Traffic flow prediction is of significant importance in traffic planning. Currently, traffic flow data are primarily collected through loop detectors. However, factors such as road conditions can affect the accuracy of these data. To address this issue, this paper proposes a traffic flow prediction method based on decomposition and machine learning. The improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) method decomposes the sequence into multiple intrinsic mode functions (IMFs). The complexity of each IMF is calculated using the sample entropy (SE), and then the IMFs are reconstructed. Parameters of the variational mode decomposition (VMD) are optimized using the whale optimization algorithm (WOA) for the secondary decomposition, and predictions are made using gated recurrent units (GRU). Finally, the prediction results are reconstructed to obtain the final prediction values. In the case study section, experiments are conducted using datasets from three detectors to explore different decomposition forms and methods.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Traffic flow prediction for highway vehicle detectors through decomposition and machine learning


    Additional title:

    W. LU ET AL.
    TRANSPORTATION LETTERS


    Contributors:
    Lu, Wanlian (author) / Hu, Yao (author) / Chen, Wangyong (author) / Qin, Yutao (author) / Wu, Chuliang (author) / He, Xinyi (author)

    Published in:

    Transportation Letters ; 17 , 2 ; 260-280


    Publication date :

    2025-02-07


    Size :

    21 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English






    Highway traffic flow prediction method

    HAO ZHIQIANG / GENG DANYANG / WEN HAO et al. | European Patent Office | 2020

    Free access

    Highway traffic flow prediction method

    LU QIRONG / JING HONGJIE / YU CHENG et al. | European Patent Office | 2020

    Free access

    Deep learning based traffic flow prediction model on highway research

    Jia, Qingyang / Zang, Jingfeng / Liu, Shuanglin | SPIE | 2024