A Clustering-based Approach for Predicting the Future Location of a Vessel

Abstract

In recent years the number of vessel accidents has increased with the growing number of vessels in the Ocean. A substantial number of these accidents are due to human errors. However, a vessel could avoid some accidents if it knows future locations or trajectories of surrounding vessels. The future location (or trajectory) prediction of vessels is also valuable for maritime traffic management and planning. Existing approaches for predicting the future location are not vessel-specific, but the behavioural pattern of each type of vessel is different. On the other hand, clustering-based approaches are computationally expensive due to computing the similarity measures among vessel trajectories. In this paper, we propose a clustering-based framework that consists of three modules: dataset preparation, route clustering, and location prediction model. The main novelty of this proposal is that the prediction models are built using only trajectories of vessels of the same type, which we hypothesise will increase their accuracy. The other main contribution of our work is the evaluation of different strategies to improve computational efficiency, which can be critical for real-time vessel location prediction. We have used a density-based hierarchical clustering algorithm, HDBSCAN, where two fast implementations of trajectory similarity measures (Hausdorff Distance and Dynamic Time Warping) are evaluated for clustering route patterns. Also, the effect of using a trajectory compression algorithm (Ramer–Douglas–Peucker) to expedite the clustering process is analyzed. The performance of our approach is evaluated using real-world AIS data. We have compared our proposal against two baseline models. Evaluation results confirm the validity of the proposed approach for prediction of the future location of vessels