Railway stations are essential aspects of railway systems, and they play a vital role in public daily life. Various types of AI technology have been utilised in many fields to ensure the safety of people and their assets. In this paper, we propose a novel framework that uses computer vision and pattern recognition to perform risk management in railway systems in which a convolutional neural network (CNN) is applied as a supervised machine learning model to identify risks. However, risk management in railway stations is challenging because stations feature dynamic and complex conditions. Despite extensive efforts by industry associations and researchers to reduce the number of accidents and injuries in this field, such incidents still occur. The proposed model offers a beneficial method for obtaining more accurate motion data, and it detects adverse conditions as soon as possible by capturing fall, slip and trip (FST) events in the stations that represent high-risk outcomes. The framework of the presented method is generalisable to a wide range of locations and to additional types of risks.

In railway systems, station safety is a critical aspect of the overall structure, and yet, accidents at stations still occur. It is time to learn from these errors and improve conventional methods by utilising the latest technology, such as machine learning (ML), to analyse accidents and enhance safety systems. ML has been employed in many fields, including engineering systems, and it interacts with us throughout our daily lives. Thus, we must consider the available technology in general and ML in particular in the context of safety in the railway industry. This paper explores the employment of the decision tree (DT) method in safety classification and the analysis of accidents at railway stations to predict the traits of passengers affected by accidents. The critical contribution of this study is the presentation of ML and an explanation of how this technique is applied for ensuring safety, utilizing automated processes, and gaining benefits from this powerful technology. To apply and explore this method, a case study has been selected that focuses on the fatalities caused by accidents at railway stations. An analysis of some of these fatal accidents as reported by the Rail Safety and Standards Board (RSSB) is performed and presented in this paper to provide a broader summary of the application of supervised ML for improving safety at railway stations. Finally, this research shows the vast potential of the innovative application of ML in safety analysis for the railway industry.

Railway safety is a very complicated subject, which is determined by numerous aspects including human error. Many of the railway risk assessment techniques currently used are comparatively mature tools. However, in many circumstances, the application of these tools may not give satisfactory results because the risk data are incomplete or there is a high level of uncertainty involved in the risk data. This article presents a risk assessment methodology for conducting systematic risk assessment using fuzzy reasoning approach and fuzzy analytical hierarchy decision-making process. In this method, qualitative descriptors are employed to describe frequency of occurrence, consequence severity, weight factor, and risk level. The proposed risk assessment method can evaluate both qualitative and quantitative risk data, and information associated with railway operation efficiently and effectively. The outcomes of risk assessment are represented as the risk degrees and the defined risk categories of risk levels (RLs) with a belief of percentage, which provide very useful risk information to decision makers. This will provide railway risk analysts, managers, and engineers with a method and tool to improve their safety management and set safety standards. A case study of risk assessment of shunting at Waterloo depot is used to illustrate the application of the proposed methodology.

The construction of railway turnout entails a complex geometry and multi-disciplinary engineering science, which makes it one of the most critical railway infrastructures. As such, these characteristics pose various risks in rail operation. A considerable number of derailment incidents at the turnouts are reported annually worldwide. Not only do these incidents cause operational downtime and financial loss, they also give rise to casualties and sometimes loss of life. One of the fundamental reasons for this may well be the fact that the railway industry pays little attention to the risk elements of railway turnouts. The paper provides an integrated approach of how to deal with the many different risks arising from various sources in railway turnout systems by suitably identifying the multi-disciplinary risk analysis methods for the complex systems. In order to do so, available open literatures are critically analysed by virtue of comparison, industry experiences and deductions. As a result, various qualitative- and quantitative-based risk analysis methods are proposed to fully understand a number of technical phenomena, e.g. ageing, degradation and signalling faults, in a railway turnout system.