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

Academic Editors: Albert Lau and Yang Song Received: 24 February 2025 Revised: 7 April 2025 Accepted: 9 April 2025 Published: 11 April 2025 Citation: Rodríguez-Hernández, M.; Crespo-Márquez, A.; Sánchez-Herguedas, A.; González-Prida, V. Digitalization as an Enabler in Railway Maintenance: A Review from “The International Union of Railways Asset Management Framework” Perspective. Infrastructures 2025,10, 96. https://doi.org/10.3390/ infrastructures10040096 Copyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/ licenses/by/4.0/). Review Digitalization as an Enabler in Railway Maintenance: A Review from “The International Union of Railways Asset Management Framework” Perspective Mauricio Rodríguez-Hernández * , Adolfo Crespo-Márquez , Antonio Sánchez-Herguedas and Vicente González-Prida * Higher Technical School of Engineering, University of Seville, P.C. 41092 Seville, Spain; [email protected] (A.C.-M.); [email protected] (A.S.-H.) *Correspondence: [email protected] (M.R.-H.); [email protected] (V.G.-P.); Tel.: +34-747432379 (M.R.-H.) 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. Keywords: digitalization; maintenance management; railway; framework criticality 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 – 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 Infrastructures 2025,10, 96 https://doi.org/10.3390/infrastructures10040096 Infrastructures 2025,10, 96 2 of 36 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. 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 5by 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. Infrastructures 2025,10, 96 3 of 36 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. 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 Infrastructures 2025,10, 96 4 of 36 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 GomezLuna [ 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 Infrastructures 2025,10, 96 5 of 36 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. Infrastructures 2025,10, 96 6 of 36 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 1shows 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. Infrastructures 2025,10, 96 7 of 36 Infrastructures 2025, 10, x FOR PEER REVIEW 7 of 37 Figure 1. Literature review methodology and research structure. 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). 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. 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 Figure 1. Literature review methodology and research structure. 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). 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. 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 Infrastructures 2025, 10, x FOR PEER REVIEW 8 of 37 Figure 2. Annual number of papers published from WoS sources. Figure 3. Annual number of papers published from Scopus sources. 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. 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, sustainab le 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 Predictive maintenance, structural analysis, reliab ility, nondestructive inspection, asset management Figure 2. Annual number of papers published from WoS sources. Infrastructures 2025,10, 96 8 of 36 Infrastructures 2025, 10, x FOR PEER REVIEW 8 of 37 Figure 2. Annual number of papers published from WoS sources. Figure 3. Annual number of papers published from Scopus sources. 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. 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, sustainab le 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 Predictive maintenance, structural analysis, reliab ility, nondestructive inspection, asset management Figure 3. Annual number of papers published from Scopus sources. 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 4shows 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. Infrastructures 2025, 10, x FOR PEER REVIEW 9 of 37 Management—Reliability—Structural Engineering 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, vib ration 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 Figure 4. Annual number of papers published from WoS and Scopus sources. Infrastructures 2025,10, 96 9 of 36 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. 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 Infrastructures 2025,10, 96 16 of 36 Table 4. Cont. Research Railway Aspect Keywords Paper Total Authors Title Year Cited by 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 Infrastructures 2025,10, 96 17 of 36 Table 4. Cont. Research Railway Aspect Keywords Paper Total Authors Title Year Cited by 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 inclu- Infrastructures 2025,10, 96 18 of 36 sive 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. Infrastructures 2025, 10, x FOR PEER REVIEW 19 of 37 Figure 8. UIC asset management framework alignment with ISO 55001[9] (ISO 55001 sections reference). 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: 1. 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. Figure 8. UIC asset management framework alignment with ISO 55001 [ 9 ] (ISO 55001 sections reference). 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: Infrastructures 2025,10, 96 19 of 36 1. 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. Infrastructures 2025,10, 96 20 of 36 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. 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 Infrastructures 2025,10, 96 21 of 36 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 6shows 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 Infrastructures 2025,10, 96 22 of 36 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). 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 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. Infrastructures 2025,10, 96 23 of 36 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. Address- Infrastructures 2025,10, 96 24 of 36 ing 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 5shows 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. Infrastructures 2025,10, 96 25 of 36 • 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 Infrastructures 2025,10, 96 32 of 36 Acknowledgments: During the preparation of this work, the authors used CHAT GPT 4 in order to improve text writing and assist in reviewing high volumes of information in specific clusters from keywords by the author. After using this tool/service, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication. Conflicts of Interest: The authors declare no conflict of interest. Abbreviations The following abbreviations are used in this manuscript: RRA Research Railway Aspect SRTIT Sustainable Railway Transport and Infrastructure Technology SEARM Structural Engineering and Advanced Railway Maintenance RMMO Railway Maintenance Management and Optimization IPMTRI Inspection and Predictive Maintenance Technologies for Railway Infrastructure RRSDO Railway Rolling Stock Design and Operation APDRT Analysis and Prediction of Degradation on Railroad Track References 1. 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