An adaptive threshold fast DBSCAN algorithm with preserved trajectory feature points for vessel trajectory clustering

Vessel navigation pattern recognition plays an important role in the research of intelligent transportation on water. Clustering using the data stored in The Automatic Identification System (AIS) is a current research hotspot. However, there are three problems in the past clustering analysis. First,...

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Vydáno v:Ocean engineering Ročník 280; s. 114930
Hlavní autoři: Bai, Xiangen, Xie, Zhexin, Xu, Xiaofeng, Xiao, Yingjie
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 15.07.2023
Témata:
AF-DP
DBSCAN
Fast-DTW
Navigation pattern recognition
Ship trajectory clustering
AF-DP
DBSCAN
Fast-DTW
Ship trajectory clustering
Navigation pattern recognition
ISSN:0029-8018, 1873-5258
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Shrnutí:Vessel navigation pattern recognition plays an important role in the research of intelligent transportation on water. Clustering using the data stored in The Automatic Identification System (AIS) is a current research hotspot. However, there are three problems in the past clustering analysis. First, the traditional Douglas-Peucker (DP) Compression Algorithm exists feature point loss and trajectory distortion when compressing trajectories. Second, Dynamic Time Warping (DTW) and the density-based spatial clustering of applications with noise (DBSCAN) algorithm require high time cost. Finally, most of the studies ignore the interaction between parameters when choosing the parameters of DBSCAN. These problems seriously affect the efficiency and accuracy of clustering. To solve these problems, this paper improves the existing methods by (1) Adaptive selection of compression thresholds and trajectory feature points for each trajectory when using the DP algorithm ensures the realism of the compressed trajectory; (2) using the Fast-DTW algorithm to improve the computation speed and ensure the accuracy of trajectory similarity; (3) Self-selection of parameter combinations based on Silhouette Coefficient (SC) scores was achieved using the similarity distribution of the trajectories in combination with an improved K-Adaptive Nearest Neighbors (KANN). The experiments show that the proposed method can greatly reduce the time cost of clustering compared to the original method and significantly outperforms the three compared algorithms in terms of clustering effect images. •In this paper, we improves the trajectory clustering by:•adaptively selecting compression thresholds and trajectory feature points for each trajectory when using the DP algorithm.•using the Fast-DTW algorithm to improve the computation speed and ensure the accuracy of trajectory similarity.•The KANN method is improved and a more stable threshold selection method is proposed.
ISSN:0029-8018
1873-5258
DOI:10.1016/j.oceaneng.2023.114930