A TDV attention-based BiGRU network for AIS-based vessel trajectory prediction

Automatic identification system (AIS) is a vessel-based system for the automatic broadcast and reception of vessel information, and it also supports data for trajectory prediction. Since the vessel’s sailing route is flexible and changeable and the AIS broadcast is unconfirmed, the trajectory varies greatly and the original AIS data contains some noisy trajectory, which leads to low prediction accuracy and stability. Therefore, to solve the above problem, this paper proposes a trajectory prediction method based on bidirectional gate recurrent unit (BiGRU) and trajectory direction vector (TDV) with attention mechanism. This paper firstly proposes a TDV to associate latitude and longitude with the course and speed. Then the paper proposes an attention mechanism to self-adaptively update weight to the TDV in different stages to eliminate unreasonable predicted trajectory points. Finally, this paper combines the TDV attention mechanism and the BiGRU network to train a vessel trajectory prediction model. [Display omitted] •A vessel trajectory prediction method based on BiGRU and TDV attention is proposed•The TDV attention self-adoptively tunes feature weights in different navigation•A BiGRU with attention mechanism is constructed to train a trajectory prediction model•A trajectory prediction prototype is implemented to predict vessel trajectory Computer science; Artificial intelligence; Machine learning