The HighD Dataset: Is This Dataset Suitable for Calibration of Vehicular Traffic Models?
The HighD Dataset: Is This Dataset Suitable for Calibration of Vehicular Traffic Models?
- Neue Suche nach: Kurtc, Valentina
- Neue Suche nach: Zuriguel, Iker
- Neue Suche nach: Garcimartin, Angel
- Neue Suche nach: Cruz, Raul
- Neue Suche nach: Kurtc, Valentina
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.
Dokumentinformationen
The HighD Dataset: Is This Dataset Suitable for Calibration of Vehicular Traffic Models?
Springer Proceedings Phys.
2020-11-17
7 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Global and platoon calibration , Trajectory data , Inter-driver and intra-driver variability Physics , Soft and Granular Matter, Complex Fluids and Microfluidics , Transportation Technology and Traffic Engineering , Complex Systems , Computer Appl. in Social and Behavioral Sciences , Statistical Physics and Dynamical Systems , Physics and Astronomy
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The HighD Dataset: Is This Dataset Suitable for Calibration of Vehicular Traffic Models?
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