Self-adaption vessel traffic behaviour recognition algorithm based on multi-attribute trajectory characteristics

Abstract

Clustering analysis is commonly used for vessel traffic behaviour recognition. The clustering results reflect the characteristics of different vessel traffic behaviours, which can assist authorities in transportation management. However, there are two drawbacks to traditional clustering analysis. First, the similarity measures among different trajectories in a clustering analysis mainly focus on spatial differentiation and frequently do not consider motion features, such as speed and course. Second, the density-based spatial clustering of applications with noise (DBSCAN) algorithm, a famous clustering analysis method, is characterized by poor self-adaption, which means that it cannot autonomously determine the best parameters based on different sample sets. These issues limit the efficiency of clustering analysis. To address these problems, a new similarity measure using statistical methods and based on multi-attribute trajectory characteristics is proposed to reflect the similarity among different trajectories. The proposed measure considers the spatial and motion features of trajectories. Furthermore, the Parzen window (a non-parametric estimation method) and the multivariate Gaussian distribution are employed to generate self-adaptive strategies to determine the optimal parameters of the DBSCAN algorithm for a given sample set. Finally, numerical experiments are conducted to verify the effectiveness of the proposed similarity measure and improved DBSCAN algorithm. The clustering analysis results obtained with the proposed method could provide insights towards better monitoring and navigation advice to help improve marine managerial effectiveness and avoid maritime accidents.