聚类分析
弹道
计算机科学
噪音(视频)
豪斯多夫距离
光学(聚焦)
相似性(几何)
流量(计算机网络)
算法
数据挖掘
人工智能
物理
天文
图像(数学)
计算机安全
光学
作者
Jiaxuan Yang,Yuan Liu,Lingqi Ma,Chengtao Ji
标识
DOI:10.1016/j.oceaneng.2022.111001
摘要
Most of the existing ship trajectory clustering algorithms focus on the properties of single AIS point or sub-trajectories: the trajectory point clustering does not consider the spatio-temporal correlation between neigh-boring points on the same ship trajectory, and is incapable to portray the overall characteristics of ship motion; the ship sub-trajectories clustering needs to discard some points in the ship trajectories, which may lose the vital part of trajectories for clustering purpose. In order to solve the mentioned problems, this paper proposes a DBTCAN (Density based Trajectory Clustering of Applications with Noise) algorithm. This algorithm is suitable for clustering complete trajectories or sub-trajectories of different lengths by using Hausdorff distance as a similarity measure, and can recognize noise trajectories. In addition, DBTCAN algorithm can adaptively determine its optimal input parameters by using adaptive parameter algorithm. We test this method by real AIS data from Bohai Sea, and the experimental results show that DBTCAN algorithm can cluster ship trajectories and extract the main routes of Bohai Sea. Furthermore, the results can provide guidance for the VTS and other agents for carrying out route planning, vessel traffic separation and regulating traffic flows.
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