Using visual analytics to make sense of railway Close Calls

Miguek Figueres-Esteban, Peter Hughes, Coen Van Gulijk

Research output: Contribution to journalArticle

Abstract

In the big data era, large and complex data sets will exceed scientists’ capacity to make sense of them in the traditional way. New approaches in data analysis, supported by computer science, will be necessary to address the problems that emerge with the rise of big data. The analysis of the Close Call database, which is a text-based database for near-miss reporting on the GB railways, provides a test case. The traditional analysis of Close Calls is time consuming and prone to differences in interpretation. This paper investigates the use of visual analytics techniques, based on network text analysis, to conduct data analysis and extract safety knowledge from 500 randomly selected Close Call records relating to worker slips, trips and falls. The results demonstrate a straightforward, yet effective, way to identify hazardous conditions without having to read each report individually. This opens up new ways to perform data analysis in safety science.
Original languageEnglish
Pages (from-to)1107-1114
Number of pages8
JournalProceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit
Volume231
Issue number10
Early online date28 Oct 2016
DOIs
Publication statusPublished - 1 Nov 2017

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Computer science
Big data

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Using visual analytics to make sense of railway Close Calls. / Figueres-Esteban, Miguek; Hughes, Peter; Van Gulijk, Coen.

In: Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, Vol. 231, No. 10, 01.11.2017, p. 1107-1114.

Research output: Contribution to journalArticle

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