AUTHOR=Ma Lin , Cao Hao , Shi Guo-You 

TITLE=Ship behavior pattern recognition method based on hybrid graph neural networks

JOURNAL=Frontiers in Marine Science

VOLUME=Volume 12 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2025.1605216

DOI=10.3389/fmars.2025.1605216

ISSN=2296-7745

ABSTRACT=IntroductionAccurate 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.MethodsTo 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.ResultsExperimental 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.DiscussionThe 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.