Vessel Crowd Movement Pattern Mining for Maritime Traffic Management

Open access

Abstract

The goal of maritime traffic management is to provide a safe and efficient maritime environment for different type of vessels facilitating port logistics and supply chain business. However, current maritime traffic management mainly relies on the massive individual vessel’s data for decision making. Lack of macro-level understanding of vessel crowd movement around port challenges maritime safety and traffic efficiency. In this paper, we describe a spatio-temporal data mining method to discover crowd movement patterns of vessels from their short-term history data. The method first captures vessels’ crowd movement features by building vessels’ tracklets with their speed and location. A movement vector clustering algorithm is developed to find different travel behaviors for different group of vessels. With nonparametric regression on the classified vessel movement vectors which represent the crowd travel behaviors, an overall vessel movement pattern can then be discovered. In this research, we tested real trajectory data of vessels near Singapore ports. Comparing with the actual massive vessel movement data, we found that this method was able to extract vessels’ crowd movement information. The hotspots on risk area in terms of vessel traffic and speed can be identified. The method can be used to provide decision-making support for maritime traffic management.

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  • [1] Kleiner F.S. Mamiya C.J. & Tansey R.G. (2001). Gardner’s art through the ages (11th ed.). Fort Worth USA: Harcourt College Publishers.

  • [2] Wen R. Yan W. Zhang A. N. Chinh N. Q. & Akcan O. (2017). Spatio-temporal route mining and visualization for busy waterways. Paper presented at the 2016 IEEE International Conference on Systems Man and Cybernetics SMC 2016 - Conference Proceedings 849-854. DOI:10.1109/SMC.2016.7844346

  • [3] Wen R. Yan W. & Zhang A. N. (2017). Adaptive spatio-temporal mining for route planning and travel time estimation. Paper presented at the Proceedings - 2017 IEEE International Conference on Big Data Big Data 2017 2018-January 3278-3284. DOI:10.1109/BigData.2017.8258311

  • [4] Tu E. Zhang G. Rachmawati L. Rajabally E. & Huang G. (2018). Exploiting AIS data for intelligent maritime navigation: A comprehensive survey from data to methodology. IEEE Transactions on Intelligent Transportation Systems 19(5) 1559-1582. DOI:10.1109/TITS.2017.2724551

  • [5] Yan W. Wen R. Zhang A. N. & Yang D. (2016). Vessel movement analysis and pattern discovery using density-based clustering approach. Paper presented at the Proceedings - 2016 IEEE International Conference on Big Data Big Data 2016 3798-3806. DOI:10.1109/BigData.2016.7841051

  • [6] Wen R. Yan W. & Zhang A. N. (2016). Weighted clustering of spatial pattern for optimal logistics hub deployment. Paper presented at the Proceedings - 2016 IEEE International Conference on Big Data Big Data 2016 3792-3797. DOI:10.1109/BigData.2016.7841050

  • [7] Song J. Wen R. & Yan W. (2016) Identification of traffic accident clusters using kulldorffs space-time scan statistics. Paper presented at the Proceedings - 2016 IEEE International Conference on Big Data Big Data 2016 3792-3797. DOI:10.1109/BigData.2016.7841050

  • [8] Lee J. G. Han J. & Whang K.-Y. (2007). Trajectory clustering: A partitionand-group framework. Proceedings of the ACM SIGMOD International Conference on Management of Data.

  • [9] Wisdom M. J. Cimon N. Johnson B. Garton E. & Thomas J. (2004). Spatial partitioning by mule deer and elk in relation to traffic. Transactions North American Wildlife and Natural Resource Conference 69(01).

  • [10] Zhen R. Jin Y. Hu Q. Shao Z. & Nikitakos N. (2017). Maritime anomaly detection within coastal waters based on vessel trajectory clustering and nave bayes classifier. Journal of Navigation 70(3) 648-670.

  • [11] Bermingham L. & Lee I. (2014). Spatio-temporal sequential pattern mining for tourism sciences. Paper presented at the Procedia Computer Science vol. 29 379-389. DOI:10.1016/j.procs.2014.05.034

  • [12] Agrafiotis D. K. (2003). Stochastic proximity embedding. Journal of Computational Chemistry 24(10) 1215-1221. DOI:10.1002/jcc.10234

  • [13] Heldens S. Litvak N. & Van Steen M. (2018). Scalable detection of crowd motion patterns. IEEE Transactions on Knowledge and Data Engineering DOI:10.1109/TKDE.2018.2879079

  • [14] Peters S. & Krisp J. M. (2010). Density calculation for moving points. in GIScience 2010.

  • [15] Liu F. T. Ting K. M. & Zhou Z. (2008). Isolation forest. Paper presented at the Proceedings - IEEE International Conference on Data Mining ICDM 413-422. DOI:10.1109/ICDM.2008.17

  • [16] Kang S. & Chien W. K. (2016). A method to group reliability data by hierarchical clustering. Paper presented at the IEEE International Conference on Industrial Engineering and Engineering Management 2016-December 345-349. DOI:10.1109/IEEM.2016.7797894

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