A New Intelligent Model for Short Time Traffic Flow Prediction via EMD and PSO–SVM

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 113)

Abstract

Accurate and reliable short time traffic flow forecasting is one of the most important issues in the traffic information management. Due to the nonlinear and stochastic of the data, it is often difficult to predict the traffic flow precisely. Hence, a new hybrid intelligent forecasting approach based on the integration of empirical mode decomposition (EMD), particle swarm optimization (PSO) and support vector machine (SVM) is proposed for the short time traffic flow prediction in this paper. The advantages of the proposed method are that the combination of EMD and PSO–SVM can deal with the nonlinear and stochastic characteristics of the original data well. The forecasting rate may be enhanced using this new technique. Seven-hundred and twenty samples of the practical traffic flow data were applied for the validation of the proposed prediction model. The analysis results show that the proposed method can extract the underlying rules of the testing data and improve the prediction accuracy by 10% or better when compared to SVM approach. Thus, the new EMD–PSO–SVM traffic flow forecasting model provides practical application.

Keywords

Traffic flow Short time prediction EMD PSO SVM 

References

  1. 1.
    Zahra Z, Mahmoud P, Hossein SM (2010) Application of data mining in traffic management: case of city of Isfahan. In: Proceedings of the 2010 2nd international conference on electronic computer technology, vol 2010, pp 102–106Google Scholar
  2. 2.
    Nejad S, Seifi F, Ahmadi H, Seifi N (2009) Applying data mining in prediction and classification of urban traffic. In: Proceedings of the 2009 WRI world congress on computer science and information engineering, vol 3, pp 674–678Google Scholar
  3. 3.
    Zhao X, Jing R, Gu M (2008) Adaptive intrusion detection algorithm based on rough sets. J T Singhua Univ (Sci & Tech) 48:1165–1168Google Scholar
  4. 4.
    Li Z, Yan X, Yuan C, Zhao J, Peng Z (2011) Fault detection and diagnosis of the gearbox in marine propulsion system based on bispectrum analysis and artificial neural networks. J Marine Sci and Appl 10:17–24CrossRefGoogle Scholar
  5. 5.
    Wen Y, Lee T (2005) Fuzzy data mining and grey recurrent neural network forecasting for traffic information systems. In: Proceedings of the 2005 IEEE international conference on information reuse and integration, vol 2005, pp 356–361Google Scholar
  6. 6.
    Hauser T, Scherer W (2001) Data mining tools for real time traffic signal decision support and maintenance. In: Proceedings of The IEEE international conference on systems, man, and cybernetics, Vol 3, pp 1471–1477Google Scholar
  7. 7.
    Park B, Lee D, Yun H (2003) Enhancement of time of day based traffic signal control. Proc IEEE Int Conf Syst Man Cybern 4:3619–3624Google Scholar
  8. 8.
    Luo X, Niu G, Pan R (2010) Short-term traffic flow prediction method based on EMD and artificial neural network. Comput Eng Appl 46:212–214Google Scholar
  9. 9.
    Huang NE, Wu ML, Qu WL et al (2003) Applications of Hilbert–Huang transform to non-stationary financial time series analysis. Appl Stoch Models Bus Ind 19:246–268MathSciNetCrossRefGoogle Scholar
  10. 10.
    Huang NE, Shen Z, Long SR et al (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc Lond A 454:903–905MATHMathSciNetCrossRefGoogle Scholar
  11. 11.
    Parey A, Tandon N (2007) Impact velocity modelling and signal processing of spur gear vibration for the estimation of defect size. Mech Syst Signal Process 21(1):234–243CrossRefGoogle Scholar
  12. 12.
    Vapnik V (1995) The nature of statistical learning theory, 1st edn. Springer, BerlinMATHGoogle Scholar
  13. 13.
    Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, Piscataway, vol 1, pp 1942–1948Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  1. 1.Henan Institute of Science and TechnologyXinxiangChina
  2. 2.NanYang institute of technologyNanyangChina

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