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Prediction of Railway Passenger Traffic Volume by means of LS-SVM

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Advances in Neural Networks - ISNN 2006 (ISNN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3973))

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Abstract

Based on Least Squares Support Vector Machine (LS-SVM), a method for the prediction of railway passenger traffic volume is proposed. The railway passenger traffic volume from 1985 to 2002, provided by National Bureau of Statistics of China, is employed as total data set. The normalized passenger volume from 1985 to1999 is used as training data set to establish LS-SVM model, while the normalized volume from 1999 to 2002 is utilized as testing data set to carry out prediction. LS-SVM is applied to establish prediction model. The prediction results by LS-SVM model are compared with those by BP neural network method. The results show that LS-SVM outperforms BP neural network in the prediction of railway passenger traffic volume.

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Author information

Authors and Affiliations

  1. School of Mechatronics, Lanzhou Jiaotong University, Lanzhou, 730070, China

    Zhen-Rui Peng & Zhao-Yuan Jiang

  2. Key Laboratory of Opto-Electronic Technology and Intelligent Control of Ministry of Education, Lanzhou Jiaotong University, Lanzhou, 730070, China

    Fu Wu

Authors
  1. Zhen-Rui Peng
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  2. Fu Wu
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  3. Zhao-Yuan Jiang
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Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

  3. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Zurada

  4. Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd, 200240, Shanghai, China

    Bao-Liang Lu

  5. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

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© 2006 Springer-Verlag Berlin Heidelberg

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Peng, ZR., Wu, F., Jiang, ZY. (2006). Prediction of Railway Passenger Traffic Volume by means of LS-SVM. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760191_2

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  • DOI: https://doi.org/10.1007/11760191_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34482-7

  • Online ISBN: 978-3-540-34483-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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