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A digital twin approach to enhancing the reliability of inland waterway transport via water-level forecasting and port call coordination

Ernest Czermański et al.

Maritime Economics & Logistics2026https://doi.org/10.1057/s41278-026-00352-9article
AJG 1ABDC B
Weight
0.50

What the paper says

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.

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https://doi.org/https://doi.org/10.1057/s41278-026-00352-9

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@article{ernest2026,
  title        = {{A digital twin approach to enhancing the reliability of inland waterway transport via water-level forecasting and port call coordination}},
  author       = {Ernest Czermański et al.},
  journal      = {Maritime Economics & Logistics},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1057/s41278-026-00352-9},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.