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An Improved Feedback Wavelet Neural Network for Short-Term Passenger Entrance Flow Prediction in Shanghai Subway System

  • Bo Zhang
  • Shuqiu Li
  • Liping HuangEmail author
  • Yongjian Yang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10638)

Abstract

Subway traffic prediction is of great significance for scheduling and anomalies detection. A novel model of multi-scale mixture feedback wavelet neural network(MMFWNN) is proposed to predict the short-term entrance flow of Shanghai subway stations. Firstly, passengers are classified into two categories of commuter and non-commuter by mining the travel pattern and identifying the travel pattern stability, which finds that the non-commuters travel is more susceptible to the meteorology status. The proposed prediction model adds a transitional layer to adapt the feedback mechanism, thus to improve the robustness with associative memorizing and optimization calculation. Thus MMFWNN is advantageous to the nonlinear time-varying short-term traffic flow prediction. We evaluate our model in the Shanghai subway system. The experimental results show that the MMFWNN model is more accurate in predicting the short-term passenger entrance flow in subway stations.

Keywords

Wavelet neural network Subway flow prediction Travel pattern Data mining 

Notes

Acknowledgement

This research is partly supported by the National Nature Science Foundation of China under Grand no. 61272412 and Jilin Province Science and Technology Development Program under Grant no. 20160204021GX.

References

  1. 1.
    Si, B., Fu, L., Liu, J., Shiravi, S., Gao, Z.: A multi-class transit assignment model for estimating transit passenger flowsa case study of Beijing subway network. J. Adv. Transp. 50(1), 50–68 (2015)CrossRefGoogle Scholar
  2. 2.
    Calheiros, R.N., Masoumi, E., Ranjan, R., Buyya, R.: Workload prediction using arima model and its impact on cloud applicationsqos. IEEE Trans. Cloud Comput. 3(4), 449–458 (2015)CrossRefGoogle Scholar
  3. 3.
    Lippi, M., Bertini, M., Frasconi, P.: Short-term traffic flow forecasting: an experimental comparison of time-series analysis and supervised learning. IEEE Trans. Intell. Transp. Syst. 14(2), 871–882 (2013)CrossRefGoogle Scholar
  4. 4.
    Sun, J.: Examples of validating an adaptive kalman filter model for short-term traffic flow prediction. In: Twelfth COTA International Conference of Transportation Professionals, pp. 912–922 (2015)Google Scholar
  5. 5.
    Wei, Y., Chen, M.C.: Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks. Transp. Res. Part C Emerg. Technol. 21(1), 148–162 (2012)CrossRefGoogle Scholar
  6. 6.
    Niu, D., Lu, Y., Xu, X., Li, B.: Short-term power load point prediction based on the sharp degree and chaotic RBF neural network. Math. Prob. Eng. 2015(3), 1–8 (2015)Google Scholar
  7. 7.
    Zhang, D.Y., Yang, H.N.: Passenger flow analysis in subway using a kind of neural network. Appl. Mech. Mater. 713–715, 2284–2287 (2015)CrossRefGoogle Scholar
  8. 8.
    Kim, Y.H., Abdallah, C.T., Lewis, F.L.: A dynamic recurrent neural-network-based adaptive observer for a class of nonlinear systems. Automatica 33(8), 1539–1543 (1997)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Gers, F.A., Schmidhuber, J., Cummins, F.: Learning to forget: continual prediction with LSTM. Neural Comput. 12(10), 2451 (2000)CrossRefGoogle Scholar
  10. 10.
    Koutnik, J., Greff, K., Gomez, F., Schmidhuber, J.: A clockwork RNN. In: Proceedings of the 31st International Conference on Machine Learning (ICML 2014) (2014)Google Scholar
  11. 11.
    Sun, Y., Leng, B., Guan, W.: A novel wavelet-SVM short-time passenger flow prediction in Beijing subway system. Neurocomputing 166(C), 109–121 (2015)CrossRefGoogle Scholar
  12. 12.
    Doucoure, B., Agbossou, K., Cardenas, A.: Time series prediction using artificial wavelet neural network and multi-resolution analysis: application to wind speed data. Renewable Energy 92, 202–211 (2016)CrossRefGoogle Scholar
  13. 13.
    Wickerhauser, M.V.: Adapted Wavelet Analysis from Theory to Software, p. 160. A.K. Peters (1994)Google Scholar
  14. 14.
    Bhat, C.: Modeling the commute activity-travel pattern of workers: formulation and empirical analysis. Transp. Sci. 35(35), 61–79 (2001)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Bo Zhang
    • 1
  • Shuqiu Li
    • 2
  • Liping Huang
    • 2
    Email author
  • Yongjian Yang
    • 1
  1. 1.College of SoftwareJilin UniversityChangchunChina
  2. 2.College of Computer Science and TechnologyJilin UniversityChangchunChina

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