Visual analytics for text-based railway incident reports

Miguel Figueres-Esteban, Peter Hughes, Coen van Gulijk

Research output: Contribution to journalArticlepeer-review

26 Citations (Scopus)


The GB railways collect about 150,000 text-based records each year on potentially dangerous events and the numbers are on the increase in the Close Call System. The huge volume of text requires considerable human effort to its interpretation. This work focuses on visual text analysis techniques of Close Call records to extract safety lessons more quickly and efficiently. This paper treats basic steps for visual text analysis based on an evaluation test using a pre-constructed test set of 150 Close Call records for "Trespass", "Slip/Trip hazards on site" and "Level crossing". The results demonstrate that visual text analysis can be used to identify the risks in a small-scale test set but differences in language use by different cohorts of people interferes with straightforward risk identification in larger sets. This work paves the way to machine-assisted interpretation of text-based safety records which can speed up risk identification in a large corpus of text. It also demonstrates how new possibilities open up to develop interactive visualisations tools that allow data analysts to use text analysis techniques for risk analysis.

Original languageEnglish
Pages (from-to)72-76
Number of pages5
JournalSafety Science
Early online date11 Jun 2016
Publication statusPublished - 1 Nov 2016


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