%0 Journal Article
	%A Thammasak Thianniwet and  Satidchoke Phosaard and  Wasan Pattara-Atikom
	%D 2011
	%J International Journal of Transport and Vehicle Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 53, 2011
	%T The Optimization of an Intelligent Traffic Congestion Level Classification from Motorists- Judgments on Vehicle's Moving Patterns
	%U https://publications.waset.org/pdf/3132
	%V 53
	%X We proposed a technique to identify road traffic
congestion levels from velocity of mobile sensors with high accuracy
and consistent with motorists- judgments. The data collection utilized
a GPS device, a webcam, and an opinion survey. Human perceptions
were used to rate the traffic congestion levels into three levels: light,
heavy, and jam. Then the ratings and velocity were fed into a
decision tree learning model (J48). We successfully extracted vehicle
movement patterns to feed into the learning model using a sliding
windows technique. The parameters capturing the vehicle moving
patterns and the windows size were heuristically optimized. The
model achieved accuracy as high as 99.68%. By implementing the
model on the existing traffic report systems, the reports will cover
comprehensive areas. The proposed method can be applied to any
parts of the world.
	%P 487 - 492