A Graph Representation Learning Approach for Imbalanced Ship Type Recognition Using AIS Trajectory Data

Marine transportation constitutes a vital segment of international trade logistics. Recognizing marine carrier ship types is essential for the governance and efficiency of the marine transportation sector. Recognizing ship types by analyzing Automatic Identification System (AIS) trajectory data represents a fundamental aspect of trajectory classification within Intelligent Transportation Systems (ITS). Due to their data representation limitations, traditional CNNs and RNNs struggle to simultaneously process the topological relationships and the attributes of trajectory points. This restriction significantly hinders the learning of micro-activity behaviors of ships. To overcome this, we introduce Graph Isomorphic Network (GIN), a new variant of graphical neural networks with enhanced representation capabilities. Therefore, this study develops a GIN-based framework for graph representation learning. Initially, ship trajectories are represented as vector graph structures, with both original and derived features incorporated into the node embeddings. Subsequently, a GIN-based model for graph representation learning is employed to extract features necessary for ship type recognition. The model’s performance is evaluated through experiments on ground truth data from the Gulf of Mexico and the New Jersey Bight, achieving recognition accuracies of 93.95% and 92.33%, respectively. The high accuracy and robustness of the model substantiate the effectiveness of the proposed method in ship type recognition. This research illustrates that GIN-based graph representation learning can efficiently capture trajectory features, offering a valuable reference for motion trajectory representation extraction and pattern recognition in diverse geographical contexts.