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Journal of Applied Mathematics
Volume 2013, Article ID 953548, 10 pages
http://dx.doi.org/10.1155/2013/953548
Research Article

Sensitivity Analysis of Wavelet Neural Network Model for Short-Term Traffic Volume Prediction

Transportation College, Southeast University, Nanjing, Jiangsu 210096, China

Received 23 August 2013; Revised 12 December 2013; Accepted 13 December 2013

Academic Editor: Han H. Choi

Copyright © 2013 Jinxing Shen and Wenquan Li. 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. S. A. Zargari, S. Z. Siabil, A. H. Alavi, and A. H. Gandomi, “A computational intelligence-based approach for short-term traffic flow prediction,” Expert Systems, vol. 29, no. 2, pp. 124–142, 2012. View at Google Scholar
  2. E. I. Vlahogianni, J. C. Golias, and M. G. Karlaftis, “Short-term traffic forecasting: overview of objectives and methods,” Transport Reviews, vol. 24, no. 5, pp. 533–557, 2004. View at Publisher · View at Google Scholar · View at Scopus
  3. L. Li, W. H. Lin, and H. Liu, Type-2 Fuzzy Logic Approach for Short-Term Traffic Forecasting, 2006.
  4. Y. Xie, Y. Zhang, and Z. Ye, “Short-term traffic volume forecasting using Kalman filter with discrete wavelet decomposition,” Computer-Aided Civil and Infrastructure Engineering, vol. 22, no. 5, pp. 326–334, 2007. View at Publisher · View at Google Scholar · View at Scopus
  5. D. Ngoduy, “Applicable filtering framework for online multiclass freeway network estimation,” Physica A, vol. 387, no. 2-3, pp. 599–616, 2008. View at Publisher · View at Google Scholar · View at Scopus
  6. B. Ghosh, B. Basu, and M. O'Mahony, “Bayesian time-series model for short-term traffic flow forecasting,” Journal of Transportation Engineering, vol. 133, no. 3, pp. 180–189, 2007. View at Publisher · View at Google Scholar · View at Scopus
  7. A. Stathopoulos and M. G. Karlaftis, “A multivariate state space approach for urban traffic flow modeling and prediction,” Transportation Research C, vol. 11, no. 2, pp. 121–135, 2003. View at Publisher · View at Google Scholar · View at Scopus
  8. J.-N. Xue and Z.-K. Shi, “Short-time traffic flow prediction using chaos time series theory,” Journal of Transportation Systems Engineering and Information Technology, vol. 8, no. 5, pp. 68–72, 2008. View at Google Scholar · View at Scopus
  9. X. Fei, C.-C. Lu, and K. Liu, “A bayesian dynamic linear model approach for real-time short-term freeway travel time prediction,” Transportation Research C, vol. 19, no. 6, pp. 1306–1318, 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 C, vol. 10, no. 4, pp. 303–321, 2002. View at Publisher · View at Google Scholar · View at Scopus
  11. R. E. Turochy and B. D. Pierce, “Relating short-term traffic forecasting to current system state using nonparametric regression,” in Proceedings of the 7th International IEEE Conference on Intelligent Transportation Systems (ITSC '04), pp. 239–244, October 2004. View at Scopus
  12. S.-Y. Yun, S. Namkoong, J.-H. Rho, S.-W. Shin, and J.-U. Choi, “A performance evaluation of neural network models in traffic volume forecasting,” Mathematical and Computer Modelling, vol. 27, no. 9–11, pp. 293–310, 1998. View at Publisher · View at Google Scholar · View at Scopus
  13. B. Ghosh, B. Basu, and M. O'Mahony, “Random process model for urban traffic flow using a wavelet-bayesian hierarchical technique,” Computer-Aided Civil and Infrastructure Engineering, vol. 25, no. 8, pp. 613–624, 2010. View at Publisher · View at Google Scholar · View at Scopus
  14. B. L. Smith and M. J. Demetsky, “Traffic flow forecasting: comparison of modeling approaches,” Journal of Transportation Engineering, vol. 123, no. 4, pp. 261–266, 1997. View at Google Scholar · View at Scopus
  15. H. Adeli and S. Hung, Machine Learning Neural Networks, Genetic Algorithms, and Fuzzy Systems, John Wiley & Sons, 1994.
  16. M. G. Karlaftis and E. Vlahogianni, “Statistical methods versus neural networks in transportation research: differences, similarities and some insights,” Transportation Research C, vol. 19, no. 3, pp. 387–399, 2011. View at Publisher · View at Google Scholar · View at Scopus
  17. S. Y. Chen and W. Wang, “Traffic volume forecasting based on wavelet transform and neural networks,” in Advances in Neural Networks—ISNN, vol. 3973, pp. 1–7, 2006. View at Google Scholar
  18. Y. Xie and Y. Zhang, “A wavelet network model for short-term traffic volume forecasting,” Journal of Intelligent Transportation Systems, vol. 10, no. 3, pp. 141–150, 2006. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  19. E. I. Vlahogianni, “Enhancing predictions in signalized arterials with information on short-term traffic flow dynamics,” Journal of Intelligent Transportation Systems, vol. 13, no. 2, pp. 73–84, 2009. View at Publisher · View at Google Scholar · View at Scopus
  20. B. Abdulhai, H. Porwal, and W. Recker, “Short-term traffic flow prediction using neuro-genetic algorithms,” ITS Journal, vol. 7, no. 1, pp. 3–41, 2002. View at Publisher · View at Google Scholar · View at Scopus
  21. R. K. Belew, J. McInerney, and N. N. Schraudolph, Evolving Networks Using the Genetic Algorithm with Connectionist Learning, 1990.
  22. J. R. Koza and J. P. Rice, “Genetic generation of both the weights and architecture for a neural network,” in Proceedings of the International Joint Conference on Neural Networks (IJCNN '91), pp. 397–404, July 1991. View at Scopus
  23. J. Guo, B. M. Williams, and B. L. Smith, “Data collection time intervals for stochastic short-term traffic flow forecasting,” Journal of the Transportation Research Board, vol. 2024, no. 1, pp. 18–26, 2007. View at Publisher · View at Google Scholar · View at Scopus
  24. C. Oh, S. G. Ritchie, and J.-S. Oh, “Exploring the relationship between data aggregation and predictability to provide better predictive traffic information,” Journal of the Transportation Research Board, vol. 1935, no. 1, pp. 28–36, 2005. View at Google Scholar · View at Scopus
  25. F. Qiao, X. Wang, and L. Yu, “Optimizing aggregation level for intelligent transportation system data based on wavelet decomposition,” Journal of the Transportation Research Board, vol. 1840, no. 1, pp. 10–20, 2003. View at Google Scholar · View at Scopus
  26. F. Qiao, L. Yu, and X. Wang, “Double-sided determination of aggregation level for intelligent transportation system data,” Journal of the Transportation Research Board, vol. 1879, no. 1, pp. 80–88, 2004. View at Google Scholar · View at Scopus
  27. D. Veitch, Wavelet neural networks and their application in the study of dynamical systems [Dissertation], MSc in Data Analysis, Networks and Nonlinear Dynamics, Department of Mathematics, University of York, Helsington, UK, 2005.
  28. A. Saltelli, M. Ratto, T. Andres et al., Global Sensitivity Analysis: The Primer, Wiley-Interscience, 2008.