Vessel trajectory clustering using historical AIS data has been a popular research topic in recent years. However, few studies have investigated applying deep learning techniques. In this study, deep representation learning is investigated for use in clustering historical AIS trajectories to provide insight into navigation patterns to support maritime situation awareness. A recurrent autoencoder and beta-variational recurrent autoencoder are investigated to generate fixed size vector representations of the AIS trajectories. Subsequently, clustering is facilitated by applying the Hierarchical Density-Based Spatial Clustering of Applications with Noise algorithm to the representations. The method was tested on historical AIS data for a region surrounding Tromsø, Norway, with successful results. The results also indicate that the beta-variational recurrent autoencoder was able to generate representations of the AIS trajectories that resulted in more compact clusters.
( Video Presentation: https://www.youtube.com/watch?v=KLQp1zwrvk8&feature=emb_logo )