Research of spatio-temporal similarity measure on network constrained trajectory data

Authors:
Ying Xia;Guo-Yin Wang;Xu Zhang;Gyoung-Bae Kim;Hae-Young Bae
Affiliations:
School of Information Science and Technology, Southwest Jiaotong University, China and College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, China;College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, China;College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, China;Department of Computer Education, Seowon University, Korea;Department of Computer Science & Engineering, Inha University, Korea
Venue:
RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
Year:
2010

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Abstract

Similarity measure between trajectories is considered as a pre-processing procedure of trajectory data mining. A lot of shaped-based and time-based methods on trajectory similarity measure have been proposed by researchers recently. However, these methods can not perform very well on constrained trajectories in road network because of the inappropriateness of Euclidean distance. In this paper, we study spatio-temporal similarity measure for trajectories in road network. We partition constrained trajectories on road network into segments by considering both the temporal and spatial properties firstly, then propose a spatio-temporal similarity measure method for trajectory similarity analysis. Experimental results exhibit the performance of the proposed methods and its availability used for trajectory clustering.