Comparison of Parametric and Nonparametric Techniques for Non-peak Traffic Forecasting

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.

Authors:

• Yang Zhang
• Yuncai Liu

Keywords:

• Parametric and Nonparametric Techniques
• Non-peak Traffic Forecasting

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.