Abstract
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 utilizing 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.
Highlights
The growth in technology has expanded into a vast variety of systems, methodologies, and tools for developing policies in society
Various machine learning (ML) methods can be applied to safety tasks in the railway industry
We have demonstrated the applicability of decision tree (DT) to this safety task for railway stations
Summary
The growth in technology has expanded into a vast variety of systems, methodologies, and tools for developing policies in society. There is a demand to implement artificial intelligence (AI) to interpret the 21st century’s ever-growing difficulties in nearly every industry and to focus on promoting intelligent systems interactively. Many of these aspects call for a move towards greater intelligence and a greater sharing of data [1]. Industrial organisations are racing into the AI domain, which is being used to improve safety, analytics and accessibility, and real-time intelligent scheduling, thereby increasing productivity. In self-driving vehicles, for instance, passive safety systems
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