Review: Ship trajectory prediction based on AIS data using machine learning methods

The comparison table shows researches that processes AIS data using machine learning methods with the aim of predicting the ship's trajectory. Ship trajectory prediction is the process of estimating the future path of a moving object based on its past behavior. Ship trajectory prediction refers to models that can capture the ship motion characteristics and estimate their future positions. The aim of ship trajectory prediction is to provide the possibility to integrate and enrich services to support decision-making including target-tracking, collision avoidance and detection of abnormal traffic behavior, but it is also possible to predict the arrival time of the ship at the destination, port of departure and port of arrival. A variety of machine learning methods have been developed for that purpose including Extended Kalman Filter (EKF), Neural Networks, Bayesian Networks, The Constant Velocity Method, The Single Point Neighbor Search Method, The Multiple Trajectory Extraction Method (MTEM), Gaussian Process, Dual linear autoencoder and etc. Ship trajectory prediction is important because it can help improve the safety and efficiency of maritime transportation by providing accurate and reliable predictions of ship trajectories.