Modeling, Evaluation, and Mitigation of Maritime Traffic Complexity in Complex Waters

Xin, X, Liu, K, Liu, J, Wang, W and Yang, Z ORCID: 0000-0003-1385-493X (2025) Modeling, Evaluation, and Mitigation of Maritime Traffic Complexity in Complex Waters. IEEE Transactions on Intelligent Transportation Systems, 26 (9). pp. 13275-13292. ISSN 1524-9050

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Abstract

Accurately interpreting regional traffic situations plays a pivotal role in emerging intelligent transportation systems, particularly in the evaluation of traffic states to realize the implementation of rational interventions. Nonetheless, existing studies face challenges when it comes to unveiling the complex nested interactions among multiple ships while simultaneously factoring in various influential elements for precise collision risk evaluation. This paper aims to develop a comprehensive methodology for collectively modeling, evaluating, and mitigating maritime traffic complexity, to enhance the comprehension of traffic patterns and guide anti-collision management in complex waters. First, a novel ship domain-based approach is proposed, incorporating individual ship attributes, relative bearing, ship motion dynamics, and restricted water geography to realize the accurate evaluation of ship-pair conflict risk. Subsequently, advanced motif structure-based indicators and a network disintegration model are merged to provide a thorough and nuanced characterization of the topological dependencies among multiple conflicts within a specified maritime region. Simultaneously, a comprehensive complexity evaluation approach, combining Principal Component Analysis (PCA) and a Fuzzy Clustering Iterative (FCI) method, is employed to achieve dependable parameterization and classification of traffic complexity levels. Finally, the collective impact of multiple interdependent conflicts on overall traffic complexity mitigation is investigated to support the identification of key influential conflicts that should take precedence in joint resolution efforts. Extensive experimental analyses based on Automatic Identification System (AIS) data are carried out to validate the effectiveness of the proposed methodology. These analyses demonstrate its applicability in accurately assessing conflict risk, hierarchically categorizing traffic complexity levels, and providing guidance for joint conflict resolution endeavors. Consequently, this methodology holds significant promise for bolstering the growth of intelligent transportation service systems and facilitating the automation of maritime traffic management.

Item Type:	Article
Uncontrolled Keywords:	Maritime risk; intelligent surveillance and man-agement; ship traffic complexity; motif structure-based indicators; motif structure-based indicators; AIS data; AIS data; AIS data; 3509 Transportation, Logistics and Supply Chains; 35 Commerce, Management, Tourism and Services; 0801 Artificial Intelligence and Image Processing; 0905 Civil Engineering; 1507 Transportation and Freight Services; Logistics & Transportation; 3509 Transportation, logistics and supply chains; 4602 Artificial intelligence; 4603 Computer vision and multimedia computation
Subjects:	T Technology > TK Electrical engineering. Electronics. Nuclear engineering V Naval Science > V Naval Science (General)
Divisions:	Engineering
Publisher:	IEEE
Date of acceptance:	23 June 2025
Date of first compliant Open Access:	13 May 2026
Date Deposited:	13 May 2026 11:08
Last Modified:	13 May 2026 11:08
DOI or ID number:	10.1109/TITS.2025.3584705
URI:	https://researchonline.ljmu.ac.uk/id/eprint/28561

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