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Nonnegative low-rank tensor completion method for spatiotemporal traffic data

  • 1229: Multimedia Data Analysis for Smart City Environment Safety
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Abstract

Although tensor completion theory performs well with high data missing rates, a lack of attention is encountered at the level of data completion non-negative constraints, and a remaining lack of effective non-negative tensor completion methods is still found. In this article, a new non-negative tensor completion model, based on the low-rank tensor completion theory, called the Nonnegative Weighted Low-Rank Tensor Completion (NWLRTC) method, is proposed. Due to the advantages of Truncated Nuclear Norm (TNN) in low-rank approximation, NWLRTC considers the TNN as the objective optimization function and adds a directional weight factor to the model to avoid its dependency on the data input direction. In addition to considering the completion accuracy, NWLRTC also imposes non-negativity constraints to meet the requirements of practical engineering applications. Finally, NWLRTC is realized by the alternating direction multiplier method. As for the experiments, they are carried out using different methods for generating missing data and for different iteration times. The experimental results show that the NWLRTC algorithm has high completion accuracy at low missing data rates, and it maintains a stable completion accuracy even when the missing rate hits 80%.

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Data availability

1. The Numerical data analysed during the current study are available in the Dataset Open Access repository, https://github.com/sysuits/urban-traffic-speed-dataset-Guangzhou.

Cited in the reference [32] of the manuscript.

2. The Image data analysed during the current study is available in the Dataset Open Access. http://tianqi.moji.com/liveview/picture/72181397.

Cited in the reference [10] of the manuscript.

3. The datasets generated during the current study are included in the manuscript.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (No. 6200238).

Author information

Authors and Affiliations

  1. School of Computer Science Engineering, Northwestern Polytechnical University, Xi’an, People’s Republic of China

    Yongmei Zhao

  2. School of Materiel Management and Unmanned aerial vehicle Engineering, Air Force Engineering University, Xi’an, 710051, People’s Republic of China

    Yongmei Zhao, Mingfu Tuo, Hongmei Zhang, Han Zhang & Jiangnan Wu

  3. Foundmental Department, Air Force Engineering University, Xi’an, People’s Republic of China

    Fengyin Gao

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  1. Yongmei Zhao
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  2. Mingfu Tuo
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Correspondence to Yongmei Zhao.

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Zhao, Y., Tuo, M., Zhang, H. et al. Nonnegative low-rank tensor completion method for spatiotemporal traffic data. Multimed Tools Appl (2023). https://doi.org/10.1007/s11042-023-15511-w

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