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Learning from text-based close call data

Hughes, Peter, Figueres-Esteban, Miguel and Van Gulijk, Coen (2015) Learning from text-based close call data. In: Safety and Reliability of Complex Engineered Systems. ESREL (2015). CRC Press: Taylor & Francis Group, Zürich, Switzerland, pp. 31-38. ISBN 978-1-138-02879-1

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Abstract

Moving away from standard approaches of safety risk analysis to new approaches that incorporate big data analytics brings with it many opportunities to include new sources of data. These data sources could be the numeric data sources that are used for traditional safety analyses, but could also include text-based sources, such as accident reports, or even social media data feeds. This paper describes an automatic text mining approach to obtain information from close call events (accident “near misses”) that can be used for safety management decision-making. The results from this work have shown how automated text mining can be used to extract information that can be used to inform safety decision-making. Further research in this area intends to look at how the techniques that have been proven to date can be improved with the use of machine-learning techniques.

Item Type: Book Chapter
Subjects: T Technology > TF Railroad engineering and operation
Schools: School of Computing and Engineering
School of Computing and Engineering > Institute of Railway Research
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Depositing User: Peter Hughes
Date Deposited: 15 Oct 2015 09:57
Last Modified: 07 Dec 2016 07:08
URI: http://eprints.hud.ac.uk/id/eprint/26075

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