AI in Digital Twins for Rail Operations

The chapter explores the integration of Artificial Intelligence (AI) within Digital Twins (DTs) for rail operations. DTs operate as virtual replicas of physical rail assets and infrastructure, continuously updated through real-time data to mirror the actual system's state. When integrated with AI techniques like Machine Learning, Natural Language Processing, and Generative AI, DTs could support various applications in rail operations, including predictive maintenance, operational optimization, strategic decision-making and real-time monitoring. Thus, the role of AI is investigated in different application areas, such as train scheduling and dispatching, anomaly detection and fault diagnosis in railway signaling systems, energy efficiency, passenger information and ticketing. The key challenges of AI and DTs in rail operations are also addressed, including the need for standardized protocols and regulations, enhanced operational safety, and improved passenger satisfaction. The integration of AI into rail operations not only enhances efficiency and safety, but also contributes to creating more sustainable and resilient transportation systems. As AI technologies continue to evolve, their applications in railway are expected to expand, offering new opportunities for innovation and improvement