Calibration of Car-Following Models Using Floating Car Data

Abstract

We study the car-following behavior of individual drivers in real city traffic on the basis of publicly available floating car datasets. By means of a nonlinear optimization procedure based on a genetic algorithm, we calibrate the “Intelligent Driver Model” and the “Optimal Velocity Model” by minimizing the deviations between the observed driving dynamics and the simulated trajectory when following the same leading vehicle. The reliability and robustness of the nonlinear fits can be assessed by applying different optimization criteria, i.e., different measures for the deviations between two trajectories. We also investigate the sensitivity of the model parameters. Furthermore, the parameter sets calibrated to a certain trajectory are applied to the other trajectories allowing for model validation. We found that the calibration errors of the Intelligent Driver Model are between 11% and 28%, while the validation errors are between 22% and 30%. The calibration of the Optimal Velocity Model led to larger calibration and validation errors, and stronger parameter variations regarding different objective measures. The results indicate that “intra-driver variability” rather than “inter-driver variability” accounts for a large part of the fit errors.