Ship Trajectory Data Compression Algorithms for Automatic Identification System: Comparison and Analysis

Le Qi; Yuanyuan Ji

doi:doi:10.11648/j.wros.20200902.11

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Ship Trajectory Data Compression Algorithms for Automatic Identification System: Comparison and Analysis

Le Qi, Yuanyuan Ji

Published in Journal of Water Resources and Ocean Science (Volume 9, Issue 2)

Received: 6 May 2020 Accepted: 26 May 2020 Published: 8 June 2020

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Abstract

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.

Published in	Journal of Water Resources and Ocean Science (Volume 9, Issue 2)
DOI	10.11648/j.wros.20200902.11
Page(s)	42-47
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Automatic Identification System, Big Data, Data Compression Algorithms, Ship Trajectory

References

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APA Style

Le Qi, Yuanyuan Ji. (2020). Ship Trajectory Data Compression Algorithms for Automatic Identification System: Comparison and Analysis. Journal of Water Resources and Ocean Science, 9(2), 42-47. https://doi.org/10.11648/j.wros.20200902.11

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ACS Style

Le Qi; Yuanyuan Ji. Ship Trajectory Data Compression Algorithms for Automatic Identification System: Comparison and Analysis. J. Water Resour. Ocean Sci. 2020, 9(2), 42-47. doi: 10.11648/j.wros.20200902.11

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AMA Style

Le Qi, Yuanyuan Ji. Ship Trajectory Data Compression Algorithms for Automatic Identification System: Comparison and Analysis. J Water Resour Ocean Sci. 2020;9(2):42-47. doi: 10.11648/j.wros.20200902.11

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@article{10.11648/j.wros.20200902.11,
  author = {Le Qi and Yuanyuan Ji},
  title = {Ship Trajectory Data Compression Algorithms for Automatic Identification System: Comparison and Analysis},
  journal = {Journal of Water Resources and Ocean Science},
  volume = {9},
  number = {2},
  pages = {42-47},
  doi = {10.11648/j.wros.20200902.11},
  url = {https://doi.org/10.11648/j.wros.20200902.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.wros.20200902.11},
  abstract = {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.},
 year = {2020}
}

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TY  - JOUR
T1  - Ship Trajectory Data Compression Algorithms for Automatic Identification System: Comparison and Analysis
AU  - Le Qi
AU  - Yuanyuan Ji
Y1  - 2020/06/08
PY  - 2020
N1  - https://doi.org/10.11648/j.wros.20200902.11
DO  - 10.11648/j.wros.20200902.11
T2  - Journal of Water Resources and Ocean Science
JF  - Journal of Water Resources and Ocean Science
JO  - Journal of Water Resources and Ocean Science
SP  - 42
EP  - 47
PB  - Science Publishing Group
SN  - 2328-7993
UR  - https://doi.org/10.11648/j.wros.20200902.11
AB  - 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.
VL  - 9
IS  - 2
ER  -

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Author Information

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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Plain Text BibTeX RIS

APA Style

Le Qi, Yuanyuan Ji. (2020). Ship Trajectory Data Compression Algorithms for Automatic Identification System: Comparison and Analysis. Journal of Water Resources and Ocean Science, 9(2), 42-47. https://doi.org/10.11648/j.wros.20200902.11

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ACS Style

Le Qi; Yuanyuan Ji. Ship Trajectory Data Compression Algorithms for Automatic Identification System: Comparison and Analysis. J. Water Resour. Ocean Sci. 2020, 9(2), 42-47. doi: 10.11648/j.wros.20200902.11

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AMA Style

Le Qi, Yuanyuan Ji. Ship Trajectory Data Compression Algorithms for Automatic Identification System: Comparison and Analysis. J Water Resour Ocean Sci. 2020;9(2):42-47. doi: 10.11648/j.wros.20200902.11

Copy | Download

@article{10.11648/j.wros.20200902.11,
  author = {Le Qi and Yuanyuan Ji},
  title = {Ship Trajectory Data Compression Algorithms for Automatic Identification System: Comparison and Analysis},
  journal = {Journal of Water Resources and Ocean Science},
  volume = {9},
  number = {2},
  pages = {42-47},
  doi = {10.11648/j.wros.20200902.11},
  url = {https://doi.org/10.11648/j.wros.20200902.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.wros.20200902.11},
  abstract = {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.},
 year = {2020}
}

Copy | Download

TY  - JOUR
T1  - Ship Trajectory Data Compression Algorithms for Automatic Identification System: Comparison and Analysis
AU  - Le Qi
AU  - Yuanyuan Ji
Y1  - 2020/06/08
PY  - 2020
N1  - https://doi.org/10.11648/j.wros.20200902.11
DO  - 10.11648/j.wros.20200902.11
T2  - Journal of Water Resources and Ocean Science
JF  - Journal of Water Resources and Ocean Science
JO  - Journal of Water Resources and Ocean Science
SP  - 42
EP  - 47
PB  - Science Publishing Group
SN  - 2328-7993
UR  - https://doi.org/10.11648/j.wros.20200902.11
AB  - 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.
VL  - 9
IS  - 2
ER  -

Copy | Download