An efficient Meta-VSW method for ship behaviors recognition and application.

Ship behaviors refer to the operational process such as sailing, entering into port/departure, etc., which indicate by their position, speed, and so on. The collected big data normally have been treated by unsupervised Machine Learning methods. However, the process is time consuming and lacks consideration of time continuity. From the unknown data to recognize and recur the ship behaviors is still a complex problem. Hence, this study proposes a universal Meta-trajectory Variable Sliding Window (Meta-VSW) method to provide an efficient and high-fidelity solution. In this method, the ship data were connected into the smallest units by the meta-trajectory coding, and combines with variable sliding windows to achieve fast, continuous and accurate recognition of ship behaviors. Taking an inland-water ship and a marine transport ship as examples, the validity of the method was fulfilled and compared with two commonly used algorithms, Affinity Propagation (AP) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). It has the fastest computational speed and can effectively classify the behaviors of massive unknown data from different ships. And it has good performance in capturing behavior boundaries, with the recognition accuracy up to 0.9. Then, the method was applied to analyze the operational effectively and fuel consumption. • An efficient and universal Meta-trajectory Variable Sliding Window (Meta-VSW) method for ship behavior recognition is proposed. • The computational speed and accuracy of the Meta-VSW method are analyzed in comparison with commonly used unsupervised algorithms. • The universality and effectiveness of the Meta-VSW method have been validated through the massive unknown data from two different ships. • The results of ship behavior recognition were applied to the analysis of operational efficiency and fuel consumption. [ABSTRACT FROM AUTHOR]