Recent text-based research and applications in railways: A critical review and future trends

Kaitai Dong, Igor Romanov, Colin Mclellan, Ahmet F. Esen

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)
80 Downloads (Pure)


In the railway industry, a significant amount of data is stored in the textual format. The advanced development of natural language processing and text mining techniques enable automatic knowledge extraction and discovery from such documents. This paper presents a systematic review with quantitative and qualitative analyses to understand the current state of text-based research in the context of railway transport. The paper collects 107 relevant publications in the past decade and identifies different channels for researchers to obtain text data in railways and the corresponding text analysis application use-cases. Moreover, a comprehensive analysis is performed on the state-of-the-art machine learning and natural language processing methods. Four key research directions, namely multilingual NLP, digital maintenance, external data integration, and railway-centred solution pipeline, are identified from Siemens Mobility’s perspective to highlight the most prominent challenges faced in the railway industry.
Original languageEnglish
Article number105435
JournalEngineering Applications of Artificial Intelligence
Early online date17 Sept 2022
Publication statusPublished - Nov 2022


  • Critical review
  • Machine learning
  • Natural language processing
  • Railway
  • Text-based analysis

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering


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