Multi-Density Adaptive Trajectory Clustering Algorithm for Ships Based on AIS Data

Yingjian Zhang

doi:10.1109/ACCESS.2023.3321270

Multi-Density Adaptive Trajectory Clustering Algorithm for Ships Based on AIS Data

2023-01-01

6

OA

AI

DOI：10.1109/ACCESS.2023.3321270

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Automatic Identification System (AIS) can obtain a large amount of data on ship trajectories and movement characteristics, which provides data support for ship route planning, navigation safety, and maritime traffic control. Therefore, it is of great importance to analyze and cluster the AIS data to obtain an understanding of the movement behavior of ships. The Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is capable of discovering arbitrarily shaped clusters and is suitable for clustering ship trajectories. However, the traditional DBSCAN algorithm suffers from the following shortcomings. First, the DBSCAN algorithm requires the input of a similarity matrix between the trajectories, and the similarity matrix obtained from the traditional Hausdorff distances does not identify the trajectory directions. Second, the DBSCAN algorithm is sensitive to the input parameters, and the clustering results obtained from different datasets with the same parameters vary widely. Finally, the DBSCAN algorithm is poor at clustering multi-density distribution datasets. To address these shortcomings, this study proposes a multi-density adaptive trajectory clustering (MDA-Traclus) algorithm, which considers the multi-density distribution trajectory dataset, adaptively determines the input parameters, and adds a trajectory direction identification mechanism to realize the clustering of trajectory clusters with different directions and different densities. Here, the classical DBSCAN algorithm, KANN-DBSCAN algorithm, and MDA-Traclus algorithm are compared and analyzed using four manually labeled trajectory datasets and a real trajectory dataset. The results show that the MDA-Traclus algorithm and KANN-DBSCAN algorithm are able to automatically determine the input parameters when clustering trajectory datasets with uniformly distributed densities and achieve the same clustering effect as the DBSCAN algorithm. When the trajectory density distribution is not uniform and the directions of the trajectory clusters are not consistent, the MDA-Traclus algorithm can effectively identify the low-density regions, differentiate the different directions of trajectories, and improve the clustering of the trajectories.

Keyword:

Trajectory

Clustering algorithms

Marine vehicles

Artificial intelligence

Prediction algorithms

Behavioral sciences

Planning

AIS data

density-based spatial clustering of applications with noise (DBSCAN) algorithm

multi-density adaptive trajectory clustering (MDA-Traclus) algorithm

trajectory clustering

Multi-Density Adaptive Trajectory Clustering Algorithm for Ships Based on AIS Data

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