# Digitalization and Dynamic Criticality Analysis for Railway Asset Management
## 2 Literature Review
These twins allow real-time simulation and analysis of asset behavior, improving criticality analysis and enabling a more proactive approach to maintenance. * Research on IoT and Big Data in rail asset management has been extensively studied in references such as [16; 17], highlighting how these technologies enable real-time data collection and analysis to optimize decision-making and improve operational efficiency. * The use of artificial intelligence (AI) in railway maintenance has shown substantial advantages, especially in identifying and predicting defects using techniques such as computer vision and machine learning [18]. For example, automated visual track inspection allows rail geometry defects and component wear to be detected with significantly higher accuracy than traditional manual inspections. In addition, deep learning algorithms can detect objects on roads and segment signals in complex environments, improving safety and reducing the risk of accidents. However, the deployment of AI in this sector faces critical challenges. One of the biggest challenges is the lack of specific standards and regulations that ensure the safety and reliability of AI-based systems. In addition, AI integration requires a large amount of high-quality data, the availability and accuracy of which can vary considerably between railway systems. Despite these challenges [19], AI has the potential to transform railway maintenance management, enabling real-time scenario simulation and predictive alert generation that improve operational efficiency and safety. * Several studies, shown in Table 3, have laid the groundwork for implementing digital technologies in rail asset management, demonstrating their potential to improve the safety, efficiency, and sustainability of rail operations. The current literature on the digitalization and management of railway assets has identified several gaps that limit the full exploitation of digital technologies in this sector. * Heterogeneous Data Integration: One of the main challenges is integrating extensive volumes of data from various sources, such as sensors and monitoring devices. Consistency in analyzing these data remains a complex task due to railway systems' dynamic and varied nature. * Scalability and Flexibility: Another critical gap is ensuring that digital solutions are scalable and flexible to adapt to different railway environments. While promising models have been proposed, such as digital twins, more studies are needed to explore their scalability in various operational contexts. * Interoperability and Standardization: A significant obstacle is a lack of standardization and interoperability between different technologies and systems. Both aspects hinder the effective integration of new technologies with legacy systems, which is crucial for efficient asset lifecycle management. * Application of Advanced Data Models: Although progress has been made in collecting and analyzing large volumes of data, few studies have succeeded in integrating these data into operational models that facilitate dynamic criticality assessment and real-time predictions. * Cybersecurity and Data Management: Secure data management and protection against cyber threats are growing concerns as more aspects of railway maintenance are digitized. Addressing these challenges is essential to maximize the potential of digitalization. These gaps highlight the need for further research and development of solutions that enable more effective and secure integration of digital technologies in rail asset management. The table shows challenges and gaps in the literature (Table 3). This study is positioned as a significant contribution to the railway digitalization and asset management field, addressing several gaps identified in the previous research. One of the main contributions is creating an innovative data model that efficiently integrates digitalization in the criticality analysis of railway systems.