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access icon free traffic flow prediction model based on deep belief network and genetic algorithm

Traffic flow prediction plays an indispensable role in the intelligent transportation system. The effectiveness of traffic control and management relies heavily on the prediction accuracy. The authors propose a model based on deep belief networks (DBNs) to predict the traffic flow. Moreover, they use Fletcher–Reeves conjugate gradient algorithm to optimise the fine-tuning of model's parameters. Since the traffic flow has various features at different times such as weekday, weekend, daytime and night-time, the hyper-parameters of the model should adapt to the time. Therefore, they employ the genetic algorithm to find the optimal hyper-parameters of DBN models for different times. The dataset from Caltrans Performance Measurement System was used to evaluate the performance of their models. The experimental results demonstrate that the proposed model achieved better performance in different times.

References

    1. 1)
      • 26. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ‘Imagenet classification with deep convolutional neural networks’. Int. Conf. Neural Information Processing Systems, 2012, pp. 10971105.
    2. 2)
      • 28. Caltrans, Performance Measurement System (PeMS)’, 2014. Available at http://pems.dot.ca.gov.
    3. 3)
      • 25. Hinton, G.E., Salakhutdinov, R.R.: ‘Reducing the dimensionality of data with neural networks’, Science, 2006, 313, (5786), p. 504.
    4. 4)
      • 11. Wu, S., Yang, Z., Zhu, X., et al: ‘Improved k-NN for short-term traffic forecasting using temporal and spatial information’, J. Transp. Eng., 2014, 140, (7), p. 04014026.
    5. 5)
      • 17. Huang, W., Song, G., Hong, H., et al: ‘Deep architecture for traffic flow prediction: deep belief networks with multitask learning’, IEEE Trans. Intell. Transp. Syst., 2014, 15, (5), pp. 21912201.
    6. 6)
      • 15. Kumar, K., Parida, M., Katiyar, V.K.: ‘Short term traffic flow prediction in heterogeneous condition using artificial neural network’, Transport, 2013, 30, (4), pp. 19.
    7. 7)
      • 14. Zheng, W., Lee, D.H.: ‘Short-term freeway traffic flow prediction: Bayesian combined neural network approach’, J. Transp. Eng., 2006, 132, (2), pp. 114121.
    8. 8)
      • 4. Voort, M.V.D., Dougherty, M., Watson, S.: ‘Combining Kohonen maps with ARIMA time series models to forecast traffic flow’, Transp. Res. C Emerg. Technol., 1996, 4, (5), pp. 307318.
    9. 9)
      • 12. Park, J., Dai, L., Yi L, M., et al: ‘Real time vehicle speed prediction using a neural network traffic model’. Int. Joint Conf. Neural Networks. IEEE, 2011, pp. 29912996.
    10. 10)
      • 9. Kim, E.Y.: ‘MRF model based real-time traffic flow prediction with support vector regression’, Electron. Lett., 2017, 53, (4), pp. 243245.
    11. 11)
      • 19. Lv, Y., Duan, Y., Kang, W., et al: ‘Traffic flow prediction with big data: a deep learning approach’, IEEE Trans. Intell. Transp. Syst., 2015, 16, (2), pp. 865873.
    12. 12)
      • 8. Jia, Y., Wu, J., Benakiva, M., et al: ‘Rainfall-integrated traffic speed prediction using deep learning method’, IET Intell. Transp. Syst., 2017, 11, (9), pp. 531536.
    13. 13)
      • 30. Goldberg, D.E.: ‘Genetic algorithms in search, optimization, and machine learning’ (Addison-Wesley, New York, 1989, 1st edn).
    14. 14)
      • 21. Tian, Y., Pan, L.: ‘Predicting short-term traffic flow by long short-term memory recurrent neural network’. IEEE Int. Conf. Smart City. IEEE, 2015, pp. 153158.
    15. 15)
      • 3. Ghosh, B., Basu, B., O'Mahony, M.: ‘Bayesian time-series model for short-term traffic flow forecasting’, J. Transp. Eng., 2007, 133, (3), pp. 180189.
    16. 16)
      • 16. Chen, D.: ‘Research on traffic flow prediction in the big data environment based on the improved RBF neural network’, IEEE Trans. Ind. Inf., 2017, 13, (4), pp. 20002008.
    17. 17)
      • 1. Highway Capacity Manual’ (Transportation Research Board, Washington, DC, USA, 2000).
    18. 18)
      • 22. Zhao, Z., Chen, W., Wu, X., et al: ‘LSTM network: a deep learning approach for short-term traffic forecast’, IET Intell. Transp. Syst., 2017, 11, (2), pp. 6875.
    19. 19)
      • 20. Duan, Y., Lv, Y., Wang, F.Y.: ‘Performance evaluation of the deep learning approach for traffic flow prediction at different times’. IEEE Int. Conf. Service Operations and Logistics, and Informatics. IEEE, 2016, pp. 223227.
    20. 20)
      • 24. Fletcher, R., Reeves, C.M.: ‘Function minimization by conjugate gradients’, Comput. J., 1964, 7, (2), pp. 149154.
    21. 21)
      • 5. Work, D.B., Tossavainen, O.P., Blandin, S., et al: ‘An ensemble Kalman filtering approach to highway traffic estimation using GPS enabled mobile devices’. IEEE Conf. Decision and Control (CDC 2008). IEEE, 2008, pp. 50625068.
    22. 22)
      • 29. Hinton, G.E., Osindero, S., Teh, Y.W..: ‘A fast learning algorithm for deep belief nets’, Neural Comput., 2006, 18, (7), p. 1527.
    23. 23)
      • 31. Le, Q.V., Ngiam, J., Coates, A., et al: ‘On optimization methods for deep learning’. Int. Conf. Machine Learning’, ICML 2011, Bellevue, Washington, USA, June 28–July. DBLP, 2011, pp. 265272.
    24. 24)
      • 10. Yang, Y., Duan, Z.: ‘A novel prediction method of traffic flow: least squares support vector machines based on spatial relation’. Cota Int. Conf. Transportation Professionals, 2014, pp. 18071818.
    25. 25)
      • 2. Thomas, T., Weijermars, W., Berkum, E.V.: ‘Predictions of urban volumes in single time series’. IEEE Trans. Intell. Transp. Syst., 2010, 11, (1), pp. 7180.
    26. 26)
      • 23. Sun, B., Cheng, W., Goswami, P., et al: ‘Short-term traffic forecasting using self-adjusting k-nearest neighbours’, IET Intell. Transp. Syst., 2018, 12, (1), pp. 4148.
    27. 27)
      • 7. Okutani, I., Stephanedes, Y.J.: ‘Dynamic prediction of traffic volume through Kalman filtering theory’, Transp. Res. B, 1984, 18, (1), pp. 111.
    28. 28)
      • 27. Hinton, G.E.: ‘Training products of experts by minimizing contrastive divergence’. Neural Comput., 2002, 14, (8), pp. 17711800.
    29. 29)
      • 13. Vlahogianni, E.I., Karlaftis, M.G., Golias, J.C.: ‘Optimized and meta-optimized neural networks for short-term traffic flow prediction: a genetic approach’, Transp. Res. C Emerg. Technol., 2005, 13, (3), pp. 211234.
    30. 30)
      • 18. Koesdwiady, A., Soua, R., Karray, F.: ‘Improving traffic flow prediction with weather information in connected cars: a deep learning approach’, IEEE Trans. Veh. Technol., 2016, 65, (12), pp. 95089517.
    31. 31)
      • 6. Ahmed, M.S., Cook, A.R.: ‘Analysis of freeway traffic time-series data by using Box–Jenkins techniques’, Transp. Res. Rec., 1979, 722, pp. 19.
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