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A Novel Similarity Measure for Clustering Vessel Trajectories Based on Dynamic Time Warping

Published online by Cambridge University Press:  09 October 2018

Liangbin Zhao  and
Guoyou Shi
Liangbin Zhao*
Affiliation:
(Navigation College, Dalian Maritime University, Dalian, China)
Guoyou Shi
Affiliation:
(Navigation College, Dalian Maritime University, Dalian, China)
*
(E-mail: [email protected])
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Abstract

Clustering methods that use a similarity measurement for evaluating vessel trajectories are important for mining spatial distribution information in water transportation. To better measure the similarity of vessel trajectories, a novel similarity measure is proposed based on the dynamic time warping distance, which considers the course change of track points and the meaning at the route level. Parallel experiments were conducted based on a month of Automatic Identification System (AIS) data collected from the Zhoushan Islands area, China. After evaluation of the accuracy and the cluster degree, the novel measure demonstrated its capabilities for distinguishing different vessel trajectories and detecting similar vessel trajectories with high accuracy and has a better performance compared to some existing methods.

Keywords

Vessel TrajectoriesSimilarity measurementTrajectory clusteringDynamic time warping
Type
Research Article
Information
The Journal of Navigation , Volume 72 , Issue 2 , March 2019 , pp. 290 - 306
Copyright
Copyright © The Royal Institute of Navigation 2018 

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