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

Abstract

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”).