Learning from accidents : machine learning for safety at railway stations

Alawad, H, Kaewunruen, S and An, M ORCID: https://orcid.org/0000-0002-1069-7492 2020, 'Learning from accidents : machine learning for safety at railway stations' , IEEE Access, 8 (1) , pp. 633-648.

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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.

Item Type: Article
Schools: Schools > School of the Built Environment > Centre for Urban Processes, Resilient Infrastructures & Sustainable Environments
Journal or Publication Title: IEEE Access
Publisher: IEEE
ISSN: 2169-3536
Related URLs:
Funders: European Union Horizon 2020 Research and Innovation Programme, University of Birmingham, Australian Academy of Science, Japan Society for the Promotion of Science Railway Technical Research Institute, University of Tokyo, European Commission Rail Infrastructure Systems Engineering Network (RISEN)
Depositing User: Professor Min An
Date Deposited: 08 Jan 2020 08:42
Last Modified: 16 Feb 2022 03:46
URI: https://usir.salford.ac.uk/id/eprint/56174

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