Maritime traffic clustering to capture high-risk multi-ship encounters in complex waters

Xin, X ORCID: 0000-0002-2362-0806, Liu, K, Loughney, S ORCID: 0000-0003-0217-5739, Wang, J ORCID: 0000-0003-4646-9106 and Yang, Z ORCID: 0000-0003-1385-493X (2022) Maritime traffic clustering to capture high-risk multi-ship encounters in complex waters. Reliability Engineering and System Safety, 230. ISSN 0951-8320

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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.

Item Type:	Article
Uncontrolled Keywords:	01 Mathematical Sciences; 09 Engineering; 15 Commerce, Management, Tourism and Services; Strategic, Defence & Security Studies
Subjects:	T Technology > TA Engineering (General). Civil engineering (General) T Technology > TC Hydraulic engineering. Ocean engineering
Divisions:	Engineering
Publisher:	Elsevier BV
Date of acceptance:	25 October 2022
Date of first compliant Open Access:	5 November 2023
Date Deposited:	21 Nov 2022 10:34
Last Modified:	05 Jul 2025 12:15
DOI or ID number:	10.1016/j.ress.2022.108936
URI:	https://researchonline.ljmu.ac.uk/id/eprint/18170

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