Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30169
Comparison of Parametric and Nonparametric Techniques for Non-peak Traffic Forecasting

Authors: Yang Zhang, Yuncai Liu

Abstract:

Accurately predicting non-peak traffic is crucial to daily traffic for all forecasting models. In the paper, least squares support vector machines (LS-SVMs) are investigated to solve such a practical problem. It is the first time to apply the approach and analyze the forecast performance in the domain. For comparison purpose, two parametric and two non-parametric techniques are selected because of their effectiveness proved in past research. Having good generalization ability and guaranteeing global minima, LS-SVMs perform better than the others. Providing sufficient improvement in stability and robustness reveals that the approach is practically promising.

Keywords: Parametric and Nonparametric Techniques, Non-peak Traffic Forecasting

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1329472

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1838

References:


[1] B. Van Arem, H. R. Kirby, M. J. M. Van Der Vlist, and J. C. Whittaker, "Recent advances and applications in the field of short-term traffic forecasting," Int. J. Forecast., vol. 13, no. 1, pp. 1-12, 1997.
[2] E. I. Vlahogianni, J. C. Golias, and M. G. Karlaftis, "Short-term forecasting: Overview of objectives and methods," Transport Rev., vol. 24, no. 5, pp. 533-557, 2004.
[3] R. Chrobok, O. Kaumann, J. Wahle, and M. Schreckenberg, "Different methods of traffic forecast based on real data," Eur. J. Oper. Res. vol. 155, no. 3, pp. 558-568, 2004.
[4] W. H. K. Lam, Y. F. Tang, K. S. Chan, and M. L. Tam, "Short-term hourly traffic forecasts using Hong Kong annual traffic census," Transp., vol. 33, no. 3, pp. 291-310, 2006.
[5] W. H. K. Lam, Y. F. Tang, and M.L. Tam, "Comparison of two non-parametric models for daily traffic forecasting in Hong Kong," J. Forecast., vol. 25, no. 3, pp. 173-192, 2006.
[6] W. H. K. Lam, and J. Xu, "Estimation of AADT from short period counts in Hong KongÔÇöA comparison between neural network method and regression analysis," J. Adv. Transp., vol. 34, no. 2, pp. 249-268, 2000.
[7] C. H. Wu, J. M. Ho, and D. T. Lee, "Travel-time prediction with support vector regression," IEEE Trans. Intell. Transp. Syst., vol. 5, no. 4, pp. 276-281, 2004.
[8] H. Yin, S. C. Wong, J. Xu, and C. K. Wong, "Urban traffic flow prediction using a fuzzy-neural approach," Transp. Res. Part C., vol. 10, no. 2, pp. 85-98, 2002.
[9] B. L. Smith, B. M. Williams, and R. K. Oswald, "Comparison of parametric and nonparametric models for traffic flow forecasting," Transp. Res. Part C, vol. 10, no. 4, pp. 303-321, 2002.
[10] B. L. Smith, and M. J. Demetsky, "Traffic flow forecasting: Comparison of modeling approaches," J. Transp. Eng., vol. 123, no. 4, pp. 261-266, 1997.
[11] S. Lee, Y. I. Lee, and B. Cho, "Short-term travel speed prediction models in car navigation systems," J. Adv. Transp., vol. 40, no. 2, pp. 123-139, 2006.
[12] M. S. Ahmed, and A. R. Cook, "Analysis of freeway traffic time-series data by using Box-Jenkins techniques," Transportation Research Record, no. 722, pp. 1-9, 1979.
[13] D. Park, and L. R. Rilett, "Forecasting multiple-period freeway link travel times using modular neural networks," Transportation Research Record, no. 1617, pp. 63-70, 1998.
[14] A. Ding, X. Zhao, and L. Jiao, "Traffic flow time series prediction based on statistics learning theory," in Proc. IEEE 5th Int. Conf. Intell. Transp. Syst., 2002, pp. 727-730.
[15] L. Vanajakshi, and L. R. Rilett, "A comparison of the performance of artificial neural network and support vector machines for the prediction of traffic speed," in Proc. IEEE Intell. Vehicles Symp., 2004, pp. 194-199.
[16] J. A. K. Suykens, J. Vandewalle, and B. De Moor, "Optimal control by least squares support vector machines," Neural Netw., vol. 14, no. 1, pp. 23-35, 1998.
[17] J. A. K. Suykens, T. Van Gestel, J. De Brabanter, B. De Moor, and J. Vandewalle, Least Squares Support Vector Machines. Singapore: World Scientific, 2002.
[18] T. Van Gestel, J. A. K. Suykens, D. Baestaens, A. Lambrechts, G. Lanckriet, B. Vandaele, B. De Moor, and J. Vandewalle, "Financial Time Series Prediction using Least Squares Support Vector Machines within the Evidence Framework," IEEE Trans. Neural Netw., vol. 12, no. 4, pp. 809-821, 2001.
[19] R. E. Turochy, "Enhancing short-term traffic forecasting with traffic condition information," J. Transp. Eng., vol. 132, no. 6, pp. 469-474, 2006.
[20] H. Drucker, C. J. C. Burges, L. Kaufman, A. Smola, and V. Vapnik. "Support vector regression machines," Adv. Neural. Info. Proc. Sys., vol. 9, NIPS, pp. 155-161, MIT Press, 1996.
[21] J. Q. Fan, and Q. W. Yao, Nonlinear Time Series: Nonparametric and Parametric Methods, Springer-Verlag, New York, 2003.
[22] S. Chen, C. F. N. Cowan, and P. M. Grant, "Orthogonal least squares learning algorithm for radial basis function networks," IEEE Trans. Neural Networks, vol. 2, no. 2, pp. 302-309, 1991.
[23] V. Vapnik, Statistical Learning Theory, New York, John Wiley, 1998.
[24] PeMS, Available: http://pems.eecs.berkeley.edu.
[25] LS-SVMlab Matlab/C toolbox, Available: http://www.esat.kuleuven.ac.be/sista/lssvmlab.