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Tensor based missing traffic data completion with spatial–temporal correlation

Author

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  • Ran, Bin
  • Tan, Huachun
  • Wu, Yuankai
  • Jin, Peter J.

Abstract

Missing and suspicious traffic data is a major problem for intelligent transportation system, which adversely affects a diverse variety of transportation applications. Several missing traffic data imputation methods had been proposed in the last decade. It is still an open problem of how to make full use of spatial information from upstream/downstream detectors to improve imputing performance. In this paper, a tensor based method considering the full spatial–temporal information of traffic flow, is proposed to fuse the traffic flow data from multiple detecting locations. The traffic flow data is reconstructed in a 4-way tensor pattern, and the low-n-rank tensor completion algorithm is applied to impute missing data. This novel approach not only fully utilizes the spatial information from neighboring locations, but also can impute missing data in different locations under a unified framework. Experiments demonstrate that the proposed method achieves a better imputation performance than the method without spatial information. The experimental results show that the proposed method can address the extreme case where the data of a long period of one or several weeks are completely missing.

Suggested Citation

  • Ran, Bin & Tan, Huachun & Wu, Yuankai & Jin, Peter J., 2016. "Tensor based missing traffic data completion with spatial–temporal correlation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 446(C), pages 54-63.
    • Handle: RePEc:eee:phsmap:v:446:y:2016:i:c:p:54-63
    DOI: 10.1016/j.physa.2015.09.105
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    Cited by:

    1. Zhao, Mingming & Yu, Hongxin & Wang, Yibing & Song, Bin & Xu, Liang & Zhu, Dianchen, 2024. "Real-time freeway traffic state estimation for inhomogeneous traffic flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 639(C).
    2. Yixian Chen & Zhaocheng He, 2020. "Vehicle Identity Recovery for Automatic Number Plate Recognition Data via Heterogeneous Network Embedding," Sustainability, MDPI, vol. 12(8), pages 1-17, April.
    3. Huiming Duan & Xinping Xiao, 2019. "A Multimode Dynamic Short-Term Traffic Flow Grey Prediction Model of High-Dimension Tensors," Complexity, Hindawi, vol. 2019, pages 1-18, June.
    4. Wang, Ning & Zhang, Kunpeng & Zheng, Liang & Lee, Jaeyoung & Li, Shukai, 2023. "Network-wide traffic state reconstruction: An integrated generative adversarial network framework with structural deep network embedding," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).

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