Call for Paper - November 2023 Edition
IJCA solicits original research papers for the November 2023 Edition. Last date of manuscript submission is October 20, 2023. Read More

Performance Improvement of Traffic Flow Prediction Model using Combination of Support Vector Machine and Rough Set

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Minal Deshpande, Preeti Bajaj

Minal Deshpande and Preeti Bajaj. Performance Improvement of Traffic Flow Prediction Model using Combination of Support Vector Machine and Rough Set. International Journal of Computer Applications 163(2):31-35, April 2017. BibTeX

	author = {Minal Deshpande and Preeti Bajaj},
	title = {Performance Improvement of Traffic Flow Prediction Model using Combination of Support Vector Machine and Rough Set},
	journal = {International Journal of Computer Applications},
	issue_date = {April 2017},
	volume = {163},
	number = {2},
	month = {Apr},
	year = {2017},
	issn = {0975-8887},
	pages = {31-35},
	numpages = {5},
	url = {},
	doi = {10.5120/ijca2017913473},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Short term traffic flow prediction has become one of the important research fields in intelligent transportation system. The prediction of this traffic flow information quickly and accurately is important for traffic control and guidance to initiate the measuring steps well in advance. It makes the transport users better informed and makes the transport network smarter, safer and more coordinated. It plays a crucial role in individual dynamic route guidance, advance traffic information system (ATIS) and advance traffic management system (ATMS). This paper discusses the implementation of traffic flow prediction model using support vector machine. Rough set is used as a post processing tool to validate the prediction result. The objective is to improve traffic flow prediction performance. Data near Perungudi toll plaza in IT corridor in Chennai, India is used for the analysis. It is found that the use of rough set results in satisfactory performance improvement which is evaluated using mean square error as the performance measures.


  1. Liang Zhao and Fei-Yue, “ Short-term Fuzzy Traffic Flow Prediction Using Self-Organizing TSK-Type Fuzzy Neural Network”, IEEE International Conference on Vehicular Electronics and Safety, ICVES, 2007.
  2. H. Chang, Y. Lee, B.Yoon and S. Baek,” Dynamic near-term traffic flow prediction : system oriented approach based on past experiences”, IET Intelligent Transportation System, 2012, Vol 6, Issue 3, pp 292-305.
  3. Qiangwei Li,” Short-time Traffic Flow Volume Prediction Based on Support Vector Machine with Time-dependent Structure”, Inetrnational Instrumentation and Measurement Technology Conference, I2MTC 2009, Singapore, 5-7 May 2009.
  4. Shiliang Sun, Changshui Zhang and Guoqiang Yu ,” A Bayesian Network Approach to Traffic FlowForecasting”, IEEE Transactions on Intelligent Transportation Systems, Vol 7, No. 1, March 2006.
  5. Wusheng HU et al.,” The Short-Term Traffic Flow Prediction Based on Neural Network”, 2nd International Conference on Future Computer and Communication, Volume 1, 2010, pp 293-296.
  6. S. Chen and P Wang, “ Computational intelligence in economics and finance”, Springer, ISBN: 3-540-44098-4, 2002, pp 15-22.
  7. L. Shen and H Loh, “ Applying rough sets to market timing decisions”, Decision Support Systems Journal , 2006, pp 65-72.
  8. K Ang and C Quek,” Stock trading using RSPOP : A novel rough set based neuro-fuzzy approach”, IEEE International conference on Trnsactions on Neural Networks, 2005, pp 105-112.
  9. Bin-Sheng Liu et al.,” Research on Forecasting Model in Short Term Traffic Flow Based on Data Mining Technology”, Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications, ISDA 2006.
  10. Zhenguo Zhou and Kun Huang,” Study of Traffic Flow Prediction Model atv Intersection Based on R-FNN”, International Seminar on Business and Information Management , 2008.
  11. PANG Ming-bao and HE Guo-guang ,” Chaos Rapid Recognition of Traffic Flow by using Rough Set Neural Network”, Inetrnational Symposiums on Information Processing, 2008.
  12. Xinrong Liang et al. ,” Freeway Traffic Flow Model Based on Rough Sets and Elman Neural Network”, IEEE 2009.
  14. Shi, D. Y. Lu, J, Lu,L.J.,” A judge model of the impact of lane closure incident on individual vehicles on freeways based on RFID technology and FOA-GRNN method,” Wuhan Univ. Technology, Vol 34, pp 63-68, 2012.
  15. Msizi Khoza and Tshilidzi Marwala,” A Rough Set Theory Based Predictive Model for Stock Prices”, 12th IEEE inetrnational Symposium on Computational Intelligence and Informatics, CINTI 2011, 21-22 November, 2011, Budapest, Hungary.


Intelligent Transportation Systems (ITS), Rough Set Theory (RST), Short term traffic Flow Prediction, Support Vector Machine (SVM).