ORIGINAL RESEARCH article
Front. Mar. Sci.
Sec. Ocean Observation
Volume 12 - 2025 | doi: 10.3389/fmars.2025.1605216
Ship Behavior Pattern Recognition Method Based on Hybrid Graph Neural Networks
Provisionally accepted
- Dalian Maritime University, Dalian, China
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Accurate ship behavioral pattern identification is crucial for maritime management and enhances regulatory efficiency, accident prevention, navigation safety, and scheduling. Conventional methods typically struggle with high-dimensional, time-series ship trajectory data. 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. The proposed models achieve over 98% accuracy for ship pattern recognition and fast training times, significantly outperforming conventional GNN models. They provide efficient and accurate pattern recognition with extensive applications in maritime supervision and management.
Keywords: Maritime traffic networks, Graph neural networks, AIS data, Behavioral pattern recognition, ship motion analysis
Received: 04 Apr 2025; Accepted: 12 May 2025.
Copyright: © 2025 Ma, Cao and Shi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Lin Ma, Dalian Maritime University, Dalian, China
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