Nothing Special   »   [go: up one dir, main page]
Skip to main content

Advertisement

Springer Nature Link
Log in

Deep belief network-based support vector regression method for traffic flow forecasting

  • Deep Learning & Neural Computing for Intelligent Sensing and Control
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Instability is a common problem in deep belief network–back propagation forecasting model, and the trend of traffic data will affect the forecasting results of the model. Therefore, this paper proposes a short-term traffic flow forecasting method based on deep belief network–support vector regression. Support vector regression classifier SVR is used at the top of the model. Data processing is from bottom to top. Firstly, at the bottom of the model, the input traffic flow data are processed differently; then, the DBN model is used to learn the traffic flow characteristics. Finally, SVR is used to predict the traffic flow at the top of the model. The average absolute error of the prediction is 9.57%, and the average relative error is 5.91%. The relationship between the predicted value and the actual traffic flow data is found through simulation experiments. The predicted value of the model proposed in this paper is in good agreement with the measured value, and the prediction accuracy is high. The model can effectively predict short-term traffic flow. Finally, compared with the traditional DBN prediction model and other common prediction models, the proposed prediction model has higher prediction accuracy.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

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

    Article  Google Scholar 

  2. Lv Y, Duan Y, Kang W et al (2015) Traffic flow prediction with big data: a deep learning approach. IEEE Trans Intell Transp Syst 16(2):865–873

    Google Scholar 

  3. Castro-Neto M, Jeong YS, Jeong MK et al (2009) Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions. Expert Syst Appl 36(3):6164–6173

    Article  Google Scholar 

  4. Abadi A, Rajabioun T, Ioannou PA (2015) Traffic flow prediction for road transportation networks with limited traffic data. IEEE Trans Intell Transp Syst 16(2):653–662

    Google Scholar 

  5. Castro-Neto M, Jeong YS, Jeong MK et al (2009) Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions. Expert Syst Appl 36(3):6164–6173

    Article  Google Scholar 

  6. Chen H, Grant-Muller S (2001) Use of sequential learning for short-term traffic flow forecasting. Transp Res C 9(5):319–336

    Article  Google Scholar 

  7. Zheng W, Lee DH (2006) Short-term freeway traffic flow prediction: Bayesian combined neural network approach. J Transp Eng 132(2):114–121

    Article  Google Scholar 

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

    Article  Google Scholar 

  9. Guo H, Fang L, Yu L (2013) A short-term traffic flow forecasting method based on fuzzy Kalman filter. J Zhejiang Univ Technol 41(2):218–221

    Google Scholar 

  10. Guo J, Huang W, Williams BM (2014) Adaptive Kalman filter approach for stochastic short-term traffic flow rate prediction and uncertainty quantification. Transp Res C 43:50–64

    Article  Google Scholar 

  11. Li D, Dong Y (2014) Deep learning: methods and applications. Now Publishers Inc, Boston

    MATH  Google Scholar 

  12. Han C, Song S, Wang CH (2004) A real-time short-term traffic flow adaptive forecasting method based on ARIMA model. Acta Simulata Syst Sin 16(7):1456–1530

    Google Scholar 

  13. Huang W, Song G, Hong H et al (2014) Deep architecture for traffic flow prediction: deep belief networks with multitask learning. IEEE Trans Intell Transp Syst 15(5):2191–2201

    Article  Google Scholar 

  14. Huang W, Song G, Hong H et al (2014) Deep architecture for traffic flow prediction: deep belief networks with multitask learning. IEEE Trans Intell Transp Syst 15(5):2191–2201

    Article  Google Scholar 

  15. Xue J, Shi Z (2008) Short-time traffic flow prediction based on chaos time series theory. J Transp Syst Eng Inf Technol 8(5):68–72

    Google Scholar 

  16. Xiaojian HU, Wang W, Sheng H (2010) Urban traffic flow prediction with variable cell transmission model. J Transp Syst Eng Inf Technol 10(4):73–78

    Google Scholar 

  17. Fernández-Navarro F, de la Cruz MA, Gutiérrez PA, Castaño A, Hervás-Martínez C (2018) Time series forecasting by recurrent product unit neural networks. Neural Comput Appl 29(3):779–791

    Article  Google Scholar 

  18. Hong WC, Dong Y, Zheng F et al (2011) Hybrid evolutionary algorithms in a SVR traffic flow forecasting model. Appl Math Comput 217(15):6733–6747

    MathSciNet  MATH  Google Scholar 

  19. Ai Y, Li Z, Gan M, Zhang Y, Yu D, Chen W, Ju Y (2019) A deep learning approach on short-term spatiotemporal distribution forecasting of dockless bike-sharing system. Neural Comput Appl 31(5):1665–1677

    Article  Google Scholar 

  20. Zhang HZ, Wang J (2008) Application of data mining on short-term traffic flow forecasting model. Comput Integr Manuf Syst 14(4):690–695

    Google Scholar 

  21. Xu JR, Li XY, Shi HJ (2010) Short-term traffic flow forecasting model under missing data. J Comput Appl 30(4):1117–1120

    Google Scholar 

  22. O’Connor P, Neil D, Liu SC et al (2013) Real-time classification and sensor fusion with a spiking deep belief network. Front Neurosci 7(7):178

    Google Scholar 

  23. Kuremoto T, Kimura S, Kobayashi K et al (2014) Time series forecasting using a deep belief network with restricted Boltzmann machines. Neurocomputing 137(15):47–56

    Article  Google Scholar 

  24. Kang S, Qian X, Meng H (2013) Multi-distribution deep belief network for speech synthesis. In: IEEE international conference on acoustics, speech and signal processing. IEEE, pp 8012–8016

  25. Shi X, Zhu Y, Sa C et al (2016) Power transformer fault classifying model based on deep belief network. Power Syst Prot Control 44(1):71–76

    Google Scholar 

  26. Ji NN, Zhang JS, Zhang CX (2014) A sparse-response deep belief network based on rate distortion theory. Pattern Recogn 47(9):3179–3191

    Article  Google Scholar 

  27. Fernandez R, Rendel A, Ramabhadran B et al (2013) F0 contour prediction with a deep belief network-Gaussian process hybrid model. In: IEEE international conference on acoustics, speech and signal processing. IEEE, pp 6885–6889

  28. Liu T (2010) A novel text classification approach based on deep belief network. Neural information processing. Theory and algorithms. Springer, Berlin, pp 314–321

    Book  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510640, China

    Haibo Xu

  2. College of Big Data and Intelligent Engineering, Yangtze Normal University, Chongqing, China

    Haibo Xu & Chengshun Jiang

Authors
  1. Haibo Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chengshun Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chengshun Jiang.

Ethics declarations

Conflict of interests

There are no conflicts of interests of this work.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xu, H., Jiang, C. Deep belief network-based support vector regression method for traffic flow forecasting. Neural Comput & Applic 32, 2027–2036 (2020). https://doi.org/10.1007/s00521-019-04339-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-019-04339-x

Keywords

Search

Navigation