Peak Traffic Prediction Using Nonparametric Approaches

Yang Zhang

doi:10.4028/www.scientific.net/AMR.378-379.196

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 378-379Peak Traffic Prediction Using Nonparametric...

Peak Traffic Prediction Using Nonparametric Approaches

Abstract:

How to accurately predict peak traffic is difficult for various forecasting models. In this paper, least squares support vector machines (LS-SVMs) are investigated to solve such a practical problem. It is the first time to apply the technique and analyze the forecast performance in the domain. For comparison purpose, other two non-parametric predictors 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.

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Periodical:

Advanced Materials Research (Volumes 378-379)

Pages:

196-199

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.378-379.196

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Online since:

October 2011

Authors:

Yang Zhang

Keywords:

Nonparametric Method, Peak Traffic, Traffic Prediction

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References

[1] B. Van Arem, H.R. Kirby, M.J.M. Van Der Vlist et al.: International Journal of Forecasting Vol. 13 (1997), pp.1-12.

Google Scholar

[2] E.I. Vlahogianni, J.C. Golias and M.G. Karlaftis: Transport Reviews Vol. 24 (2004), pp.533-557.

Google Scholar

[3] R. Chrobok, O. Kaumann, J. Wahle et al: European Journal of Operational Research Vol. 155 (2004), pp.558-568.

DOI: 10.1016/j.ejor.2003.08.005

Google Scholar

[4] W.H.K. Lam, Y.F. Tang and M.L. Tam: Journal of Forecasting Vol. 25 (2006), pp.173-192.

Google Scholar

[5] B.L. Smith, B.M. Williams and R.K. Oswald, Transportation Research Part C Vol. 10 (2002), pp.303-321.

Google Scholar

[6] J.A.K. Suykens, J. Vandewalle and B. De Moor: Neural Networks Vol. 14 (1998), pp.23-35.

Google Scholar

[7] R.E. Turochy: Journal of Transportation Engineering Vol. 132 (2006), pp.469-474.

Google Scholar

[8] PeMS, Information on http: /pems. eecs. berkeley. edu.

Google Scholar

[9] LS-SVMlab Matlab/C toolbox, Information on http: /www. esat. kuleuven. ac. be/sista/lssvmlab.

Google Scholar