Machine Learning Models for Vessel Route Forecasting: An Experimental Comparison

Maritime transport systems are essential to human mobility. A vital part of the maritime transport systems is the accurate vessel route forecasting (VRF). However, accurate VRF is a challenging task due to the fact that maritime traffic conditions are complex and dynamic. Machine learning (ML) methods can leverage from the ”explosion” of vessel surveillance information in order to encourage, enable deeper digitalization in the shipping industries and tackle the VRF problem. In this paper we investigate some of the most popular ML methods to address the VRF problem and we present an overview of these methods through an experimental testbed based on real vessel surveillance data. This work results in introducing baseline ML models for VRF purposes.