Accurate ship track prediction plays a pivotal role in maritime operations, enabling proactive decision-making, enhancing safety, and optimizing vessel routing. We propose ship trajectory prediction - a threefold technique for facilitating accurate predictions, which involves clustering historical AIS trajectories into maritime de facto routes, classifying new trajectory to one of these routes, and conducting predictions along the identified route. To overcome the challenges of capturing the latent structure in high-dimensional and heterogeneous space imposed by AIS data, we introduce a new similarity technique that automatically determines the number of clusters. Furthermore, we introduce a method to automatically annotate the feature space, enhancing the efficiency of data analysis tasks like clustering. This not only improves performance but also ensures transparency, allowing for effective performance evaluation. Our approach demonstrates an accuracy of over 88% and an accuracy of 78% in predicting routes for tanker vessels.

The automated extraction of maritime routes that accurately resembles the real traffic of vessels is crucial for intelligent traffic management systems to understand vessel behaviour in sea areas, identify events, and support decision-making. Available solutions for maritime traffic route extraction utilize traditional clustering algorithms, which have high computational costs. Data reduction methods are proposed for use with these clustering methods to improve clustering performance, which involves a loss in movement pattern quality. Such solutions often result in low quality representations of traffic routes, which poorly estimate sailing distances for long journeys and time of arrivals and poorly identify non-conformities. In this paper, we propose a spatial clustering method (SPTCLUST-II) to extract spatial representations of sailing routes from historical Automatic Identification System (AIS) data. Our method can cluster huge volumes of trajectory data in a minimal amount of time without using any of the traditional clustering algorithms and with no reduction or modification of the spatio-temporal predicates of the original trajectories. A real-world AIS dataset captured in the area of the Gulf of Mexico is used for the evaluation of the proposed method. The results demonstrate that the proposed method extracts tankers maritime traffic routes with an accuracy of 97% and a f1-measure of 98.5% and cargoes maritime traffic routes with an accuracy of 98.1% and a f1-measure of 99%. This method can be utilized by surveillance authorities for stable and sustainable vessel traffic management.

Most of the trajectory datasets only record the spatio-temporal position of the moving object, thus lacking semantics and this is due to the fact that this information mainly depends on the domain expert labeling, a time-consuming and complex process. This paper is a contribution in facilitating and supporting the manual annotation of trajectory data thanks to a visual-analytics-based platform named VISTA. VISTA is designed to assist the user in the trajectory annotation process in a multi-role user environment. A session manager creates a tagging session selecting the trajectory data and the semantic contextual information. The VISTA platform also supports the creation of several features that will assist the tagging users in identifying the trajectory segments that will be annotated. A distinctive feature of VISTA is the visual analytics functionalities that support the users in exploring and processing the trajectory data, the associated features and the semantic information for a proper comprehension of how to properly label trajectories.