Skip to main content
SpringerLink
Log in

Research on prediction of traffic flow based on dynamic fuzzy neural networks

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Combining the advantages of the neural network and fuzzy system, this paper makes a further research on the dynamic fuzzy neural networks (D-FNN) traffic flow prediction. Instead of being in consistence with growth of the input number, the fuzzy rule number of the D-FNN increases exponentially in the whole training network structure. In particular, this method can establish a required network structure automatically. This method is applied to the traffic flow time series to analyze and compare the predicting performance of the predicting model based on the neural network method and the adaptive neural fuzzy inference system by combining with the chaos theory. The simulation result shows that this method is quite effective and can improve the predicting accuracy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Smith B (2002) Comparison of parametric and nonparametric models for traffic flow forecasting. Transp Res Part C 10(4):303–321

    Article  Google Scholar 

  2. Rice J (2004) A simple and effective method for predicting travel times on freeways. IEEE Trans Intell Transp Syst 5(3):200–207

    Article  Google Scholar 

  3. Giovanni P (2006) A processing architecture for associative short-term memory in electronic noses. Meas Sci Technol 17(11):3066–3072

    Article  Google Scholar 

  4. Vladimir N (2014) Comparison of classical statistical methods and artificial neural network in traffic noise prediction. Environ Impact Assess Rev 49:24–30

    Article  Google Scholar 

  5. Marfia G, Roccetti M (2010) Vehicular congestion detection and short-term forecasting: a new model with results. IEEE Trans Veh Technol 60(7):2936–2948

    Article  Google Scholar 

  6. Vlahogianni EI, Karlaftis MG, Golias JC (2005) Optimized and meta-optimized neural networks for short-term traffic flow prediction: a genetic approach. Transp Res Part C 13(3):211–234

    Article  Google Scholar 

  7. Li MT, Yuan J (2013) WT-AOSVR models for short-time traffic flow prediction. Comput Appl Softw 1(30):277–280

    Google Scholar 

  8. Hamed M, Al-Masaeid H (1995) Short-term prediction of traffic volume in urban arterials. J Transp Eng (ASCE) 121(3):249–254

    Article  Google Scholar 

  9. Okutani I, Stephanedes YJ (1984) Dynamic prediction of traffic volume through Kalman filtering theory. Transp Res B 18(1):1–11

    Article  Google Scholar 

  10. Hong WC, Pai PF (2006) Predicting engine reliability by support vector machines. Int J Adv Manuf Technol 28(1–2):154–161

    Article  Google Scholar 

  11. Hong WC (2012) Application of seasonal SVR with chaotic immune algorithm in traffic flow forecasting. Neural Comput Appl 21:583–593

    Article  Google Scholar 

  12. Zhang WB, Hu HZ, Liu WJ (2007) traffic flow forecast based on type-2 fuzzy logic approach. J Xi’an Jiaotong Univ 41(10):1160–1163

    MATH  Google Scholar 

  13. Loukas D, Theodore T, Antony S (2008) Adaptive hybrid fuzzy rule-based system approach for modeling and predicting urban traffic flow. Transp Res Part C 16(5):554–573

    Article  Google Scholar 

  14. Abdulhai B, Porwal H, Recker W (2002) Short term traffic-flow forecasting using neuro-genetic algorithms. J Intell Transp Syst Technol Plann Oper 7(1):3–41

    Article  MATH  Google Scholar 

  15. Park B (2002) Hybrid neuro-fuzzy application in short term freeway traffic volume predicting. Transp Res Rec 1802:190–196

    Article  Google Scholar 

  16. Zhang L, Liu QC, Yang WC (2013) An improved k-nearest neighbor model for short-term traffic flow prediction. Procedia—Soc Behav Sci 96:653–662

    Article  Google Scholar 

  17. Zhang C, Sun S, Yu G (2004) Short-term traffic flow forecasting using expanded Bayesian network for incomplete data. In: Proceedings international symposium on neural networks, Dalian, China. Lecture Notes in Computer Science (LNCS 3174), Springer, Berlin

  18. Enrique C, Jose MM, Santos SC (2008) Predicting traffic flow using Bayesian networks. Transp Res Part B 42(5):482–509

    Article  Google Scholar 

  19. Hodge V, Jackson T, Austin J (2012) A binary neural network framework for attribute selection and prediction. In: Proceedings 4th international conference on neural computation theory and applications (NCTA 2012), SciTePress, Barcelona, pp 510–515

  20. Victoria JH, Rajesh K, Jim A et al (2014) Short -term prediction of traffic flow using a binary neural network. Neural Comput Appl 25:1639–1655

    Article  Google Scholar 

  21. Hou Y, Mai YM (2013) Chaotic prediction for traffic flow of improved BP neural network. Indones J Electr Eng 11(3):1682–1690

    Google Scholar 

  22. Lu JJ, Wang ZQ (2006) Internet traffic data flow forecast by RBF neural network based on phase space reconstruction. Trans Nanjing Univ Aeronaut Astronaut 23(4):316–322

    MATH  Google Scholar 

  23. Vlahogianni EI, Karlaftis MG, Golias JC (2007) Spatio-temporal urban traffic volume forecasting using genetically optimized modular networks. Comput-Aided Civil Infrastruct Eng 22(5):317–325

    Article  Google Scholar 

  24. Zhong M, Sharma S, Lingras P (2005) Short-term traffic prediction on different types of roads with genetically designed regression and time delay neural network models. J Comput Civil Eng 19(1):94–103

    Article  Google Scholar 

  25. Vlahogianni EI (2009) Enhancing predictions in signalized arterials with information on short-term traffic flow dynamics. J Intell Transp Syst 13(2):73–84

    Article  Google Scholar 

  26. Roger J (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685

    Article  Google Scholar 

  27. Zhu WF, Zhao SJ (2007) Optimal design of structure for neural networks based on rough sets. Comput Eng Des 28(17):4210–4212

    Google Scholar 

  28. Fu H, Xu LH, Hu G et al (2010) Traffic flow state-forecasting algorithm based on Sugeno neural fuzzy system. Control Theory Appl 12(27):1637–1640

    Google Scholar 

  29. Er MJ, Wu SQ, Gao YA (2001) fast approach for automatic generation of fuzzy rules by generalized dynamic fuzzy neural networks. IEEE Trans Fuzzy Syst 9(4):578–594

    Article  Google Scholar 

  30. Er MJ, Wu SQ (2002) A fast learning algorithm for parsimonious fuzzy neural system [J]. Fuzzy Sets Syst 126(3):337–351

    Article  MathSciNet  MATH  Google Scholar 

  31. Tan W, Wang YN (2008) Fuzzy neural network control theory and method of chaotic system. Science Press, Beijing, pp 108–150

  32. Yu JH, Peng YH (2011) Predictability study of ENSO with mutual information and Cao methods. Meteorol Sci Technol 39(1):9–12

    MathSciNet  Google Scholar 

  33. Zhang SQ, Jia J (2010) Study on the parameters determination for reconstructing phase-space in chaos time series. Acta Phys Sin 59(3):1576–1582

    MATH  Google Scholar 

  34. Lv JH, Lu JN (2001) The chaotic time series analysis and its application. Wuhan University Press, Wuhan, pp 15–93

    Google Scholar 

Download references

Acknowledgments

This work is supported by the Young Teachers Subsidy Scheme in 2013 of the Shangqiu Normal University, China (No. 2013GGJS12), and the basic and frontier technology research projects of Henan Province, China (No. 132300410203).

Author information

Authors and Affiliations

  1. College of Computer and Information Technology, Shangqiu Normal University, Shangqiu, 476000, China

    Haitao Li

Authors
  1. Haitao Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Haitao Li.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, H. Research on prediction of traffic flow based on dynamic fuzzy neural networks. Neural Comput & Applic 27, 1969–1980 (2016). https://doi.org/10.1007/s00521-015-1991-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-015-1991-z

Keywords

Search

Navigation