@article{(Open Science Index):https://publications.waset.org/pdf/3132,
	  title     = {The Optimization of an Intelligent Traffic Congestion Level Classification from Motorists- Judgments on Vehicle's Moving Patterns},
	  author    = {Thammasak Thianniwet and  Satidchoke Phosaard and  Wasan Pattara-Atikom},
	  country	= {},
	  institution	= {},
	  abstract     = {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.},
	    journal   = {International Journal of Transport and Vehicle Engineering},
	  volume    = {5},
	  number    = {5},
	  year      = {2011},
	  pages     = {487 - 492},
	  ee        = {https://publications.waset.org/pdf/3132},
	  url   	= {https://publications.waset.org/vol/53},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 53, 2011},
	}