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An Improved DBSCAN Clustering Method for AIS Trajectories Incorporating DP Compression and Discrete Fréchet Distance

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Spatial Data and Intelligence (SpatialDI 2024)

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Abstract

AIS provides a huge amount of maritime traffic data containing spatial and temporal information in a limited area. Trajectory clustering based on AIS data is a pre-task in intelligent maritime domain, providing typical movement patterns of vessels for follow-up studies in navigation safety and maritime supervision. This paper presents an AIS trajectory clustering method incorporating discrete Fréchet distance and Douglas-Peucker (DP) algorithm, based on improved density-based spatial clustering of applications with noise (DBSCAN). Experimental results on the dataset of vessels entering and leaving the Taiwan Strait in November 2017 demonstrate the effectiveness of our method.

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Acknowledgments

This study was funded by the National Key R&D Program (2020YFB2104400), the Beijing Natural Science Foundation (Grant No. L222048), and the Young Scientists Fund of the National Natural Science Foundation of China (No. 62202018).

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Correspondence to Xiaoying Zhi or Peng Wang .

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Liu, X., Zhi, X., Wang, P., Mei, Q., Su, H., He, Z. (2024). An Improved DBSCAN Clustering Method for AIS Trajectories Incorporating DP Compression and Discrete Fréchet Distance. In: Meng, X., Zhang, X., Guo, D., Hu, D., Zheng, B., Zhang, C. (eds) Spatial Data and Intelligence. SpatialDI 2024. Lecture Notes in Computer Science, vol 14619. Springer, Singapore. https://doi.org/10.1007/978-981-97-2966-1_4

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  • DOI: https://doi.org/10.1007/978-981-97-2966-1_4

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-2965-4

  • Online ISBN: 978-981-97-2966-1

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