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At the US passenger stations, train operations approaching terminating tracks rely on the engineer’s compliant behavior to safely stop before the end of the tracks. Noncompliance actions from the disengaged or inattentive engineers would result in hazards to train passengers, train crewmembers and bystanders at passenger stations. Over the past decade, a series of end-of-track collisions occurred at passenger stations with substantial property damage and casualties. This study’s developed systemic model and discussions present policymakers, railway practitioners and academic researchers with a flexible approach for qualitatively assessing railroad safety.

To achieve a system-based, micro-level analysis of end-of-track accidents and eventually promote the safety level of passenger stations, the systems-theoretic accident modeling and processes (STAMP), as a practical systematic accident model widely used in the complex systems, is developed in view of environmental factors, human errors, organizational factors and mechanical failures in this complex socio-technical system.

The developed STAMP accident model and analytical results qualitatively provide an explicit understanding of the system hazards, constraints and hierarchical control structure of train operations on terminating tracks in the US passenger stations. Furthermore, the safety recommendations and practical options related to obstructive sleep apnea screening, positive train control-based collision avoidance mechanisms, robust system safety program plans and bumping posts are proposed and evaluated using the STAMP approach.

The findings from STAMP-based analysis can serve as valid references for policymakers, government accident investigators, railway practitioners and academic researchers. Ultimately, they can contribute to establishing effective emergent measures for train operations at passenger stations and promote the level of safety necessary to protect the public. The STAMP approach could be adapted to analyze various other rail safety systems that aim to ultimately improve the safety level of railroad systems.

The operation safety of the high-speed railway has been widely concerned. Due to the joint influence of the environment, equipment, personnel and other factors, accidents are inevitable in the operation process. However, few studies focused on identifying contributing factors affecting the severity of high-speed railway accidents because of the difficulty in obtaining field data. This study aims to investigate the impact factors affecting the severity of the general high-speed railway.

A total of 14 potential factors were examined from 475 data. The severity level is categorized into four levels by delay time and the number of subsequent trains that are affected by the accident. The partial proportional odds model was constructed to relax the constraint of the parallel line assumption.

The results show that 10 factors are found to significantly affect accident severity. Moreover, the factors including automation train protection (ATP) system fault, platform screen door and train door fault, traction converter fault and railway clearance intrusion by objects have an effect on reducing the severity level. On the contrary, the accidents caused by objects hanging on the catenary, pantograph fault, passenger misconducting or sudden illness, personnel intrusion of railway clearance, driving on heavy rain or snow and train collision against objects tend to be more severe.

The research results are very useful for mitigating the consequences of high-speed rail accidents.

As drones are rapidly transforming tasks such as mapping and surveying, safety inspection and progress monitoring, human operators continue to play a critical role in ensuring safe drone missions in compliance with safety regulations and standard operating procedures. Research shows that operator's stress and fatigue are leading causes of drone accidents. Building upon the authors’ past work, this study presents a systematic approach to predicting impending drone accidents using data that capture the drone operator's physiological state preceding the accident.

The authors collect physiological data from 25 participants in real-world and virtual reality flight experiments to design a feedforward neural network (FNN) with back propagation. Four time series signals, namely electrodermal activity (EDA), skin temperature (ST), electrocardiogram (ECG) and heart rate (HR), are selected, filtered for noise and used to extract 92 time- and frequency-domain features. The FNN is trained with data from a window of length t = 3…8 s to predict accidents in the next p = 3…8 s.

Analysis of model performance in all 36 combinations of analysis window (t) and prediction horizon (p) combinations reveals that the FNN trained with 8 s of physiological signal (i.e. t = 8) to predict drone accidents in the next 6 s (i.e. p = 6) achieved the highest F1-score of 0.81 and AP of 0.71 after feature selection and data balancing.

The safety and integrity of collaborative human–machine systems (e.g. remotely operated drones) rely on not only the attributes of the human operator or the machinery but also how one perceives the other and adopts to the evolving nature of the operational environment. This study is a first systematic attempt at objective prediction of potential drone accident events from operator's physiological data in (near-) real time. Findings will lay the foundation for creating automated intervention systems for drone operations, ultimately leading to safer jobsites.

The aviation industry recognised the significance of human error in accidents in the 1970s, and has been instrumental in the development of special training, designed to reduce error and increase the effectiveness of flight crews. These crew resource management (CRM) programmes focus on “non‐technical skills” critical for enhanced operational performance, such as leadership, situation awareness, decision making, team work and communication. More recently CRM has been adopted by other “high reliability” team environments including anaesthesiology, air traffic control, the Merchant Navy, the nuclear power industry, aviation maintenance, and the offshore oil industry. This review paper describes the basic principles of crew resource management, then outlines recent developments in aviation and other high reliability work environments.

The study aims to examine the stock price performance of publicly owned railroad companies following severe railroad accidents that resulted in the loss of human lives and/or hazardous material spills. The focus is on legal liability considerations as one of the primary factors that drives a firm's abnormal performance following a given accident.

This paper employs a sample of 97 railroad accidents that occurred between January 1967 and December 2006 and involved equipment (tracks and/or locomotives) owned by publicly traded US and Canadian railroad companies. The stock price reaction of the affected firms is examined following these disasters and a series of univariate and multivariate tests is used to investigate whether differences in abnormal returns following a given accident can be related to various factors that characterize the affected firm or the accident it was involved in.

The results suggest that legal liability considerations are one of the primary factors that determine a company's stock price reaction following a railroad disaster. Specifically, it is observed that firms that are likely to be sued in connection with an accident tend to incur larger stock price losses. On the other hand, it is found that firms that are protected through indemnification agreements suffer only insignificant price declines, even if initial accident reports hold them responsible for causing the accident.

The paper extends the prior literature on the stock market's reaction to firm‐specific catastrophic events. While there are a number of studies that examine the financial consequences of aviation disasters, there is to the authors' knowledge only one prior study that performs a similar analysis for railroad accidents.

The purpose of this paper is to focus on the corporate response of Chinese railway companies after the deadly Wenzhou train accident in China which happened on July 23, 2011. Few studies on corporate social responsibility (CSR) in developing countries have looked into whether the information disclosed by companies is satisfactory with sufficient response after a major incident has happened.

Five companies with the largest market value in the Chinese railway industry involved in the production of trains and railway systems connected to the “7.23” incident were taken as the observations in this study. Information published by the companies and the media related to the accident, including CSR and sustainability reports, company Web sites, news and press releases and Internet postings, were investigated in detail in a qualitative manner.

The findings show that disclosure of information related to the “7.23” incident was very low or almost inexistent in the observed companies. For those that claimed that they had followed CSR reporting standards and guidelines, the disclosed information appeared to be insufficient to reveal practical information and fulfill stakeholders’ requirements. The study also sheds light on the corporate reporting behaviors of Chinese state-owned enterprises by applying legitimacy, stakeholder and institutional theories to the unique social and political environment in the country.

This paper critically reveals the poor corporate response after the “7.23” incident in Chinese railway companies. The case serves as an example for the companies to ponder on what improvements are called for in terms of social reporting and relevant corporate actions after a major accident. Also, the study contributes to the CSR disclosure literature concerning developing countries by examining the case of China and the little studied railway industry run by the state.

The manufacturing industry perceives government standards as an attempt to unnecessarily increase production cost. This may be due to lack of acceptable models for demonstrating the associated benefits to industry. It was the goal of this study to develop a simulation model for predicting the performance of a manufacturing safety programme (SP).

The principles of system‐dynamics were applied to identify the relevant safety‐related components and their relationships. A simulation model for evaluating periodic performance of a manufacturing SP was then developed. A set a dynamic equations for predicting factory accidents or preventions and the monetary saving were the performance measures. Two set of factory data: non‐SP (1979) and SP (1991‐2004) were collected from a bottling company. The parameters of the model were estimated using the first set while it was validated with the second and associated monetary saving computed.

Solutions to factory accidents or preventions yielded exponential functions. The means and standard deviations of the predicted and actual accidents were 32 and 5.66; and 30 and 7.46, respectively. The corresponding values for predicted and actual preventions were 55 and 10.47; and 59 and 7.45, respectively. There were no significant differences between the predicted and actual for the accidents and preventions, respectively, at 5 per cent level. The predicted SP saving per annum was 6.96 millions.

The model is a useful tool for setting profitable manufacturing safety standards and effective SP management.

The research covers the period April 1983 to March 1986 and looks at 5,002 cases of injury (including fatalities) amongst YOP and YTS participants.

Effective construction safety training has been considered to play a significant role in reducing the incidence of accidents. However, the current safety training methods pay less attention to the relationship between workers' personalized characteristics and their learning needs, which results in workers' low learning participation and poor training effect. The purpose of this paper is to improve the participation and effect of safety training for construction workers with a persona-based approach.

This paper presents a persona-based approach to safety tag generation and training material recommendation. By extracting the demographic characteristics and behavior patterns tags of construction workers, a neural network algorithm is introduced to calculate the learning needs tags of workers, and the collaborative filtering recommendation method is integrated to enrich the innovation of recommendation results. Offline experiments and online experiments are designed to verify the rationality of the proposed method.

The results show that the learning needs of workers are closely related to their background. The proposed method can effectively improve workers' interest in materials and the training effect compared with conventional safety training methods. The research provides a theoretical and practical reference for promoting active safety management and achieving worker-centered safety management.

First, a persona-based approach is introduced to establish a novel framework for solving the problem of personalized construction safety management. Second, an artificial intelligence algorithm is used to automatically extract the learning needs tag values and design a hybrid recommendation method for construction workers' personalized safety training. The collaborative filtering method is integrated to enrich the innovation of recommendation results.