AUTHORS: R. Sujatha, S. Srinithibharathi, S. Subhapradha
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ABSTRACT: Traffic congestion could be a challenge in Gauteng province of Republic of South Africa and it's a negative impact on the economy of this province therein services and merchandise aren't being rendered on time. Holdup affects the standard of lives of Gauteng residents and guests alike. This information was used for constructing the traffic flow prediction models. To propose a way to spot road holdup levels from rate of mobile sensors with high accuracy .According to Human perceptions holdup levels divided into 3 levels: lightweight, heavy, and jam. The ratings and rate were fed into a choice tree learning model (J48).The slippery windows technique to capture the consecutive moving average velocities, that was referred to as a moving pattern .Then road users’ judgments and connected info were learned utilizing a decision tree model (J48) classification algorithmic program. The evaluations disclosed that the decision tree model. The achieved Associate in Nursing overall accuracy as high as 91% with a precision as high as 94%.
KEYWORDS: - Classification , Decision tree, HoldupREFERENCES:
 E. Chung, “Classification of traffic pattern,' In Proc. of the 11th World Congress on ITS, pp. 687-694, 2003.
 W. Weijermars and Eric Van Berkum, “Analyzing highway flow patterns using cluster analysis,' In Proceedings of the 8th International IEEE Conference on Intelligent Transportation Systems, pp. 308-313, 2005.
 Lin Xu, Yang Yue and Qingquan Li, “Identifying Urban Traffic Congestion Pattern from Historical Floating Car Data,' In Procedings of Social and Behavioral Sciences, Vol. 96, pp. 2084– 2095, 2013.
 A. Amelia and P. Saptawati, “Detection of potential traffic jam based on traffic characteristic data analysis,' In International Conference on Data and Software Engineering (ICODSE), pp.1-5, 2014.
 R. Ong, F. Pinelli, R. Trasarti, M. Nanni, C. Renso, S. Rinzivillo and F. Giannotti, “Traffic Jams Detection Using Flock Mining,' In European Conference (ECML PKDD-11), pp. 650-653, 2011.
 Z. Wang, Min Lu and X. Yuan, J. Zhang and Van de Wetering H, “Visual Traffic Jam Analysis Based on Trajectory Data,' In IEEE Transactions on Visualization and Computer Graphics, Vol. 19, No. 12, pp. 2159 – 2168, 2013.
 A. Gupta, Netaji Subhas, S. Choudhary and S. Paul, “DTC: A framework to Detect Traffic Congestion by mining versatile GPS data,' In 1st International Conference Proceedings of Emerging Trends and Applications in Computer Science (ICETACS), pp. 97 – 103, 2013.
 E Florido, O Castaño, A Troncoso and F Martínez-Álvarez, “Data Mining for Predicting Traffic Congestion and Its Application to Spanish Data,' In 10th International Conference on Soft Computing Models in Industrial and Environmental Applications Volume 368 of the series Advances in Intelligent Systems and Computing, pp. 341-351. 2015 .
 Y. Yang, Zhiming Cui, Jian Wu a Guangming Zhang and X. Xian, “Fuzzy C-means Clustering and Opposition-based Reinforcement Learning for Traffic Congestion Identification,' In Journal of Information & Computational Science, Vol. 9, No. 9, pp. 2441-2450, 2012.