Ship behavior pattern recognition method based on hybrid graph neural networks
Lin Ma, Hao Cao, Guoyou Shi
Abstract
Introduction Accurate identification of ship behavioral patterns is essential for maritime management, contributing to improved regulatory efficiency, accident prevention, navigation safety, and scheduling. However, traditional methods often struggle with the complexity of high-dimensional, time-series trajectory data. Methods To overcome these challenges, this study proposes the following optimized graph neural network (GNN) models: an optimized adjacency matrix graph convolutional network, a hybrid model combining a graph convolutional network with a graph attention network (GAT), and an integrated model of GAT and long short-term memory. These models leverage standardized automatic identification system data to improve feature extraction and recognition accuracy. Results Experimental results demonstrate that the proposed models achieve over 98% accuracy in ship behavioral pattern recognition, with fast convergence and superior performance compared to conventional GNN-based methods. Discussion The models provide robust and efficient solutions for maritime traffic analysis, offering significant potential for real-world applications in ship monitoring, intelligent navigation, and maritime safety management.