Obtaining high resolution traffic states of large-scale freeway is always a significant topic for both transportation engineers and researchers. This paper presents a machine learning based high resolution speed estimation method for large-scale freeway using two data sources. Two low resolution heterogeneous traffic data are collected from microscopic simulations with different error distributions. A neural network based model is implemented fusing the two data sources and improving both time and space resolution of traffic estimations. The validation results and the sensitivity analysis indicate that the proposed method is feasible and suitable for large-scale freeway speed estimation. The performance of the model is acceptable, and the model could indeed improve both time and space resolutions of the estimations.


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    Titel :

    High Resolution Speed Estimation for Large-Scale Freeway Based on Data Fusion Technology


    Beteiligte:
    Gui, Yanjie (Autor:in) / Ding, Fan (Autor:in) / Dong, Hanxuan (Autor:in) / Peng, Jiankun (Autor:in) / Tan, Huachun (Autor:in)

    Kongress:

    20th COTA International Conference of Transportation Professionals ; 2020 ; Xi’an, China (Conference Cancelled)


    Erschienen in:

    CICTP 2020 ; 654-664


    Erscheinungsdatum :

    12.08.2020




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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