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

Peter Hughes, David Shipp, Miguel Figueres Esteban, Coen Van Gulijk

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

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 categorisation 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 categorisation 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.
Original languageEnglish
Pages (from-to)11-19
Number of pages9
JournalSafety Science
Volume110
Early online date13 Mar 2018
DOIs
Publication statusPublished - Dec 2018

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Safety Management
German Federal Railways
Hazards
Safety
management
Natural Language Processing
threat
Industry
Databases
industry
language
Processing

Cite this

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abstract = "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 categorisation 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 categorisation 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.",
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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. / Hughes, Peter; Shipp, David; Figueres Esteban, Miguel; Van Gulijk, Coen.

In: Safety Science, Vol. 110, 12.2018, p. 11-19.

Research output: Contribution to journalArticle

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