A Genetic-Algorithm-Based Neural Network Approach for Short-Term Traffic Flow Forecasting

  • Mingzhe Liu
  • Ruili Wang
  • Jiansheng Wu
  • Ray Kemp
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3498)


In this paper, a Genetic-Algorithm-based Artificial Neural Network (GAANN) model for short-term traffic flow forecasting is proposed. GAANN can integrate capabilities of approximation of Artificial Neural Networks (ANN) and of global optimization of Genetic Algorithms (GA) so that the hybrid model can enhance capability of generalization and prediction accuracy, theoretically. With this model, both the number of hidden nodes and connection weights matrix in ANN are optimized using genetic operation. The real data sets are applied to the introduced method and the results are discussed and compared with the traditional Back Propagation (BP) neural network, showing the feasibility and validity of the proposed approach.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Vlahogianni, E.I., Golias, J.C., Karlaftis, M.G.: Short-term Traffic Forecasting: Overview of Objectives and Methods. Transport Review 24, 533–557 (2004)CrossRefGoogle Scholar
  2. 2.
    Smith, B.I., Demetsky, M.J.: Traffic Flow forecasting: Comparison of Modeling Approaches. Journal of Transportation Engineering 4, 261–265 (1997)CrossRefGoogle Scholar
  3. 3.
    William, B.M.: Modeling and Forecasting Vehicular Traffic Flow as a Seasonal Stochastic Time Series process. Doctoral dissertation. Department of Civil Engineering, University of Virginia, Charlottesville (1999)Google Scholar
  4. 4.
    Okutani, I., Stephanides, Y.I.: Dynamic Prediction of Traffic Volume through Kalman Theory. Transportation Research Part B 1, 1–11 (1984)CrossRefGoogle Scholar
  5. 5.
    Sun, H., Liu, H., Xiao, H., He, R., Ran, B.: Short Term Traffic Forecasting Using the Local Linear Regression Model. Journal of Transportation Research Board 1836, 143–150 (2003)CrossRefGoogle Scholar
  6. 6.
    Hu, J., Zong, C., Song, J., Zhang, Z., Ren, J.: An Applicable Short-term Traffic Flow Forecasting Method Based on Chaotic Theory. In: Proceedings of the 2003 IEEE Intelligent Transportation Systems, vol. 1, pp. 608–613 (2003)Google Scholar
  7. 7.
    Yu, G., Hu, J., Zhang, C., Zhuang, L., Song, J.: Short-term Traffic Flow Forecasting Based on Markov Chain Model. In: Proceedings of The 2003 IEEE Intelligent Vehicles Symposium, pp. 208–212 (2003)Google Scholar
  8. 8.
    Dochy, T., Danech-Pajooh, M., Lechevallier, Y.: Short-term Road Forecasting Using Neural Network. Recherché-transports-Securite. English issue 11, 73–82 (1995)Google Scholar
  9. 9.
    Messai, N., Thomas, P., Lefebvre, D., Moudni, A.E.: A Neural Network Approach for Freeway Traffic Flow Prediction. In: Proceedings of The 2002 IEEE International Conference on Control Applications, Glasgow, Scotland, U.K, pp. 18–20 (2002)Google Scholar
  10. 10.
    Hornik, K.: Multilayer Feedforward Networks Are Universal Approximates. Neural Networks 2, 359–366 (1989)CrossRefGoogle Scholar
  11. 11.
    White, H.: Connectionist non-parametric regression: Multilayer Feed Forward Networks Can Learn Arbitrary Mapping. Neural Networks 3, 535–549 (1990)CrossRefGoogle Scholar
  12. 12.
    Gallant, P.J., Aitken, J.M.: Genetic Algorithm Design of Complexity-controlled Timeseries Predictors. In: Proceedings of the 2003 IEEE XIII Workshop on Neural Networks for Signal Processing (2003)Google Scholar
  13. 13.
    Tian, L., Noore, A.: Evolutionary Neural Network Modeling for Software Cumulative Failure Time Prediction. Reliability Engineering and System Safety 87, 45–51 (2005)CrossRefGoogle Scholar
  14. 14.
    Pedrycz, W.: Heterogeneous Fuzzy Logic Networks: Fundamentals and Development Studies. IEEE Transactions on Neural Networks 15, 1466–1481 (2004)CrossRefGoogle Scholar
  15. 15.
    Ahmad, A.L., Azid, I.A., Yusof, A.R., Seetharamu, K.N.: Emission Control in Palm Oil Mills Using Artificial Neural Network and Genetic Algorithm. Computers and Chemical Engineering 28, 2709–2715 (2004)CrossRefGoogle Scholar
  16. 16.
    Niska, H., Hiltunen, T., Karppinen, A., Ruuskanen, J., Kolehmainen, M.: Evoling The Neural Network Model for Forecasting Air Pollution Time Series. Engineering Application of Artificial Intelligence 17, 159–167 (2004)CrossRefGoogle Scholar
  17. 17.
    Kim, G., Yoon, J., An, S., Cho, H., Kang, K.: Neural Network Model Incorporating a Genetic Algorithm in Estimating Construction Costs. Building and Environment 39, 1333–1340 (2004)CrossRefGoogle Scholar
  18. 18.
  19. 19.
    Hopfield, J.J.: Neural Networks and Physical Systems with Emergent Collective Computation Abilities. Proceedings of The National Academy of Science, USA, 2554–2558 (1982)Google Scholar
  20. 20.
    Dia, H.: An Object-Oriented Neural Network Approach to Short-Term Traffic Forecasting. European Journal Of Operational Research 131, 253–261 (2001)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Mingzhe Liu
    • 1
  • Ruili Wang
    • 1
  • Jiansheng Wu
    • 2
  • Ray Kemp
    • 1
  1. 1.Institute of Information Sciences and TechnologyMassey UniversityPalmerston NorthNew Zealand
  2. 2.Department of Mathematics and ComputerLiuzhou Teachers CollegeGuangxiChina

Personalised recommendations