A Deep Learning Approach Towards Railway Safety Risk Assessment

Hamad Ali H Alawad, Sakdirat Kaewunruen, Min An

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)
281 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)102811-102832
Number of pages22
JournalIEEE Access
Issue number1
Early online date27 May 2020
Publication statusPublished - 11 Jun 2020


  • Artificial intelligence
  • Convolutional neural network
  • Deep learning
  • Railway station
  • Risk management

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)


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