Digital Twin and Artificial Intelligence for Railway Hubs Design and Management

The use of artificial intelligence in transportation systems represents a promising research direction for improving the performance of railway hubs and upgrading the criteria and methods for their design and optimal management. The advent of big data sources and machine learning techniques has expanded the capabilities of traditional simulation tools, paving the way for real-time estimated models (online) and automated control of real-world systems (closed-loop optimization). This chapter delves into the concept of digital twins of multimodal hubs, combining integrated micro- and macro-simulation models with real-time data collection and policy implementation. The use of digital twins for railway hub planning and management is examined, and their functional architecture and the challenges related to real-world implementation are discussed.