Evaluation of Ship Importance in Offshore Wind Farm Area Based on Fusion Gravity Model in Complex Network

Jian Liu, Keteng Ke, Shimin Yang, Chuang Yang, Zhongyi Sui (), Chunhui Zhou and Lichuan Wu
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Jian Liu: Shanghai Investigation, Design & Research Institute Co., Ltd., Shanghai 200335, China
Keteng Ke: Shanghai Investigation, Design & Research Institute Co., Ltd., Shanghai 200335, China
Shimin Yang: Shanghai Investigation, Design & Research Institute Co., Ltd., Shanghai 200335, China
Chuang Yang: Shanghai Investigation, Design & Research Institute Co., Ltd., Shanghai 200335, China
Zhongyi Sui: Department of Logistics and Maritime Studies, Faculty of Business, The Hong Kong Polytechnic University, Hong Kong 999077, China
Chunhui Zhou: School of Navigation, Wuhan University of Technology, Wuhan 430063, China
Lichuan Wu: Department of Earth Sciences, Uppsala University, 75236 Uppsala, Sweden

Sustainability, 2025, vol. 17, issue 18, 1-25

Abstract: With the rapid expansion of offshore wind farms (OWFs), ensuring maritime safety in adjacent waters has become an increasingly critical challenge. This study proposes an innovative dynamic risk assessment method that integrates a fusion gravity model into a complex network framework to comprehensively evaluate ship importance in OWF areas. By treating ships and wind farms as network nodes and modeling their interactions using AIS data, the method effectively captures spatiotemporal traffic dynamics and precisely quantifies ship importance. Multiple network indicators, including centrality, clustering coefficient, and vertex strength, are fused to comprehensively assess node criticality. A case study in the Yangtze River Estuary empirically demonstrates that ship importance is not static but dynamically and significantly changes with trajectories, interactions with other vessels, and proximity to OWFs, successfully identifying high-risk ships and sensitive OWF areas. The contribution of this research lies in providing a data-driven, quantifiable, novel framework capable of real-time identification of potential threats in maritime traffic. This approach offers direct and practical insights for traffic control, early warning system development, and optimizing maritime traffic management policies, facilitating a shift from reactive response to proactive prevention. Ultimately, it enhances safety supervision efficiency and decision-making support in complex maritime environments, safeguarding the sustainable development of the offshore wind industry.

Keywords: traffic management; situation awareness; offshore wind farm; complex network; gravity model (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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