Trajectory pattern mining via clustering based on similarity function for transportation surveillance

Choong, Mei Yeen and Chin, Renee Ka Yin and Yeo Kiam Beng @ Abdul Noor and Teo, Kenneth Tze Kin (2016) Trajectory pattern mining via clustering based on similarity function for transportation surveillance. International Journal of Simulation: Systems, Science & Technology (IJSSST), 17. 19.1-19.7. ISSN 1473-8031 (P-ISSN) , 1473-804X (E-ISSN)

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URL: https://ijssst.info/Vol-17/No-34/paper19.pdf

Abstract

Recently, surveillance on moving vehicles for traffic flow monitoring has emerging in rapid rate. A comprehensive traffic data, that is vehicle trajectory, is selected as reliable data for discovering the underlying pattern via trajectory mining. As the task of monitoring moving vehicles via vehicle trajectory dataset can be tedious, researchers are keen to provide solutions that reducing the tedious task performed by the traffic operators. One of the solutions is to group the vehicle trajectory data according to the shape of the patterns. This grouping task is called as clustering. Each of the clusters formed represents a pattern. In this paper, the analysis of the implemented clustering algorithm on the trajectory data with similarity function is presented. Discussion on the issues concerning the trajectory clustering is also presented.

Item Type:	Article
Keyword:	Vehicle trajectory , Trajectory mining , Trajectory clustering
Subjects:	Q Science > QA Mathematics > QA1-939 Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science T Technology > TA Engineering (General). Civil engineering (General) > TA1-2040 Engineering (General). Civil engineering (General) > TA1001-1280 Transportation engineering
Department:	FACULTY > Faculty of Engineering
Depositing User:	SAFRUDIN BIN DARUN -
Date Deposited:	25 Apr 2022 09:33
Last Modified:	25 Apr 2022 09:33
URI:	https://eprints.ums.edu.my/id/eprint/32418

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