The Optimization of an Intelligent Traffic Congestion Level Classification from Motorists- Judgments on Vehicle's Moving Patterns

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.

[1] S. Phoosuphanusorn, "New mobile-phone users up 30%," Bangkok Post,
May 2007.
[2] W. Pattara-atikom and R. Peachavanish, "Estimating Road Traffic
Congestion from Cell Dwell Time using Neural Network", the 7th
International Conference on ITS Telecommunications (ITST 2007),
Sophia Antipolis, France, June 2007.
[3] P. Pongpaibool, P. Tangamchit, K. Noodwong, "Evaluation of Road
Traffic Congestion Using Fuzzy Techniques," Proceeding of IEEE
TENCON 2007, Taipei, Taiwan, October 2007.
[4] F. Porikli and X. Li, "Traffic congestion estimation using hmm models
without vehicle tracking" in IEEE Intelligent Vehicles Symposium, June
2004, pp. 188-193.
[5] J. Lu and L. Cao, "Congestion evaluation from traffic flow information
based on fuzzy logic" in IEEE Intelligent Transportation Systems, Vol.
1, 2003, pp. 50-33.
[6] B. Krause and C. von Altrock, "Intelligent highway by fuzzy
logic:Congestion detection and traffic control on multi-lane roads with
variable road signs" in 5th International Conference on Fuzzy Systems,
vol. 3,September 1996, pp. 1832-1837.
[7] R. B. A. Alessandri and M. Repetto. "Estimating of freeway traffic
variables using information from mobile phones," in IEEE American
Control Conference, 2003.
[8] J. T. Lomax, S. M. Tuner, G. Shunk, H.S. Levinson, R. H. Pratt, P. N.
Bay and B. B. Douglas. "Quantifying Congestion:Final Report" National
Cooperative Highway Research Program Report 398, TRB, Washington
D.C., 1997.
[9] R. L. Bertini, 2004. Congestion and Its Extent. "Access to Destinations:
Rethinking the Transportation Future of our Region", Minnesota, U.S.A.
[10] K. Choocharukul, "Congestion Measures in Thailand: State of the
Practice." Proceedings of the10th National Convention on Civil
Engineering, May 2005, pp. TRP111-TRP118.
[11] W. Pattara-atikom, P Pongpaibool, and S. Thajchayapong, "Estimating
Road Traffic Congestion using Vehicle Velocity", Proceedings of the
6th Intertional Conference on ITS Telecommunications, Chengdu,
CHINA, June 2006, pp. 1001-1004.
[12] D. J. Drown, T. M. Khoshgoftaar, and R. Narayanan, "Using
Evolutionary Sampling to Mine Imbalanced Data", Proceedings of the
6th International Conference on Machine Learning and Applications
(ICMLA 2007), OH, USA, December 2007, pp. 363-368.
[13] T. Thianniwet, S. Phosaard, and W. Pattara-Atikom, "Classification of
Road Traffic Congestion Levels from GPS Data using a Decision Tree
Algorithm and Sliding Windows" in Proc. of the World Congress on
Engineering (WCE 2009), vol. I, London, U.K., 2009, pp. 105-109.
[14] T. Thianniwet, S. Phosaard, and W. Pattara-Atikom, "Classification of
Road Traffic Congestion Levels from Vehicle-s Moving Patterns: A
Comparison between Artificial Neural Network and Decision Tree
Algorithm" in Electronic Engineering and Computing Technology:
Lecture Notes in Electrical Engineering, vol. 60, S.-L. AO and L.
Gelman, Eds. Netherlands: Springer, 2010, pp. 261-271.