Exploiting Natural Language Processing for Analysing Railway Incident Reports

In this work, we focus accident causation for the railway industry by exploiting text analysis approaches mainly Natural Language Processing (NLP). We review and analyse investigation reports of railway accidents in the UK published by the Rail Accident Investigation Branch (RAIB), aiming to unleash the presence of entities which are informative of causes and failures such as human, technical and external. We give an overview of framework based on NLP and machine learning to analyse the raw text from RAIB reports which would assist risk and incident analysis experts to study causal relationship between causes and failures towards overall safety in rail industry. The approach can also be generalized to other safety critical domains such as aviation etc.