Application of switching-input LSTM network for vessel trajectory prediction

Due to the rapid economic development of modern society, the demand for cargo in the shipping industry has experienced unprecedented growth in recent years. The introduction of a large number of ships, especially large, new, and intelligent ships, has made shipping networks more complex. Controlling transportation risks has become more challenging than ever before. Ship trajectory prediction based on automatic identification system (AIS) data can effectively help identify abnormal ship behaviors and reduce maritime risks such as collisions, grounding, and contacts. In recent years, with the rapid development of deep learning theories, recurrent neural network models (long short-term memory and gated recurrent unit) have been widely used in ship trajectory prediction due to their powerful ability to capture hidden information in time-series data. However, these models struggle with tasks involving high complexity of trajectory features. To address this issue, this paper introduces a switching-input mechanism based on LSTM, constructing a ship trajectory prediction model based on the SI-LSTM model. The switching-input mechanism enables the model to adjust its processing of important information according to dynamic changes in input data, effectively capturing local features of complex trajectories. The experimental section, which includes eight cases of complex trajectories, demonstrates the competitive generalization ability and prediction accuracy of SI-LSTM.