Unsupervised hierarchical methodology of maritime traffic pattern extraction for knowledge discovery
Li, H.; Lam, J.S.L.; Yang, Z.; Liu, J.; Liu, R.W.; Liang, M.; Li, Y. (2022). Unsupervised hierarchical methodology of maritime traffic pattern extraction for knowledge discovery. Transportation Research Part C-Emerging Technologies 143: 103856. https://dx.doi.org/10.1016/j.trc.2022.103856
In: Transportation Research Part C-Emerging Technologies. PERGAMON-ELSEVIER SCIENCE LTD: Oxford. ISSN 0968-090X, more
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| Keyword |
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| Author keywords |
Pattern extraction; Knowledge discovery; Trajectory compression; Trajectory clustering; Maritime traffic safety management |
| Authors | | Top |
- Li, H.
- Lam, J.S.L., more
- Yang, Z.
- Liu, J.
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- Liu, R.W.
- Liang, M.
- Li, Y.
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| Abstract |
Owing to the space–air–ground integrated networks (SAGIN), seaborne shipping has attracted increasing interest in the research on the motion behavior knowledge extraction and navigation pattern mining problems in the era of maritime big data for improving maritime traffic safety management. This study aims to develop a novel unsupervised methodology for feature extraction and knowledge discovery based on automatic identification system (AIS) data, allowing for seamless knowledge transfer to support trajectory data mining. The unsupervised hierarchical methodology is constructed from three parts: trajectory compression, trajectory similarity measure, and trajectory clustering. In the first part, an adaptive Douglas–Peucker with speed (ADPS) algorithm is created to preserve critical features, obtain useful information, and simplify trajectory information. Then, dynamic time warping (DTW) is utilized to measure the similarity between trajectories as the critical indicator in trajectory clustering. Finally, the improved spectral clustering with mapping (ISCM) is presented to extract vessel traffic behavior characteristics and mine movement patterns for enhancing marine safety and situational awareness. Comprehensive experiments are conducted and implemented in the Chengshan Jiao Promontory in China to verify the feasibility and effectiveness of the novel methodology. Experimental results show that the proposed methodology can effectively compress the trajectories, determine the number of clusters in advance, guarantee the clustering accuracy, and extract useful navigation knowledge while significantly reducing the computational cost. The clustering results are further explored and follow the Gaussian mixture distribution, which can help provide new discriminant criteria for trajectory clustering. |
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