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A Novel Similarity Measure for Clustering Vessel Trajectories Based on Dynamic Time Warping

Published online by Cambridge University Press:  09 October 2018

Liangbin Zhao  and
Guoyou Shi
Liangbin Zhao*
Affiliation:
(Navigation College, Dalian Maritime University, Dalian, China)
Guoyou Shi
Affiliation:
(Navigation College, Dalian Maritime University, Dalian, China)
*
(E-mail: vszlb@126.com)
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Abstract

Clustering methods that use a similarity measurement for evaluating vessel trajectories are important for mining spatial distribution information in water transportation. To better measure the similarity of vessel trajectories, a novel similarity measure is proposed based on the dynamic time warping distance, which considers the course change of track points and the meaning at the route level. Parallel experiments were conducted based on a month of Automatic Identification System (AIS) data collected from the Zhoushan Islands area, China. After evaluation of the accuracy and the cluster degree, the novel measure demonstrated its capabilities for distinguishing different vessel trajectories and detecting similar vessel trajectories with high accuracy and has a better performance compared to some existing methods.

Keywords

Vessel TrajectoriesSimilarity measurementTrajectory clusteringDynamic time warping
Type
Research Article
Information
The Journal of Navigation , Volume 72 , Issue 2 , March 2019 , pp. 290 - 306
Copyright
Copyright © The Royal Institute of Navigation 2018 

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References

REFERENCES

De Vries, G. and Van Someren, M. (2012). Machine learning for vessel trajectories using compression, alignments and domain knowledge. Expert Systems with Applications, 39(18), 13426–13439.Google Scholar
De Vries, G. and Van Someren, M. (2010). Clustering vessel trajectories with alignment kernels under trajectory compression. Machine Learning and Knowledge Discovery in Databases, 296–311.Google Scholar
Gong, X., Pei, T., Sun, J. and Luo, M. (2011). Review of the Research Progresses in Trajectory Clustering Methods. Progress in Geography, 30(5), 522–534. (In Chinese)Google Scholar
International Telecommunications Union (ITU). (2010). Technical characteristics for an automatic identification system using time-division multiple access in the VHF maritime mobile band, Recommendation ITU-R M. 1371-4.Google Scholar
Kaufmann, L. and Rousseeuw, P. J. (1987). Clustering by Means of Medoids. Statistical Data Analysis Based on the L1-norm & Related Methods, 405–416.Google Scholar
Laxhammar, R. and Falkman, G. (2011). Sequential conformal anomaly detection in trajectories based on Hausdorff distance. IEEE 2011 Proceedings of the 14th International Conference on Information Fusion, 1–8.Google Scholar
Le Guillarme, N. and Lerouvreur, X. (2013). Unsupervised extraction of knowledge from S-AIS data for maritime situational awareness. IEEE 2013 16th International Conference on Information Fusion, 2025–2032.Google Scholar
Li, H., Liu, J., Liu, R. W., Xiong, N., Wu, K. and Kim, T. H. (2017). A Dimensionality Reduction-Based Multi-Step Clustering Method for Robust Vessel Trajectory Analysis. Sensors, 17(8), 1792.Google Scholar
Lin, B. and Su, J. (2008). One way distance: For shape based similarity search of moving object trajectories. GeoInformatica, 12(2), 117–142.Google Scholar
Ma, W., Wu, Z., Yang, J. and Li, W. (2015). Vessel Motion Pattern Recognition Based on One-Way Distance. Journal of Chongqing Jiaotong University (Natural Science), 34(5), 130–134. (In Chinese)Google Scholar
Pallotta, G., Vespe, M. and Bryan, K. (2013). Vessel pattern knowledge discovery from AIS data: a framework for anomaly detection and route prediction. Entropy, 15(6), 2218–2245.Google Scholar
Wang, J., Zhu, C., Zhou, Y. and Zhang, W. (2017a). Vessel Spatio-temporal Knowledge Discovery with AIS Trajectories Using Co-clustering. The Journal of Navigation, 70(6), 1383–1400.Google Scholar
Wang, J., Zhou, Y., Cao, X., Wang, Y., Zhu, C. and Zhang, W. (2017b). Shape-Based Analysis for Vessel Trajectories. SIGSPATIAL'17.Google Scholar
Zhang, Z., Huang, K. and Tan, T. (2006). Comparison of similarity measures for trajectory clustering in outdoor surveillance scenes. 18th International Conference on Pattern Recognition, 3, 1135–1138.Google Scholar
Zhao, L., Shi, G. and Yang, J. (2018). Ship Trajectories Pre-processing Based on AIS Data. Journal of Navigation, 1–21. doi:10.1017/S0373463318000188Google Scholar
Zhen, R., Jin, Y., Hu, Q., Shao, Z. and Nikitas, N. (2017). Maritime anomaly detection within coastal waters based on vessel trajectory clustering and naïve Bayes classifier. Journal of Navigation, 70(3), 648–670.Google Scholar