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A dynamic adaptive grating algorithm for AIS-based ship trajectory compression

Published online by Cambridge University Press:  18 October 2021

Yuanyuan Ji ,
Le Qi Open the ORCID record for Le Qi [Opens in a new window]  and
Robert Balling
Yuanyuan Ji
Affiliation:
School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ 85281, USA.
Le Qi*
Affiliation:
School of Navigation, Wuhan University of Technology, Wuhan 430063, China. Hubei Key Laboratory of Inland Shipping Technology, Wuhan 430063, China
Robert Balling
Affiliation:
School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ 85281, USA.
*
*Corresponding author. E-mail: leqiem@hotmail.com
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Abstract

Automatic identification system (AIS)-based ship trajectory data are important for analysing maritime activities. As the data accumulate over time, trajectory compression is needed to alleviate the pressure of data storage, migration and usage. The grating algorithm, as a vector data compression algorithm with high compression performance and low computation complexity, has been considered as a very promising approach for ship trajectory compression. This algorithm needs the threshold to be set for each trajectory which limits the applicability over a large number of different trajectories. To solve this problem, a dynamic adaptive threshold grating compression algorithm is developed. In this algorithm, the threshold for each trajectory is dynamically generated using an effective approaching strategy. The developed algorithm is tested with a complex trajectory dataset from the Qiongzhou Strait, China. In comparison with the traditional grating method, our algorithm has improved advantages in the ease of use, the applicability to different trajectories and compression performance, all of which can better support relevant applications, such as ship trajectory data storage and rapid cartographic display.

Keywords

ship trajectorydynamic adaptive thresholdgrating algorithmvector data compression
Type
Research Article
Information
The Journal of Navigation , Volume 75 , Issue 1 , January 2022 , pp. 213 - 229
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Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of The Royal Institute of Navigation

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