Research on Ship Trajectory Prediction Method Based on CNN-RGRU-Attention Fusion Model
Wei Liu, Yu Cao, M.‐X. GUAN, Linlin Liu
Abstract
Based on Automatic Identification System (AIS) data in maritime settings, this paper explores the limitations of traditional Recurrent Neural Networks in extracting features from complex vessel trajectory sequences. We propose a fusion model, namely CNN-RGRU-Attention, for vessel trajectory prediction. The model integrates Convolutional Neural Network (CNN), Attention Mechanism, and Gated Recurrent Unit (GRU). The effectiveness of the model is validated using actual AIS data, demonstrating significant improvements in metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). The CNN-RGRU-Attention model provides crucial theoretical support for enhancing the safety management of maritime traffic services.