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Navigation pattern extraction from AIS trajectory big data via topic model

Published online by Cambridge University Press:  10 July 2023

Iwao Fujino  and
Christophe Claramunt
Iwao Fujino*
Affiliation:
School of Information and Telecommunication Engineering, Tokai University, Tokyo, Japan
Christophe Claramunt
Affiliation:
Naval Academy Research Institute, French Naval Academy, Lanvéoc-Poulmic, France
*
*Corresponding author: Iwao Fujino; Email: fujino@tokai.ac.jp
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Abstract

This paper introduces a novel approach for extracting vessel navigation patterns from very large automatic identification system (AIS) trajectory big data. AIS trajectory data records are first converted to a series of code documents using vector quantisation, such as k-means and PQk-means algorithms, whose performance is evaluated in terms of precision and computational time. Therefore, a topic model is applied to these code documents from which vessels’ navigation patterns are extracted and identified. The potential of the proposed approach is illustrated by several experiments conducted with a practical AIS dataset in a region of North West France. These experimental results show that the proposed approach is highly appropriate for mining AIS trajectory big data and outperforms common DBSCAN algorithms and Gaussian mixture models.

Keywords

automatic identification systemtrajectoryship behaviournavigation
Type
Research Article
Information
The Journal of Navigation , Volume 76 , Issue 4-5 , July 2023 , pp. 506 - 524
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Copyright
Copyright © The Author(s), 2023. Published by Cambridge University Press on behalf of The Royal Institute of Navigation

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