AKurtc, Valentina large-scale naturalistic vehicle trajectory dataset from German highways called highD is used to investigate the car-following behavior of individual drivers. These data include trajectories of 1,10,000 vehicles with the total length of 16.5  h. Solving a nonlinear optimization problem, the Intelligent Driver Model is calibrated by minimizing the deviations between observed and simulated gaps, when following the prescribed leading vehicle. The averaged calibration error is 7.6%, which is a little bit lower compared to previous findings (NGSIM I-80). It can be explaind by the shorter highD trajectories, predominantly free flow traffic and good precision metrics of this dataset. The ratio between inter-driver and intra-driver variabilityInter-driver and intra-driver variability is inversigated by performing global and platoon calibrationGlobal and platoon calibration. Inter-driver variation accounts for a larger part of the calibration errors than intra-driver variation does.


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    Title :

    The HighD Dataset: Is This Dataset Suitable for Calibration of Vehicular Traffic Models?


    Additional title:

    Springer Proceedings Phys.


    Contributors:

    Published in:

    Traffic and Granular Flow 2019 ; Chapter : 64 ; 523-529


    Publication date :

    2020-11-17


    Size :

    7 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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