A Hybrid-Clustering Model of Ship Trajectories for Maritime Traffic Patterns Analysis in Port Area

Lei Liu,Yue Hu,Xiaoxiao Dong,Jingyi Sun,Yong Zhang,Yongming Wang

doi:10.3390/jmse10030342

Abstract

A hybrid-clustering model is presented for the probabilistic characterization of ship traffic and anomaly detection. A hybrid clustering model was proposed to increase the efficiency of trajectory clustering in the port area and analyze the maritime traffic patterns in port. The model identified dissimilarities between trajectories based on characteristics, using K-Means and the density-based spatial clustering of applications with noise algorithm (DBSCAN). Firstly, the ship’s trajectory characteristics are constructed based on real ship trajectories considering static characteristics and dynamic characteristics of ship trajectories to calculate the characteristic dissimilarity between trajectories. Simultaneously, the spatial dissimilarity could be quantified using the Hausdorff algorithm. Then, the ship trajectory is clustered initially based on the departure and destination characteristics using K-Means algorithms to obtain various sub-trajectories. However, there are still different types of trajectories in each sub-trajectory. Thus, the DBSCAN algorithm is adopted to cluster the sub-trajectory based on the analysis of the different trajectory characteristics. Finally, the proposed model is applied to the characterization of the Zhanjiang Port, and the results show that the hybrid-clustering method can effectively cluster ship trajectory and present probabilistic characterization of ship traffic and anomaly detection. This lays a solid theoretical foundation for the supervision and risk control of intelligent ships.

Highlights

A ship trajectory dissimilarity metric and quantitative method are proposed based on different ship trajectory characteristics, including static characteristic dissimilarities, dynamic characteristic dissimilarities, and spatial dissimilarity; The hybrid clustering model is used to realize the division of ship trajectory, improving the efficiency of ship trajectory recognition; ing the efficiency of ship trajectory recognition; (3) Based on the results of ship traffic clustering, analysis and anomaly recogn ship behaviors can be identified and ships can be classified into various rou ages in the port area
Since results based on K-Means clustering are significantly different and the process of parameter selection is relatively simple, we mainly consider the evaluation of DBSCAN clustering
This paper proposes a hybrid-clustering model based on the K-Means and DBSCAN

Summary

Introduction

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. More than 50,000 vessels navigate around the world each day, including shipping on the Arctic Sea Route [2]. Moving ship trajectory is an extremely valuable spatial-temporal data source that could be used to analyze the ship travel behaviors and provide empirical support for ship path planning, grounding risk analysis, anomaly detection, traffic complexity metric, etc. Surveillance of ship travel behaviors is of great importance for maritime safety and security. Accidents frequently occur due to high traffic density, human errors, and severe weather conditions, which cause casualties, property damage, and environmental damage [3]. To enhance navigation safety and reduce the number of maritime accidents, the maritime risk awareness alert system is being gradually developed and established.

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