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Research on ship trajectory compression based on a Dynamic Programming algorithm

Published online by Cambridge University Press:  13 January 2025

Yinjie Hu ,
Le Qi Open the ORCID record for Le Qi [Opens in a new window] ,
Yuanyuan Ji ,
Robert Balling  and
Dexiang Shen
Yinjie Hu
Affiliation:
State Key Laboratory of Maritime Technology and Safety, Wuhan 430063, China School of Navigation, Wuhan University of Technology, Wuhan 430063, China
Le Qi*
Affiliation:
State Key Laboratory of Maritime Technology and Safety, Wuhan 430063, China School of Navigation, Wuhan University of Technology, Wuhan 430063, China
Yuanyuan Ji
Affiliation:
School of Geographical Sciences and Urban Planning, Arizona State University, Tempe 85287, USA Environmental Systems Research Institute, Redlands 92373, USA
Robert Balling
Affiliation:
School of Geographical Sciences and Urban Planning, Arizona State University, Tempe 85287, USA
Dexiang Shen
Affiliation:
State Key Laboratory of Maritime Technology and Safety, Wuhan 430063, China School of Navigation, Wuhan University of Technology, Wuhan 430063, China
*
*Corresponding author: Le Qi; Email: leqiem@hotmail.com
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Abstract

The Automatic Identification System (AIS) is extensively used in monitoring vessel traffic, and ship navigation related information can be obtained from the AIS data. However, AIS data contain extensive redundant information, which leads to the general need to compress the data when applying it in practice or conducting research. In this paper, a three-dimensional compression of ship trajectories using the Dynamic Programming algorithm has been proposed. The AIS data near the ports of Long Beach and San Francisco in the United States were used to test and compare the Dynamic Programming algorithm with the Top-down Time-ratio algorithms. The experimental results show that the proposed algorithm can better retain the position and time information at low compression ratio such as 1%, 20% and 40%. Moreover, the algorithm is applicable to ship trajectories with different motion modes such as steering, mooring and straight ahead. The results show that the proposed algorithm can reasonably solve the problem of AIS data redundancy and ensure the quality of data, which is of practical significance for water transport, transport planning and other related research.

Keywords

ship trajectoryDynamic Programming algorithmoptimised compressiondata compression
Type
Research Article
Information
The Journal of Navigation , First View , pp. 1 - 19
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Copyright
Copyright © The Author(s), 2025. Published by Cambridge University Press on behalf of The Royal Institute of Navigation

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