Commenced in January 2007
Paper Count: 30184
The Optimization of an Intelligent Traffic Congestion Level Classification from Motorists- Judgments on Vehicle's Moving Patterns
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.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1058552Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1445
 S. Phoosuphanusorn, "New mobile-phone users up 30%," Bangkok Post, May 2007.
 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.
 P. Pongpaibool, P. Tangamchit, K. Noodwong, "Evaluation of Road Traffic Congestion Using Fuzzy Techniques," Proceeding of IEEE TENCON 2007, Taipei, Taiwan, October 2007.
 F. Porikli and X. Li, "Traffic congestion estimation using hmm models without vehicle tracking" in IEEE Intelligent Vehicles Symposium, June 2004, pp. 188-193.
 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.
 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.
 R. B. A. Alessandri and M. Repetto. "Estimating of freeway traffic variables using information from mobile phones," in IEEE American Control Conference, 2003.
 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.
 R. L. Bertini, 2004. Congestion and Its Extent. "Access to Destinations: Rethinking the Transportation Future of our Region", Minnesota, U.S.A.
 K. Choocharukul, "Congestion Measures in Thailand: State of the Practice." Proceedings of the10th National Convention on Civil Engineering, May 2005, pp. TRP111-TRP118.
 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.
 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.
 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.
 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.