  Microscopic traffic simulations are used to evaluate the impact of
infrastructure modifications and evolving vehicle technologies, such as
connected and automated driving. Simulated vehicles are controlled via
car-following, lane-changing and junction models, which are designed to imitate
human driving behavior. However, physics-based car-following models (CFMs)
cannot fully replicate measured vehicle trajectories. Therefore, we present
model extensions for the Intelligent Driver Model (IDM), of which some are
already included in the Extended Intelligent Driver Model (EIDM), to improve
calibration and validation results. They consist of equations based on vehicle
dynamics and drive off procedures. In addition, parameter selection plays a
decisive role. Thus, we introduce a framework to calibrate CFMs using drone
data captured at a signalized intersection in Stuttgart, Germany. We compare
the calibration error of the Krauss Model with the IDM and EIDM. In this setup,
the EIDM achieves a 17.78 % lower mean error than the IDM, based on the
distance difference between real world and simulated vehicles. Adding vehicle
dynamics equations to the EIDM further improves the results by an additional
18.97 %. The calibrated vehicle-driver combinations are then investigated by
simulating the traffic in three different scenarios: at the original
intersection, in a closed loop and in a stop-and-go wave. The data shows that
the improved calibration process of individual vehicles, openly available at
https://www.github.com/stepeos/pycarmodel_calibration, also provides more
accurate macroscopic results.
