  Calibration and validation techniques are crucial in assessing the
descriptive and predictive power of car-following models and their suitability
for analyzing traffic flow. Using real and generated floating-car and
trajectory data, we systematically investigate following aspects: Data
requirements and preparation, conceptional approach including local
maximum-likelihood and global LSE calibration with several objective functions,
influence of the data sampling rate and measuring errors, the effect of data
smoothing on the calibration result, and model performance in terms of fitting
quality, robustness, parameter orthogonality, completeness and plausible
parameter values.
