Advertisement

Back propagation bidirectional extreme learning machine for traffic flow time series prediction

Original Article
  • 3 Downloads

Abstract

On account of transportation management, a predictive model of the traffic flow is built up that would precisely predict the traffic flow, reduce longer travel delays. In prediction model of traffic flow based on traditional neural network, the parameters of prediction model need to be tuned through iterative processing, and these methods easily get stuck in local minimum. The paper presents a novel prediction model based on back propagation bidirectional extreme learning machine (BP-BELM). Parameters of BP-BELM are not tuned by experience. Compared with back propagation neural network, radial basis function, support vector machine and other improved incremental ELM, the combined simulations and comparisons demonstrate that BP-BELM is used in predicting the traffic flow for its suitability and effectivity.

Keywords

Traffic flow Transportation management Hidden nodes parameters Back propagation bidirectional extreme learning machine 

Notes

Compliance with ethical standards

Conflict of interest

The authors (Weidong Zou, Yuanqing Xia) of paper (Title: Back propagation bidirectional extreme learning machine for traffic flow time series prediction, NCAA-D-17-01893) declare that there is no conflict of interests.

References

  1. 1.
    Lv Y, Duan Y, Wang W, Li Z, Wang F (2015) Traffic flow prediction with big data: a deep learning approach. IEEE Trans Intell Transp Syst 2(16):865–873Google Scholar
  2. 2.
    Huang W, Song G, Hong H, Xie K (2014) Deep architecture for traffic flow prediction: deep belief networks with multitask learning. IEEE Trans Intell Transp Syst 5(15):2191–2201CrossRefGoogle Scholar
  3. 3.
    Kumar SV (2017) Traffic flow prediction using kalman filtering technique. Procedia Eng 187:582–587CrossRefGoogle Scholar
  4. 4.
    Koesdwiady A, Soua R, Karray F (2016) Improving traffic flow prediction with weather information in connected cars: a deep learning approach. IEEE Trans Veh Technol 12(65):9508–9517CrossRefGoogle Scholar
  5. 5.
    Xu Y, Kong Q, Klette R, Liu Y (2014) Accurate and interpretable bayesian MARS for traffic flow prediction. IEEE Trans Intell Transp Syst 6(15):2457–2469CrossRefGoogle Scholar
  6. 6.
    Oh S, Kim Y, Hong J (2015) Urban traffic flow prediction system using a multifactor pattern recognition model. IEEE Trans Intell Transp Syst 5(16):2744–2755CrossRefGoogle Scholar
  7. 7.
    Moretti F, Pizzuti S, Panzieri S, Annunziato M (2015) Urban traffic flow forecasting through statistical and neural network bagging ensemble hybrid modeling. Neurocomputing 167:3–7CrossRefGoogle Scholar
  8. 8.
    Chan K, Dillon T, Singh J, Chang E (2012) Neural-network-based models for short-term traffic flow forecasting using a hybrid exponential smoothing and levenberg-marquardt algorithm. IEEE Trans Intell Transp Syst 2(13):644–654CrossRefGoogle Scholar
  9. 9.
    Chan K, Dillon T (2013) On-road sensor configuration design for traffic flow prediction using fuzzy neural networks and taguchi method. IEEE Trans Instrum Meas 1(62):50–59CrossRefGoogle Scholar
  10. 10.
    Jeong Y, Byon Y, Castro-Neto M, Easa S (2013) Supervised weighting-online learning algorithm for short-term traffic flow prediction. IEEE Trans Intell Transp Syst 4(14):1700–1707CrossRefGoogle Scholar
  11. 11.
    Polson N, Sokolov V (2017) Deep learning for short-term traffic flow prediction. Transp Res Part C Emerg Technol 79:1–17CrossRefGoogle Scholar
  12. 12.
    Guo D, Zhang Y, Xiao Z, Mao M, Liu J (2015) Common nature of learning between bp-type and hopfiled-type neural networks. Neurocomputing 167:439–448CrossRefGoogle Scholar
  13. 13.
    Qi XX, Yuan ZH, Han XW (2015) Diagnosis of misalignment faults by tacholess order tracking analysis and RBF networks. Neurocomputing 169:439–448CrossRefGoogle Scholar
  14. 14.
    Ekici S, Yildirim S, Poyraz M (2009) A transmission line fault locator based on Elman recurrent networks. Appl Soft Comput 9(1):341–347CrossRefGoogle Scholar
  15. 15.
    Huang GB, Chen L, Siew CK (2006) Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Trans Neural Netw 17(4):879–892CrossRefGoogle Scholar
  16. 16.
    Huang GB, Chen L (2007) Convex incremental extreme learning machine. Neurocomputing 70(16–18):3056–3062CrossRefGoogle Scholar
  17. 17.
    Huang GB, Chen L (2008) Enhanced random search based incremental extreme learning machine. Neurocomputing 71(16–18):3460–3468CrossRefGoogle Scholar
  18. 18.
    Miche Y, Sorjamaa A, Bas P, Jutten C, Lendasse A (2010) OP-ELM: optimally pruned extreme learning machine. IEEE Trans Neural Netw 21(1):158–162CrossRefGoogle Scholar
  19. 19.
    Feng G, Huang GB, Lin Q, Gay R (2009) Error minimized extreme learning machine with growth of hidden nodes and incremental learning. IEEE Trans Neural Netw 20(8):1352–1357CrossRefGoogle Scholar
  20. 20.
    Lan Y, Soh YC, Huang GB (2010) Two-stage extreme learning machine for regression. Neurocomputing 73(16):3028–3038CrossRefGoogle Scholar
  21. 21.
    Yang YM, Wang Y, Yuan X (2012) Bidirectional extreme learning machine for regression problem and its learning effectiveness. IEEE Trans Neural Netw Learn Syst 23(9):1498–1505CrossRefGoogle Scholar
  22. 22.
    Horn RA, Johnson CR (2012) Matrix analysis. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  23. 23.
    He WW, Wang ZZ, Jiang H (2008) Model optimizing and feature selecting for support vector regression in time series forecasting. Neurocomputing 72(1):600–611CrossRefGoogle Scholar
  24. 24.
    Wu YK, Tan HC, Qin LQ, Ran B, Jiang ZX (2018) A hybrid deep learning based traffic flow prediction method and its understanding. Transp Res Part C 90:166–180CrossRefGoogle Scholar
  25. 25.
    Zhang HJ, Li JX, Ji YZ, Yue H (2017) Understanding subtitles by character-level sequence-to-sequence learning. IEEE Trans Industr Inf 13(2):616–624CrossRefGoogle Scholar

Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  1. 1.School of AutomationBeijing Institute of TechnologyBeijingPeople’s Republic of China

Personalised recommendations