Revolutionizing railway systems: A systematic review of digital twin technologies

Abstract

Digital Twin (DT) technology is revolutionizing the railway sector by providing a virtual replica of physical systems, enabling real-time monitoring, predictive maintenance, and enhanced decision-making. This systematic literature review examines the status, enabling technologies, case studies, and frameworks for DT applications in railway systems with 91 selected papers from Scopus, Web of Science, IEEE, and the Snowballing Technique. The review focuses on four primary subsystems: tracks, civil structures, vehicles, and overhead contact line structures. Key findings reveal that DT has successfully optimized maintenance strategies, improved operational efficiency, and enhanced system safety. Internet of Things (IoT) devices, Artificial Intelligence (AI), machine learning, and cloud computing are critical in implementing DT models. However, challenges like data integration, high implementation costs, and cybersecurity risks remain, necessitating the discussed implications. Future research should focus on improving data interoperability, reducing costs through scalable cloud-based solutions, and addressing cybersecurity vulnerabilities. DT technology has the potential to revolutionize railway infrastructure management, ensuring greater efficiency, safety, and sustainability.

Keywords

Digital twin / Railway systems / Transportation / Machine learning / Industry 4.0

CRediT authorship contribution statement

Emmanuel Anu Thompson: Writing – review & editing, Writing – original draft, Visualization, Methodology, Formal analysis, Data curation, Conceptualization. Pan Lu: Writing – review & editing, Writing – original draft, Supervision, Resources, Project administration. Philip Kofi Alimo: Writing – review & editing, Writing – original draft, Visualization, Validation, Formal analysis, Conceptualization. Herman Benjamin Atuobi: Writing – original draft, Resources, Project administration, Methodology, Formal analysis, Conceptualization. Evans Tetteh Akoto: Writing – review & editing, Writing – original draft, Visualization, Software, Formal analysis, Data curation. Cephas Kenneth Abbew: Writing – original draft, Visualization, Software, Methodology, Formal analysis.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgment

The work presented in this paper conducted with support from the North Dakota State University and the Center for Multimodal Mobility in Urban, Rural, and Tribal Areas (CMMM), a TIER one University Transportation Center funded by the U.S. Department of Transportation. The contents of this paper reflect the views of the authors, who are responsible for the facts and accuracy of the information presented.

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