ABSTRACT: With the increasing maritime traffic, the effective identification of multi-ship encounter scenarios has become an urgent demand for maritime management. Traditional clustering-based methods tend to generate identification errors in complex environments. This paper proposes a community detection-based approach for recognizing multi-ship encounter scenarios. Community detection is a technique that discovers collective behavior patterns through network topology analysis. In this study, we first construct a ship encounter network model incorporating dynamic ship features such as positions and headings to characterize encounter relationships among ships. Subsequently, we employ the Louvain community detection algorithm to identify communities within the network, where each community represents a multi-ship encounter scenario. Finally, a case study using real AIS data from the Yangtze River Estuary demonstrates that the proposed method can effectively identify multi-ship encounter scenarios.

KEYWORDS: Multi-Ship Encounter Identification, Complex Network Modeling, Community Detection (Louvain), AIS Data Preprocessing, Encounter Influence Weighting, Graph-Based Maritime Traffic Analysis, Network Modularity Optimization, Yangtze River Estuary Case Study

REFERENCES