A deep learning approach towards railway safety risk assessment

Alawad, H; Kaewunruen, S; An, M

Abstract

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.

Citation

Alawad, H., Kaewunruen, S., & An, M. (2020). A deep learning approach towards railway safety risk assessment. IEEE Access, 9(2020), 1-23. https://doi.org/10.1109/ACCESS.2020.2997946

• Journal Article Type: Article
• Acceptance Date: May 19, 2020
• Online Publication Date: May 27, 2020
• Publication Date: May 27, 2020
• Deposit Date: Jun 5, 2020
• Publicly Available Date: Jun 5, 2020
• Journal: IEEE Access
• Electronic ISSN: 2169-3536
• Publisher: Institute of Electrical and Electronics Engineers
• Volume: 9
• Issue: 2020
• Pages: 1-23
• DOI: https://doi.org/10.1109/ACCESS.2020.2997946
• Publisher URL: https://doi.org/10.1109/ACCESS.2020.2997946
• Related Public URLs: https://ieeexplore-ieee-org.salford.idm.oclc.org/xpl/RecentIssue.jsp?punumber=6287639
• Additional Information: Projects : RISEN: Rail Infrastructure Systems Engineering Network