Reconstructing high-resolution fully sampled vehicle trajectories is very challenging due to the limited deployment of fixed and moving sensors. Existing studies are mostly based on microscopic car-following models that require high-resolution (HR) trajectory data with a certain penetration rate, making their methods vulnerable in low-penetration data environments. This study proposes a data-robust and scenario-flexible method for high-resolution trajectory reconstruction (TR), in which the macroscopic traffic state and microscopic vehicle behavior are both exploited. First, the macroscopic module recovers high-resolution spatiotemporal traffic velocity, providing constraints for subsequent trajectory reconstruction. Then, the microscopic module generates candidate vehicle trajectories, which are further fused to reconstruct high-resolution trajectories through an error-resistance method. The proposed method significantly improves the flexibility and robustness of high-resolution trajectory reconstruction on freeways and achieves a 57.54% improvement (in MAPE) even under an extremely low penetration rate of probe vehicles.


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    Titel :

    A Robust Fully Sampled High-Resolution Vehicle Trajectory Reconstruction Method Fusing Aggregate Traffic State and Microscopic Vehicle Behavior


    Beteiligte:
    Zhao, Jiayue (Autor:in) / He, Yang (Autor:in) / Xia, Jingxin (Autor:in) / Lu, Zhenbo (Autor:in)

    Kongress:

    24th COTA International Conference of Transportation Professionals ; 2024 ; Shenzhen, China


    Erschienen in:

    CICTP 2024 ; 55-65


    Erscheinungsdatum :

    11.12.2024




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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