Deep learning based ship trajectory prediction with AIS

Abstract

It is crucial to monitor and predict the trajectory of ships for preventing maritime accidents and enhancing maritime efficiency. Traditional data mining algorithms face serious challenges in terms of computational complexity and generalization capability. To address these challenges, a deep learning-based ship trajectory prediction method is proposed in this paper. By performing clustering on trajectory data, similar trajectories are grouped into clusters, effectively reducing the complexity of the data. Subsequently, the Seq2Seq model is employed to model and predict trajectories within each cluster. Experimental results demonstrates effectiveness and superiority of the proposed method in ship motion trajectory prediction tasks.