Maritime traffic clustering to capture high-risk multi-ship encounters in complex waters. (February 2023)

• Record Type: Journal Article
• Authors: Xin, Xuri; Liu, Kezhong; Loughney, Sean; Wang, Jin; Yang, Zaili
• Is Part Of: Reliability engineering & system safety. Volume 230(2023)
• Publication Date: 2023-02
• Subjects: Maritime safety -- Intelligent maritime monitoring -- Traffic cluster identification -- Clustering technique -- AIS data; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2022.108936
• Languages: English
• ISSNs: 0951-8320

Abstract

Highlights: A traffic partition framework to incorporate conflict and distance relations. A novel hybrid clustering approach to achieve robust traffic partition. A hierarchical optimization algorithm to search for optimal clustering solutions. Effective and reliable performance in capturing high-risk ship traffic clusters. Abstract: Maritime traffic situational awareness is fundamental to the safety of maritime transportation. The state-of-the-art research primarily attaches importance to collision risk estimation and evaluation between/among ships but encounters the challenges of capturing the high-risk traffic clusters in complex waters. This paper develops a systematic traffic clustering approach to enhance traffic pattern interpretability and proactively discover high-risk multi-ship encounter scenarios, in which both the conflict connectivity and spatial compactness of encounter ships are considered. Specifically, a novel hybrid clustering approach that integrates a composite distance measure, a constrained Shared Nearest Neighbour clustering, and a fine-tuning strategy is developed to segment maritime traffic into multiple conflict-connected and spatially compact clusters. Meanwhile, a hierarchical bi-objective optimization algorithm is introduced to search for optimal clustering solutions. Through maritime traffic data obtained from the Ningbo-Zhoushan Port, a thorough methodology performance evaluation is carried out through application demonstration and validation. Experiment results reveal that the new approach: 1) can effectively capture the high-risk/density traffic clusters; 2) is robust with respect to various traffic scenarios; and 3) can be extended to assist in collision risk management. It therefore offers new insights into enhancing maritime traffic surveillance capabilities and eases the design of risk management strategy.