The Study of Combination Model Forecasting of Dynamic Traffic Volumes Based on MATLAB

Abstract:

The most important part of ITS (Intelligent Technology System) is the forecasting of dynamic traffic information reporting in a timely manner. Based on the results of past empirical studies, this article is presenting and analyzing two models based on Grey Theory and the BP (Back propagation) Neural Network. In accordance with the advantages and disadvantages of these two models, we established a new combination model. This article also presents the calculation of dynamic traffic stream volumes based on the application of two models (Grey Theory and BP Neutral Network).The results predicted an increase in the overall prediction accuracy of road traffic volume.

• Periodical: Advanced Materials Research (Volumes 228-229)
• Pages: 327-331
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.228-229.327
• Online since: April 2011
• Authors: Qian Sun, Wei Dong Ding, Han Li Liu, Sheng Pin Du
• Keywords: Dynamic Volumes Forecast, Grey—BP Combination Forecasting Model, ITS, MATLAB Programming

References

[1] C.B. Xu, Y.M. Liu: Dynamic Traffic Volumes Forecasting Model Combined with Traffic Information, Special Technology (May 2008).

[2] S.F. Liu, Y. Lin: Grey Information, Theory and Practical Applications, Science Publishers, pp.97-99.

[3] B.J. Wang, Y.H. Wang, L.P. Niu: Pseudo Random Number Generator Based on Back Propagation Neural Network, Semiconductor Photonics and Technology (May 2007), Vol. 13, No. 2.

[4] C.B. Xiu, Y.Q. Li: BP neural network based on chaos simulated annealing [A]. The International Conference on Neural Networks and Brain, 2005, 1(13-15): 358-360.

DOI: 10.1109/icnnb.2005.1614632

[5] G.W. Yang, R. Xing, S.J. Wang: A hidden trouble theorem for model classing by BP algorithm [A]. The Sixth World Congress on Intelligent Control and Automation, 2006, 1: 2742-2745.

DOI: 10.1109/wcica.2006.1712863

[6] S.Y. Chen, W. Wang: Grey neural network forecasting for traffic flow, Journal of Southeast University (Natural Science Edition), July 2004, Vol. 34, No. 4.

[7] Q.Y. Chen, X.L. Liu: Short Term Load Forecasting by Using Neural Networks with Variable Activation Functions and Embedded Chaos Algorithm.

[8] S.F. Wang, L.P. Lu, Y.Q. Tan, H.L. Cui: A application of artificialneural network based on the technology of Matlab in the traffic volume. Journal of Hebei Institute of Architectural Science and Technology, Vol. 2l, No. 2, Jun. (2004).

[9] S.Q. Wen, P.F. Zhou, H.G. Kang: Study of combining traffic forecasting model based on gray theory and BP neural network. Journal of Dalian University of Technology, Vol. 50, No. 4, July (2010).

[10] D.M. Shi, L.C. Li, J.W. Song: Power System Load Forecasting Based Upon Combination of Grey Forecast and Artificial Neural Network. Power System Technology, Vol. 25, No. 12, Dec. (2001).