Artificial Intelligence in Railway Transport: Taxonomy, Regulations, and Applications

TLDR

Artificial Intelligence is increasingly pervasive across engineering domains, including railway transport, yet the proliferation of ambiguous terminology risks practitioners overlooking the real opportunities offered by machine learning, computer vision, and big‑data analytics. This paper aims to introduce foundational AI concepts and their potential applications to railway academics and practitioners. To achieve this, the authors present a structured taxonomy that maps AI techniques, research fields, and disciplines to railway applications such as autonomous driving, maintenance, and traffic management, while also addressing ethics and explainability. The taxonomy is supported by relevant research linking AI concepts to railway subdomains, offering pointers to promising directions for existing and planned applications.

Abstract

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.