Artificial Intelligence in Railway Transport: Taxonomy, Regulations and Applications

Nikola Besinovic, Lorenzo De Donato, Francesco Flammini, Rob M.P. Goverde, Zhiyuan Lin, Ronghui Liu, Stefano Marrone, Roberto Nardone, Tianli Tang, Valeria Vittorini

Research output: Contribution to journalArticleScientificpeer-review

14 Citations (Scopus)


Artificial Intelligence (AI) is becoming pervasive in most engineering domains, and railway transport is no exception. However, due to the plethora of different new terms and meanings associated with them, there is a risk that railway practitioners, as several other categories, will get lost in those ambiguities and fuzzy boundaries, and hence fail to catch the real opportunities and potential of machine learning, artificial vision, and big data analytics, just to name a few of the most promising approaches connected to AI. The scope of this paper is to introduce the basic concepts and possible applications of AI to railway academics and practitioners. To that aim, this paper presents a structured taxonomy to guide researchers and practitioners to understand AI techniques, research fields, disciplines, and applications, both in general terms and in close connection with railway applications such as autonomous driving, maintenance, and traffic management. The important aspects of ethics and explainability of AI in railways are also introduced. The connection between AI concepts and railway subdomains has been supported by relevant research addressing existing and planned applications in order to provide some pointers to promising directions.

Original languageEnglish
Pages (from-to)14011-14024
Number of pages14
JournalIEEE Transactions on Intelligent Transportation Systems
Issue number9
Publication statusPublished - 2021


  • Artificial intelligence
  • computer vision
  • machine learning
  • Maintenance engineering
  • predictive maintenance.
  • Rail transportation
  • Rails
  • railway transport
  • Safety
  • Software
  • Taxonomy
  • traffic management


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