Ship trajectory prediction with high accuracy plays a significant role in maritime traffic management. The collision can be effectively decreased with the help of real-time prediction to plan a navigation course and monitor the ship's travel status. We propose a short-term trajectory prediction method based on bidirectional long short-term memory (Bi-LSTM) network by using AIS data from the Hainan-1 satellite. The main steps include (1) eliminating the abnormal data by filtering the historical data, smoothing the trace by linear interpolation, and normalizing into uniformly distributed time-series data; (2) creating the Bi-LSTM model; (3) predicting the next position of the ship. The experimental results show that the model has a relatively low root-mean-square error, which demonstrates its efficiency for trajectory prediction and can be utilized to avoid collisions and improve the safety of maritime traffic.
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