A context-aware approach for vessels' trajectory prediction*

An accurate vessel trajectory prediction can improve safety management at sea. Although the vessels' trajectories can be affected by complex and high dimensional context factors such as wind, wave size, and current, the research in incorporating them in trajectory prediction is still burgeoning. This paper proposes a context-aware data-driven framework for vessel trajectory prediction. In this regard, trajectories are enriched with context information at the first stage. This includes evaluating methods of trajectory annotation. Next, feature selection techniques are applied to solve the problems caused by high dimensionality, i.e., incensement in the complexity and the computational load. Finally, selected factors are used in a Context-aware Long Short-Term Memory (CLSTM) network. The implementations on the Automatic Identification System (AIS), weather, and ocean condition dataset around the English Channel collected during February 2016, indicated that the spline interpolation is suitable for trajectory annotation. Water depth, wave direction, and height are the most influencing context in vessels' data-driven movement prediction. The predictions made by the proposed framework are 15.31% more accurate than the prediction of a Long Short-Term Memory (LSTM) network. Therefore, the proposed context-aware trajectory prediction framework might suit maritime systems and applications like collision avoidance, vessel route planning, and anomaly detection.

Automated summary

Accurate vessel trajectory prediction is important for safety management at sea. This paper proposes a context-aware data-driven framework for vessel trajectory prediction, which includes trajectory annotation and feature selection. Results showed that the predictions made by this framework are more accurate than those made by an LSTM network.