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Xin, X, Liu, K, Loughney, S, Wang, J and Yang, Z (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 |
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| 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 |
| SWORD Depositor: | A Symplectic |
| Date Deposited: | 21 Nov 2022 10:34 |
| Last Modified: | 05 Nov 2023 00:50 |
| DOI or ID number: | 10.1016/j.ress.2022.108936 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/18170 |
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