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Vessel Trajectory Online Multi-Dimensional Simplification Algorithm

Published online by Cambridge University Press:  22 August 2019

Yuan-qiang Zhang ,
Guo-you Shi ,
Song Li  and
Shu-kai Zhang
Yuan-qiang Zhang
Affiliation:
(Navigation College, Dalian Maritime University, Dalian116026, China) (Faculty of Maritime and Transportation, Ningbo University, Ningbo315211, China)
Guo-you Shi*
Affiliation:
(Navigation College, Dalian Maritime University, Dalian116026, China)
Song Li
Affiliation:
(Faculty of Maritime and Transportation, Ningbo University, Ningbo315211, China)
Shu-kai Zhang
Affiliation:
(Merchant Marine College, Shanghai Maritime University, Shanghai, 201306, China)
*
(E-mail: shiguoyoudmu@163.com)
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Abstract

Facilitated by the establishment of terrestrial networks and satellite constellations of Automatic Identification System (AIS) receivers, large quantities of spatial and temporal information that trace ships' paths have been collected. The exponential increase in the amount of AIS data has caused expensive and time-consuming transmission, calculation and storage problems. Using appropriate trajectory simplification methods in a timely manner to compress redundant information while minimising the loss of importation information is important. To minimise the simplification error, this paper proposes an online multi-dimensional simplification algorithm for AIS trajectory streaming data. The simplification algorithm takes into account position, direction and speed preservation. Finally, a comparison experiment with other algorithms is made to examine the effectiveness of this algorithm. The results indicate that the proposed online multi-dimensional simplification algorithm can effectively preserve a ship's motion state, including its position, speed and course.

Keywords

AIS TrajectoriesTrajectory simplificationOnline algorithmDOTS algorithm
Type
Research Article
Information
The Journal of Navigation , Volume 73 , Issue 2 , March 2020 , pp. 342 - 363
Copyright
Copyright © The Royal Institute of Navigation 2019

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