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28 August 2019 Detecting Anomalous Vessel Dynamics with Functional Data Analysis
He Huang, Kaiyue Qiu, Myeong-Hun Jeong, Seung Bae Jeon, Woo Pyeong Lee
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Huang, H.; Qie, K.; Jeong, M.-H.; Jeon, S.B., and Lee, W.P., 2019. Detecting anaomalous vessel dynamics with functional data analysis. In: Lee, J.L.; Yoon, J.-S.; Cho, W.C.; Muin, M., and Lee, J. (eds.), The 3rd International Water Safety Symposium. Journal of Coastal Research, Special Issue No. 91, pp. 406-410. Coconut Creek (Florida), ISSN 0749-0208.

Advances in location-acquisition technology open up new areas of applications in maritime monitoring and security. Automatic identification system (AIS) data provide dynamic information on vessel movements. This research proposes a new method for detecting anomalous vessel dynamics using functional data analysis. Empirical investigations of this approach demonstrate the effective detection of outlier flows in terms of ship traffic volume. However, alternative methods such as the 3-sigma rule and the MAD-Median rule fail to detect anomalous vessel traffic. This investigation suggests that the method proposed can improve the safety and operation of ship-to-shore vessel traffic management.

©Coastal Education and Research Foundation, Inc. 2019
He Huang, Kaiyue Qiu, Myeong-Hun Jeong, Seung Bae Jeon, and Woo Pyeong Lee "Detecting Anomalous Vessel Dynamics with Functional Data Analysis," Journal of Coastal Research 91(sp1), 406-410, (28 August 2019).
Received: 9 October 2018; Accepted: 14 December 2018; Published: 28 August 2019

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