Hostname: page-component-8448b6f56d-dnltx Total loading time: 0 Render date: 2024-04-16T20:30:33.615Z Has data issue: false hasContentIssue false
  • English
  • Français

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: gshu@whut.edu.cn)
Get access Rights & Permissions [Opens in a new window]

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 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

*

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

References

REFERENCES

Ali, T., Asghar, S. and Sajid, N.A. (2010). Critical analysis of DBSCAN variations. Proceedings of 2010 International Conference on Information and Emerging Technologies, 24–29.Google Scholar
Cazzanti, L., Davoli, A. and Millefiori, L.M. (2016). Automated Port Traffic Statistics: From Raw Data to Visualisation. Proceedings of 2016 IEEE International Conference on Big Data, 1569–1573Google Scholar
Ferreira, N., Poco, J., Vo, H.T., Freire, J. and Silva, C.T. (2013). Visual Exploration of Big Spatio-Temporal Urban Data: A Study of New York City Taxi Trips. Transactions on Visualisation and Computer Graphics, 19(12), 2149–2158.Google Scholar
Lu, C. and Gao, S. (2016). Visual Analysis of Multi-factor Association on Inland Waterway Accident. Proceedings of 2016 International Conference on Engineering and Advanced Technology, 110–115.Google Scholar
Piciarelli, C., Foresti, G.L. and Snidaro, L. (2005). Trajectory clustering and its applications for video surveillance. Advanced Video and Signal Based Surveillance. Proceedings of AVSS 2005. IEEE, 40–45.Google Scholar
Riveiro, M. and Falkman, G. (2009). Interactive Visualisation of Normal Behavioural Models and Expert Rules for Maritime Anomaly Detection. Proceedings of Sixth International Conference on Computer Graphics, Imaging and Visualisation, 459–466.Google Scholar
Scheepens, R., Hurter, C., Van de Wetering, H. and Van Wijk, J.J. (2016). Visualisation, selection, and analysis of traffic flows. IEEE Transactions on Visualisation and Computer Graphics, 22(1), 379–388.Google Scholar
Sidibé, A. and Gao, S. (2017). Study of Automatic Anomalous Behaviour Detection Techniques for Maritime Vessels. Journal of Navigation, 70(4), 847–858.Google Scholar
Thomas, J.J. and Cook, K.A. (2006). A visual analytics agenda. Computer Graphics and Applications, IEEE, 26(1), 10–13.Google Scholar
Tominski, C., Schumann, H., Andrienko, G. and Andrienko, N. (2012). Stacking-Based Visualisation of Trajectory Attribute Data. IEEE Transactions on Visualisation & Computer Graphics, 18(12), 2565–2574.Google Scholar
Wang, G.Z., Malik, A., Yau, C., Surakitbanharn, C. and Ebert, D.S. (2017). TraSeer: A Visual Analytics Tool for Vessel Movements in the Coastal Areas. Proceedings of IEEE International Symposium on Technologies for Homeland Security, 1–6Google Scholar
Wang, Z., Lu, M., Yuan, X.R., Zhang, J.P and Wetering, H.V.D. (2013). Visual Traffic Jam Analysis Based on Trajectory Data. Transactions on Visualisation and Computer Graphics, 19(12), 2159–2168.Google Scholar
Willems, N. (2011). Visualisation of vessel traffic. PhD thesis, Eindhoven University of Technology. http://alexandria.tue.nl/extra2/719764.pdf.Google Scholar