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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Xiao, Z., Fu, X., Zhang, L., Goh, R.S.M.: Traffic pattern mining and forecasting technologies in maritime traffic service networks: a comprehensive survey. IEEE Trans. Intell. Transp. Syst. 21(5), 1796–1825 (2019)
Tetreault, B.J.: Use of the Automatic Identification System (AIS) for maritime domain awareness (MDA). In: Proceedings of OCEANS 2005 MTS/IEEE, 17–23 September 2005, Washington, DC, USA, pp. 1590–1594. IEEE (2005)
Yang, D., Wu, L., Wang, S., Jia, H., Li, K.X.: How big data enriches maritime research–a critical review of automatic identification system (AIS) data applications. Transp. Rev. 39(6), 755–773 (2019)
Lu, N., Liang, M., Yang, L., Wang, Y., Xiong, N., Liu, R.W.: Shape-based vessel trajectory similarity computing and clustering: a brief review. In: 2020 5th IEEE International Conference on Big Data Analytics (ICBDA), 8–11 May 2020, Xiamen, China, pp. 186–192. IEEE (2020)
Besse, P.C., Guillouet, B., Loubes, J.-M., Royer, F.: Review and perspective for distance-based clustering of vehicle trajectories. IEEE Trans. Intell. Transp. Syst. 17(11), 3306–3317 (2016)
Zhao, L., Shi, G.: A novel similarity measure for clustering vessel trajectories based on dynamic time warping. J. Navig. 72(2), 290–306 (2019)
Zhai, W., Bai, X., Peng, Z.-R., Gu, C.: From edit distance to augmented space-time-weighted edit distance: detecting and clustering patterns of human activities in Puget Sound region. J. Transp. Geogr. 78, 41–55 (2019)
Park, J., Jeong, J., Park, Y.: Ship trajectory prediction based on Bi-LSTM using spectral-clustered AIS data. J. Marine Sci. Eng. 9(9), 1037 (2021)
Nie, P., Chen, Z., Xia, N., Huang, Q., Li, F.: Trajectory similarity analysis with the weight of direction and k-neighborhood for AIS data. ISPRS Int. J. Geo Inf. 10(11), 757 (2021)
Li, H., Liu, J., Liu, R.W., Xiong, N., Wu, K., Kim, T.H.: A Dimensionality reduction-based multi-step clustering method for robust vessel trajectory analysis. Sensors 17(8), 1792 (2017)
Yoo, W., Kim, T.W.: Statistical trajectory-distance metric for nautical route clustering analysis using cross-track distance. J. Comput. Design Eng. 9(2), 731–754 (2022)
Wang, L., Chen, P., Chen, L., Mou, J.: Ship AIS trajectory clustering: an HDBSCAN-based approach. J. Marine Sci. Eng. 9(6), 566 (2021)
Yang, J., Liu, Y., Ma, L., Ji, C.: Maritime traffic flow clustering analysis by density based trajectory clustering with noise. Ocean Eng. 249, 111001 (2022)
Eiter, T., Mannila, H.: Computing discrete Fréchet distance (1994)
Nuocheng, X.: Study on the risk calculation model for traffic conflicts in intersecting waters. In: 2022 7th International Conference on Big Data Analytics (ICBDA), 4–6 March 2022, Guangzhou, China, pp. 115–122. IEEE (2022)
Cao, J., et al.: PCA-based hierarchical clustering of AIS trajectories with automatic extraction of clusters. In: 2018 IEEE 3rd International Conference on Big Data Analysis (ICBDA), March 9–12, 2018, Shanghai, China, pp. 448–452. IEEE (2018)
Chen, Z., Guo, J., Liu, Q.: DBSCAN algorithm clustering for massive AIS data based on the Hadoop platform. In: 2017 International Conference on Industrial Informatics-Computing Technology, Intelligent Technology, Industrial Information Integration (ICIICII), 2–3 December 2017, Wuhan, China, pp. 25–28. IEEE (2017)
Deng, D.: Application of DBSCAN algorithm in data sampling. J. Phys. Conf. Ser. 1617(1), 012088 (2020)
Wang, X., Liu, X., Liu, B., de Souza, E.N., Matwin, S.: Vessel route anomaly detection with Hadoop MapReduce. In: 2014 IEEE International Conference on Big Data (Big Data), 27–30 October 2014, Washington, DC, USA, pp. 25–30. IEEE (2014)
Han, X., Armenakis, C., Jadidi, M.J.S.: Modeling vessel behaviours by clustering ais data using optimized DBSCAN. Sustainability 13(15), 8162 (2021)
Wang, C., Li, G., Han, P., Osen, O., Zhang, H.: Impacts of COVID-19 on ship behaviours in port area: an AIS data-based pattern recognition approach. IEEE Trans. Intell. Transp. Syst. 1–12 (2022)
Lee, H.T., Lee, J.S., Yang, H., Cho, I.S.: An AIS data-driven approach to analyze the pattern of ship trajectories in ports using the DBSCAN algorithm. Appl. Sci. 11(2), 799 (2021)
Zhao, L., Shi, G.: Maritime anomaly detection using density-based clustering and recurrent neural network. J. Navig. 72(4), 894–916 (2019)
Rahmah, N., Sitanggang, I.S.: Determination of optimal epsilon (Eps) value on DBSCAN algorithm to clustering data on peatland hotspots in sumatra. In: IOP Conference Series: Earth and Environmental Science, vol. 31, p. 012012 (2016)
Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Satopaa, V., Albrecht, J., Irwin, D., Raghavan, B.: Finding a “Kneedle” in a haystack: detecting knee points in system behavior. In: Proceedings of the 2011 31st International Conference on Distributed Computing Systems Workshops, pp. 166–171. IEEE Computer Society (2011)
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).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-981-97-2966-1_4
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-97-2965-4
Online ISBN: 978-981-97-2966-1
eBook Packages: Computer ScienceComputer Science (R0)