TrAISformer-A generative transformer for AIS trajectory prediction
  Modelling trajectory in general, and vessel trajectory in particular, is a
difficult task because of the multimodal and complex nature of motion data. In
this paper, we present TrAISformer-a novel deep learning architecture that can
forecast vessel positions using AIS (Automatic Identification System)
observations. We address the multimodality by introducing a discrete
representation of AIS data and re-frame the prediction, which is originally a
regression problem, as a classification problem. The model encodes complex
movement patterns in AIS data in high-dimensional vectors, then applies a
transformer to extract useful long-term correlations from sequences of those
embeddings to sample future vessel positions. Experimental results on real,
public AIS data demonstrate that TrAISformer significantly outperforms
state-of-the-art methods.
