Traffic Flow Analysis Based on the Real Data Using Neural Networks

  • Teresa Pamuła
Part of the Communications in Computer and Information Science book series (CCIS, volume 329)


The paper presents the analysis of traffic data for determining classes of time series of traffic flow intensity for use in traffic forecasting employing neural networks. Data from traffic detectors on the main access road to the city of Gliwice in the period of past year is the basis for statistical analysis. Four classes of time series are proposed as representative of the traffic flow. The time series map temporarily smoothed detector counts. Different smoothing periods are used to retain the dynamic characteristics of the flows. A neural network is developed to classify incoming traffic data into the proposed time series classes. The specific time series implies a traffic control or management strategy, which indicates the capability of the NN to work out decisions for use in Intelligent Transportation Systems (ITS) applications.


traffic flow analysis traffic flow classification prediction time series neural network 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Karlaftis, M.G., Vlahogianni, E.I.: Statistical methods versus neural networks in transportation research: differences, similarities and some insights. Transportation Research, Part C. Emerging Technologies 19(3), 387–399 (2011)CrossRefGoogle Scholar
  2. 2.
    Pamuła, T.: Road traffic parameters prediction in urban traffic management systems using neutal networks. Transport Problems 6(3), 123–129 (2011)Google Scholar
  3. 3.
    Chrobok, R., Kaumann, O., Wahle, J., Schreckenberg, M.: Different methods of traffic forecast based on real data. European Journal of Operational Research 155(3), 558–568 (2004)MathSciNetzbMATHCrossRefGoogle Scholar
  4. 4.
    Vlahogianni, E.I., Karlaftis, M.G., Golias, J.C.: Optimized and meta-optimized neural networks for short-term traffic flow prediction:agenetic approach. Transportation Research Part C 13, 211–234 (2005)CrossRefGoogle Scholar
  5. 5.
    Dia, H.: An object-oriented neural network approach to short-term traffic forecasting. Eur. J. Oper. Res. 131(2), 253–261 (2001)zbMATHCrossRefGoogle Scholar
  6. 6.
    Cai, C., Wong, C.K., Heydecker, B.G.: Adaptive traffic signal control using approximate dynamic programming. Transportation Research Part C 17(5), 456–474 (2009)CrossRefGoogle Scholar
  7. 7.
    Srinivasan, D., Choy, M.C., Cheu, R.L.: Neural networks for real-time traffic signal control. IEEE Trans. Intelligent Transportation Systems 7(3), 261–271 (2006)CrossRefGoogle Scholar
  8. 8.
    Chen, H., Grant-Muller, S., Mussone, L., Montgomery, F.: A study of hybrid neural network approaches and the effects of missing data on traffic forecasting. Neural Computing and Applications 10, 277–286 (2001)zbMATHCrossRefGoogle Scholar
  9. 9.
    Quek, C., Pasquier, M., Boon, B., Lim, S.: POP-TRAFFIC A Novel Fuzzy Neural Approach to Road Traffic Analysis and Prediction. IEEE Transactions on Intelligent Transportation Systems 7(2), 133–146 (2006)CrossRefGoogle Scholar
  10. 10.
    Tan, M.-C., Wong, S.C., Xu, J.-M., Guan, Z.-R., Zhang, P.: An Aggregation Approach to Short-Term Traffic Flow Prediction. IEEE Transactions on Intelligent Transportation Systems 10, 60–69 (2009)CrossRefGoogle Scholar
  11. 11.
    Płaczek, B.: A Real Time Vehicle Detection Algorithm for Vision-Based Sensors. In: Bolc, L., Tadeusiewicz, R., Chmielewski, L.J., Wojciechowski, K. (eds.) ICCVG 2010, Part II. LNCS, vol. 6375, pp. 211–218. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  12. 12.
    Pamula, W.: Vehicle Detection Algorithm for FPGA Based Implementation. In: Kurzynski, M., Wozniak, M. (eds.) Computer Recognition Systems 3. AISC, vol. 57, pp. 585–592. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  13. 13.
    Michalik, K.: Neuronix – Symulator sztucznych sieci neuronowych. Podręcznik użytkownika, Katowice (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Teresa Pamuła

    There are no affiliations available

    Personalised recommendations