The sparse problem of traffic volume data is unavoidable due to budget limits and device malfunctions in traffic systems. To address this problem, we propose a license plate recognition (LPR) data and collaborative tensor decomposition (CTD)-based method to estimate the sparse traffic volume data. The method works in two phases: first, a vehicle-time matrix is created based on LPR data, and non-negative matrix factorization is employed to analyze vehicle types; second, a road traffic volume tensor and the corresponding matrix of vehicle types are created, and people’s check-in data and point of interest information are introduced to complement the sparse tensor with CTD. Experimental results show that our method outperforms traditional estimation methods, and it can estimate traffic volume data even when the missing rate is high.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    License Plate Recognition Data-Based Traffic Volume Estimation Using Collaborative Tensor Decomposition


    Beteiligte:
    Shao, Wei (Autor:in) / Chen, Ling (Autor:in)


    Erscheinungsdatum :

    2018-11-01


    Format / Umfang :

    1570401 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Speed profile estimation using license plate recognition data

    Mo, Baichuan / Li, Ruimin / Zhan, Xianyuan | Elsevier | 2017





    Improving Actuated Traffic Signal Control Using License Plate Recognition Data

    Nie, Chunting / Wei, Heng / Shi, Jianjun et al. | TIBKAT | 2020