Traffic volume data have been collected and used for various purposes in some aspects of intelligent transportation systems (ITS) applications. However, the unavoidable detector malfunction can cause data to be missing. It is often necessary to develop an effective approach to recover the missing data. In most previous methods, temporal correlation is explored to reconstruct missing traffic volume. In this article, a new missing traffic volume estimation approach based on tensor completion is proposed by exploring traffic spatial–temporal information. The tensor model is utilized to represent traffic volume, which allows for exploring the multicorrelation of traffic volume in spatial and temporal information simultaneously. In order to estimate the missing traffic volume represented by the tensor model, a novel tensor completion algorithm, called low multilinear rank tensor completion, is proposed to reconstruct the missing entries. The proposed approach is evaluated on the PeMS database. Experimental results demonstrate that the proposed method is more effective than the state-of-art methods, especially when the ratio of missing data is high.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Estimating Missing Traffic Volume Using Low Multilinear Rank Tensor Completion


    Contributors:
    Ran, Bin (author) / Tan, Huachun (author) / Feng, Jianshuai (author) / Wang, Wuhong (author) / Cheng, Yang (author) / Jin, Peter (author)

    Published in:

    Publication date :

    2016-03-03


    Size :

    10 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





    Low-Rank Hankel Tensor Completion for Traffic Speed Estimation

    Wang, Xudong / Wu, Yuankai / Zhuang, Dingyi et al. | IEEE | 2023


    Low-Rank Autoregressive Tensor Completion for Spatiotemporal Traffic Data Imputation

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


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

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


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

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