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Vessel Spatio-temporal Knowledge Discovery with AIS Trajectories Using Co-clustering

Published online by Cambridge University Press:  03 July 2017

Jiang Wang ,
Cheng Zhu ,
Yun Zhou  and
Weiming Zhang
Jiang Wang
Affiliation:
(Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, P. R. China)
Cheng Zhu*
Affiliation:
(Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, P. R. China)
Yun Zhou
Affiliation:
(Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, P. R. China)
Weiming Zhang
Affiliation:
(Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, P. R. China)
*
(E-mail: [email protected])
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Abstract

Large volumes of data collected by the Automatic Identification System (AIS) provide opportunities for studying both single vessel motion behaviours and collective mobility patterns on the sea. Understanding these behaviours or patterns is of great importance to maritime situational awareness applications. In this paper, we leveraged AIS trajectories to discover vessel spatio-temporal co-occurrence patterns, which distinguish vessel behaviours simultaneously in terms of space, time and other dimensions (such as ship type, speed, width etc.). To this end, available AIS data were processed to generate spatio-temporal matrices and spatio-temporal tensors (i.e., multidimensional arrays). We then imposed a sparse bilinear decomposition on the matrices and a sparse multi-linear decomposition on the tensors. Experimental results on a real-world dataset demonstrated the effectiveness of this methodology, with which we show the existence of connection among regions, time, and vessel attributes.

Keywords

Co-clusteringTrajectoriesSpatio-temporal Data MiningAIS Data
Type
Research Article
Information
The Journal of Navigation , Volume 70 , Issue 6 , November 2017 , pp. 1383 - 1400
Copyright
Copyright © The Royal Institute of Navigation 2017 

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