  In order to improve the vessel's capacity and ensure maritime traffic safety,
vessel intelligent trajectory prediction plays an essential role in the
vessel's smart navigation and intelligent collision avoidance system. However,
current researchers only focus on short-term or long-term vessel trajectory
prediction, which leads to insufficient accuracy of trajectory prediction and
lack of in-depth mining of comprehensive historical trajectory data. This paper
proposes an Automatic Identification System (AIS) data-driven long short-term
memory (LSTM) method based on the fusion of the forward sub-network and the
reverse sub-network (termed as FRA-LSTM) to predict the vessel trajectory. The
forward sub-network in our method combines LSTM and attention mechanism to mine
features of forward historical trajectory data. Simultaneously, the reverse
sub-network combines bi-directional LSTM (BiLSTM) and attention mechanism to
mine features of backward historical trajectory data. Finally, the final
predicted trajectory is generated by fusing output features of the forward and
reverse sub-network. Based on plenty of experiments, we prove that the accuracy
of our proposed method in predicting short-term and mid-term trajectories has
increased by 96.8% and 86.5% on average compared with the BiLSTM and Seq2seq.
Furthermore, the average accuracy of our method is 90.1% higher than that of
compared the BiLSTM and Seq2seq in predicting long-term trajectories.
