Abstract:
Digital Twin (DT) technology is emerging as a transformative tool in inland waterway transport, enhancing operational reliability and efficiency. This paper explores the application of DTs in predicting water levels and optimizing transport infrastructure, based on the experience of the European Union’s CRISTAL project. The study highlights challenges such as data availability constraints and the need for continuous monitoring. By leveraging real-time data, historical records, and machine learning, DTs provide predictive analytics that improve navigability, reduce maintenance costs, and enhance decision-making for port operations. Key functionalities of DTs in Inland Waterway Transport (IWT) include real-time condition monitoring, advanced forecasting capabilities, and early warning systems. The findings demonstrate that DT implementation leads to increased resilience, sustainability, and efficiency in inland navigation. The study concludes that while DTs offer significant advantages, further improvements in data granularity and interoperability are necessary to maximize their potential in the maritime transport sector.
