Multi-Density Adaptive Trajectory Clustering Algorithm for Ships Based on AIS Data
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...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10268431/ |
| _version_ | 1797357946396475392 |
|---|---|
| author | Yingjian Zhang Xiaoyu Yuan Meng Li Guang Zhao Hongbo Wang |
| author_facet | Yingjian Zhang Xiaoyu Yuan Meng Li Guang Zhao Hongbo Wang |
| author_sort | Yingjian Zhang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T14:52:49Z |
| format | Article |
| id | doaj.art-cef37d90e0ed43928c5cebe502b321a9 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T14:52:49Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-cef37d90e0ed43928c5cebe502b321a92024-01-11T00:01:49ZengIEEEIEEE Access2169-35362023-01-011110819810821010.1109/ACCESS.2023.332127010268431Multi-Density Adaptive Trajectory Clustering Algorithm for Ships Based on AIS DataYingjian Zhang0https://orcid.org/0000-0002-7637-9286Xiaoyu Yuan1https://orcid.org/0000-0003-0635-7046Meng Li2Guang Zhao3Hongbo Wang4https://orcid.org/0000-0002-4947-750XState Key Laboratory on Integrated Optoelectronics, College of Electronic Science and Engineering, Jilin University, Changchun, ChinaState Key Laboratory on Integrated Optoelectronics, College of Electronic Science and Engineering, Jilin University, Changchun, ChinaState Key Laboratory on Integrated Optoelectronics, College of Electronic Science and Engineering, Jilin University, Changchun, ChinaTianjin Navigation Instrument Research Institute, Tianjin, ChinaState Key Laboratory on Integrated Optoelectronics, College of Electronic Science and Engineering, Jilin University, Changchun, ChinaAutomatic 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.https://ieeexplore.ieee.org/document/10268431/AIS datadensity-based spatial clustering of applications with noise (DBSCAN) algorithmmulti-density adaptive trajectory clustering (MDA-Traclus) algorithmtrajectory clustering |
| spellingShingle | Yingjian Zhang Xiaoyu Yuan Meng Li Guang Zhao Hongbo Wang Multi-Density Adaptive Trajectory Clustering Algorithm for Ships Based on AIS Data IEEE Access AIS data density-based spatial clustering of applications with noise (DBSCAN) algorithm multi-density adaptive trajectory clustering (MDA-Traclus) algorithm trajectory clustering |
| title | Multi-Density Adaptive Trajectory Clustering Algorithm for Ships Based on AIS Data |
| title_full | Multi-Density Adaptive Trajectory Clustering Algorithm for Ships Based on AIS Data |
| title_fullStr | Multi-Density Adaptive Trajectory Clustering Algorithm for Ships Based on AIS Data |
| title_full_unstemmed | Multi-Density Adaptive Trajectory Clustering Algorithm for Ships Based on AIS Data |
| title_short | Multi-Density Adaptive Trajectory Clustering Algorithm for Ships Based on AIS Data |
| title_sort | multi density adaptive trajectory clustering algorithm for ships based on ais data |
| topic | AIS data density-based spatial clustering of applications with noise (DBSCAN) algorithm multi-density adaptive trajectory clustering (MDA-Traclus) algorithm trajectory clustering |
| url | https://ieeexplore.ieee.org/document/10268431/ |
| work_keys_str_mv | AT yingjianzhang multidensityadaptivetrajectoryclusteringalgorithmforshipsbasedonaisdata AT xiaoyuyuan multidensityadaptivetrajectoryclusteringalgorithmforshipsbasedonaisdata AT mengli multidensityadaptivetrajectoryclusteringalgorithmforshipsbasedonaisdata AT guangzhao multidensityadaptivetrajectoryclusteringalgorithmforshipsbasedonaisdata AT hongbowang multidensityadaptivetrajectoryclusteringalgorithmforshipsbasedonaisdata |