Scalable In-Database Machine Learning for the Prediction of Port-to-Port Routes


  • Dennis Marten Research and Development Department JAKOTA Cruise Systems GmbH | FleetMon Rostockti Germany
  • Carsten Hilgenfeld Research and Development Department JAKOTA Cruise Systems GmbH | FleetMon Rostockti Germany
  • Andreas Heuer Chair of Database and Information Systems Institute of Computer Scienceti Rostock University Rostock, Germany



Digitalization, Big Data, Artificial Intelligence, Markov Process, Port Destination Prediction, Traffic Management


The correct prediction of subsequential port-to-port routes plays an integral part in maritime logistics and is therefore essential for many further tasks like accurate predictions of the estimated time of arrival. In this paper we present a scalable AI-based approach to predict upcoming port destinations from vessels based on historical AIS data. The presented method is mainly intended as a fill in for cases where the AIS destination entry of a vessel is not interpretable. We describe how one can build a stable and efficient in-database AI solution built on Markov models that are suited for massively parallel prediction tasks with high accuracy. The presented research is part of the PRESEA project (“Real-time based maritime traffic forecast”).


United Nations, "Review of Maritime Transport 2019," in United Nations Conference on Trade and Development, Geneva, 2019.

FleetMon, "FleetMon - Tracking the Seven Seas," 2020. [Online]. Available:

G. Pallotta, M. Vespe and K. Bryan, "Vessel Pattern Knowledge Discovery from AIS Data: A Framework for Anomaly Detection and Route Prediction," Entropy, vol. 15, no. 1, pp. 2218-2245, 2013.

I. Parolas, ETA prediction for containerships at the Port of Rotterdam using Machine Learning Techniques, Delft: Delft University of Technology, 2016.

K. F. Toloue and M. V. Jahan, "Anomalous Behavior Detection of Marine VesselsBased on Hidden Markov Model," in 6th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS), Kerman, 2018.

J. d. Toit and J. v. Vurren, "Semi-automated maritime vessel activity detectionusing hidden Markov models," in Proceedings of the 2014 ORSSA Annual Conference, Parys, 2014.

S. Russel and P. Norvig, Artificial Intelligence: A Modern Approach, Pearson, 2020.

D. Marten and A. Heuer, "Machine Learning on Large Databases: Transforming Hidden Markov Models to SQL Statements," Open Journal of Databases, pp. 22-42, 2017.

D. Marten, H. Meyer, D. Dietrich and A. Heuer, "Sparse and Dense Linear Algebra for Machine Learning on Parallel-RDBMS using SQL," Open Journal of Big Data, pp. 1-34, 2019.

L. R. Rabiner, "A tutorial on hidden Markov models and selected applications in speech

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