Purpose: In recent years, railway systems worldwide have faced challenges such as the modernization of engineering projects, efficient management of intelligent digital railway equipment, rapid growth in passenger and freight transport demands, customized transport services and ubiquitous transport safety. The transformation toward intelligent digital transformation in railways has emerged as an effective response to the formidable challenges confronting the railway industry, thereby becoming an inevitable global trend in railway development.Design/methodology/approach: This paper, therefore, conducts a comprehensive analysis of the current state of global railway intelligent digital transformation, focusing on the characteristics and applications of intelligent digital transformation technology. It summarizes and analyzes relevant technologies and applicable scenarios in the realm of railway intelligent digital transformation, theoretically elucidating the development process of global railway intelligent digital transformation and, in practice, providing guidance and empirical examples for railway intelligence and digital transformation.Findings: Digital and intelligent technologies follow a wave-like pattern of continuous iterative evolution, progressing from the early stages, to a period of increasing attention and popularity, then to a phase of declining interest, followed by a resurgence and ultimately reaching a mature stage.Originality/value: The results offer reference and guidance to fully leverage the opportunities presented by the latest wave of the digitalization revolution, accelerate the overall upgrade of the railway industry and promote global collaborative development in railway intelligent digital transformation.

Purpose: This paper analyzes the application of digital twin technology in the field of intelligent operation and maintenance of high-speed railway infrastructure from the perspective of top-level design.Design/methodology/approach: This paper provides a comprehensive overview of the definition, connotations, characteristics and key technologies of digital twin technology. It also conducts a thorough analysis of the current state of digital twin applications, with a particular focus on the overall requirements for intelligent operation and maintenance of high-speed railway infrastructure. Using the Jinan Yellow River Bridge on the Beijing–Shanghai high-speed railway as a case study, the paper details the construction process of the twin system from the perspectives of system architecture, theoretical definition, model construction and platform design.Findings: Digital twin technology can play an important role in the whole life cycle management, fault prediction and condition monitoring in the field of high-speed rail operation and maintenance. Digital twin technology is of great significance to improve the intelligent level of high-speed railway operation and management.Originality/value: This paper systematically summarizes the main components of digital twin railway. The general framework of the digital twin bridge is given, and its application in the field of intelligent operation and maintenance is prospected.

As the informatization, digitization, and intelligence of railways continues to progress, new advantages, such as massive data resources and rich application scenarios, promote the generation of the autonomous intelligent high-speed railway system (AIHSRS). Based on an analysis of the current state of autonomous transportation system research, this article proposes an overall architecture of the AIHSRS, with its connotations and characteristics. Specifically, the fundamental platform of the AIHSRS consists of an entity model layer, data fusion layer, mechanism model layer, and application interface layer. As a result, the AIHSRS supports implementations of autonomous infrastructure, autonomous mobile equipment, autonomous safety and early warnings, autonomous transportation organization, and autonomous transportation service. Finally, the development road map of the AIHSRS is given.

Purpose: This paper aims to provide top-level design and basic platform for intelligent application in China high-speed railway.Design/methodology/approach: Based on the analysis for the future development trends of world railway, combined with the actual development needs in China high-speed railway, The definition and scientific connotation of intelligent high-speed railway (IHSR) are given at first, and then the system architecture of IHSR are outlined, including 1 basic platform, 3 business sectors, 10 business fields, and 18 innovative applications. At last, a basic platform with cloud edge integration for IHSR is designed.Findings: The rationality, feasibility and implementability of the system architecture of IHSR have been verified on and applied to the Beijing–Zhangjiakou high-speed railway, providing important support for the construction and operation of the world’s first IHSR.Originality/value: This paper systematically gives the definition and connotation of the IHSR and put forward the system architecture of IHSR for first time. It will play the most important role in the design, construction and operation of IHSR.

This paper concerns a fractional modeling and prediction method directly oriented toward an industrial time series with obvious non-Gaussian features. The hidden long-range dependence and the multifractal property are extracted to determine the fractional order. A fractional autoregressive integrated moving average model (FARIMA) is then proposed considering innovations with stable infinite variance. The existence and convergence of the model solutions are discussed in depth. Ensemble learning with an autoregressive moving average model (ARMA) is used to further improve upon accuracy and generalization. The proposed method is used to predict the energy consumption in a real cooling system, and superior prediction results are obtained.

The deformation monitoring system of railway infrastructure comes with many non-Gaussian behaviors. These behaviors are the typical fractional order characteristics which are hard to analyze by traditional methods. This paper presents a detail fractional order statistical theory to capture the key deformation feature and further achieve active warning of railway infrastructure. Initially, α-stable distribution is applied to reveal the non-Gaussian features hidden in the monitored time series. Then, long-range correlation and multifractal properties are extracted by the fractional order statistical method. After that, a novel fractional Bi-long short term memory model (F-BiLSTM) capture long-term trends characteristic and simulate stochastic process of the monitoring system. The proposed method is used to predict the deformation of railway infrastructure and obtained the superior prediction performances.