An improved AIS trajectory prediction model based on TrAISformer

As a key technology for maritime applications, trajectory prediction can effectively help ships reduce risks such as collisions and groundings at sea. Currently, although the combination of rich automatic identification system (AIS) data and deep learning brings new possibilities for ship trajectory prediction, ship trajectory prediction is still hugely challenging due to the complexity of ship motion. In this paper, we improved a new ship prediction model based on TrAISformer. On the one hand, we sparse the multi-dimensional data through dictionary coding, map it into probability space, and use a new loss function to measure network performance. On the other hand, we proposed a An MLP algorithm enables it to effectively learn the timing characteristics of AIS data while avoiding destroying encoding correlation. Finally, we conducted experiments on public AIS data, and the experiments showed that the improved model improved the prediction performance by about 12.5 % compared with TrAISformer.