Missing data is a common and critical problem in the stage of traffic data collection and processing. How to impute the missing values in spatio-temporal traffic data has been a challenging topic for a long time. Recently, a variety of methods have been proposed to impute the missing values. Among them, the tensor-based methods show higher competence in multi-dimensional traffic data imputation. However, the previous studies of tensor factorization rarely considered the joint imputation of multiple correlative data such as traffic speed and traffic volume. In this paper, a novel method called Multi-Task Neural Tensor Factorization (MTNTF) is proposed to learn the non-linear correlation patterns of traffic speed-volume, and then address the joint imputation of traffic speed and traffic volume. Extensive experiments on a real dataset show our MTNTF significantly outperforms the state-of-the-art methods in element-wise missing and fiber-wise missing cases. In addition, our method can impute the slice-wise missing values of traffic volume based on incomplete traffic speed.


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    Titel :

    Multitask Neural Tensor Factorization for Road Traffic Speed-Volume Correlation Pattern Learning and Joint Imputation


    Beteiligte:
    Zhu, Yiting (Autor:in) / Wang, Jiyu (Autor:in) / Wang, Junbo (Autor:in) / He, Zhaocheng (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2022-12-01


    Format / Umfang :

    4017267 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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