In the intelligent transportation system, the collected traffic data are usually incomplete. The low-rank completion models are effective in processing missing traffic data (MTD) imputation. However, the existing low-rank completion models encounter the following challenges: 1) Incorporating spatio-temporal information of traffic data often leads to significant time overhead; 2) To reduce the time overhead, some models based on the low-rank completion only embed the temporal information and discard the spatial information, thereby resulting in low imputation accuracy; 3) Missing traffic data carries complex spatio-temporal information, requiring multi-faceted analysis to extract valuable insights. To address these issues, we propose an efficient fourth-order dimension preserved tensor completion (FDPTC) with temporal constraint model. It works based on our proposed fourth-order dimension preserved (FDP) tensor decomposition model to capture the spatio-temporal information of traffic data from a high-dimensional perspective by extending dimensions. Additionally, we embed a temporal constraint into FDP tensor decomposition model to ensure consistency of MTD and introduce a non-negative constraint to accelerate convergence speed. By constructing this model, we successfully avoid direct operations on the extra information matrix/tensor, thereby optimizing efficiency. Experimental results on four real traffic datasets demonstrate that our proposed model achieves significantly higher imputation accuracy at an affordable computational burden compared with state-of-the-art models.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Fourth-Order Dimension Preserved Tensor Completion With Temporal Constraint for Missing Traffic Data Imputation


    Contributors:
    Chen, Hong (author) / Lin, Mingwei (author) / Zhao, Liang (author) / Xu, Zeshui (author) / Luo, Xin (author)


    Publication date :

    2025-05-01


    Size :

    4462140 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Missing Traffic Data Imputation based on Tensor Completion and Graph Network Fusion

    Xia, Chengliang / Yin, Xiang / Yu, Junyang et al. | Transportation Research Record | 2025


    Graph Spectral Regularized Tensor Completion for Traffic Data Imputation

    Deng, Lei / Liu, Xiao-Yang / Zheng, Haifeng et al. | IEEE | 2022


    Low-Rank Autoregressive Tensor Completion for Spatiotemporal Traffic Data Imputation

    Chen, Xinyu / Lei, Mengying / Saunier, Nicolas et al. | IEEE | 2022


    Spatiotemporal Tensor Completion for Improved Urban Traffic Imputation

    Ben Said, Ahmed / Erradi, Abdelkarim | IEEE | 2022


    Convolutional Low-Rank Tensor Representation for Structural Missing Traffic Data Imputation

    Li, Ben-Zheng / Zhao, Xi-Le / Chen, Xinyu et al. | IEEE | 2024