Prediction of Hourly Vehicle Flows by Optimized Evolutionary Fuzzy Rules

  • Pavel KrömerEmail author
  • Jana Nowaková
  • Martin Hasal
  • Jan Platoš
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 554)


The prediction of traffic situation at different time periods is essential for intelligent management of transportation systems and represents a key concept of smart cognitive environments. Road traffic is a complex dynamic system with many stochastic elements and many internal and external dependencies. Real–world traffic patterns in large cities are very complicated to model and simulate analytically. Road traffic monitoring, on the other hand, can be easily achieved by inexpensive sensing and monitoring systems and is often readily available. It can be even obtained as a by–product of other transportation services, for example, toll collection. In this work, we use a modified version of a recent machine–learning method, evolutionary fuzzy rules, to learn location–specific estimators of hourly traffic flow at specific locations.



This work was supported by the European Regional Development Fund under the project AI&Reasoning (reg. no. CZ.02.1.01/0.0/0.0/15_003/0000466), by the Czech Science Foundation under the grant no. GJ16-25694Y, and by the project SP2018/126 of the Student Grant System, VŠB-Technical University of Ostrava.


  1. 1.
    Affenzeller, M., Winkler, S., Wagner, S., Beham, A.: Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications. Chapman & Hall/CRC, New York (2009)CrossRefGoogle Scholar
  2. 2.
    Bezdek, J.C., Keller, J., Krisnapuram, R., Pal, N.R.: Fuzzy Models and Algorithms for Pattern Recognition and Image Processing (The Handbooks of Fuzzy Sets). Springer, New York (2005)zbMATHGoogle Scholar
  3. 3.
    Castro-Neto, M., Jeong, Y.S., Jeong, M.K., Han, L.D.: Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions. Expert Syst. Appl. 36(3), 6164–6173 (2009)CrossRefGoogle Scholar
  4. 4.
    Dia, H.: An object-oriented neural network approach to short-term traffic forecasting. Eur. J. Oper. Res. 131(2), 253–261 (2001)CrossRefGoogle Scholar
  5. 5.
    Dimitriou, L., Tsekeris, T., Stathopoulos, A.: Adaptive hybrid fuzzy rule-based system approach for modeling and predicting urban traffic flow. Transp. Res. Part C Emerg. Technol. 16(5), 554–573 (2008)CrossRefGoogle Scholar
  6. 6.
    Krömer, P., Owais, S.S.J., Platos, J., Snásel, V.: Towards new directions of data mining by evolutionary fuzzy rules and symbolic regression. Comput. Math. Appl. 66(2), 190–200 (2013)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Krömer, P., Platos, J.: Simultaneous prediction of wind speed and direction by evolutionary fuzzy rule forest. In: International Conference on Computational Science, ICCS 2017, 12-14 June 2017, Zurich, Switzerland, pp. 295–304 (2017)Google Scholar
  8. 8.
    Ledoux, C.: An urban traffic flow model integrating neural networks. Transp. Res. Part C Emerg. Technol. 5(5), 287–300 (1997)CrossRefGoogle Scholar
  9. 9.
    Lippi, M., Bertini, M., Frasconi, P.: Short-term traffic flow forecasting: an experimental comparison of time-series analysis and supervised learning. IEEE Trans. Intell. Transp. Syst. 14(2), 871–882 (2013)CrossRefGoogle Scholar
  10. 10.
    Osorio, C., Selvam, K.K.: Solving large-scale urban transportation problems by combining the use of multiple traffic simulation models. Transp. Res. Procedia 6, 272–284 (2015). 4th International Symposium of Transport Simulation (ISTS 2014) Selected Proceedings, Ajaccio, France, 1-4 June 2014Google Scholar
  11. 11.
    Pan, B., Demiryurek, U., Shahabi, C.: Utilizing real-world transportation data for accurate traffic prediction. In: IEEE 12th International Conference on Data Mining, pp. 595–604 (2012)Google Scholar
  12. 12.
    Pasi, G.: Fuzzy sets in information retrieval: state of the art and research trends. In: Bustince, H., Herrera, F., Montero, J. (eds.) Fuzzy Sets and Their Extensions: Representation, Aggregation and Models, Studies in Fuzziness and Soft Computing, vol. 220, pp. 517–535. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  13. 13.
    Price, K.V., Storn, R.M., Lampinen, J.A.: Differential Evolution A Practical Approach to Global Optimization. Natural Computing Series. Springer, Heidelberg (2005)zbMATHGoogle Scholar
  14. 14.
    Smith, B.L., Demetsky, M.J.: Traffic flow forecasting: comparison of modeling approaches. J. Transp. Eng. 123(4), 261–266 (1997)CrossRefGoogle Scholar
  15. 15.
    Stathopoulos, A., Dimitriou, L., Tsekeris, T.: Fuzzy modeling approach for combined forecasting of urban traffic flow. Comput. Aided Civ. Inf. Eng. 23(7), 521–535 (2008)CrossRefGoogle Scholar
  16. 16.
    Vlahogianni, E.I., Karlaftis, M.G., Golias, J.C.: Optimized and meta-optimized neural networks for short-term traffic flow prediction: a genetic approach. Transp. Res. Part C Emerg. Technol. 13(3), 211–234 (2005)CrossRefGoogle Scholar
  17. 17.
    Yin, H., Wong, S., Xu, J., Wong, C.: Urban traffic flow prediction using a fuzzy-neural approach. Transp. Res. Part C Emerg. Technol. 10(2), 85–98 (2002)CrossRefGoogle Scholar
  18. 18.
    Zadeh, L.A.: Fuzzy sets. Inf. Control 8, 338–353 (1965)CrossRefGoogle Scholar
  19. 19.
    Zheng, B., Chen, J., Xia, S., Jin, Y.: Data analysis of vessel traffic flow using clustering algorithms. In: International Conference on Intelligent Computation Technology and Automation (ICICTA) 2008, vol. 2, pp. 243–246. IEEE (2008)Google Scholar
  20. 20.
    Zheng, W., Lee, D.H., Shi, Q.: Short-term freeway traffic flow prediction: Bayesian combined neural network approach. J. Transp. Eng. ASCE 132(2), 114–121 (2006)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Pavel Krömer
    • 1
    Email author
  • Jana Nowaková
    • 1
  • Martin Hasal
    • 2
  • Jan Platoš
    • 1
  1. 1.Department of Computer ScienceVŠB Technical University of OstravaOstravaCzech Republic
  2. 2.IT4InnovationsVŠB Technical University of OstravaOstravaCzech Republic

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