering rail sections, sleeper materials, fastener stiffness and initial imperfections. Results demonstrate that ballast resistance is the most critical factor, with compacted ballast increasing buckling temperatures by up to 127%. Additionally, rail imperfections significantly impact temperature increases. A new design guide is proposed for operating conditions under different conditions. [ABSTRACT FROM AUTHOR]

Railway accidents, particularly serious derailments, can lead to catastrophic consequences. Therefore, it is essential to prevent derailment escalation to reduce the likelihood of severe derailments. Train post-derailment behaviours and containment methods play a critical role in preventing derailment escalation and providing passive safety protection and accident prevention in the event of a derailment. However, despite the increasing attention on this field from academia and industry in recent years, there is a lack of systematic exploration and summarization of emerging applications and containment methods in train post-derailment research. For this reason, this paper presents a comprehensive review of existing studies on train post-derailment behaviours, encompassing various topics such as post-derailment contact–impact models, dynamic modelling and simulation techniques, and the primary factors influencing post-derailment behaviours. Significantly, this review introduces and elucidates substitute guidance mechanisms (SGMs), which serve as railway-specific passive safety protection and accident prevention measures. The various types of SGMs are depicted, and their ongoing developments and applications are explored in depth. The review additionally points out several unresolved challenges including the adverse effects of SGMs, and proposes future research directions to advance the theoretical understanding and practical application of train post-derailment behaviours and containment methods. This review seeks to be a valuable reference for railway industry professionals in preventing catastrophic derailment consequences through post-derailment containment methods. [ABSTRACT FROM AUTHOR]

Railroads play a pivotal role in the Korean national economy, necessitating a thorough understanding of factors influencing accidents for effective mitigation strategies. Unlike prior research focused on accident frequency and severity, this study delves into the often-overlooked aspect of time delays resulting from railroad accidents. Analyzing 15 years of nationwide data (2008–2022), encompassing 3244 human-related and 3350 technical events, this research identifies key factors influencing delay likelihood and duration. Factors considered include event type, season, train type, location, operator size, person type involved, facility type, and causes. Despite an overall decrease in events, variable delay times highlight the need to comprehend specific contributing factors. To address excess zeros, the study employs a two-stage model and a zero-inflated negative binomial (ZINB) model, alongside artificial neural networks (ANNs) for non-linear pattern recognition. Human-related delays are influenced by event types, seasons, and passenger categories, exhibit nuanced impacts. Technical-related delays are influenced by incident types and facility involvement. Regarding model performance, the ANN models outperform regression-based models consistently in all cases. This study emphasizes the importance of considering both human and technical factors in predicting and understanding railroad accident delays, offering valuable insights for formulating strategies to mitigate service disruptions associated with these incidents. [ABSTRACT FROM AUTHOR]

The deadliest peacetime railway accident in French history occurred at 7:52 p.m. on December 23, 1933, at Pomponne on the outskirts of Paris. Over two hundred people died as a result of the tragedy. This article explores the responses to the disaster. These drew on long‐standing fears over the power of modern technology, not to mention its potential destructiveness as witnessed over a decade previously during the Great War. Yet understandings of the accident and its causes also spoke to anxieties over the contemporary French sense of political crisis, a crisis that the crash at Pomponne helped nurture. The Right and extreme Right came to subsume the accident into its broader campaign against the Third Republic that would end in the violence of February 6, 1934. Study of the Pomponne disaster thus sheds new light both on French anxieties about "modernity" and on the emergence of right‐wing antiparliamentarianism in 1930s France. [ABSTRACT FROM AUTHOR]

Foreign object encroachment is the presence of persons or items in the contact network and railroad track area, which can result in accidents, electrical failures, and train damage on the railroad. By keeping an eye on the lookout, seeing the intrusion of a foreign object, and braking quickly, train drivers can lessen the risks brought on by foreign object invasion. The question of how to maintain and monitor the continuous attention level of train drivers has been a hot issue, because of the monotonous environment of railroad operating makes the alertness decline during threat perception. Based on the theory of situational awareness, this study posits that training drivers in foreign object encroachment experience schemas can enhance their sustained attention levels, leading to improved hazard perception sensitivity. The hypothesis is subsequently verified in different route types and road types. We utilized a set of 24 videos depicting railroad traffic scenarios, generated by a driving simulator, to examine the sustained attention levels and hazard perception capabilities of train drivers. The participants in our study were divided into two groups: one group consisted of train drivers with recent experience of foreign object intrusion (n = 37), and the other group comprised train drivers without such recent experience (n = 35). The study's findings suggest that schemas have an impact on hazard sensitivity by influencing the extent of sustained attention. This effect is further moderated by the pseudoneglect effect and prioritization of task goals. The results provide confirmation that the situational awareness theory holds valuable insights for enhancing the training of train drivers in hazard perception and sustaining attention. And it also addresses a previously existing gap in the literature regarding the factors affecting train drivers' hazard perception. These findings can be instrumental in shaping training programs aimed at safety awareness, as well as the design and implementation of alertness monitoring systems for train drivers. [ABSTRACT FROM AUTHOR]

In the research on the new generation of intelligent rail transit system, the prevention of train collision accidents is still a focus. Virtual coupling technology controls the running speed and interval distance of two or more trains cooperatively through Train-to-Train (T2T) direct communication technology. The aim is to form a coordinated logical system and jointly complete the train control and dispatching. In this paper, a virtual coupling train formation control method based on the model predictive control (MPC) framework is proposed. From the establishment of the virtual coupling system, the construction of an MPC model and decoupling method, how to integrate virtual coupling technology into the rail transit train collision protection is studied. The safety and reliability of the system are calculated through fault tree and the method of constructing reliability function. The research shows that if virtual coupling technology is used for train collision protection, the overall safety and reliability of the train control system is improved. [ABSTRACT FROM AUTHOR]

The majority of Train Accidents (TA) in Indonesia from 2015 to 2020 were caused by infrastructure factors, namely railway tracks, bridges, and signals. To mitigate these TAs, infrastructure maintenance is required, prioritizing locations with a high risk of TA. The prioritization of these locations can be accomplished through a model that estimates TA based on infrastructure risk factors. Locations with the highest TA estimates will be prioritized for infrastructure maintenance. This model illustrates the associative relationship between KKA as the dependent variable and exposure (train frequency and track length) and infrastructure risk factors (railway tracks, bridges, and signals) as independent variables. The model is constructed using the Generalized Linear Model (GLM), considering Poisson Regression (PR), Negative Binomial (NB), Zero Inflated Poisson (ZIP), and Zero Inflated Negative Binomial (ZINB) models. TA data from the Operational Areas (OA) of Jakarta, Bandung, and Cirebon during 2015-2020 were used to build the model, with the model entity being the segment between two train stations. The selection of the best model is based on tests of dispersion value, goodness-of-fit test, and Vuong test. Modeling results indicate that the NB model is the most suitable for illustrating the associative relationship between TA and infrastructure factors in the Indonesian Railway. The variables are train frequency (trains/day), track length (km), train speed (km/h), length of curves with a radius of 500 m to ≤ 1000 m (km), number of vulnerable areas (points), length of electricity network (km), and track type (single or double). [ABSTRACT FROM AUTHOR]

The structural performance of derailment containment provision (DCP) was investigated using a grid steel frame. The DCP which was installed within the gauge of a track was capable of resisting the impact loading in the case of derailed wheels. It was also possible to control the excessive lateral movement of the derailed train. In this study, the structural performance of DCP with a post-installed anchor to a railway concrete sleeper was evaluated. For this purpose, a total of nine specimens were manufactured and static tests were conducted to investigate the structural performance. Furthermore, the shear resistance of the connecting anchor was also evaluated using grid steel frame specimens. The initial test indicated that the developed DCP for railway using grid steel frames had sufficient load-carrying capacity and performance equivalent to about 150% of the design load. The developed DCP also demonstrated sufficient load-carrying capacity up to about 140% of the designed load in combination tests that simulated conceivable boundary conditions. As analytical results, the overall DCP behavior for the specimen railway that utilized grid steel frames was appropriately tracked, and detailed information was presented. [ABSTRACT FROM AUTHOR]

This study proposes a theoretical method for evaluating the "safety against derailment" indicator of a specialized train composition for the transportation of very long rails. A composition of nine wagons, suitable for the transportation of rails with a length of 120 m in three layers, is considered. For the remaining recommended rail lengths, the number of wagons is reduced or increased, with the calculation model being modified depending on the required configuration. When the composition is in a curve with the minimum radius (R = 150 m), the rails bend, and some of them come into contact with the vertical stanchions of the wagon and cause additional lateral forces. These forces are then transferred through the wagon body, central pivot, bogie frame, and wheels and act on the wheel–rail contact points. They could potentially lead to derailment of the train composition. The goal of this study is to determine the additional lateral forces that arise because of the bent rails. For the purposes of this study, the finite element method was used. Based on the displacements of the support points of the rails (caused by the geometry of the curve), the bending line of the elastic load is determined and the forces in the supports are calculated. The resulting forces are considered when determining the derailment safety criterion. The analysis of the results shows that the wagon with fixing blocks is the most at risk of derailment. The front and intermediate wagons have criterion values very close to that of the empty wagon. This shows that the emerging horizontal elastic forces do not significantly influence the derailment process. The obtained results show that the transportation of long rails with specialized train composition can be realized on four layers. This will significantly increase the efficiency of delivering new long rails. [ABSTRACT FROM AUTHOR]

The information that automated train control (ATO) systems use to improve safety and reduce power usage is limited by on-board and wayside monitoring applications and computing power. This paper presents an augmented digital twin for railway applications that enables real-time consideration of derailment risk in train operations. The augmented digital twin implements a surrogate model with the results of a massive multibody dynamics numerical program and machine learning models to predict the instantaneous wagon derailment risk. A case study for a heavy haul iron ore wagon with three-piece bogies was conducted to test the augmented digital twin. A multibody simulation numerical program comprising 2100 simulation cases was completed. The surrogate model was developed using linear, polynomial, decision tree and ensemble forest regression models on the results of the numerical program. A longitudinal train simulator was used to calculate the speed and lateral coupler force throughout a train trip. The surrogate model effectively predicted the derailment index for empty and loaded conditions accounting for lateral coupler forces, vehicle speeds and curve radius. The proposed augmented digital twin can be further developed to accomplish other train operational benefits such as the reduction of rail damage. [ABSTRACT FROM AUTHOR]

This paper proposes a stochastic method to solve the weak correlation of lateral-vertical forces when the train crosses a bridge. This method treats the Kalker creep equation with Taylor series and uses the moment statistical method to approximate the numerical characteristics of the nonlinear terms of the equation. The variance recursive method, mean recursive method, and lateral linear creep force modulation function are proposed. Based on these methods, the approximate numerical solutions for the standard deviation of the lateral linear creep force and the correlation coefficient of the lateral-vertical forces are obtained and verified by Monte Carlo method (MCM). Under certain preconditions, the derailment factor considering the correlation of the wheel-rail forces obeys the Approximate Cauchy Distribution, which is verified by the Kolmogorov–Smirnov test. The correctness of the proposed method is validated comparing the similarity of the derailment factor extreme value cumulative distribution function curve obtained by MCM. The case of a 6-car Pioneer EMU running through a 3-span 32 m simply-supported bridge is concerned by the proposed method and it is found that there is a significant difference with different traffic intense of the railway. [ABSTRACT FROM AUTHOR]