A machine learning method for the recognition of ship behavior using AIS data

The efficiency of maritime traffic management and the safety of ship navigation have become top priorities. This study introduces a ship behavior recognition method that utilizes the Extreme Gradient Boosting (XGBoost) classification model, in conjunction with the Sparrow Search Algorithm (SSA), to enhance proactive maritime traffic management. The method leverages Automatic Identification System (AIS) data as its primary source and involves several critical steps. Initially, the AIS data is preprocessed, and ship behaviors are encoded. Subsequently, the encoded behaviors are clustered using spectral clustering to create a labeled dataset. Then, this dataset is employed to train and validate the SSA-XGBoost classification algorithm for identifying ship behaviors. Finally, an example analysis is performed in the Yangtze River. The results indicate that the proposed method can accurately and swiftly identify ship behaviors, achieving an accuracy of 97.28%, precision of 96.97%, recall of 97.43%, and an F1 score of 97.19%, surpassing the performance of the existing algorithms. The findings have the potential to aid maritime supervision authorities in promptly assessing ship navigation statuses and provide a valuable reference for developing ship scheduling decisions. • Proposes a ship behavior recognition method using the SSA-XGBoost algorithm with AIS data. • Employs UMAP for dimensionality reduction and spectral clustering for behavior analysis. • Achieves 97.28% accuracy, outperforming Random Forest, GBDT, and standard XGBoost. • Validated through a case study on the Yangtze River, aligning with real ship behaviors. • Supports maritime authorities in enhancing traffic management and navigation safety.