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Ship Trajectory Data Compression Algorithms for Automatic Identification System: Comparison and Analysis
Journal of Water Resources and Ocean Science
Volume 9, Issue 2, April 2020, Pages: 42-47
Received: May 6, 2020; Accepted: May 26, 2020; Published: Jun. 8, 2020
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Le Qi, School of Navigation, Wuhan University of Technology, Wuhan, China; Hubei Key Laboratory of Inland Shipping Technology, Wuhan, China
Yuanyuan Ji, College of Information Science Technology, Dalian Maritime University, Dalian, China; Ira A. Fulton Schools of Engineering, Arizona State University, Tempe, USA
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With the development of Internet of Things (IoT) technology and its vast applications in ship transportation systems, such as the Automatic Identification System (AIS), a large quantity of ship trajectory data have been recorded and stored. Nowadays ship transportation has also entered the age of big data, which can support IoT applications in Intelligent Transportation System (ITS), e.g. traffic monitoring, fleet management and traffic safety enhancement. However, the redundancy of ship trajectory data considerably reduces the effectiveness and efficiency of large scale traffic data storage, mining and visualization. Therefore, compression processing of the data becomes a very important issue for these applications. Because ship trajectory is a type of vector data, employing the vector data compression algorithms is an efficient way to solve the data redundancy problem. In this paper, the pseudo-code of five typical vector data compression algorithms for ship trajectory data compression is introduced. The performances of these algorithms were tested by the compression experiments of actual ship trajectories in the Qiongzhou Strait. The results show that ships’ speeds and rate of turns, the requirement of real time processing can affect the option of the most appropriate algorithm, and the algorithm selection in different applications is suggested. The results and conclusions lay the foundation for the future development of ship transportation intelligentization.
Automatic Identification System, Big Data, Data Compression Algorithms, Ship Trajectory
To cite this article
Le Qi, Yuanyuan Ji, Ship Trajectory Data Compression Algorithms for Automatic Identification System: Comparison and Analysis, Journal of Water Resources and Ocean Science. Vol. 9, No. 2, 2020, pp. 42-47. doi: 10.11648/j.wros.20200902.11
Copyright © 2020 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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