Title :
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
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;
Conference_Titel :
Multi Topic Conference (INMIC), 2013 16th International
Conference_Location :
Lahore
DOI :
10.1109/INMIC.2013.6731330
