Using Scaling Methods to Improve Support Vector Regression’s Performance for Travel Time and Traffic Volume Predictions

  • Amanda Yan Lin
  • Mengcheng Zhang
  • SelpiEmail author
Conference paper
Part of the Contributions to Statistics book series (CONTRIB.STAT.)


Long queues often happen on toll roads, especially at the tollgates. These create many problems including having an impact on the regular roads nearby. If travel time and traffic volume at the tollgates can be predicted accurately in advance, this would allow traffic authorities to take appropriate measures to improve traffic flow and the safety of road users. This paper describes a novel combination of scaling methods with Support Vector Machines for Regression (SVR) for travel time and tollgate volume prediction tasks, as part of the Knowledge Discovery and Data Mining (KDD) Cup 2017. A new method is introduced to handle missing data by utilising the structure of the road network. Moreover, experiments with reduced data were conducted to evaluate whether the conclusions from combining scaling methods with SVR could be generalised.


Travel time prediction Traffic volume prediction Tollgate SVR Time series analysis SVR with scaling Support vector regression 



S. acknowledges strategic funding support from Chalmers Area of Advance Transport while writing this paper.


  1. 1.
    Lin, A.Y., Zhang, M.C., Selpi.: Combining support vector regression with scaling methods for highway tollgates travel time and volume predictions. In: Proceedings of International Work-Conference on Time Series Analysis. ITISE (2017)Google Scholar
  2. 2.
  3. 3.
    Drucker, H., Burges, C.J., Kaufman, L., Smola, A., Vapnik, V.: Support vector regression machines. Advanc Neural Inf. Process. Syst. 9, 155–161 (1997)Google Scholar
  4. 4.
    Lu, C.J., Lee, T.S., Chiu, C.C.: Financial time series forecasting using independent component analysis and support vector regression. Decision Support Syst. 47(2), 115–125 (2009)CrossRefGoogle Scholar
  5. 5.
    Yeh, C.Y., Huang, C.W., Lee, S.J.: A multiple-kernel support vector regression approach for stock market price forecasting. Exp. Syst. Appl. 38(3), 2177–2186 (2011)CrossRefGoogle Scholar
  6. 6.
    Yu, P.S., Chen, S.T., Chang, I.F.: Support vector regression for real-time flood stage forecasting. J. Hydrol. 328(3), 704–716 (2006)CrossRefGoogle Scholar
  7. 7.
    Wu, C.H., Ho, J.M., Lee, D.T.: Travel-time prediction with support vector regression. IEEE Trans. Intell. Trans. Syst. 5(4), 276–281 (2004)CrossRefGoogle Scholar
  8. 8.
    Oh, S., Byon, Y.J., Jang, K., Yeo, H.: Short-term travel-time prediction on highway: a review of the data-driven approach. Transp. Rev. 35(1), 4–32 (2015)CrossRefGoogle Scholar
  9. 9.
    Van Lint, J.W.C., Hoogendoorn, S.P., van Zuylen, H.J.: Accurate freeway travel time prediction with state-space neural networks under missing data. Transp. Res. Part C Emerg. Technol. 13(5), 347–369 (2005)CrossRefGoogle Scholar
  10. 10.
    Smola, A.J., Schlkopf, B.: A tutorial on support vector regression. Stat. Comput. 14(3), 199–222 (2004)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Basak, D., Pal, S., Patranabis, D.C.: Support vector regression. Neural Inf. Process. Lett. Rev. 11(10), 203–224 (2007)Google Scholar
  12. 12.
  13. 13.
    Crone, S.F., Guajardo, J., Weber, R.: The impact of preprocessing on support vector regression and neural networks in time series prediction. In: DMIN, pp. 37–44 (2006)Google Scholar
  14. 14.
  15. 15.
    Cherkassky, V., Ma, Y.: Practical selection of SVM parameters and noise estimation for SVM regression. Neural Netw. 17(1), 113–126 (2004)CrossRefGoogle Scholar
  16. 16.
    van Lint., J.W.C.: Reliable travel time prediction for freeways: bridging artificial neural networks and traffic flow theory. In: TRAIL Research School (2004)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Mechanics and Maritime SciencesChalmers University of TechnologyGöteborgSweden
  2. 2.Department of Computer Science and EngineeringChalmers University of TechnologyGöteborgSweden

Personalised recommendations