Abstract
A 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 variability is inversigated by performing global and platoon calibration. Inter-driver variation accounts for a larger part of the calibration errors than intra-driver variation does.
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Kurtc, V. (2020). The HighD Dataset: Is This Dataset Suitable for Calibration of Vehicular Traffic Models?. In: Zuriguel, I., Garcimartin, A., Cruz, R. (eds) Traffic and Granular Flow 2019. Springer Proceedings in Physics, vol 252. Springer, Cham. https://doi.org/10.1007/978-3-030-55973-1_64
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DOI: https://doi.org/10.1007/978-3-030-55973-1_64
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