Machine Learning Models for Vessel Traffic Flow Forecasting: An Experimental Comparison

Within the last years the shipping industry invest-ments continue to grow to improve maritime transport systems. A vital part of the maritime transport systems is the accurate Vessel Traffic Flow Forecasting (VTFF). In this paper, we approach the VTFF problem from two different perspectives: a) indirect - as a vessel route forecasting application via employing predicted vessels locations in the future, and b) direct - as a flow sequence forecasting problem. In both strategies, machine learning methods are employed because they can leverage from the massive vessel surveillance information to enable deeper digitalization in the shipping industry. This work performs an experimental comparative study between the two approaches over a real dataset from the maritime domain.