The destination of vessels is important decision makers of maritime trading. However, shipping companies keep this kind of data inclusively, which results in the absence of complete information of destination for every vessel. However, other information such as the position can be available due to Automated Identification Systems (AIS). Hence, predicting the vessels’ destination port becomes possible. To give a baseline of how to make use of AIS data for vessel destination prediction with machine learning, several AIS data preprocessing approaches and machine learning approaches for vessel destination prediction are introduced in the literature. The chapter aims to give the audience an idea of how to link between AIS data, trajectories, and numerous machine learning models for the purpose of predicting arrival ports for maritime services. Furthermore, the discussion points out the current state of researches on this topic and where the potential future work may possibly lie in.
Keywords: AIS, Bayesian estimation, Deep learning, Destination prediction, Machine learning, Maritime analysis, Nearest neighbor search, Sequence to sequence, Similarity measures, Spatial-temporal data, Trajectory.