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Modeling, Evaluation, and Mitigation of Maritime Traffic Complexity in Complex Waters
Author(s) -
Xuri Xin,
Kezhong Liu,
Jiongjiong Liu,
Weiqiang Wang,
Zaili Yang
Publication year - 2025
Publication title -
ieee transactions on intelligent transportation systems
Language(s) - English
Resource type - Magazines
SCImago Journal Rank - 1.591
H-Index - 153
eISSN - 1558-0016
pISSN - 1524-9050
DOI - 10.1109/tits.2025.3584705
Subject(s) - transportation , aerospace , communication, networking and broadcast technologies , computing and processing , robotics and control systems , signal processing and analysis
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.

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