Advertisement

Deep Bi-directional Long Short-Term Memory Model for Short-Term Traffic Flow Prediction

  • Jingyuan Wang
  • Fei Hu
  • Li LiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10638)

Abstract

Short-term traffic flow prediction plays an important role in intelligent transportation system. Numerous researchers have paid much attention to it in the past decades. However, the performance of traditional traffic flow prediction methods is not satisfactory, for those methods cannot describe the complicated nonlinearity and uncertainty of the traffic flow precisely. Neural networks were used to deal with the issues, but most of them failed to capture the deep features of traffic flow and be sensitive enough to the time-aware traffic flow data. In this paper, we propose a deep bi-directional long short-term memory (DBL) model by introducing long short-term memory (LSTM) recurrent neural network, residual connections, deeply hierarchical networks and bi-directional traffic flow. The proposed model is able to capture the deep features of traffic flow and take full advantage of time-aware traffic flow data. Additionally, we introduce the DBL model, regression layer and dropout training method into a traffic flow prediction architecture. We evaluate the prediction architecture on the dataset from Caltrans Performance Measurement System (PeMS). The experiment results demonstrate that the proposed model for short-term traffic flow prediction obtains high accuracy and generalizes well compared with other models.

Keywords

Traffic flow prediction Long short-term memory Deep learning PeMS 

References

  1. 1.
    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. Part C: Emerg. Technol. 13(3), 211–234 (2005)CrossRefGoogle Scholar
  2. 2.
    Huang, W., Song, G., Hong, H., Xie, K.: Deep architecture for traffic flow prediction: deep belief networks with multitask learning. IEEE Trans. Intell. Transp. Syst. 15(5), 2191–2201 (2014)CrossRefGoogle Scholar
  3. 3.
    Abadi, A., Rajabioun, T., Ioannou, P.A.: Traffic flow prediction for road transportation networks with limited traffic data. IEEE Trans. Intell. Transp. Syst. 16(2), 653–662 (2015)Google Scholar
  4. 4.
    Moorthy, C.K., Ratcliffe, B.G.: Short term traffic forecasting using time series methods. Transp. Plan. Technol. 12(1), 45–56 (1988)CrossRefGoogle Scholar
  5. 5.
    Thomas, T., Weijermars, W., Van Berkum, E.: Predictions of urban volumes in single time series. IEEE Trans. Intell. Transp. Syst. 11(1), 71–80 (2010)CrossRefGoogle Scholar
  6. 6.
    Sun, S., Zhang, C., Yu, G.: A Bayesian network approach to traffic flow forecasting. IEEE Trans. Intell. Transp. Syst. 7(1), 124–132 (2006)CrossRefGoogle Scholar
  7. 7.
    Yu, G., Hu, J., Zhang, C., Zhuang, L., Song, J.: Short-term traffic flow forecasting based on Markov chain model. In: Proceedings of IEEE Intelligent Vehicles Symposium, pp. 208–212 (2003)Google Scholar
  8. 8.
    Castro-Neto, M., Jeong, Y.S., Jeong, M.K., Han, L.D.: Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions. Expert Syst. Appl. 36(3), 6164–6173 (2009)CrossRefGoogle Scholar
  9. 9.
    Shuai, M., Xie, K., Pu, W., Song, G., Ma, X.: An online approach based on locally weighted learning for short-term traffic flow prediction. In: Proceedings of the 16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, p. 45 (2008)Google Scholar
  10. 10.
    Chan, K.Y., Dillon, T.S., Singh, J., Chang, E.: Neural-network-based models for short-term traffic flow forecasting using a hybrid exponential smoothing and Levenberg-Marquardt algorithm. IEEE Trans. Intell. Transp. Syst. 13(2), 644–654 (2012)CrossRefGoogle Scholar
  11. 11.
    Lv, Y., Duan, Y., Kang, W., Li, Z., Wang, F.Y.: Traffic flow prediction with big data: a deep learning approach. IEEE Trans. Intell. Transp. Syst. 16(2), 865–873 (2015)Google Scholar
  12. 12.
    Tian, Y., Pan, L.: Predicting short-term traffic flow by long short-term memory recurrent neural network. In: IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity), pp. 153–158 (2015)Google Scholar
  13. 13.
    Highway Capacity Manual, Special report, vol. 1, pp. 5–7, Washington D.C. (2000)Google Scholar
  14. 14.
    Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRefGoogle Scholar
  15. 15.
    Cornegruta, S., Bakewell, R., Withey, S., Montana, G.: Modelling radiological language with bidirectional long short-term memory networks (2016)Google Scholar
  16. 16.
    Bin, Y., Yang, Y., Shen, F., Xu, X., Shen, H.T.: Bidirectional long-short term memory for video description. In: ACM on Multimedia Conference, pp. 436–440 (2016)Google Scholar
  17. 17.
    Wu, Y., Schuster, M., Chen, Z., Le, Q.V., Norouzi, M., Macherey, W., Klingner, J.: Google’s neural machine translation system: bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144 (2016)
  18. 18.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)Google Scholar
  19. 19.
    Hawkins, D.M.: The problem of overfitting. J. Chem. Inf. Comput. Sci. 44(1), 1 (2004)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.R.: Improving neural networks by preventing co-adaptation of feature detectors. Comput. Sci. 3(4), 212–223 (2012)Google Scholar
  21. 21.
    Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)MathSciNetzbMATHGoogle Scholar
  22. 22.
    Caltrans Performance Measurement System (PeMS). http://pems.dot.ca.gov

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.School of Computer and Information ScienceSouthwest UniversityChongqingChina
  2. 2.Network CentreChongqing University of EducationChongqingChina

Personalised recommendations