Open Access Subscription Access
Open Access Subscription Access
Neuro-Fuzzy and Rough Set Based Traffic Flow Prediction
With the rapid growth in urban population and vehicle ownership, traffic congestion has become a severe problem everywhere in the world and is only expected to rise. This problem can be avoided by knowing the traffic situation in advance which is achieved with the help of traffic flow prediction. In the proposed work, traffic flow is predicted on short term basis using neuro-fuzzy hybrid system in combination with rough set. The neuro-fuzzy hybrid system combines the complementary capabilities of both neural networks and fuzzy logic. The work has attempted to study the effect of aggregation intervals and past samples on the prediction performance using MSE threshold variation. Rough set is used as a post processing tool. The objective is to improve prediction accuracy. Data from highway of Chennai, India is used for the analysis. It is found that use of rough set results in considerable improvement in the prediction performance as indicated by performance measures like MSE, RMSE etc.
Intelligent Transportation Systems (ITS), Rough Set Theory (RST), Short Term Traffic Flow Prediction, Neuro-Fuzzy Hybrid System.
- Lelitha Vanajakshi and Laurence Rilett, “A Comparison of the Performance of Artificial Neural Network and Support Vector Machine for the Prediction of Traffic Speed”, Proceedings of IEEE International Symposium on Intelligent Vehicles, pp. 14-17, 2004.
- T. Thomas, W. Weijermars and E.V. Berkum, “Predictions of Urban Volumes in Single Time Series”, IEEE Transactions on Intelligent Transportation Systems, Vol. 11, No. 1, pp. 71-80, 2010.
- M.C. Tan, S.C. Wong, J.M. Xu, Z.R. Guan and P. Zhang, “An Aggregation Approach to Short Term Traffic Flow Prediction”, IEEE Transactions on Intelligent Transportation Systems, Vol. 10, No. 1, pp. 60-69, 2009.
- Y. Mingheng and G. Ganglong, “Accurate Multisteps Traffic Flow Prediction Based on SVM”, Mathematical Problems in Engineering, Vol. 2013, pp. 1-14, 2013.
- Y. Cong, J. Wang and X. Li, “Traffic Flow Forecasting by a Least Squares Support Vector Machine with a Fruit Fly Optimization Algorithm”, Procedia Engineering, Vol. 137, pp. 59-68, 2016.
- Wusheng Hu, “The Short-Term Traffic Flow Prediction Based on Neural Network”, Proceedings of IEEE International Conference on Future Computer and Communication, pp. 293-296, 2010.
- C. Quek and B.B.S. Lim, “Pop-Traffic: A Novel Fuzzy Neural Approach to Road Traffic Analysis and Prediction”, IEEE Transactions on Intelligent Transportation Systems, Vol. 7, No. 2, pp. 133-146, 2006.
- C.B. Ping and M.Z. Qiang, “Short-Term Traffic Flow Prediction Based on ANFIS”, Proceedings of IEEE International Conference on Communication Software and Networks, pp. 1-8, 2009.
- S. Chen and P Wang, “Computational Intelligence in Economics and Finance”, Springer, 2002.
- L. Shen and H. Loh, “Applying Rough Sets to Market Timing Decisions”, Decision Support Systems, Vol. 37, No. 4, pp. 65-72, 2006.
- K Ang and C Quek, “Stock Trading using RSPOP: A Novel Rough Set based Neuro-Fuzzy Approach”, Proceedings of IEEE International Conference on Neural Networks, pp. 105-112, 2005.
- Zhenguo Zhou and Kun Huang, “Study of Traffic Flow Prediction Model at Intersection Based on R-FNN”, Proceedings of International Seminar on Business and Information Management, pp. 1-7, 2008.
- CEOS, Available at: http://www.ceos.com.au.
- Minal Deshpande and Preeti Bajaj, “Performance Improvement of Traffic Flow Prediction using Combination of Support Vector Machine and Rough Set”, International Journal of Computer Applications, Vol. 163, No. 2, pp. 31-35, 2017.
Abstract Views: 1
PDF Views: 0