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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

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


    Contributors:
    Shao, Wei (author) / Chen, Ling (author)


    Publication date :

    2018-11-01


    Size :

    1570401 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Improving Actuated Traffic Signal Control Using License Plate Recognition Data

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



    Improving Actuated Traffic Signal Control Using License Plate Recognition Data

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



    Traffic control optimization strategy based on license plate recognition data

    Ruimin Li / Shi Wang / Pengpeng Jiao et al. | DOAJ | 2023

    Free access