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An Efficient Traffic Prediction Model Using Deep Spatial-Temporal Network

  • Jie XuEmail author
  • Yong Zhang
  • Yongzheng Jia
  • Chunxiao Xing
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 268)

Abstract

Recently years, traffic prediction has become an important and challenging problem in smart urban traffic computing, which can be used for government for road planning, detecting bottle-neck congestions roads, pollution emissions estimating and so on. However, former data mining algorithms mainly address the problem by using the traditional mathematical or statistical theories, and they were impossible to model the spatial and temporal relationship simultaneously. To address these issues, we propose an end-to-end neural network named C-LSTM to predict the traffic congestion at next time interval. More specifically, the C-LSTM is based on CNN and LSTM to collectively capture the spatial-temporal dependencies on the road network. Inspired by the procedure of handling the image by CNN, the city-wide traffic maps are first converted into a series of static images like the video frame and then are fed into a deep learning architecture, in which CNN extracts the spatial characteristics, and LSTM extracts the temporal characteristics. In addition, we also consider some external factors to further improve the prediction accuracy. Extensive experiments on reality Beijing transportation datasets demonstrate the superiority of our method.

Keywords

Road network Traffic prediction Residual CNN LSTM 

Notes

Acknowledgments

This research was financially supported by NSFC (91646202), the National High-tech R&D Program of China (SS2015AA020102), Research/Project 2017YB142 supported by Ministry of Education of The People’s Republic of China, the 1000-Talent program, Tsinghua University Initiative Scientific Research Program.

References

  1. 1.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105. Hinton (2012)Google Scholar
  2. 2.
    Shu, Y., Jin, Z., Zhang, L., et al.: Traffic prediction using FARIMA models. In: IEEE International Conference on Communications, ICC 1999, vol. 2, pp. 891–895. IEEE (1999)Google Scholar
  3. 3.
    Wang, D., Zhang, J., Cao, W., et al.: When will you arrive? Estimating travel time based on deep neural networks. In: AAAI (2018)Google Scholar
  4. 4.
    Yu, H., Wu, Z., Wang, S., et al.: Spatiotemporal recurrent convolutional networks for traffic prediction in transportation networks. Sensors 17(7), 1501 (2017)CrossRefGoogle Scholar
  5. 5.
    Yao, H., Wu, F., Ke, J., et al.: Deep multi-view spatial-temporal network for taxi demand prediction. arXiv preprint arXiv:1802.08714 (2018)
  6. 6.
    Ke, J., Zheng, H., Yang, H., et al.: Short-term forecasting of passenger demand under on-demand ride services: a spatio-temporal deep learning approach. Transp. Res. Part C Emerg. Technol. 85, 591–608 (2017)CrossRefGoogle Scholar
  7. 7.
    Zhang, J., Zheng, Y., Qi, D.: Deep spatio-temporal residual networks for citywide crowd flows prediction. In: AAAI, pp. 1655–1661 (2017)Google Scholar
  8. 8.
    Xingjian, S.H.I., Chen, Z., Wang, H., et al.: Convolutional LSTM network: a machine learning approach for precipitation nowcasting. In: Advances in Neural Information Processing Systems, pp. 802–810. MLA (2015)Google Scholar
  9. 9.
    Zheng, Y.: Trajectory data mining: an overview. ACM Trans. Intell. Syst. Technol. (TIST) 6(3), 29 (2015)Google Scholar
  10. 10.
    Clark, S.: Traffic prediction using multivariate nonparametric regression. J. Transp. Eng. 129(2), 161–168 (2003)CrossRefGoogle Scholar
  11. 11.
    Min, W., Wynter, L.: Real-time road traffic prediction with spatio-temporal correlations. Transp. Res. Part C Emerg. Technol. 19(4), 606–616 (2011)CrossRefGoogle Scholar
  12. 12.
    Song, X., Kanasugi, H., Shibasaki, R.: DeepTransport: prediction and simulation of human mobility and transportation mode at a citywide level. In: IJCAI, vol. 16, pp. 2618–2624 (2016)Google Scholar
  13. 13.
    Liao, S., Zhou, L., Di, X., et al.: Large-scale short-term urban taxi demand forecasting using deep learning. In: Proceedings of the 23rd Asia and South Pacific Design Automation Conference, pp. 428–433. IEEE Press (2018)Google Scholar
  14. 14.
    Zhang, S., Wu, G., Costeira, J.P., et al.: FCN-rLSTM: Deep spatio-temporal neural networks for vehicle counting in city cameras. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 3687–3696. IEEE (2017)Google Scholar
  15. 15.
    He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)Google Scholar
  16. 16.
    Ma, X., Tao, Z., Wang, Y., et al.: Long short-term memory neural network for traffic speed prediction using remote microwave sensor data. Transp. Res. Part C Emerg. Technol. 54, 187–197 (2015)CrossRefGoogle Scholar
  17. 17.
    Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRefGoogle Scholar
  18. 18.
    Gers, F.A., Schmidhuber, J., Cummins, F.: Learning to forget: continual prediction with LSTM (1999)Google Scholar
  19. 19.
    Wang, J., Gu, Q., Wu, J., et al.: Traffic speed prediction and congestion source exploration: a deep learning method. In: 2016 IEEE 16th International Conference on Data Mining (ICDM), pp. 499–508. IEEE (2016)Google Scholar
  20. 20.
    Zheng, Y.: Methodologies for cross-domain data fusion: an overview. IEEE Trans. Big Data 1(1), 16–34 (2015)CrossRefGoogle Scholar
  21. 21.
    Ta, N., Li, G., Zhao, T., et al.: An efficient ride-sharing framework for maximizing shared route. IEEE Trans. Knowl. Data Eng. (TKDE) 30, 219–233 (2017)CrossRefGoogle Scholar
  22. 22.
    Tong, Y., Chen, Y., Zhou, Z., et al.: The simpler the better: a unified approach to predicting original taxi demands based on large-scale online platforms. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1653–1662. ACM (2017)Google Scholar
  23. 23.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)Google Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  • Jie Xu
    • 1
    • 2
    Email author
  • Yong Zhang
    • 1
    • 2
  • Yongzheng Jia
    • 3
  • Chunxiao Xing
    • 1
    • 2
  1. 1.Department of Computer Science and TechnologyTsinghua UniversityBeijingChina
  2. 2.Research Institute of Information TechnologyBeijing National Research Center for Information Science and TechnologyBeijingChina
  3. 3.Institute of Interdisciplinary Information SciencesTsinghua UniversityBeijingChina

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