基于改进TRACLUS算法的船舶轨迹聚类研究

doi:10.12305/j.issn.1001-506X.2025.04.18

Abstract

Abstract:

With the advancement of ship automatic identification system (AIS), clustering analysis through ship trajectory data has become a critical component in marine traffic management and ship behavior analysis. However, the existing clustering methods based on trajectory points do not adequately consider the temporal aspect of trajectories, and clustering the entire trajectory leads to low computational efficiency and overlook of local information. To address these issues, this paper proposes a ship behavior analysis framework based on an improved trajectory clustering (TRACLUS) algorithm, utilizing Hausdorff distance to assess trajectory similarity. The proposed framework comprises three parts: Trajectory segmentation based on feature points, which segments the entire trajectory to account for common sub-trajectories; Trajectory segment clustering, using the density-based spatial clustering of application with noise (DBSCAN) algorithm to form trajectory clusters; Representative trajectory extraction, employing a scanning line method to identify paths with similar behavior patterns within the clusters. Utilizing data from the ports as a dataset, the results demonstrate that the improved clustering algorithm surpasses the original TRACLUS algorithm in both effectiveness and efficiency, effectively extracting the ship overall movement trends and local similar navigation behaviors within the study area.

Key words: trajectory clustering, ship behavior, automatic identification system (AIS)

CLC Number:

TP751

Wenjuan QI, Zhiheng LIU, Suiping ZHOU, Cheng JIANG, Yongshi JIE, Lei SHI. Research on ship trajectory clustering based on improved TRACLUS algorithm[J]. Systems Engineering and Electronics, 2025, 47(4): 1214-1221.

Figures/Tables 9

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聚簇数量K	运行时间/s	轮廓系数值
4	0.167	0.508
5	0.211	0.507
6	0.251	0.522
7	0.493	0.534

Research on ship trajectory clustering based on improved TRACLUS algorithm

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