Extracting Shipping Route Patterns by Trajectory Clustering Model Based on Automatic Identification System Data

Shipping route analysis is essential for vessel traffic management and relies on professional technical facilities for collecting and recording specific information about vessel behaviors. The recent Automatic Identification System (AIS) onboard has been made available to provide ship-related information for the research. However, the complexity and large quantity of AIS data overload traditional surveillance operations and increase the difficulty of vessel traffic analysis. An unsupervised approach is urgently desired to effectively convert the raw AIS data to regular shipping route patterns. In this paper, we proposed a trajectory clustering model based on AIS data to analyze the shipping routes. The whole model consists of four parts: Data preprocessing, structure similarity measurement, clustering, and representative trajectory extraction. Our model comprehensively considered the geospatial information and contextual features of ship trajectory. The revised density-based clustering algorithm could automatically classify different shipping routes with trajectory features without prior knowledge. The experimental evaluation showed the effectiveness of the proposed model by real AIS data from Port of Tianjin. The results contribute to the further understanding of shipping route patterns and assists maritime authorities and the officers in stable and sustainable vessel traffic management.