Railway accident causation analysis: Current approaches, challenges and potential solutions

Railway accident causation analysis is fundamental to understanding the nature of railway safety. Although a considerable number of prior studies have investigated this context, many of them suffer from the need to deal with a large amount of textual data given that most railway safety-related information is recorded and stored in the form of text. To gain a better understanding of the limitations imposed by overreliance on textual analysis, a scoping review of the academic literature on how railway accident causation analysis is addressed has been conducted. The results confirm the high frequency of using textual data, a single case study, and in-depth analysis frameworks. While the value of exploring causational factors is clear, the high level of human intervention and the labor-intensive analysis processes based on a large volume of textual data hinder researchers from understanding the complex nature of the rail safety system. Recently, growing attention has been given to the application of Natural Language Processing (NLP) to aid the practice of analyzing a large corpus of textual data, but only limited studies to date in railway safety use such techniques and none address railway accident causation analysis. To fill this gap, a supplementary review is conducted to identify opportunities, challenges, boundaries and limitations in the application of NLP approaches to railway accident causation analysis. Findings indicate that novel techniques using off-the-shelf tools have strong potential to overcome the limitations of overreliance on manual analysis in practice and theory, but the absence of shared railway safety-related benchmark corpora restricts implementation. This study sheds light on a new approach to railway accident causation analysis and clarifies future applicable utilizations for further research.


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  • Accession Number: 01882340
  • Record Type: Publication
  • Files: TRIS
  • Created Date: May 22 2023 1:28PM