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. 2022 Jul 14;22(14):5281.
doi: 10.3390/s22145281.

A Formal and Visual Data-Mining Model for Complex Ship Behaviors and Patterns

Yongfeng Suo  1 , Yuxiang Ji  1 , Zhenye Zhang  1 , Jinhai Chen  1 , Christophe Claramunt  2
Affiliations

A Formal and Visual Data-Mining Model for Complex Ship Behaviors and Patterns

Yongfeng Suo et al. Sensors (Basel). .

Abstract

The successful emergence of real-time positioning systems in the maritime domain has favored the development of data infrastructures that provide valuable monitoring and decision-aided systems. However, there is still a need for the development of data mining approaches oriented to the detection of specific patterns such as unusual ship behaviors and collision risks. This research introduces a CSBP (complex ship behavioral pattern) mining model aiming at the detection of ship patterns. The modeling approach first integrates ship trajectories from automatic identification system (AIS) historical data, then categorizes different vessels' navigation behaviors, and introduces a visual-oriented framework to characterize and highlight such patterns. The potential of the model is illustrated by a case study applied to the Jiangsu and Zhejiang waters in China. The results show that the CSBP mining model can highlight complex ships' behavioral patterns over long periods, thus providing a valuable environment for supporting ship traffic management and preventing maritime accidents.

Keywords: AIS data; CSBP mining; complex behavioral pattern; spatiotemporal analysis.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Event time series cutting diagram.
Figure 2
Figure 2
Visual representation of an event flow.
Figure 3
Figure 3
Visualization of the sequential combination of event flows.
Figure 4
Figure 4
Visualization of the potential sequential combinations of event flows.
Figure 5
Figure 5
Visualization of the event flow iteration composition.
Figure 6
Figure 6
Visualization of the event flow embedded composition.
Figure 7
Figure 7
Visualization of the event flow composite composition.
Figure 8
Figure 8
Visualization of a ship CSBP.
Figure 9
Figure 9
Cargo ship complex behavioral pattern diagram.
Figure 10
Figure 10
(a) The jumping behavior pattern of the cargo ship; (b) The stay behavior pattern of the cargo ship.
Figure 11
Figure 11
Involved ship complex behavioral pattern diagram.
Figure 12
Figure 12
Flow chart of ship frequent behavioral pattern matching.
Figure 13
Figure 13
Histogram of the degree of matching of frequent behavioral patterns between the ships and the ship involved.
Figure 14
Figure 14
(a) Visualization of complex behavior of the first suspicious ship; (b) Visualization of complex behavior of the second suspicious ship; (c) Visualization of complex behavior of the third suspicious ship.
Figure 14
Figure 14
(a) Visualization of complex behavior of the first suspicious ship; (b) Visualization of complex behavior of the second suspicious ship; (c) Visualization of complex behavior of the third suspicious ship.
See this image and copyright information in PMC

References

    1. Wrobel K. Searching for the origins of the myth: 80% human error impact on maritime safety. Reliab. Eng. Syst. Saf. 2021;216:107942. doi: 10.1016/j.ress.2021.107942. - DOI
    1. Gao M., Shi G.Y. Ship-handling behavior mode recognition using AIS sub-trajectory clustering analysis based on the T-SNE and spectral clustering algorithms. Ocean Eng. 2020;205:106919. doi: 10.1016/j.oceaneng.2020.106919. - DOI
    1. Pan X.L., He Y., Wang H.P., Xiong W., Peng X. Mining regular behaviors based on multidimensional trajectories. Expert Syst. Appl. 2016;66:106–113. doi: 10.1016/j.eswa.2016.09.015. - DOI
    1. Zhao L.B., Shi G.Y. A trajectory clustering method based on Douglas-Peucker compression and density for marine traffic mode recognition. Ocean Eng. 2019;172:456–467. doi: 10.1016/j.oceaneng.2018.12.019. - DOI
    1. Murray B., Perera L.P. Ship behavior prediction via trajectory extraction-based clustering for maritime situation awareness. J. Ocean Eng. Sci. 2021;7:1–13. doi: 10.1016/j.joes.2021.03.001. - DOI

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