Recognition Model and Simulation of Busy Waters in Fishing Area Based on Density Clustering

Recognition Model and Simulation of Busy Waters in Fishing Area Based on Density Clustering

In this paper, the characteristics of the potential behavior of ships contained in AIS information are con-sidered, in order to ensure the safety of navigation in merchant ships’ fishing areas and reduce the occur-rence of collisions between commercial fishing boats. Based on the DBSCAN density clustering algorithm, the identification model of busy water in fishing areas is developed and applied to Minnan fishery in the Taiwan Strait. In addition, a real ship simulation is performed based on real traffic flow data. The results demonstrate that the proposed model can accurately identify the spatial distribution and scale of busy fishing area, adjust the algorithm parameters according to the merchant ship scale, and generate the target-ed recommended route decision for the fishing area. Finally, the STELLAR EXPRESS is compared with the original route. The obtained results show that the recommended route provides a high safety in the fishing area, while retaining the ship operating costs. The model results can be used as a reference for maritime security departments in order to divide the fishing area warning water, and the identification method provides a novel approach for the safety supervision of maritime department.