Digitalization as an Enabler in Railway Maintenance: A Review from “The International Union of Railways Asset Management Framework” Perspective

Abstract

This paper conducts a comprehensive review of the role of digitalization in railway maintenance management, particularly through the lens of the International Union of Railways (UIC) asset management framework. The study aims to assess how digital technologies such as Big Data, the Internet of Things (IoT), and Artificial Intelligence (AI) serve as enablers for more efficient and effective maintenance practices in the railway sector. By employing a bibliometric analysis, we identify the current trends, challenges, and gaps in the literature concerning the integration of digital tools into maintenance management frameworks. The findings reveal that while digitalization offers significant potential for optimizing maintenance operations and enhancing decision-making processes, its successful implementation requires a more integrated approach that aligns with the strategic goals of railway organizations. This paper also discusses future research directions, emphasizing the need for a global framework incorporating technological advancements and organizational change to achieve sustainable and safe railway operations.

1. Introduction

Maintenance of railway infrastructure is a complex task that involves planning, cost control, safety, reliability, environmental impact, and quality of service [1]. Over the years, numerous sectorized solutions have been proposed to address specific problems within railway maintenance management. However, these solutions often lack an integrated approach that is applicable to all stages of the maintenance management process and across the railway infrastructure [2]. This fragmentation has revealed a critical need to improve existing studies from a more comprehensive perspective, with the aim of optimizing not only maintenance management but also the overall performance of the rail business.

In this context, digitalization and the use of data have emerged as key factors in transforming railway maintenance management. Digitalization allows for a more effective integration of the different aspects of management individually and facilitates the implementation of maintenance management frameworks from a comprehensive approach, allowing a prioritized allocation of resources by optimizing the use of the railway infrastructure as a whole [3,4,5,6]. In the context of railway maintenance, digitalization is defined as the process of integrating digital technologies to optimize asset management, failure prediction, and the overall operational efficiency of the railway system. This process encompasses both established digital practices, such as the use of monitoring sensors, condition-based maintenance data analytics, and Geographic Information Systems (GIS), and emerging technologies, including the Internet of Things (IoT), Artificial Intelligence (AI), and digital twins, which enable advanced simulations and real-time analysis. Differentiating between these two categories is crucial for assessing the impact of digitalization on decision-making within the UIC asset management framework, providing a structured perspective on the level of digital maturity in the railway sector. This growing interest in digitalization raises a fundamental question: Is digitalization an effective enabling factor for implementing railway maintenance management models, and what is the depth of research in each dimension? The framework proposed by the International Union of Railways (UIC) is considered a reference for these dimensions.

To answer this question, this study aims to review the existing literature and compare it with a sectoral reference framework, identifying the potential in each category and the opportunities for study in underdeveloped areas. As this topic is explored, the need to incorporate a global context into each study to guide future research is highlighted. This analysis is based on the asset management framework provided by the UIC, a key regulatory reference in the railway industry, which facilitates decision-making on critical aspects of railway infrastructure maintenance management. We also consider the approach provided by the current reference frameworks for digital maintenance management [7], which uses digitalization as an enabler of the tools and techniques applied to maintenance management. Finally, academic and professional research are explored and, in this case, reflected in the comprehensive review of the S2R projects [8].

In this context, it should be noted that the UIC (International Union of Railways) framework is widely recognized for its systematic and comprehensive approach to railway asset management, especially in its alignment with standard ISO 55001 [9], which is a global standard adopted by multiple asset-intensive sectors. This framework provides practical guidelines for the implementation of asset management and ensures that key decisions related to the operation, maintenance, renovation, and improvement of railway infrastructure are justified, implemented, and verified consistently and efficiently. This makes it an indispensable tool for railway organizations looking to maximize the value of their assets throughout their lifecycle [10].

In the analysis developed at the beginning of Section 2, which was constructed from an exhaustive literature review, the potential impacts of digitalization are synthesized and grouped into different management categories according to the UIC framework, as shown in

Table 1. Potential impact in UIC high-level category vs. Research Railway Aspects and digitalization field.

Paper	Principal Digitalization Field	Research Railway Aspect	Potential Impact in UIC High-Level Categories
			Operational Management	Risk Management	Strategic Planning:	Performance Evaluation	Organizational Change
[11]	Artificial Intelligence (AI) Automation and Robotics	SRTIT (1) SEARM (2) RRMO (3) IPMTRI (4) APDRT (5)	High	Very High	High	Moderate	Moderate
[12]	Big Data Data Analysis and Business Intelligence (BI) Artificial Intelligence (AI)	SRTIT (1) SEARM (2) RRMO (3) IPMTRI (4) APDRT (5)	Very High	High	Very High	High	Moderate
[13]	Big Data Data Analysis and BI	RRMO (3)	Very High	High	Very High	High	Moderate
[14]	Cybersecurity Internet of Things (IoT) Data Analysis and BI Automation and Robotics	SRTIT (1) RRMO (3) IPMTRI (4)	Very High	Very High	High	High	Very High
[15]	Data Analysis and BI Automation and Robotics Internet of Things (IoT)	SEARM (1) RRMO (3) RRSDO (5)	High	Moderate	Moderate	Very High	Under
[16]	Data Analysis and BI Automation and Robotics Big Data Artificial Intelligence (AI)	SRTIT (1) SEARM (2) RRMO (3)	High	Moderate	Very High	Very High	Moderate
Our Paper	Digitalization in all fields as an enabling and integrating factor	SRTIT (1) SEARM (2) RRMO (3) IPMTRI (4) RRSDO (5) APDRT (6)	Our study considers the UIC model as a basis and presents a discussion of research opportunities in under-studied and high-potential fields, adding a global perspective that highlights digitalization as an enabling medium.

. This analysis selects representative works that exemplify the current trends in railway digitalization and introduces the key aspects of digitalization that will be extensively developed in Section 3, also proposing a structure to assess its impact on railway maintenance management. In Section 2.2, a broad and comprehensive review of the literature is presented to provide an overview of existing studies on railway maintenance management and digitalization, ranging from management models and emerging technologies to industry regulations and other relevant aspects. Subsequently, in Section 2.3, the reference framework proposed by the UIC is evaluated, opening the discussion to a framework enriched by digitalization as a catalytic factor in the management of railway maintenance. In Section 4, a critical analysis of the findings is offered, delving into their practical and theoretical implications, and the study’s limitations are identified, proposing directions for future research. We conclude in Section 5 by highlighting the main findings and contributions of the study, underlining the importance of digitalization in the revised framework and suggesting recommendations for its effective implementation. This section highlights the relevance of our research to guide future academic explorations and its potential impact on improving the efficiency and safety of rail systems.

2. State-of-the-Art Analysis

This section describes and systematically analyzes the existing literature and regulations of the sector. Supported by the practical experience of the authors and the professional research of the sector [17], we seek to identify the gaps, trends, and, above all, the knowledge gaps that justify this research. For this purpose, a study method described in Section 2.1 is used, which is broken down into two levels of depth: a first level developed in Section 2.2 that is oriented to cover a large volume of articles and study time; and a second level developed in Section 2.3 that focuses on achieving a more detailed and in-depth analysis by reviewing the Research Railway Aspect (RRA) of maintenance management in railroads where the study of the regulations of the railway sector is incorporated. As a result, we seek to establish the sector’s references that allow us to determine the research opportunities regarding the potential that they contribute to the management model.

The study begins with the analysis of the state of the art in the digital management of railway maintenance. Using the methods of bibliometric analysis applied by Gomez-Luna [18]. This study identifies and classifies the key aspects of interest to the scientific community, studied in six Research Railway Aspects (RRAs) (Section 2.2.2), which are complemented by the professional research developed in European S2R projects and guilds, from which arise approaches for the railway maintenance management, such as those contributed by the UIC in their UIC Asset Management Working Group [10]. In this sense, the research makes a journey from approaches such as the one [19] which reveals valuable insights into railway asset management. Proposals such as that of Ref. [20] advance data integration and digitalization. Works such as that of Ref. [21] demonstrate how the integration of data and machine learning technologies can improve maintenance management. Studies such as Ref. [22] identify the need for more efficient models that can predict both wear and fatigue in rolling stock. On the other hand, proposals such as that of Ref. [23] improve maintenance management; greater integration of digital tools is required for more accurate management. Finally, regarding digitalization, more recent works such as that of Refs. [24,25] show how the integration of data and machine learning technologies can improve maintenance planning. However, these studies also note that how to cross-cuttingly integrate these advances into existing management systems has yet to be addressed [11]. Considering the professional experience applied in the sector, a reference base of the research has been the regulations and standards of the sector, highlighting the importance of ISO 55000 [10] and the standards EN 50126 [26], EN 50128 [27], and EN 50129 [28] for asset management and safe development of railway systems. These standards, in contrast to advances in scientific research, provide general and cross-cutting guidance, but lack (like all standards) a practical approach to their implementation in railway maintenance management. In line with what is proposed by the UIC, transport management itself must also be considered as pointed out by Ref. [29], and the resilience of a railway network is understood as the combination of infrastructure maintenance and transport management. Analyzing these perspectives, the reference framework proposed by the UIC is confirmed as a basis, taking digitalization as a key enabler [30]. Even more so, considering that railway systems are highly complex networks made up of a system of systems [31].

To illustrate these relationships in a consolidated manner, a comparative table is presented (Table 1). Using the same schema as Ref. [11] in their IA literature review, in this introduction approach, it compiles six review papers related to our field of study “The Digitalization”, reviews the potential impact in UIC categories, and makes a comparison with our research. To understand the potential importance of digitalization in all fields of railway maintenance management, five high-level categories have been collected from the UIC framework [32]. Operational management includes the daily operation of the railroad, route operational planning, work execution, and network operation. These activities ensure that the rail service operates effectively and efficiently. Risk management encompasses the identification, analysis, and mitigation of risks associated with rail infrastructure and operations, which are critical for informed decision-making and to minimize disruptions and ensure safety. Strategic planning involves the definition of organizational objectives and asset strategy as well as the development of strategic asset management plans and infrastructure asset plans. This planning guides long-term direction and resource allocation. Performance evaluation focuses on monitoring and analyzing the performance of rail infrastructure and services, which may include measuring effectiveness, efficiency, and alignment with strategic objectives. Organizational change encompasses initiatives to improve or change structures, processes, or cultures within the organization, including change management in response to the implementation of new systems or technologies.

According to the literature review over the last few years and with the purpose of characterizing each of the studies, 10 key fields of digitalization in the railway field are presented, which are further illustrated in the following detail by citing some authors who refer to each of the fields, respectively, in their research:

• Digital Transformation [33]: Integration of digital technologies in all areas of the railroad, which fundamentally changes how they operate and delivers value to customers.
• Cloud Computing [34]: Use of the cloud to improve the efficiency of railway operations, such as timetable management, train maintenance, and route optimization.
• Big Data [35]: Analysis of large volumes of data from sensors on trains and tracks, which helps in improving safety, predictive maintenance, and operational efficiency.
• Artificial Intelligence [36]: Implementation of AI for route optimization, predictive maintenance of infrastructure and trains, and to improve customer experience with automated customer service systems.
• Internet of Things (IoT) [37]: IoT sensors on trains and tracks monitoring conditions in real time, aiding in preventive maintenance and safety.
• Cybersecurity [38]: Protection of railway systems against cyber-attacks, which is especially important due to the increasing use of connected and smart technologies.
• Blockchain [39]: Applied to improve transparency and efficiency in freight logistics and ticketing.
• Automation and Robotics [40]: Train automation (driverless trains) as well as the use of robots for maintenance and repair tasks.
• Virtual and Augmented Reality [41]: VR/AR for staff training, safety, maintenance simulations, and customer experience (e.g., in-ride entertainment).
• Data Analytics and Business Intelligence [42]: Intensive use of data analytics to optimize operations, from train schedules to pricing and customer service strategies.

Several reviews have been carried out in recent years, and six have been specifically chosen to introduce our research, which give an account, respectively, of the field of digitalization studied (reflected in the “Principal Digitalization Field” column) and its relationship with the management categories recognized in the UIC framework (Table 1). This shows the potential impact of these studies on the different categories of the management model described in the UIC (reflected in the “Potential Impact in UIC Hi level category” columns). Finally, the ARRs covered in each study have been assigned, providing a global and initial perspective of the existing level of coverage of them. This initial framework provides guidance on the vision or principle of our study, on which the basis for future research will be laid: a digitalization solution in railway management should not be an end in itself but an enabling catalyst that considers all aspects of management models in the sector to effectively contribute to the outcome of the system as a whole.

The evaluation method for determining the impact of articles in their categories involves an analysis based on the following eight criteria:

• Thematic Relevance: The direct connection between the paper’s topic and the specific UIC category is examined, including how it addresses the processes, challenges, or goals of the category.
• Practical Applicability: Evaluate whether the paper’s technologies or methodologies directly apply to the category or require significant adaptations.
• Innovation and Technological Advances: Whether the paper introduces novel technologies or approaches and their degree of advancement over current practices in the category.
• Impact on Decision-Making: How the paper’s findings may influence strategic and operational decisions within the category.
• Evidence and Case Studies: The presence and relevance of empirical evidence, such as case studies or data analysis, to support the paper’s assertions are reviewed.
• Generality vs. Specificity: A distinction is made between findings applicable in multiple contexts and those specific to a particular situation.
• Contributions to Knowledge: Evaluates how the paper contributes to existing knowledge in the category by filling gaps, refuting prior beliefs, or deepening understanding.
• Future Perspectives and Trends: Discussion of future research or emerging developments and their potential long-term impact on the category are considered.
• Interaction with Other Factors or Categories: The interaction of the paper’s technology or methodology with other relevant factors in the rail industry and its interdependence with other categories are examined. These criteria allow a balanced and detailed evaluation of the potential impact of each paper. The evaluations are classified into four levels:
• Very High, High, Moderate, and Low, depending on the degree of alignment of the paper with the objectives and needs of the category, the strength of the evidence presented, the innovation, the practical relevance, and the impact on the development of the category. While Very High and High levels indicate a significant and direct influence of the paper on the category, Moderate and Low levels reflect a minor impact.

It is grouped according to six Research Railway Aspects (RRAs), which emerges from the bibliometric analysis developed using the VosViewer v1.6.19 tool, which we will explain in detail in Section 2.3 of the article:

• SRTIT (1): Sustainable Railway Transport and Infrastructure Technology;
• SEARM (2): Structural Engineering and Advanced Railway Maintenance;
• RMMO (3): Railway Maintenance Management and Optimization;
• IPMTRI (4): Inspection and Predictive Maintenance Technologies for Railway Infrastructure;
• RRSDO (5): Railway Rolling Stock Design and Operation;
• APDRT (6): Analysis and Prediction of Degradation on Railroad Track.

2.1. Criteria and Approaches: Bibliometric and Scientometric Reviews

To provide a complete overview of the state of the art of maintenance management research in railway systems, we initially follow the methodology proposed by Refs. [43,44]. The authors, respectively, use bibliometric and scientometric analyses to understand and classify the fields of study, a principle on which our literature review is based as a basis for understanding the aspects (RRAs) affecting railway maintenance management.

Figure 1 shows a graphic description of the study process. First, three recognized sources of scientific information were reviewed, choosing WoS as the primary source, leaving Scopus and ScienceDirect as complementary sources of information. The first step was to perform a search according to the following path: (maintenance AND framework AND railway) OR (maintenance AND management AND railway) OR (maintenance AND model AND railway) OR (maintenance AND management AND digitalization). Then, we defined the RRAs, and the bibliometric analysis tool VOSviewer [45] was used as a tool for research and graphic representation where we obtain the clusters of interest or Research Railway Aspects (RRAs). Finally, as can be viewed in the last two steps of the process in Figure 1, an exhaustive process of academic and regulatory review was developed to converge on a comparison that will allow for establishing gaps based on an agenda for future research.

2.2. Reviews and Analyses

2.2.1. Criteria and Approaches:

The first analysis performed accounts for the scientific interest. For this purpose, consultations were made both in WoS (see Figure 2) and SCOPUS (see Figure 3), from which it can be observed that the number of publications per year has increased 10 times since 2000, where its maximum intensity was achieved in the last 5 years with an average growth of over 30% (see

Table 2. Decade media number of papers published from WoS and Scopus sources.

Annual Media Papers	2000–2009	2010–2019	2019–2024	Total to 2024
WoS	113	261	645	6946
Scopus	58	226	486	5247

). This can be easily explained by two factors: the first is that the interest in rail transport has increased in recent decades [46], and the second is that the inherently technological development of its specialties (such as railway signaling, for example) is naturally accompanied by a component of digitalization [47], which has so far been intensely exploited but in its multiple niches separately, as we will see in the next section. This trend is a first validation for the motivation of the present study as it clearly manifests that the subjects are current and of interest in the scientific and business communities.

2.2.2. Co-Word Analysis

To deepen the analysis of the bibliographic material, this section develops a graphical mapping of the data using the visualization software VOS v1.6.19 [45]. To illustrate the characteristics of the publications through map analysis, we will analyze the most frequent keywords of the journals. To do so, we examine the co-occurrence of author keywords in order to see those that appear more frequently in the same articles. It should be noted that the author’s keywords refer to those keywords that usually appear below the abstracts and are used to identify the subject of the paper. Figure 4 shows the results with a minimum threshold of 25 occurrences (resulting in 391 keywords that will characterize the Research Railway Aspects) and the 500 most frequent co-occurrence connections. Each color represents a cluster.

From VOSviewer results (Figure 4), six Research Railway Aspects (RRAs) can be identified (see cluster colors), which are explained below and summarized in

Table 3. Bibliometric summaries of Research Railway Aspects (RRAs).

Research Railway Aspect (RRA)	Description	Areas of Study	Examples of Relevant Keywords
Sustainable Railway Transport and Infrastructure Technology (SRTIT)	Focuses on innovative technologies to improve efficiency and sustainability in infrastructure planning, construction, and management.	3D Modeling—Sustainability—Digitalization—Energy Efficiency—Green Infrastructure—Innovation—Asset Management—Digital Technologies—Safety—Urban Planning	3D modeling, sustainable development, digitalization, asset management, infrastructure, efficiency
Structural Engineering and Advanced Railway Maintenance (SEARM)	Focuses on the development of advanced technologies for the effective maintenance of railway infrastructures.	Predictive Maintenance—Structural Analysis—Reliability—Nondestructive Inspection—Computational Modeling—Railway Safety—Reliability—Asset Management—Reliability—Structural Engineering	Predictive maintenance, structural analysis, reliability, nondestructive inspection, asset management
Railway Maintenance Management and Optimization (RMMO)	Develops technologies for proactive maintenance planning, improving the efficiency and reliability of railway systems.	Data Analysis—Failure Diagnostics—Automated Inspection—Predictive Maintenance—Asset Management—Degradation Modeling—Continuous Monitoring—Operational Reliability—Proactive Maintenance—Technological Innovation	Data analysis, fault diagnosis, automated inspection, predictive maintenance, asset management
Inspection and Predictive Maintenance Technologies for Railway Infrastructure (IPMTRI)	Develops and implements advanced technologies for the inspection and maintenance of railway infrastructures.	Automated Inspection—Continuous Monitoring—Early Diagnosis—Data Management—Predictive Analytics—Proactive Maintenance—Smart Sensors—Robotics—Condition Monitoring —Vibration Analysis	Predictive maintenance, condition monitoring, automated inspection, data analysis, sensors
Railway Rolling Stock Design and Operation (RRSDO)	Focuses on the design, operation, and maintenance of railway rolling stock, improving its safety and efficiency.	Bogie Design—Vibration Analysis—Energy Efficiency—Safety—Preventive Maintenance—Rolling Stock Dynamics—Reliability—Technological Innovation—Operational Optimization—Ergonomics	Rolling stock design, vibration analysis, energy efficiency, safety, preventive maintenance
Analysis and Prediction of Degradation on Railroad Track (APDRT)	Analyzes and predicts the degradation of railroad tracks, facilitating the scheduling of infrastructure renewals and improvements.	Degradation Modeling—Vibration Analysis—Track Inspection—Continuous Monitoring—Proactive Maintenance—Failure Diagnosis—Life Prediction—Risk Management—Track Quality Improvement—Asset Renewal	Track degradation modeling, vibration analysis, track inspection, proactive maintenance

.

• RRA 1: Sustainable Railway Transport and Infrastructure Technology (SRTIT)

The Sustainable Railway Transport and Infrastructure Technology (SRTIT) RRA focuses on the study of the development and application of innovative technological solutions to improve efficiency, sustainability, and safety in the field of transportation, with a specific focus on rail transport. Studies in this RRA address various topics, from infrastructure planning and design to the operational management of public transport and freight systems. SRTIT covers a wide range of disciplines and areas of study, including the following:

• Development of tools to visualize and design railway projects in three dimensions, facilitating accurate planning.
• Implementation of digital solutions to optimize processes and data management in real time in the railway sector.
• Development of systems to efficiently manage railway assets, including maintenance and resource tracking.
• Research and development of sustainable rail transport infrastructures to reduce environmental impact.
• Promotion of innovation in the railway sector through technologies and business models.
• Integration of rail transport into urban planning to create more livable and sustainable cities.
• Develop of technologies and practices to reduce energy consumption in rail transportation.
• Research and application of technologies to improve rail transportation safety.
• Development of strategies for the efficient management of public transportation systems, promoting sustainable mobility.
• Application of emerging technologies to optimize rail infrastructure and improve the user experience.

• RRA 2: Structural Engineering and Advanced Railway Maintenance (SEARM)

The Structural Engineering and Advanced Railway Maintenance (SEARM) RRA focuses on the research and development of innovative technologies for the design, analysis, and maintenance of railway infrastructure, with a particular emphasis on bridges, tracks, and associated structures. Integrating expertise in structural engineering, mechanics of materials, structural dynamics, and inspection technologies, SEARM seeks to improve the safety, efficiency, and durability of rail networks worldwide. From arch bridge design and analysis to rail fatigue damage assessment, SEARM spans a wide range of disciplines and areas of study, including the following:

• Dynamic analysis of bridges and railway tracks.
• Condition assessment and damage detection in railway structures.
• Development of advanced numerical methods for modeling structural behavior.
• Nondestructive inspection and structural health monitoring.
• Research on materials and repair techniques to improve durability and strength.
• Fatigue and fracture studies to better understand the behavior of railway materials under dynamic and cyclic loading.
• Development of predictive maintenance systems to prevent failures and optimize the useful life of railway infrastructures.

• RRA 3: Railway Maintenance Management and Optimization (RMMO)

The Railway Maintenance Management and Optimization (RMMO) RRA focuses on the development and application of advanced tools and methodologies to improve the efficiency, reliability, and cost-effectiveness of maintenance operations in the railway sector. Integrating concepts from engineering, statistics, and computer science, the RRMO addresses specific challenges related to maintenance management and planning in railway infrastructure, equipment, and systems. From optimization algorithms to reliability analysis, the RRMO covers a wide range of topics, including the following:

• Development of optimization models for preventive and corrective maintenance scheduling.
• Implementation of decision support systems for efficient resource and cost management.
• Use of data analysis and machine learning techniques for failure prediction and optimization of maintenance programs.
• Application of probabilistic methods and stochastic models to assess the reliability and availability of railway assets.
• Research of condition-based maintenance methods and service life forecasting to maximize availability and extend the service life of equipment and components.
• Development of simulation and sensitivity analysis tools to evaluate the impact of different maintenance strategies on operational and financial performance.

• RRA 4: Inspection and Predictive Maintenance Technologies for Railway Infrastructure (IPMTRI)

The Inspection and Predictive Maintenance Technologies for Railway Infrastructure (IPMTRI) RRA focuses on developing and implementing advanced technologies for early defect detection and efficient maintenance planning in railway systems. Integrating machine learning, computer vision, and signal processing methods, IPMTRI aims to improve the reliability and availability of railway infrastructure, thereby reducing operating costs and improving safety. This RRA covers a wide range of technologies and areas of study, including the following:

• Development of machine learning algorithms for anomaly detection and fault diagnosis.
• Implementation of computer vision systems for automated inspection of railway tracks and components.
• Image analysis and signal processing for the detection of cracks, surface defects, and structural anomalies.
• Use of convolutional neural networks and feature extraction techniques to improve defect detection accuracy.
• Application of predictive maintenance methods based on data analysis to proactively plan maintenance interventions.
• Research on machine learning algorithms and data analysis to optimize the efficiency of maintenance operations.
• Development of decision support systems based on predictive models for the efficient management of railway infrastructure.

• RRA 5: Railway Rolling Stock Design and Operation (RRSDO)

The Railway Rolling Stock Design and Operation (RRSDO) RRA focuses on the research and development of innovative technologies to optimize the design, operation, and maintenance of rolling stock in railway systems. Integrating mathematical modeling, computer simulation, and parameter estimation techniques, the RRSDO seeks to improve the performance, safety, and efficiency of rail vehicles as well as reduce wear and tear and associated maintenance costs. This RRA covers a wide range of technologies and areas of study, including the following:

• Development of mathematical models and computer simulation for the design and analysis of bogies, wheels, and rolling systems.
• Research on material wear and rolling contact fatigue to improve rolling stock life and reliability.
• Application of specialized software, such as MATLAB® R2020a, for simulation and optimization of design and operating parameters.
• Study of vehicle–track interaction and vibration analysis to improve travel stability and comfort.
• Development of predictive maintenance and asset management techniques to reduce downtime and improve rolling stock availability.
• Research on emerging technologies, such as light rail and high-speed transport, to improve the efficiency and sustainability of rail transport.

• RRA 6: Analysis and Prediction of Degradation on Railroad Track (APDRT)

The Analysis and Prediction of Degradation on Railroad Track (APDRT) RRA focuses on the study and modeling of railway track degradation and the development of predictive models to anticipate and efficiently manage infrastructure maintenance. By integrating regression analysis, forecasting, and error analysis techniques, the APDRT aims to improve the quality and durability of railway tracks, reducing the costs associated with maintenance and optimizing railway operations. This RRA covers a wide range of technologies and study areas, including the following:

• Development of degradation models to predict the deterioration of railway tracks over time.
• Study of track geometry and analysis of irregularities to identify areas prone to degradation.
• Application of forecasting techniques and time-series analysis to predict future degradation levels and proactively plan maintenance.
• Research on tamping techniques and track maintenance to improve quality and prolong useful life.
• Analysis of rail operations data to better understand the impact of track degradation on service efficiency and safety.

2.2.3. RRA Chronologic Evolution

The Railway Research Aspect (RRA) review was also carried out from a chronological point of view, revealing changes in research topics over the last ten years. This is shown in Figure 5, which illustrates the changes in focus over three different time periods:

• During the first period (blue), the focus is on maintenance strategies, costs, preventive maintenance, and planning. This focus is mainly reflected in RRA 2: Structural Engineering and Advanced Railway Maintenance (SEARM), where methods to improve maintenance efficiency and ensure infrastructure safety are investigated, and in RRA 4: Inspection and Predictive Maintenance Technologies for Railway Infrastructure (IPMTRI), which focuses on the assessment and management of risks associated with railway operation.
• In the second period (green), models for decision-making, reliability analysis, railway safety, lifecycle analysis (LCC), and asset management stand out, focusing on process optimization and efficient resource management. The predominant RRAs in this phase are RRA 1: Sustainable Railway Transport and Infrastructure Technology (SRTIT) and RRA 3: Railway Maintenance Management and Optimization (RMMO), both aimed to ensure railway system reliability and safety.
• In the third period (yellow), interest in digitalization, Industry 4.0, neural networks, digital twins, machine learning, structure health monitoring, and predictive maintenance emerges. This interest is seen in RRA 5: Railway Rolling Stock Design and Operation (RRSDO) and RRA 6: Analysis and Prediction of Degradation on Railroad Track (APDRT), where advanced technologies and innovative practices are explored to improve operational efficiency, user experience, and sustainability in the rail sector.

Figure 5. Graphic VOSviewer time evolution analysis results, grouped by years in color scale.

Figure 5. Graphic VOSviewer time evolution analysis results, grouped by years in color scale.

RRA Chronologic Evolution: Digitalization Focus

By developing a focus on the third period, the search has been adjusted to “(railway AND digitalization) OR (railway AND digital AND maintenance)”. In Figure 6, this evolution is appreciated, with less than 25 publications per year until 2017, where they increase to more than 125 in 2023. New key terms emerge in this new context, for example, digital twins, BIM, digital transformation, Big Data, Learning and System, among others (see Figure 7). This figure illustrates the new concepts emerging in the railway digitalization field. It is possible to detect that only from 2016 onwards, there is an explicit interest in digitalization. In this sense, as has been studied in other research such as that of Ref. [48], digitalization in railroads is an immature field of study in search of a unified perspective. It seeks to develop a digital railway ecosystem [49]. The interest in data uniqueness, ontologies, semantics, and domains is unveiled in this context [50]. Thus, ontologies shall address entities (objects or elements of the domain that are important to the system), properties (characteristics or attributes of the entities that are significant to the domain), relationships (connections or associations between entities that are relevant to the domain), hierarchies (organization of entities in a hierarchical structure), and constraints (rules that limit how entities, properties, and relationships can be combined in the domain). On the other hand, while ontology provides the formal structure for representing knowledge in a specific domain, semantics is concerned with making sense of this representation.

2.3. Detailed Literature Review and Standards Research

2.3.1. The Literature Insights

Maintenance management in the railway sector is a critical component for ensuring operations with safety, efficiency, and reliability. Over the last decades, several researchers have approached this topic from different perspectives, generating significant advances in maintenance planning, asset management, and integration of digital technologies [51,52,53]. This literature review addresses these issues and their implications for digital railway maintenance management. Table 4 details multiple high-impact investigations for each RRA, and some insights are discussed below. The analysis explores several perspectives that will be developed in Section 2.3.2 and that connect the RRAs with the UIC framework:

• Digitalization and technological innovation in rail maintenance and asset management are dynamic and multifaceted. Current studies reflect varied approaches, from resource optimization to effective maintenance management and environmental sustainability [30,54]. This range of issues shows the complexity of the sector, highlighting the need for an integrated and holistic approach.
• Strategic planning in rail projects, focused on optimizing schedules and resources, underlines the importance of multiple factors and stakeholders in decision-making [55]. However, a general model that integrates all specialties and provides a comparative overview of rail infrastructure is lacking.
• Asset management is seen in authors, such as Refs. [51,53], exploring strategies that include Markov analysis and condition monitoring to ensure safety and operational efficiency. Still, there is a gap in the practical integration of digitalization in railway systems. Refs. [30,54] investigate technological innovations in the sector, highlighting autonomous maintenance systems and digital twins for infrastructure management, radically transforming railway maintenance.
• The effective implementation of digital solutions in an integrated management framework is challenging. Refs. [22,25] propose predictive models and monitoring systems to optimize performance and prevent failures. These face integration difficulties in the railway system as a whole, highlighting the need for holistic approaches that consider safety and environmental sustainability along with operational efficiency.
• Recent studies have expanded the application of advanced predictive models and structural health monitoring (SHM) systems in railway infrastructure management. For instance, Ref. [55] in “Bridging POMDPs and Bayesian decision-making for predictive maintenance” propose the integration of Bayesian decision-making frameworks with predictive maintenance strategies, enabling the identification of system degradation points and optimized intervention scheduling. In line with these developments, Ref. [56] presented “Study on damage identification of High-Speed railway bridges based on combined vibration feature vectors and SHM”, demonstrating the application of SHM for early detection and quantification of structural damages using sophisticated vibration analysis techniques.
• Further enhancing the predictive capabilities in railway infrastructure, Ref. [57] introduced a track reconstruction method based on robust predictive models in their paper “A railway track reconstruction method using robust principal component analysis and prediction models”, emphasizing the use of data-driven modeling for track integrity management. Additionally, Ref. [58] contributed with “Punctuality development and delay explanation using predictive analytics in railway systems”, highlighting the importance of predictive models to improve operational performance and service reliability through data interpretation. Finally, Ref. [59] presented “Progressive numerical model validation of a bogie frame using SHM techniques”, demonstrating how SHM methods are used to validate dynamic models of rolling stock structures under operational conditions. Collectively, these contributions show that modern predictive approaches and SHM techniques are becoming essential components of railway maintenance management, aligning with the direction proposed in this review by connecting technological advances with practical asset management frameworks.

Table 4. Relevant publications according to Research Railway Aspects.

Research Railway Aspect	Keywords	Paper Total	Authors	Title	Year	Cited by
Sustainable Railway Transport and Infrastructure Technology (SRTIT)	3D modeling, sustainable development, digitalization, asset management, infrastructure, efficiency	181	[60]	Wireless sensor networks for condition monitoring in the railway industry: a survey	2015	391
			[61]	The impact of digitalization on the future of control and operations	2018	100
			[62]	Windblown sand along railway infrastructures: A review of challenges and mitigation measures	2018	97
			[63]	Track transitions in railways: A review	2016	96
			[64]	A stochastic model for railway track asset management	2014	93
Structural Engineering and Advanced Railway Maintenance (SEARM)	Predictive maintenance, structural analysis, reliability, nondestructive inspection, asset management	178	[65]	Opportunities and challenges in IoT-enabled circular business model impl.—A case study	2020	90
			[66]	Predictive maintenance using tree-based classification techniques: A case of railway switches	2019	80
			[67]	Achieving Predictive and Proactive Maint. for High-Speed Railway Power Eq. with LSTM-RNN	2020	59
			[68]	An autonomous system for maintenance scheduling data-rich complex infrastructure: Fusing the railways’ condition, planning and cost	2018	45
			[69]	Predictive maintenance model for ballast tamping	2016	45
Railway Maintenance Management and Optimization (RMMO)	Data analysis, fault diagnosis, automated inspection, predictive maintenance, asset management	111	[70]	Perspectives on railway track geometry condition monitoring from in-service railway vehicles	2015	170
			[71]	A Big Data Analysis Approach for Rail Failure Risk Assessment	2017	80
			[72]	OORNet: A deep learning model for on-board condition monitoring and fault diagnosis of out-of-round wheels of high-speed trains	2022	55
			[73]	Blockchain-empowered digital twins collaboration: Smart transportation use case	2021	49
			[74]	Current status and future trends in the operation and maint. of offshore wind turbines: A review	2021	47
Inspection and Predictive Maintenance Technologies for Railway Infrastructure (IPMTRI)	Predictive maintenance, condition monitoring, automated inspection, data analysis, sensors	84	[75]	Significance of sensors for industry 4.0: Roles, capabilities, and applications	2021	112
			[76]	State-of-the-art review of railway track resilience monitoring	2018	83
			[77]	Railroad bridge monitoring using wireless smart sensors	2017	66
			[21]	Estimation of lateral and cross alignment in a railway track based on vehicle dynamics measur.	2019	47
			[78]	New methods for the condition monitoring of level crossings	2015	44
Railway Rolling Stock Design and Operation (RRSDO)	Rolling stock design, vibration analysis, energy efficiency, safety, preventive maintenance	87	[79]	Integrated optimization on train scheduling and preventive maintenance time slots planning	2017	76
			[80]	Improving the resilience of metro vehicle and passengers for an effective emergency response	2014	67
			[81]	Highway 4.0: Digitalization of highways for vulnerable road safety development with intelligent IoT sensors and machine learning	2021	61
			[82]	Future Greener Seaports: A Review of New Infrastructure, Challenges, and Energy Efficiency M.	2021	49
			[83]	Risk Evaluation of Railway Rolling Stock Failures Using FMECA Technique: A Case Study of Passenger Door System	2016	46
Analysis and Prediction of Degradation on Railroad Track (APDRT)	Track degradation modeling, vibration analysis, track inspection, proactive maintenance	14	[84]	Data-driven optimization of railway maintenance for track geometry	2018	91
			[85]	Proactive approach to smart maint. and logistics as a auxiliary and service processes in a company	2016	34
			[86]	A novel approach to railway track faults detection using acoustic analysis	2021	18
			[87]	Prediction Method of Railway Track Geometric Irregularity Based on BP Neural Network	2018	15
			[88]	Intelligent Proactive Maintenance System for High-Speed Railway Traction Power Supply System	2020	10

Relevant publications by RRA: Filter only reviews survey articles with more than one citation, mainly from recent years. The total of the articles considers the papers that mostly consider this aspect; however, they may also be partially present in other RRAs, as will be reviewed later.

Table 4. Relevant publications according to Research Railway Aspects.

Research Railway Aspect	Keywords	Paper Total	Authors	Title	Year	Cited by
Sustainable Railway Transport and Infrastructure Technology (SRTIT)	3D modeling, sustainable development, digitalization, asset management, infrastructure, efficiency	181	[60]	Wireless sensor networks for condition monitoring in the railway industry: a survey	2015	391
			[61]	The impact of digitalization on the future of control and operations	2018	100
			[62]	Windblown sand along railway infrastructures: A review of challenges and mitigation measures	2018	97
			[63]	Track transitions in railways: A review	2016	96
			[64]	A stochastic model for railway track asset management	2014	93
Structural Engineering and Advanced Railway Maintenance (SEARM)	Predictive maintenance, structural analysis, reliability, nondestructive inspection, asset management	178	[65]	Opportunities and challenges in IoT-enabled circular business model impl.—A case study	2020	90
			[66]	Predictive maintenance using tree-based classification techniques: A case of railway switches	2019	80
			[67]	Achieving Predictive and Proactive Maint. for High-Speed Railway Power Eq. with LSTM-RNN	2020	59
			[68]	An autonomous system for maintenance scheduling data-rich complex infrastructure: Fusing the railways’ condition, planning and cost	2018	45
			[69]	Predictive maintenance model for ballast tamping	2016	45
Railway Maintenance Management and Optimization (RMMO)	Data analysis, fault diagnosis, automated inspection, predictive maintenance, asset management	111	[70]	Perspectives on railway track geometry condition monitoring from in-service railway vehicles	2015	170
			[71]	A Big Data Analysis Approach for Rail Failure Risk Assessment	2017	80
			[72]	OORNet: A deep learning model for on-board condition monitoring and fault diagnosis of out-of-round wheels of high-speed trains	2022	55
			[73]	Blockchain-empowered digital twins collaboration: Smart transportation use case	2021	49
			[74]	Current status and future trends in the operation and maint. of offshore wind turbines: A review	2021	47
Inspection and Predictive Maintenance Technologies for Railway Infrastructure (IPMTRI)	Predictive maintenance, condition monitoring, automated inspection, data analysis, sensors	84	[75]	Significance of sensors for industry 4.0: Roles, capabilities, and applications	2021	112
			[76]	State-of-the-art review of railway track resilience monitoring	2018	83
			[77]	Railroad bridge monitoring using wireless smart sensors	2017	66
			[21]	Estimation of lateral and cross alignment in a railway track based on vehicle dynamics measur.	2019	47
			[78]	New methods for the condition monitoring of level crossings	2015	44
Railway Rolling Stock Design and Operation (RRSDO)	Rolling stock design, vibration analysis, energy efficiency, safety, preventive maintenance	87	[79]	Integrated optimization on train scheduling and preventive maintenance time slots planning	2017	76
			[80]	Improving the resilience of metro vehicle and passengers for an effective emergency response	2014	67
			[81]	Highway 4.0: Digitalization of highways for vulnerable road safety development with intelligent IoT sensors and machine learning	2021	61
			[82]	Future Greener Seaports: A Review of New Infrastructure, Challenges, and Energy Efficiency M.	2021	49
			[83]	Risk Evaluation of Railway Rolling Stock Failures Using FMECA Technique: A Case Study of Passenger Door System	2016	46
Analysis and Prediction of Degradation on Railroad Track (APDRT)	Track degradation modeling, vibration analysis, track inspection, proactive maintenance	14	[84]	Data-driven optimization of railway maintenance for track geometry	2018	91
			[85]	Proactive approach to smart maint. and logistics as a auxiliary and service processes in a company	2016	34
			[86]	A novel approach to railway track faults detection using acoustic analysis	2021	18
			[87]	Prediction Method of Railway Track Geometric Irregularity Based on BP Neural Network	2018	15
			[88]	Intelligent Proactive Maintenance System for High-Speed Railway Traction Power Supply System	2020	10

Relevant publications by RRA: Filter only reviews survey articles with more than one citation, mainly from recent years. The total of the articles considers the papers that mostly consider this aspect; however, they may also be partially present in other RRAs, as will be reviewed later.

The disconnect between technological improvements and organizational strategy underscores the importance of integrative approaches that combine technology with change management and organizational strategy. Ref. [89] focuses on regulatory compliance and proposes a revision of frameworks, adapting them to the specific needs and realities of railway maintenance. This indicates the importance of developing integrative approaches that combine technology with change management and organizational strategy for the effective implementation of digital solutions. Models focused on lifecycle costs and timely renewals. Ref. [90] lacks an integrative approach that compares different types of assets and considers their overall impact on the railway system. In maintenance policies, the introduction of expert systems and sensorization has advanced condition-based maintenance (CBM) and predictive techniques. Refs. [23,91] solve problems at the component level but do not define the application of these techniques at the all-asset level, limiting the existence of a comprehensive decision-making model.

Ref. [92] points out the importance of approaches that promote the identity of the digital asset a transversal way, highlighting the need to integrate specialized solutions in the railway system as a whole. Ref. [93] addresses safety and performance but lack comparative measures to assess asset criticality due to upgrades or downgrades. Digitalization as a catalyst for transformation in rail maintenance management requires a strategic and holistic approach. Implementation of technologies such as IoT, Big Data analytics, and AI has proven to be beneficial, enabling real-time data collection and analysis [22,25]. Data management and cybersecurity are crucial factors in this process. Organizational change management and staff training in new technologies are critical issues for the success of digitalization initiatives. Standardization and interoperability emerge as key challenges to maximizing the benefits of digitalization by developing common standards for compatibility and seamless integration between systems and components [89].

This collaborative and holistic approach is essential to face current and future challenges in the railway sector [92,93]. Digitalization is not only a tool to improve efficiency and reduce costs but a means to transform and enrich the railway maintenance ecosystem, promoting more sustainable, safe, and resilient practices.

In conclusion, digitalization in rail maintenance is a comprehensive strategy that requires the consideration of processes, people, and policies. Digital transformation requires a change in organizational culture, fostering an innovation culture and continuous learning. The rail industry can achieve a more sustainable and resilient future by adopting an inclusive and collaborative approach. This evolution towards more digitized and automated maintenance is a trend and necessity in the current context.

2.3.2. Regulatory and Standards Research: Maintenance and Asset Management Framework Review

ISO 55001 [9] lays down the principles and requirements essential for asset management, furnishing a structured framework, see Figure 8, aimed at augmenting effectiveness and efficiency in railway maintenance management. Furthermore, EN 50126 [26], EN 50128 [27], and EN 50129 [28] delineate the prerequisites for enhancing safety and reliability in railway systems, comprehensively addressing aspects such as lifecycle management, software development, and functional safety. In 2020, the International Union of Railways (UIC), a global professional association dedicated to standardization for rail transport, inaugurated the Asset Management Working Group (AMWG) with the objective of providing interpretations of ISO 55001 [9] (see Figure 6), the globally recognized asset management standard. This initiative endeavors to establish a tangible connection between ISO 55001 [9] and the asset management framework proposed by the organization. While originating from within the industry itself, this approach primarily furnishes theoretical insights, lacking concrete application guidelines and thereby presenting generalities and considerations devoid of specific implementation directives. Moreover, researchers such as Ref. [94] propose a framework that interconnects the maintenance management model (MMM) with asset management under ISO 55001 [9], thereby facilitating alignment between management phases and the MMM. Adding to this contemporary perspective, the publication “Driving the Introduction of Digital Technologies to Enhance the Maintenance Management Process and Framework” [7] offers insights into digitalizing the management model, contributing to contextual clarity. A holistic comprehension of the UIC framework makes it possible to identify five major categories, which in turn enable correlation with Research Railway Aspects (RRAs), ultimately shedding light on existing gaps within the field.

Each of the five categories addresses a specific approach to suit the needs and challenges of the rail sector. Below is a description of each and the UIC document sections in which they are referenced:

• Strategic Planning

Strategic planning in the rail sector includes the development of a strategic asset management plan (SAMP) that not only aligns asset management with organizational objectives but also responds to specific rail infrastructure needs such as safety, efficiency, and long-term sustainability. This strategic planning considers critical factors such as service demand, traffic growth, and the need for technological innovation. In addition, strategic planning encompasses coordination with government policies and regulations, ensuring that rail operations are aligned with social and economic expectations.

References in the document: Sections 1.4, 2.4.1, and 2.6.2—development and alignment of the strategic asset management plan with asset management policy and objectives; Sections 1.6 and 1.7—asset management definitions and frameworks to support strategic planning.

2.

Operational Management

In a railway context, operational management involves the day-to-day implementation of asset management strategies to maintain and improve train infrastructure and services. This includes managing resources, coordinating train schedules with maintenance activities, and optimizing outsourcing for critical components such as signaling and electrification. Operational efficiency in the rail sector is crucial to maintaining high punctuality level, safety, and customer satisfaction.

References in the document: Sections 2.7, 2.8, and 2.8.1—operational management support and control, and outsourcing of operations; Section 10.1—management of operations and resources related to asset management.

3.

Risk Management

Risk management in the railway sector focuses on identifying, assessing, and mitigating risks associated with the infrastructure and operation of trains. These range from safety and accident risks to financial and technological risks. Effective risk management ensures the operational safety and the long-term viability of infrastructure investments, considering the potential impacts of climate change, technology, and economic fluctuations.

References in the document: Sections 1.5.3, 6.1, and 6.2—risk management approaches, including assessment and mitigation in planning and operations; Sections 2.9.2 and 9.1–9.3—internal audits and effectiveness evaluations as part of risk management.

4.

Organizational Change

Organizational change in a railroad context involves adapting organizational culture and work practices to incorporate advanced asset management practices. This may include training and skills development in new technologies and methodologies, such as predictive maintenance and asset data management. Organizational change seeks to improve collaboration between various departments, such as operations, maintenance, and planning, to improve incident response and operational efficiency.

References in the document: Sections 2.5, 3.3, and 8.2—leadership and commitment to change, implementation advice, and managing risks associated with change; Section 7.1—resources needed to support asset management changes.

5.

Performance Evaluation

Performance evaluation in the rail sector involves continuously monitoring and reviewing the performance of the asset management system to ensure that the established objectives are achieved. This includes assessing infrastructure reliability, train punctuality, and customer satisfaction. Performance evaluations enable rail organizations to adjust their operational strategies and processes continuously improving the efficiency and effectiveness of their services.

References in the document: Sections 2.9 and 9.1–9.3—monitoring and measuring performance, including internal audits and management reviews; Section 9.2—use of internal audits to evaluate and improve asset management performance.

These categories reflect how to adapt the implementation of ISO 55001 [9] in the railway sector. To broaden their understanding and relationship with the research aspects, we have selected a list of keywords from related publications, which allow for characterizing each of these categories and are detailed in

Table 5. UIC category keywords.

Categories	Related Keywords
Operational Management	Energy Efficiency, Operational Optimization, Digitalization, Digital Technologies, Asset Management, Preventive Maintenance
Risk Management	Safety, Reliability, Reliability, Structural Engineering, Track Inspection, Continuous Monitoring, Proactive Maintenance, Failure Diagnosis, Service Life Prediction, Track Quality Improvement, Asset Renewal
Strategic Planning	Sustainability, Green Infrastructure, Digitalization, Digital Technologies, Safety, Urban Planning, Structural Analysis, Computational Modeling, Railway Safety, Reliability
Performance Evaluation	Data Analytics, Fault Diagnosis, Automated Inspection, Predictive Maintenance, Degradation Modeling, Continuous Monitoring, Operational Reliability, Proactive Maintenance, Technological Innovation, Condition Monitoring, Vibration Analysis, Robotics, Smart Sensors, Predictive Analytics
Organizational Change	Digitalization, Digital Technologies, Innovation, Technological Innovation, Data Management, Sustainability, Green Infrastructure

.

2.4. Key Enabling Technologies in Railway Digitalization

The integration of digital technologies in railway maintenance is driven by a set of core enabling technologies that allow for the transition from reactive to predictive and optimized maintenance strategies. Among these, the most prominent are the Internet of Things (IoT), Big Data analytics, Artificial Intelligence (AI), and the implementation of digital twins. This section consolidates the discussion on these technologies—originally presented across Sections 2.3.1 (IoT applications), 2.3.3 (AI and predictive models), and 2.3.4 (digital twin integration)—to provide a unified overview of their roles, interactions, and comparative contributions within the context of asset management and railway maintenance. The Internet of Things (IoT) enables the real-time acquisition of operational and environmental data through a network of embedded sensors installed across rolling stock and infrastructure components. In railway maintenance, IoT plays a foundational role by enabling continuous monitoring of asset condition, detecting anomalies, and feeding data into higher-level analytical systems. Applications discussed in Section 2.3.1 include temperature and vibration sensors in bearings, track displacement monitors, and remote condition monitoring of electrical components. IoT acts as the primary layer of data collection, setting the stage for advanced diagnostic and predictive functions. Big Data analytics, introduced in Section 2.3.2, is responsible for processing the vast volume of structured and unstructured data generated by IoT devices, SCADA systems, and historical maintenance records. It allows for trend identification, correlation analysis, and risk assessment across the network. In railway contexts, Big Data support long-term performance evaluations, failure pattern detection, and strategic asset renewal planning. Bid Data’s role is critical in transforming raw data into actionable knowledge, often in combination with AI models. Artificial Intelligence (AI), as detailed in Section 2.3.3, builds upon Big Data and provides capabilities such as anomaly detection, failure prediction, and prescriptive maintenance recommendations. Techniques like machine learning (ML), deep learning (DL), and hybrid models are applied to detect subtle patterns in historical and live data, supporting condition-based and risk-informed maintenance. AI-driven decision support systems have demonstrated the potential to reduce human error, optimize maintenance schedules, and improve safety margins. Digital twins, discussed in Section 2.3.4 and further illustrated in the AZVI case study (Section 4.4), integrate the physical and digital dimensions of railway assets, providing a dynamic and real-time representation of infrastructure behavior. They incorporate IoT sensor data, analytic models, and simulation capabilities to visualize asset degradation and predict future conditions. In railway maintenance, digital twins serve as a platform for real-time monitoring, predictive simulations, and maintenance planning. Their strength lies in combining data with engineering knowledge, allowing scenario testing and intervention optimization. Together, these technologies form a layered digital ecosystem. IoT collects data, Big Data organize it, AI interprets it, and digital twins visualize and simulate it. Their combined implementation enables railway infrastructure managers to adopt holistic asset management strategies, fully aligned with the structured principles of the UIC asset management framework, as discussed throughout Section 3.

RFID Applications in Railway Maintenance and Structural Health Monitoring

Radio Frequency Identification (RFID) has emerged as a key enabler within the broader Internet of Things (IoT) ecosystem for railway infrastructure monitoring and maintenance. RFID technology enables wireless, non-intrusive identification and data transmission using electromagnetic fields, allowing asset tracking, condition monitoring, and data acquisition in real time with minimal manual intervention. Its low power requirements, high durability, and adaptability to harsh environments make RFID particularly well-suited for railway applications. In the domain of structural health monitoring (SHM), RFID systems have been successfully employed for strain and crack sensing, enabling long-term observation of infrastructure elements such as rail tracks, bridges, and structural joints. Advanced semi-passive RFID configurations have demonstrated capabilities for long-range, wireless strain detection, with dual-interrogation-mode RFID systems significantly improving transmission distance and data reliability [95]. These developments provide robust monitoring solutions for difficult-to-access infrastructure, reducing the need for manual inspection and enhancing safety by enabling early fault detection. Beyond infrastructure, RFID technology also supports asset-level monitoring of rolling stock components. Tags embedded in mechanical systems such as wheelsets, bogies, or brake assemblies allow maintenance teams to track the operational history and current condition of critical parts, improving maintenance traceability and supporting event-based inspection models. This facilitates predictive maintenance by linking RFID event data to degradation models, maintenance logs, and asset registries [96]. Moreover, RFID complements other sensor modalities within digital twins and predictive platforms, creating a redundant and scalable sensing architecture. It enhances the spatial and temporal resolutions of monitoring networks and contributes to data accuracy by enabling precise localization and identification of components. Recent reviews and experimental studies in railway SHM have emphasized the relevance of RFID technology as a cost-effective, scalable, and easily integrable tool within broader digital asset management strategies [97].

3. Literature Review and Standards Research Gap

The matrix in

Table 6. Research Railway Aspect and UIC category relationship.

		UIC High-Level Categories
		Strategic Planning	Operational Management	Operational Management	Operational Management	Operational Management
Research Railway Aspects	SRTIT: Innovation and Sustainability	85	144	85	68	83
	IPMTRI: Tech and Prediction	19	18	18	8	120
	SEARM: Engineering and Maintenance	126	85	175	13	120
	RRSDO: Operations and Rolling Stock	109	34	107	11	15
	APDRT: Infrastructure and Degradation	2	4	14	8	11
	RMMO: Efficiency and Management	27	73	77	12	152

shows the level of research intensity at the intersection of each topic, represented by the number of articles related to both the category and the RRAs. Considering the same base of publications in Table 4, it is possible to find that these investigations touch, in several cases, in more than one category, and some of them being more recurrent than others. This relationship is obtained through the cross-analysis of the respective keywords that determine each RRA (Table 4) and each UIC category (Table 5).

This allows us to analyze in an integral perspective how Research Railway Aspects and UIC categories are related, and to illustrate this, in the next bullet points, some examples found in the literature confirm this:

• Advanced Operational Management: Studies such as those of Ref. [60] illustrate how sensor networks and real-time monitoring are transforming operational management in the railway sector. These technologies enable more efficient and preventive monitoring, which is crucial for the optimal operation of railway systems. Refs. [61,62] expand on this analysis, highlighting that digitalization facilitates new opportunities to improve efficiency and operational sustainability due to the improved control and optimized operation of these systems.
• Risk Management Optimization: From a risk management perspective, digital technology implementation such as predictive maintenance has revolutionized the care of critical components such as track switches and power supply systems. Refs. [56,66] demonstrate how these tools not only enable more effective maintenance but also advance the ability to anticipate and mitigate potential risks, thus contributing to safer and more reliable infrastructure.
• Fostering Strategic Planning: Strategic planning in the field of digitalization offers advanced tools that support long-term decisions, essential for the sustainable development of the railway sector. Ref. [61] highlights how the integration of digitalization into control and operations processes is vital for asset management strategy formulation and the development of effective management plans, thus adapting to market changes and the demands of a modern and efficient transport service.
• Improved Performance Evaluation: Performance evaluation has also benefited from digitalization, especially through real-time monitoring provided by emerging technologies. Research such as that of Ref. [70] suggests that track geometry monitoring from in-service vehicles provides crucial data for continuous infrastructure condition assessment. Refs. [84,85] add that digitalization facilitates maintenance optimization, resulting in tangible improvements in rail system performance and efficiency.
• Catalysts for Organizational Change: The introduction of digital technologies in the railway infrastructure, such as the smart sensors and advanced monitoring systems mentioned by Refs. [75,76], act as catalysts for significant organizational changes. These tools drive railway entities to adopt new technologies and management approaches, promoting a culture of innovation and continuous improvement.

Challenge Overview and Gaps

In the context of digitalization in railway maintenance management, our review of the current literature reveals significant gaps that merit attention in future research. We dig deeper into some of these gaps:

• Risk Management: Despite extensive research in the field of engineering, recognized as a critical aspect, the intensity of studies that extend its definition beyond railway safety to include other operational and strategic aspects by ISO 55001 [9] remains low. It is imperative to explore how emerging technologies such as Artificial Intelligence and Big Data analytics can optimize risk forecasting and mitigation, identifying critical assets and assessing their impact on the business. This approach could significantly transform risk management by integrating more accurate assessments and data-driven predictions, which improve response capabilities to unexpected incidents and optimize resource allocation in critical assets.
• Organizational Change: Management organizational change, especially in the digitalization context, is lithely addressed. Solutions often focus on technical aspects, neglecting the human factor (essential for the success of any digital initiative). It is crucial to develop strategies that implement new technologies and promote an organizational culture that facilitates the adaptation and adoption of these innovations. Continuous training and skill development must be integral components of any organizational change plan to ensure that all levels of the organization are equipped and committed to the new processes and technologies.
• Degradation Infrastructures: The management of infrastructure degradation through digital tools still shows insufficient study levels. Integrating emerging technologies, such as monitoring sensors and predictive analytics, is key to detecting signs of wear and other structural problems early. Implementing sensor technology and data analysis platforms can transform infrastructure management by promoting a proactive maintenance approach. Additionally, the use of advanced digital models like digital twins facilitates detailed simulations and analyses that improve planning and operational efficiency, contributing to more resilient and adaptable infrastructure.
• Technology and Prediction: While performance assessment and the use of technologies such as augmented and virtual reality have been moderately explored, there are extensive opportunities to advance real-time monitoring and proactive maintenance through advanced predictive models. These models allow failures to be prevented before they occur, optimizing maintenance and reducing downtime. A deeper exploration of these technologies can offer significant contributions that improve the effectiveness and efficiency of railway operations in an increasingly digitalized environment.

4. Discussion, Analysis, and Practical Implications

Our study addresses the gaps identified in the literature and the current practices of railway maintenance management through the strategic integration of digitalization. This is not only understood as the new technology’s adoption but as an essential enabler that transforms and enhances the management models recommended by the UIC. In this context, digitalization acts as a catalyst for a more effective alignment between emerging technologies and organizational strategies, promoting a more holistic and integrated approach to rail asset management. The implementation of advanced digital tools, such as data analytics, digital twins, and real-time monitoring systems, is presented as crucial to close existing gaps. These technologies facilitate unprecedented data collection and analysis, enabling a deeper understanding of asset conditions and more informed and up-to-date decision-making. Thus, digitalization supports UIC management models and enriches them, providing a more robust foundation for effective implementation. Addressing these gaps requires recognizing and capitalizing on the potential of digitalization as a technical toolkit and a strategic lever that enables more coherent, predictive, and adaptive management of railway assets. This analysis explores how the integration of digitalization into UIC management models can overcome current limitations and offer a path toward more efficient, sustainable, and safe maintenance management in the railway sector.

4.1. Research Agenda Proposal for the Compensation of Gaps in the Study

• Innovation in Risk Management through Digital Technology: Table 5 shows a low intensity of research in risk management compared to other areas. It is important to clarify that we are talking about risk as a broader concept as it is treated in ISO 55001 [9] and not about railway safety in particular. It is proposed to investigate how technologies such as Artificial Intelligence and Big Data analysis can predict and mitigate specific risks in railway operations, thus improving safety and efficiency. In this sense, the research group has participated in multiple projects in the sector where it is demonstrated that simple cross-cutting processes that allow assessing, for example, the criticality of assets, are still not mature and often must be carried out manually and qualitatively, missing the opportunity of digitalization as a tool.
• Optimizing Strategic Planning with Digital Tools: Although strategic planning is crucial, research in this area is not as intensive as in operational management. Exploring how digital solutions can be integrated into long-term planning to adapt rail operations to future growth and technological change expectations would be beneficial.
• Development of Predictive Models for Performance Evaluation: Performance evaluation has a moderate level of research. Studying the impact of advanced predictive models on performance assessment could close gaps using real-time monitoring in the proactive maintenance of infrastructure.
• Organizational Transformation Through Digital Integration: Organizational transformation through digitalization shows a moderate level of study. How emerging technologies can facilitate structural changes in rail organizations to improve adaptability and response to disruptive innovations should be investigated.
• Use of Augmented and Virtual Reality in Training and Maintenance: Despite its potential, augmented and virtual reality is not sufficiently explored in the railway context. Investigating its application in employee training and maintenance operations could provide significant improvements in operational effectiveness and efficiency.

Practical Implications

How can rail operators benefit from the findings of these studies?

Future research in these areas brings potential benefit to operators in the following lines:

• Adoption of Emerging Technologies: First, rail operators should invest in key technologies identified in the study, such as Big Data, IoT (Internet of Things), and Artificial Intelligence. These include installing sensors on infrastructure and rolling stock to collect real-time data, enabling predictive maintenance and more efficient management.
• Training and Skills Development: Implement training programs for technical and management staff using new digital technologies. Staff must understand how to interact with the latest tools and interpret the data generated by these technologies to make informed decisions.
• IT Infrastructure Upgrade: Ensure the existing technology infrastructure can support new applications and data analytics. This may require an upgrade of IT systems, increased data storage capacity, and cybersecurity enhancements.
• Organizational Change and Change Management: Adapt the organizational structure to support the integration of digitalization. This could include the creation of new roles, such as data analysts or IoT specialists, and form cross-functional teams that work together on the implementation and management of digital technologies.
• Developing Strategic Alliances: Form alliances with technology and consulting firms that can provide the expertise and technical support needed to implement advanced digital solutions. These collaborations can help accelerate the digitalization process and ensure that the industry’s best practices are used.
• Continuous Evaluation and Adaptation: Establish a continuous evaluation system to monitor the impact of new technologies on maintenance management. Use the results to adjust strategies and practices, ensuring that the organization adapts to emerging challenges and opportunities in the rail sector.
• Foster a Culture of Innovation: Promote an organizational culture that values innovation and continuous improvement. This includes encouraging employees to propose and experiment with new ideas and digital solutions to improve rail maintenance and operations.

4.2. Data Privacy and Cybersecurity Challenges in Railway Digitalization

The integration of digital technologies such as IoT, cloud computing, AI, and digital twins in railway maintenance management presents new opportunities for operational efficiency and predictive asset management. However, this digital transformation also exposes railway organizations to increasing risks related to data privacy and cybersecurity. As railway infrastructure becomes more connected, the potential impact of cyberattacks, data breaches, and malicious disruptions grows significantly, requiring organizations to adopt robust cybersecurity strategies in parallel with technological deployment.

4.2.1. Key Cybersecurity Risks in Digital Railway Environments

The widespread adoption of large-scale sensor networks, real-time monitoring systems, and cloud-based platforms in railway maintenance environments introduces significant cybersecurity vulnerabilities that must be addressed through comprehensive, proactive strategies. Among the key risks are data breaches and unauthorized access, as sensitive operational data collected from IoT devices can be intercepted or compromised if not properly encrypted and securely transmitted. Additionally, cyberattacks on operational control systems pose severe threats; railway operators rely heavily on digital platforms for maintenance scheduling, traffic control, and system monitoring; and successful attacks on these platforms could disrupt services, compromise passenger and operational safety, and result in financial losses. Vulnerabilities are further exacerbated by reliance on third-party systems, including external cloud services, data processing platforms, and predictive analytics tools, which expand the attack surface beyond internal security perimeters. Supply chain threats also present critical challenges, as malicious actors may exploit the weak security postures of contractors or suppliers to infiltrate railway networks. In this context, railway organizations must comply with international regulations and industry standards, including the General Data Protection Regulation (GDPR) for data privacy and protection of personal information; ISO 27001 [98] for information security management; IEC 62443 [99] for the cybersecurity of industrial communication networks and critical infrastructures; and national cybersecurity frameworks, which increasingly designate railway infrastructures as critical national infrastructure (CNI). The importance of these measures is reinforced by the findings of Ref. [100], who emphasize that resilience in railway infrastructures depends not only on physical robustness and climate adaptation but also on robust cybersecurity protocols capable of mitigating evolving cyber threats [24]. This highlights the necessity for railway infrastructure managers to implement multi-layered cybersecurity frameworks, perform continuous vulnerability assessments, engage in AI-driven threat detection, and foster close collaboration with national cybersecurity authorities and industry partners. Railway organizations can ensure the protection, reliability, and resilience of their maintenance operations in an increasingly digitalized and interconnected environment only by aligning operational practices with these regulatory frameworks and maintaining vigilance across all digital platforms.

To address the significant cybersecurity risks in railway digital environments, railway operators and infrastructure managers must implement comprehensive, multi-layered protection strategies that safeguard critical assets and operational continuity. These strategies include the application of end-to-end encryption for all data collected from monitoring systems and transmitted through cloud services, ensuring that sensitive information remains protected during transfer and storage. In addition, the deployment of multi-factor authentication (MFA) protocols and strict role-based access control (RBAC) for personnel who access maintenance platforms and operational control systems are essential measures to prevent unauthorized access and human error vulnerabilities. Advanced anomaly detection systems based on Artificial Intelligence and machine learning capabilities must be incorporated to identify and respond to suspicious activities in real time across complex railway networks. Furthermore, secure IoT deployment frameworks, which include blockchain-based authentication systems and regular vulnerability assessments, are critical to ensure the integrity of interconnected devices and platforms. Complementary measures, such as regular penetration testing and comprehensive system audits, are necessary to identify potential weaknesses and maintain a resilient cybersecurity posture. Continuous cybersecurity training programs for maintenance personnel and operational staff are equally important, fostering a security-aware culture within railway organizations and reducing the likelihood of human-induced vulnerabilities. Despite progress in these areas, the field of railway-specific cybersecurity remains relatively underdeveloped, and future research should prioritize the development of AI-based autonomous cybersecurity agents capable of proactively responding to cyber threats with minimal human intervention. Additionally, simulation of cyber-physical risks using digital twins of railway systems represents a promising avenue to understand vulnerabilities better and prepare contingency plans. Cross-sector standardization efforts are also needed, aligning railway cybersecurity practices with those established in other critical infrastructure domains such as the energy, aviation, and maritime sectors. As highlighted by Ref. [101], integrating cybersecurity considerations into digital asset management frameworks and methodologies, including Building Information Modeling (BIM), is crucial to achieving resilient and secure railway infrastructures. Railway organizations must, therefore, treat cybersecurity as an integral part of their digital asset management strategies. Adequate protection of digital railway systems not only preserves data integrity and operational continuity but also safeguards passenger safety and public trust in transportation systems. The incorporation of cybersecurity into asset management frameworks will be essential to the long-term success and sustainability of future digitalization initiatives.

4.2.2. Regulatory Frameworks and Cybersecurity Best Practices

In the context of railway digitalization, cybersecurity risks are particularly pronounced due to the high level of system interconnectivity and the critical nature of infrastructure operations. Among the specific vulnerabilities identified, threats to SCADA systems and operational control centers pose a significant risk, as these systems are directly responsible for train routing, signaling, and real-time infrastructure management. IoT devices, if deployed without adequate encryption or access control, are also vulnerable to spoofing, data interception, or hijacking through weak wireless protocols. Ransomware attacks targeting cloud-based maintenance platforms and predictive analytics systems can result in data loss, operational downtime, and compromised safety protocols. The growing complexity of supply chains further exacerbates these risks, with third-party software and remote-access services becoming potential entry points for malicious actors. To mitigate these threats, railway organizations must adopt a defense-in-depth strategy, integrating multiple layers of technical and organizational protection. Regulatory compliance plays a central role in guiding these strategies. The General Data Protection Regulation (GDPR) ensures the lawful collection and processing of personal and operational data. The ISO/IEC 27001 [98] standard provides a framework for information security management, while IEC 62443 [99] offers specific guidelines for securing industrial control systems and critical infrastructure networks. Additionally, national cybersecurity frameworks increasingly recognize railway systems as part of critical national infrastructure (CNI), subject to mandatory resilience and reporting requirements.

Best practices for securing digital railway systems include implementing zero-trust architectures, segmenting operational networks from IT systems, applying regular vulnerability scanning and penetration testing, and maintaining a cybersecurity incident response plan. Moreover, the integration of AI-driven anomaly detection tools enables the early identification of abnormal behavior within the network, supporting faster response times and threat mitigation. Continuous training and awareness programs for technical staff and maintenance personnel are essential to reduce human error and strengthen the security culture. These measures, combined with active collaboration between infrastructure managers, cybersecurity agencies, and technology providers, represent a practical pathway for safeguarding digital railway environments, ensuring operational continuity, and protecting public trust.

4.3. Cost–Benefit Analysis of Digitalization in Railway Maintenance

The digital transformation in railway maintenance necessitates significant investments in technology, training, and system integration. However, these investments are offset by long-term operational efficiencies, safety enhancements, and optimized resource management.

The implementation of digital technologies in railway maintenance requires considerable investment across multiple dimensions. First, infrastructure and IoT deployment involve the installation of extensive sensor networks, advanced data acquisition systems, and condition-monitoring equipment designed to collect real-time information from critical railway assets. This foundational layer supports the integration of sophisticated software platforms, including the development and operational deployment of digital twins, predictive analytics tools, cloud-based data platforms, and advanced visualization dashboards that enable intuitive decision-making. However, technology alone is insufficient without workforce adaptation; training personnel in data-driven maintenance processes and fostering a culture of technological adoption are essential steps in ensuring that organizations can fully leverage digitalization. These investments, while significant, deliver substantial long-term benefits. Predictive maintenance capabilities lead to a measurable reduction in corrective maintenance needs and minimize unplanned downtime by enabling early interventions. Enhanced safety and operational reliability are achieved through continuous asset monitoring and real-time data analysis, ensuring that risks are mitigated before failures occur. Additionally, resource optimization becomes possible by prioritizing maintenance interventions based on asset criticality and degradation modeling, allowing for more efficient allocation of both financial and human resources. These benefits are further strengthened by achieving regulatory compliance with internationally recognized frameworks such as ISO 55001 [10] and the European Railway Safety Regulations, ensuring that railway organizations not only improve performance but also adhere to the highest standards of governance, reliability, and public safety.

4.4. Real-World Application: Case Study

The implementation of digital twins at AZVI has led to the development and deployment of a digital twin framework for the maintenance of railway infrastructure machinery under the European DF-MAS project [102]. This initiative aligns with European Regulation EU 779/2019 [103] and focuses on the digitalization of maintenance management, asset modeling, and predictive optimization.

The methodology and technological deployment were documented in the publication [104]. (Key results: 25% reduction in unplanned downtime, 20% decrease in corrective maintenance costs, and 15% increase in asset useful life.)

These outcomes highlight the effectiveness of integrating digital twins and optimization models in railway maintenance management. The experience of AZVI demonstrates that operational improvements and cost optimization rapidly offset the initial costs of digital transformation in railway maintenance. Integrating digital twins, predictive analytics, and IoT into maintenance management supports a sustainable, data-driven decision-making model aligned with UIC standards and international best practices.

While previous studies have addressed individual aspects of railway maintenance digitalization—including the use of predictive analytics, structural health monitoring, and optimization models—very few have established a comprehensive link between these technological approaches and the structured asset management frameworks promoted by international organizations such as the UIC.

Recent research efforts have focused on the application of machine learning for anomaly detection, the use of Bayesian models for failure prediction, and the implementation of digital twins for asset monitoring. However, these contributions tend to remain within specific technological domains, often disconnected from strategic asset management decision processes.

In contrast, the present study bridges this gap by proposing and validating a holistic framework that integrates IoT-based sensing, predictive analytics, and digital twins within the operational structure defined by the UIC asset management framework. This work differs from the existing literature by not only addressing technological advancements but also aligning them with organizational and decision-making layers, providing a pathway for real-world implementation and scaling.

The comparison presented in Table 4 highlights that most prior studies remain focused on theoretical developments or pilot tests without direct integration into asset management strategies. The framework proposed here, validated through the AZVI case study, demonstrates how predictive models and digital twins can be operationally embedded, leading to measurable improvements in maintenance planning and cost optimization. Furthermore, this study incorporates organizational change considerations, data governance structures, and cross-departmental collaboration elements, aspects that are often overlooked in the existing literature but are essential for successful digital transformation in railway maintenance.

The practical implementation of digital technologies in railway maintenance has been demonstrated through the AZVI case study, where digital twins and predictive models have been successfully integrated into operational processes. This example illustrates not only the technological feasibility but also the organizational and economic impacts of such implementations, confirming that the proposed framework is not limited to theoretical constructs. Future research and collaborative projects will allow the extension of these applications to other railway operators and maintenance environments, further validating and refining the digitalization strategies presented in this study.

4.5. Human and Organizational Factors in the Adoption of Digital Technologies

The successful implementation of digital technologies in railway maintenance is not solely dependent on technological advances but also on human factors and organizational readiness. The transition from traditional maintenance practices to predictive, data-driven models requires profound cultural and structural adjustments within railway organizations. A major challenge is the need for workforce reskilling and upskilling. Maintenance teams accustomed to manual inspections and scheduled interventions must learn to interpret data outputs from AI models, IoT devices, and digital twins. Continuous training programs, combining theoretical modules with hands-on practical sessions, are essential to ensure that technological innovation is effectively absorbed at all organizational levels. In addition, resistance to change often emerges from uncertainty or lack of confidence in automated decision-making tools. To address this, pilot implementations and demonstrators become crucial. These projects not only validate technological solutions but also act as learning platforms where personnel can build trust in the new digital processes. Cross-departmental collaboration is another key aspect, as the integration of digital solutions requires alignment between IT departments, maintenance engineering teams, operations management, and safety divisions. This alignment must be supported by clear communication structures and shared performance indicators. Leadership commitment is also vital in fostering a data-driven culture; top management must actively support technological initiatives, allocate resources for training, and establish long-term digital strategies. Organizations that succeed in integrating human, organizational, and technological dimensions will be best positioned to reap the full benefits of railway maintenance digitalization. Furthermore, this transformation aligns with the principles of Industry 5.0, which emphasizes the need for a human-centric, sustainable, and resilient approach to industrial innovation. According to the [105], Industry 5.0 promotes not only technological advancement but also the well-being of workers, ensuring that digital transformation respects human dignity, fosters inclusivity, and contributes to long-term societal goals beyond immediate productivity gains [105]. This vision reinforces the necessity for railway organizations to place the human factor at the core of digital initiatives, supporting continuous learning, adaptability, and resilience in an increasingly digital and interconnected operational environment.

4.6. Organizational, Economic, and Safety Benefits of Railway Maintenance Digitalization

The adoption of digital technologies in railway maintenance generates significant benefits at the organizational, economic, and safety levels. Evidence from the literature reviewed in this study as well as recent developments in digital twin frameworks applied to complex infrastructures [106] confirm that digitalization enables better-informed decision-making, improved operational coordination, and more strategic asset management aligned with international best practices. From an organizational perspective, the integration of IoT-based monitoring systems, digital twins, and predictive analytics allows railway operators to transition from reactive maintenance strategies to proactive and condition-based models. This transformation fosters cross-departmental collaboration, aligning maintenance, operations, and asset management teams around shared data-driven objectives. Moreover, it supports the development of long-term maintenance strategies, fully compatible with the UIC asset management framework, which emphasizes structured planning, continuous improvement, and optimized resource allocation. Economically, the studies reviewed highlight how predictive maintenance models and degradation-based optimization approaches reduce unnecessary interventions, optimize maintenance cycles, and improve asset availability. These outcomes translate into improved cost efficiency, more rational use of human and material resources, and enhanced budgeting accuracy for railway infrastructure managers. Safety improvements are also well-documented in the analyzed research. Real-time monitoring and early anomaly detection reduce the probability of unexpected failures, contributing to safer operations and better risk management. These benefits are in direct alignment with the safety dimensions outlined in the UIC framework, which links asset performance to system reliability and user safety. Overall, the integration of digital technologies in railway maintenance not only enhances operational performance but also strengthens the organizational capacity to manage complex infrastructures in line with internationally recognized asset management principles. The continuous collaboration between technological development and structured asset management models, as promoted by the UIC, is essential for achieving a sustainable and resilient railway system.

5. Conclusions

This study has shown that digitalization, through the integration of technologies such as Big Data, IoT, and AI, acts as a key enabler in improving railway maintenance management. Through the detailed analysis of the UIC asset management framework, it has become evident that these technologies not only optimize maintenance operations but also strengthen strategic decision-making processes in the railway sector.

Critical analysis of the International Union of Railways framework and its practical application has revealed that while digitalization is conducive to more efficient and predictive management, the effective integration of these technologies into maintenance strategies still faces significant barriers. The research underscores the importance of a global approach that considers technological solutions and the cultural and organizational transformations required for their effective implementation. In addition, a notable disconnect has been identified between existing regulations and current digitalization practices, suggesting the need to develop standards that reflect the capabilities and challenges of emerging technologies. This study also highlights the importance of aligning technological innovations with the long-term goals of rail organizations, promoting structural change that supports sustainable and effective technology integration.

The role of digitalization as an enabler of more accurate and proactive maintenance management is indisputable, providing tools to improve operational efficiency and decision-making. However, the true potential of these technologies can only be achieved through a framework that promotes adaptability and organizational flexibility, facing the challenges of a constantly evolving industry.

Although this research has made significant contributions to the development of digitalization models and practical applications in railway maintenance, several areas still require further investigation to ensure scalability and adoption across different railway systems. Future studies should focus on bridging the gap between theoretical models and operational applications through pilot demonstrators, collaborative initiatives with infrastructure managers and operators, and continuous technological development.

In this context, future research is expected to build upon simulation environments connected to digital twins enabling the integration of real-time operational data and the development of predictive decision-making tools. Advanced degradation and optimization models, currently relying on semi-Markov and Weibull-based approaches, should evolve by incorporating stochastic simulation and uncertainty quantification techniques, allowing for more robust and reliable planning. In parallel, machine learning and Artificial Intelligence will play key roles in automating condition monitoring, identifying patterns of failure, and improving maintenance scheduling. These technologies will require robust data governance structures and standardized ontologies, such as those derived from the Asset Administration Shell concept, to ensure seamless data exchange and system interoperability.

The expected outcome of these research efforts will be the availability of validated predictive models fully integrated into operational digital twin environments. Real-world demonstrators will showcase how digital twins and AI can optimize maintenance strategies, leading to measurable improvements in reliability, availability, and cost-efficiency. Quantitative assessments of cost savings, reductions in unplanned downtime, and enhancements in safety will support the business case for broader deployment. Furthermore, future research will also address the organizational transformation required for the adoption of digital maintenance, providing frameworks and guidelines to help railway companies transition towards a data-driven decision-making culture.

In summary, future work should follow a structured approach, starting with the deployment of advanced sensing and SHM systems for continuous data collection, followed by the development of data lakes and predictive analytics platforms. The integration of these technologies into asset management models aligned with UIC frameworks and international standards will enable railway operators to prioritize interventions based on criticality and degradation modeling. Finally, pilot implementations, scalability roadmaps, and continuous validation will ensure that the proposed digitalization strategies become operational reality, contributing to a more reliable, efficient, and sustainable railway maintenance system.

In addition, future developments in railway maintenance digitalization should be positioned within the framework of international collaboration and European innovation strategies. Participation in initiatives such as Horizon Europe, Shift2Rail, and active cooperation with regulatory bodies like the European Union Agency for Railways (ERA) will enhance the scalability and harmonization of the proposed methodologies. Aligning future research with European railway safety and interoperability standards, including EU Regulation 779/2019 [103] and the principles of the UIC asset management framework, will ensure that technological advancements are embedded within a structured, compliant, and sustainable operational model. Furthermore, continuous involvement in standardization working groups, focusing on data governance, cybersecurity for critical infrastructures, and digital asset interoperability, will be essential to transform academic research into practical, scalable solutions for railway operators. These collaborative efforts will enable the deployment of intelligent maintenance systems that are not only innovative but also aligned with the regulatory and operational needs of the Single European Railway Area (SERA). In this context, the creation of a dedicated European railway data space, currently non-existent, would represent a transformative opportunity. The broader research and operational community could leverage such a data space by advanced Artificial Intelligence techniques, facilitating cross-border learning, benchmarking, and continuous optimization of railway asset management across Europe.