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Big Data and Natural Language Processing for Analysing Railway Safety: Analysis of Railway Incident Reports
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Author(s): Kanza Noor Syeda (Lancaster University, UK), Syed Noorulhassan Shirazi (Lancaster University, UK), Syed Asad Ali Naqvi (Lancaster University, UK), Howard J. Parkinson (Digital Rail Limited, UK)and Gary Bamford (Digital Rail Limited, UK)
Copyright: 2019
Pages: 29
Source title:
Human Performance Technology: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-5225-8356-1.ch040
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Abstract
Due to modern powerful computing and the explosion in data availability and advanced analytics, there should be opportunities to use a Big Data approach to proactively identify high risk scenarios on the railway. In this chapter, we comprehend the need for developing machine intelligence to identify heightened risk on the railway. In doing so, we have explained a potential for a new data driven approach in the railway, we then focus the rest of the chapter on Natural Language Processing (NLP) and its potential for analysing accident data. We review and analyse investigation reports of railway accidents in the UK, published by the Rail Accident Investigation Branch (RAIB), aiming to reveal the presence of entities which are informative of causes and failures such as human, technical and external. We give an overview of a framework based on NLP and machine learning to analyse the raw text from RAIB reports which would assist the risk and incident analysis experts to study causal relationship between causes and failures towards the overall safety in the rail industry.
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