Advertisement

Analysis and Short-Term Forecasting of Highway Traffic Flow in Slovenia

  • Primož Potočnik
  • Edvard Govekar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6593)

Abstract

Analysis and short-term forecasting of traffic flow data for several locations of the Slovenia highway network are presented. Daily and weekly seasonal components of the data are analysed and several features are extracted to support the forecasting. Various short-term forecasting models are developed for one hour ahead forecasting of the traffic flow. Models include benchmark models (random walk, seasonal random walk, naive model), AR and ARMA models, and various configuration of feedforward neural networks. Results show that the best forecasting results (correlation coefficient R > 0.99) are obtained by a feedforward neural network and a selected set of inputs but this sophisticated model surprisingly only slightly surpasses the accuracy of a simple naive model.

Keywords

traffic flow analysis forecasting neural networks 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Schadschneider, A., Knospe, W., Santen, L., Schreckenberg, M.: Optimization of highway networks and traffic forecasting. Physica A: Statistical Mechanics and its Applications 346, 165–173 (2005)CrossRefGoogle Scholar
  2. 2.
    Sun, S., Zhang, C., Zhang, Y.: Traffic Flow Forecasting Using a Spatio-temporal Bayesian Network Predictor. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds.) ICANN 2005. LNCS, vol. 3697, pp. 273–278. Springer, Heidelberg (2005)Google Scholar
  3. 3.
    Smith, B.L., Williams, B.M., Oswald, R.K.: Comparison of parametric and nonparametric models for traffic flow forecasting. Transportation Research Part C: Emerging Technologies 10, 303–321 (2002)CrossRefGoogle Scholar
  4. 4.
    Kirby, H.R., Watson, S.M., Dougherty, M.S.: Should we use neural networks or statistical models for short-term motorway traffic forecasting? International Journal of Forecasting 13, 43–50 (1997)CrossRefGoogle Scholar
  5. 5.
    Grabec, I., Kalcher, K., Švegl, F.: Modeling and Forecasting of Traffic Flow. Nonlinear Phenomena in Complex Systems 12, 1–10 (2009)Google Scholar
  6. 6.
    Dia, H.: An object-oriented neural network approach to short-term traffic forecasting. European Journal of Operational Research 131, 253–261 (2001)CrossRefzbMATHGoogle Scholar
  7. 7.
    Chen, H., Grant-Muller, S.: Use of sequential learning for short-term traffic flow forecasting. Transportation Research Part C: Emerging Technologies 9, 319–336 (2001)CrossRefGoogle Scholar
  8. 8.
    Vlahogianni, E.I., Karlaftis, M.G., Golias, J.C.: Optimized and meta-optimized neural networks for short-term traffic flow prediction: A genetic approach. Transportation Research Part C: Emerging Technologies 13, 211–234 (2005)CrossRefGoogle Scholar
  9. 9.
    Wong, W.K., Xia, M., Chu, W.C.: Adaptive neural network model for time-series forecasting. European Journal of Operational Research 207, 807–816 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Yin, H., Wong, S.C., Xu, J., Wong, C.K.: Urban traffic flow prediction using a fuzzy-neural approach. Transportation Research Part C: Emerging Technologies 10, 85–98 (2002)CrossRefGoogle Scholar
  11. 11.
    Dimitriou, L., Tsekeris, T., Stathopoulos, A.: Adaptive hybrid fuzzy rule-based system approach for modeling and predicting urban traffic flow. Transportation Research Part C: Emerging Technologies 16, 554–573 (2008)CrossRefGoogle Scholar
  12. 12.
    Stathopoulos, A., Karlaftis, M.G.: A multivariate state space approach for urban traffic flow modeling and prediction. Transportation Research Part C 11, 121–135 (2003)CrossRefGoogle Scholar
  13. 13.
    Jin, X., Zhang, Y., Yao, D.: Simultaneously Prediction of Network Traffic Flow Based on PCA-SVR. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds.) ISNN 2007. LNCS, vol. 4492, pp. 1022–1031. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  14. 14.
    Hong, W.-C., Dong, Y., Zheng, F., Lai, C.-Y.: Forecasting urban traffic flow by SVR with continuous ACO. Applied Mathematical Modelling (2010), article in pressGoogle Scholar
  15. 15.
    Castillo, E., Menéndez, J.M., Sánchez-Cambronero, S.: Predicting traffic flow using Bayesian networks. Transportation Research Part B: Methodological 42, 482–509 (2008)CrossRefGoogle Scholar
  16. 16.
    Xue, J., Shi, Z.: Short-Time Traffic Flow Prediction Based on Chaos Time Series Theory. Journal of Transportation Systems Engineering and Information Technology 8, 68–72 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Primož Potočnik
    • 1
  • Edvard Govekar
    • 1
  1. 1.Faculty of Mechanical EngineeringUniversity of LjubljanaSlovenia

Personalised recommendations