Vessel trajectory prediction with the introduction of channel information

Automatic Identification System (AIS) data mining is an important research direction in water transportation, and vessel trajectory prediction is one of its applications. Accurately predicting the future trajectory of a vessel is of great importance for situational awareness and risk warning in maritime traffic. However, the complexity of waterways, bank effect and navigational danger pose challenges to vessel trajectory prediction. To solve this problem, we proposed the Context-aware LSTM network(CaLSTM). This model is an encoder-decoder structure, which constructs channel information as an information matrix and uses convolutional neural network (CNN) and long short-term memory network (LSTM) to predict vessel trajectories. At the same time, a new loss function, enviro-MSE loss, was proposed to evaluate the environment of prediction results and reduce the possibility of prediction trajectory stranding. Finally, the proposed model was tested using data from Ningbo-Zhoushan port waters. The results show that the CaLSTM model can make more accurate and reasonable predictions, and the prediction results are more consistent with the ship navigation pattern than the baseline model under the complex environment.