Affiliation:
1. Dalian University of Technology
Abstract
Accurate traffic flow forecasting is crucial to the development of intelligent transportation systems and advanced traveler information systems. Since Support Vector Machine (SVM)have better generalization performance and can guarantee global minima for given training data, it is believed that SVR is an effective method in traffic flow forecasting. But with the sharp increment of traffic data, traditional serial SVM can not meet the real-time requirements of traffic flow forecasting. Parallel processing has been proved to be a good method to reduce training time. In this paper, we adopt a parallel sequential minimal optimization (Parallel SMO) method to train SVM in multiple processors. Our experimental and analytical results demonstrate this model can reduce training time, enhance speed-up ratio and efficiency and better satisfy the real-time demands of traffic flow forecasting.
Publisher
Trans Tech Publications, Ltd.
Reference13 articles.
1. G.G. He, Y. Li and S.F. Ma: Discussion on Short-Term Traffic Flow Forecasting Methods Based on Mathematical Models. System Engineering Theory Practice, Volume. 12(2000), pp.51-56.
2. C. Han and S. Song: A review of some main models for traffic flow forecasting. IEEE Intelligent Transportation Systems Proceedings, Volume. 1 (2003), pp.216-219.
3. V. N. Vapnik: The Nature of Statistical Learning Theory. New York, Springer (1995).
4. F. Wang, G. Z. Tan, C. Deng, and Z. Tian: Real-time Traffic Flow Forecasting Model and Parameter Selection based on ε-SVR. The 7th World Congress on Intelligent Control and Automation (wcica'08), China, (2008), pp.2870-2875.
5. C.H. Wu, J.M. Ho, and D.T. Lee: Travel-time prediction with support vector regression. IEEE Transactions on Intelligent Transportation Systems, Vol. 5(4)(2004), pp.276-281.
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1 articles.
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1. Traffic Flow Forecasting Based on Combination of Multidimensional Scaling and SVM;International Journal of Intelligent Transportation Systems Research;2013-11-07