Clustering the driving features based on data streams

Author

Kalsoom, Rizwana ; Halim, Zahid

Author_Institution

Fac. of Comput. Sci. & Eng., Ghulam Ishaq Khan Inst. of Eng. Sci. & Technol., Topi, Pakistan

fYear

2013

fDate

19-20 Dec. 2013

Firstpage

89

Lastpage

94

Abstract

This paper presents an innovative idea for the classification of individual drivers. The classification is based on each driver´s driving features like, ratio of indicators to turns, number of brakes, number of time horn used, average gear, average speed, maximum speed and gear. K-means and hierarchical clustering is used to separate out the slow, normal and fast driving styles based on recorded data. Experimental result shows that k-means outperformed hierarchical clustering for recorded multi-attribute data.

Keywords

behavioural sciences computing; pattern classification; pattern clustering; road safety; average gear; average speed; brakes; data streams; driver classification; driver driving features; driving feature clustering; fast driving style; gears; hierarchical clustering; indicator-to-turn ratio; k-means clustering; maximum speed; multiattribute data; normal driving style; slow driving style; time horn; Acceleration; Clustering algorithms; Conferences; Feature extraction; Gears; Roads; Vehicles; Clustering; data stream; driver profiling; k-means clustering; road safety;

fLanguage

English

Publisher

ieee

Conference_Titel

Multi Topic Conference (INMIC), 2013 16th International

Conference_Location

Lahore

Type

conf

DOI

10.1109/INMIC.2013.6731330

Filename

6731330