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Efficient and Accurate Traffic Flow Prediction via Fast Dynamic Tensor Completion

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Traffic Mining Applied to Police Activities (TRAP 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 728))

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Abstract

Timely and accurate prediction of traffic flow plays an important role in improving living quality of the public, which greatly influences the polices and regulations to be enforced and abided by. In this paper, we focus on urban highway traffic prediction, and present a tensor completion based method, namely, DTC-F. It is conceived on the solid basis of dynamic tensor model for traffic prediction, and in this paper, fast low rank tensor completion and dynamic tensor structure are first combined to pursue high prediction performance. The proposed DTC-F method excavates the inner law of traffic flow data by taking account of multi-mode features, such as daily and weekly periodicity, spatial information, and temporal variations, etc. Empirical evaluation demonstrates the superiority of DTC-F, and indicates that the proposed method is potentially applicable in large and dynamic highway networks.

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Notes

  1. 1.

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Acknowledgements

This work was in part supported by NSFC under grants Nos. 61402498, 71331008 and 71690233.

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Authors and Affiliations

  1. National University of Defense Technology, Changsha, China

    Jinzhi Liao, Xiang Zhao, Jiuyang Tang, Chong Zhang & Mingke He

  2. Collaborative Innovation Center of Geospatial Information Technology, Wuhan, China

    Xiang Zhao, Jiuyang Tang, Chong Zhang & Mingke He

Authors
  1. Jinzhi Liao
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  2. Xiang Zhao
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  3. Jiuyang Tang
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  4. Chong Zhang
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  5. Mingke He
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Corresponding author

Correspondence to Xiang Zhao .

Editor information

Editors and Affiliations

  1. Italian National Police, Rome, Italy

    Fabio Leuzzi

  2. University of Bari, Bari, Italy

    Stefano Ferilli

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Liao, J., Zhao, X., Tang, J., Zhang, C., He, M. (2018). Efficient and Accurate Traffic Flow Prediction via Fast Dynamic Tensor Completion. In: Leuzzi, F., Ferilli, S. (eds) Traffic Mining Applied to Police Activities. TRAP 2017. Advances in Intelligent Systems and Computing, vol 728. Springer, Cham. https://doi.org/10.1007/978-3-319-75608-0_6

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  • DOI: https://doi.org/10.1007/978-3-319-75608-0_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-75607-3

  • Online ISBN: 978-3-319-75608-0

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