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Literature review | Open Access

Digital twin technology in railway infrastructure

Shubao Song1( )Jingyu Zhang2
Postgraduate Department, China Academy of Railway Sciences Corporation Limited, Beijing, China
Railway Science and Technology Research and Development Center, China Academy of Railway Sciences Corporation Limited, Beijing, China
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Abstract

Purpose

This review aims to provide insights for researchers and practitioners to utilize the full potential of digital twins (DT) in railway infrastructure, furthermore, promoting the future advances of DT technology in the field.

Design/methodology/approach

This paper comprehensively reviews the latest progress in the application of digital twins in railway infrastructure (Digital Twin for Railway Infrastructure (DTRI)). It systematically summarizes the application scenarios and elaborates on the core components of DTRI.

Findings

The core components of DTRI include virtual entity models, twin data, and virtual-physical connections. Emerging developments such as artificial intelligence (AI), data fusion, the Internet of Things (IoT) and advanced algorithms have been incorporated as the key technologies. The primary application scenarios focus on monitoring and maintenance, failure prediction and prevention and life cycle management.

Originality/value

DT technology has emerged as an innovative framework in the railway infrastructure sector, offering unprecedented opportunities for real-time monitoring, predictive maintenance and optimization control. The paper analyzes current and future challenges alongside emerging development directions, highlighting the transformative potential of DT technology in promoting intelligent and efficient railway infrastructure operations.

References

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Railway Sciences
Pages 505-523

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Cite this article:
Song S, Zhang J. Digital twin technology in railway infrastructure. Railway Sciences, 2026, 5(4): 505-523. https://doi.org/10.1108/RS-02-2026-0013

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Received: 27 February 2026
Revised: 07 April 2026
Accepted: 30 April 2026
Published: 01 August 2026
© Shubao Song and Jingyu Zhang. Published in Railway Sciences.

This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/