Calibrating Car-Following Models using Trajectory Data: Methodological Study

TL;DR

This study calibrates car-following models using real trajectory data and genetic algorithms, revealing insights into driver behavior, model robustness, and the importance of intra-driver variability for model accuracy.

Contribution

It introduces a methodological framework for calibrating car-following models with trajectory data and assesses the robustness and variability of model parameters.

Findings

Calibration errors range between 11% and 29%.

Intelligent Driver Model shows more robustness than Velocity Difference Model.

Reaction time has negligible influence due to driver anticipation.

Abstract

The car-following behavior of individual drivers in real city traffic is studied on the basis of (publicly available) trajectory datasets recorded by a vehicle equipped with an radar sensor. By means of a nonlinear optimization procedure based on a genetic algorithm, we calibrate the Intelligent Driver Model and the Velocity Difference 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 are assessed by applying different optimization criteria, i.e., different measures for the deviations between two trajectories. The obtained errors are in the range between~11% and~29% which is consistent with typical error ranges obtained in previous studies. In addition, we found that the calibrated parameter values of the Velocity Difference Model strongly depend on the optimization criterion, while the Intelligent Driver Model is more robust in this respect. By applying an explicit delay to the model input, we investigated the influence of a reaction time. Remarkably, we found a negligible influence of the reaction time indicating that drivers compensate for their reaction time by anticipation. Furthermore, the parameter sets calibrated to a certain trajectory are applied to the other trajectories allowing for model validation. The results indicate that ``intra-driver variability'' rather than ``inter-driver variability'' accounts for a large part of the calibration errors. The results are used to suggest some criteria towards a benchmarking of car-following models.