Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2016, Article ID 5128528, 13 pages
http://dx.doi.org/10.1155/2016/5128528
Research Article

Fuzzy Prediction for Traffic Flow Based on Delta Test

Beijing Key Lab of Traffic Engineering, Beijing University of Technology, 100 Pingleyuan, Chaoyang District, Beijing 100124, China

Received 15 March 2016; Revised 9 July 2016; Accepted 4 August 2016

Academic Editor: Alberto Borboni

Copyright © 2016 Yang Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. I. Okutani and Y. J. Stephanedes, “Dynamic prediction of traffic volume through Kalman filtering theory,” Transportation Research Part B, vol. 18, no. 1, pp. 1–11, 1984. View at Publisher · View at Google Scholar · View at Scopus
  2. D. Gazis and C. Liu, “Kalman filtering estimation of traffic counts for two network links in tandem,” Transportation Research Part B: Methodological, vol. 37, no. 8, pp. 737–745, 2003. View at Publisher · View at Google Scholar · View at Scopus
  3. N. Zhang, Y. Zhang, and H. Lu, “Seasonal autoregressive integrated moving average and support vector machine models: prediction of short-term traffic flow on freeways,” Transportation Research Record, vol. 2215, pp. 85–92, 2011. View at Publisher · View at Google Scholar · View at Scopus
  4. E. Castillo, J. M. Menéndez, and S. Sánchez-Cambronero, “Predicting traffic flow using Bayesian networks,” Transportation Research Part B: Methodological, vol. 42, no. 5, pp. 482–509, 2008. View at Publisher · View at Google Scholar · View at Scopus
  5. K. Kuhn and A. Nicholson, “Traffic flow forecasting and spatial data aggregation,” Transportation Research Record, no. 2260, pp. 16–23, 2011. View at Publisher · View at Google Scholar · View at Scopus
  6. Y. L. Zhang and Z. R. Ye, “Short-term traffic flow forecasting using fuzzy logic system methods,” Journal of Intelligent Transportation Systems, vol. 12, no. 3, pp. 102–112, 2008. View at Publisher · View at Google Scholar · View at Scopus
  7. G.-J. Shen, “An intelligent hybrid forecasting model for short-term traffic flow,” in Proceedings of the 8th World Congress on Intelligent Control and Automation (WCICA '10), pp. 486–491, Jinan, China, July 2010. View at Publisher · View at Google Scholar · View at Scopus
  8. L. Dimitriou, T. Tsekeris, and A. Stathopoulos, “Adaptive hybrid fuzzy rule-based system approach for modeling and predicting urban traffic flow,” Transportation Research Part C: Emerging Technologies, vol. 16, no. 5, pp. 554–573, 2008. View at Publisher · View at Google Scholar · View at Scopus
  9. C. M. Zhu, C. P. Yan, X. L. Xu, and G. X. Wu, “Research on the application of the prediction of the expressway traffic flow based on the neural network with genetic algorithm,” Advanced Materials Research, vol. 189–193, pp. 4400–4404, 2011. View at Publisher · View at Google Scholar · View at Scopus
  10. B. L. Smith, B. M. Williams, and R. Keith Oswald, “Comparison of parametric and nonparametric models for traffic flow forecasting,” Transportation Research Part C: Emerging Technologies, vol. 10, no. 4, pp. 303–321, 2002. View at Publisher · View at Google Scholar · View at Scopus
  11. Y. S. Gu, Y. C. Li, J. C. Xu, and Y. P. Liu, “An aggregation approach to short-term traffic flow prediction, novel model based on wavelet transform and GA-Fuzzy neural network applied to short time traffic flow prediction,” Information Technology Journal, vol. 10, pp. 2105–2111, 2011. View at Google Scholar
  12. Y. Wang, P. Beullens, H. H. Liu, D. J. Brown, T. Thornton, and R. Proud, “A practical intelligent navigation system based on travel speed prediction,” in Proceedings of the 11th International IEEE Conference on Intelligent Transportation Systems (ITSC '08), pp. 470–475, IEEE, Beijing, China, October 2008. View at Publisher · View at Google Scholar · View at Scopus
  13. L. Zhao and F.-Y. Wang, “Short-term fuzzy traffic flow prediction using self-organizing TSK-type fuzzy neural network,” in Proceedings of the IEEE International Conference on Vehicular Electronics and Safety (ICVES '07), Beijing, China, December 2007. View at Publisher · View at Google Scholar · View at Scopus
  14. Y. C. Jin, “Fuzzy modeling of high-dimensional systems: complexity reduction and interpretability improvement,” IEEE Transactions on Fuzzy Systems, vol. 8, no. 2, pp. 212–221, 2000. View at Publisher · View at Google Scholar · View at Scopus
  15. F. M. Pouzols, A. Lendasse, and A. B. Barros, “Autoregressive time series prediction by means of fuzzy inference systems using nonparametric residual variance estimation,” Fuzzy Sets and Systems, vol. 161, no. 4, pp. 471–497, 2010. View at Publisher · View at Google Scholar · View at Scopus
  16. L. X. Wang, Adaptive Fuzzy Systems and Control—Design and Stability Analysis, Prentice Hall, Englewood Cliffs, NJ, USA, 1994.
  17. Z. Ju and H. Liu, “Fuzzy Gaussian mixture models,” Pattern Recognition, vol. 45, no. 3, pp. 1146–1158, 2012. View at Publisher · View at Google Scholar · View at Scopus
  18. G. Tsekouras, H. Sarimveis, E. Kavakli, and G. Bafas, “A hierarchical fuzzy-clustering approach to fuzzy modeling,” Fuzzy Sets and Systems, vol. 150, no. 2, pp. 245–266, 2005. View at Publisher · View at Google Scholar · View at Scopus
  19. H. Ying, “General Takagi-Sugeno fuzzy systems with simplified linear rule consequent are universal controllers, models and filters,” Information Sciences, vol. 108, no. 1–4, pp. 91–107, 1998. View at Publisher · View at Google Scholar · View at MathSciNet
  20. H. Ying, “Sufficient conditions on uniform approximation of multivariate functions by general Takagi-Sugeno fuzzy systems with linear rule consequent,” IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans., vol. 28, no. 4, pp. 515–520, 1998. View at Publisher · View at Google Scholar · View at Scopus
  21. K. Zeng, N.-Y. Zhang, and W.-L. Xu, “A comparative study on sufficient conditions for Takagi-Sugeno fuzzy systems as universal approximators,” IEEE Transactions on Fuzzy Systems, vol. 8, no. 6, pp. 773–780, 2000. View at Publisher · View at Google Scholar · View at Scopus
  22. H. Pi and C. Peterson, “Finding the embedding dimension and variable dependencies in time series,” Neural Computation, vol. 6, pp. 509–520, 1994. View at Google Scholar
  23. A. J. Jones, “New tools in non-linear modeling and prediction,” Computational Management Science, vol. 1, pp. 109–149, 2004. View at Google Scholar
  24. A. Lendasse, F. Corona, J. Hao, N. Reyhani, and M. Verleysen, “Determination of the mahalanobis matrix using nonparametric noise estimations,” in Proceedings of the European Symposium on Artificial Neural Networks, Bruges, Belgium, April 2006.
  25. F. Mateo, D. Sovilj, and R. Gadea, “Approximate k-NN delta test minimization method using genetic algorithms: application to time series,” Neurocomputing, vol. 73, no. 10–12, pp. 2017–2029, 2010. View at Publisher · View at Google Scholar · View at Scopus
  26. M. Misiti, Y. Misiti, G. Oppenheim, and J. M. Poggi, Wavelets and Their Applications, ISTE Publishing Knowledge, London, UK, 2007.
  27. D. L. Donoho, “De-noising by soft-thresholding,” IEEE Transactions on Information Theory, vol. 41, no. 3, pp. 613–627, 1995. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  28. D. L. Donoho, I. M. Johnstone, G. Kerkyacharian, and D. Picard, “Wavelet shrinkage: asymptopia?” Journal of the Royal Statistical Society, Series B: Methodological, vol. 57, no. 2, pp. 301–369, 1995. View at Google Scholar
  29. Y. F. Ren and X. Z. Ke, “Selection of wavelet decomposition level in multi-scale sensor data fusion of MEMS gyroscope,” International Journal of Digital Content Technology and its Applications, vol. 4, pp. 209–215, 2010. View at Google Scholar
  30. T. Cai and J. Zhu, “Wavelet de-noising of speech using singular spectrum analysis for decomposition level selection,” Journal of Shanghai Jiaotong University (Science), vol. 12, no. 2, pp. 190–196, 2007. View at Google Scholar · View at Scopus
  31. Q. Zhang, W. Chen, and Y. Tang, “Method of choosing the adaptive level of discrete wavelet decomposition to eliminate zero component,” Optics Communications, vol. 282, no. 5, pp. 778–785, 2009. View at Publisher · View at Google Scholar · View at Scopus
  32. S. R. Messer, J. Agzarian, and D. Abbott, “Optimal wavelet denoising for phonocardiograms,” Microelectronics Journal, vol. 32, no. 12, pp. 931–941, 2001. View at Publisher · View at Google Scholar · View at Scopus
  33. C. McLachlan and T. Krishnan, The EM Algorithm and Extensions, Wiley-Interscience, New York, NY, USA, 2nd edition, 2008.
  34. W. Sun and M. Liu, “Wind speed forecasting using FEEMD echo state networks with RELM in Hebei, China,” Energy Conversion and Management, vol. 114, pp. 197–208, 2016. View at Publisher · View at Google Scholar · View at Scopus