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Visual Analytics Approach to Vessel Behaviour Analysis

Published online by Cambridge University Press:  02 April 2018

Liang Jin ,
Zhengyi Luo  and
Shu Gao
Liang Jin
Affiliation:
(School of Computer Science and Technology, Wuhan University of Technology, Wuhan 430063, China)
Zhengyi Luo
Affiliation:
(School of Engineering and Applied Science, University of Pennsylvania, Philadelphia 19104 USA)
Shu Gao*
Affiliation:
(School of Computer Science and Technology, Wuhan University of Technology, Wuhan 430063, China)
*
(E-mail: [email protected])
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Abstract

Vessel behaviour analysis plays an important role in maritime situational awareness. However, available technology still provides only limited approaches to vessel behaviour analysis. In this paper, we propose a visual analytics framework to interactively explore the characteristics of vessel behaviour by means of integrating visualisation with data mining and a human-computer interaction controlling model, which combines human insight with the enormous storage and processing capacities of computers to gain insight into vessel behaviour. In addition, we provide multiple views for visually analysing vessel trajectories, densities and speeds. Case studies with 15 days' AIS data collected from the middle Hankou channel to Yangluo channel in the Yangtze River demonstrate the effectiveness of our approach.

Keywords

Automatic Identification System (AIS)Vessel behaviourVessel trajectoryVisual Analytics
Type
Research Article
Information
The Journal of Navigation , Volume 71 , Issue 5 , September 2018 , pp. 1195 - 1209
Copyright
Copyright © The Royal Institute of Navigation 2018 

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Footnotes

*

Liang Jin and Zhengyi Luo contributed equally to the work and should be considered co-first authors.

References

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