From Free-Text to Structured Safety Management: Introduction of A Semi-Automated Classification Method of Railway Hazard Reports to Elements on a Bow-Tie Diagram

This paper introduces a semi-automated technique for classifying text-based close call reports from the GB railway industry. The classification schema uses natural language processing techniques to classify close call reports in accordance with the threat pathways shown on bow-tie diagrams. The method enables categorization of a very large number of unstructured text documents where safety-related information has not previously been extracted due to the infeasibility of analysis by human readers. The results demonstrate mixed accuracy in the categorization of close calls, with the highest accuracy being for the threat pathways that are more frequently reported. This work paves the way to machine-assisted analysis of text-based safety and risk databases, and provides a step forward in the introduction of data analytics in the safety and risk domain. Others working in this area have speculated that approaches such as this could be mandatory for safety management in the future.

• Record URL:
  https://doi.org/10.1016/j.ssci.2018.03.011
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0925753517310160
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/09257535
• Supplemental Notes:
  • © 2018 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Hughes, Peter
  • 0000-0002-1063-4897
  • Shipp, David
  • 0000-0002-8495-5597
  • Figueres-Esteban, Miguel
  • van Gulijk, Coen
• Publication Date: 2018-12

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 11-19
• Serial:
  • Safety Science
  • Volume: 110
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0925-7535
  • Serial URL: http://www.sciencedirect.com/science/journal/09257535

Subject/Index Terms

• TRT Terms: Crash analysis; Data analysis; Databases; Near crashes; Railroad safety; Risk assessment; Safety management
• Identifier Terms: GB Railway
• Uncontrolled Terms: Natural language processing (Computer science)
• Subject Areas: Data and Information Technology; Railroads; Safety and Human Factors;

Filing Info

• Accession Number: 01684473
• Record Type: Publication
• Files: TRIS
• Created Date: Oct 29 2018 9:34AM