Over the last years multiple variations of the Social Force model have been proposed. While most of the available force-based
models are calibrated on observed human movement data, validation for investigating the model characteristics, e.g. variance in
parameter values, is still sparse. We present a novel methodology for validating Social Force based models which investigates
the reproducibility of human movement behavior on the individual trajectory level with real-world movement data. Our approach
estimates model parameter values and their distribution with non-linear regression on observed trajectory data, where the resulting
variances of the parameter values represent the model’s validity. We demonstrate our approach on a comprehensive (235 pedestrians)
and highly accurate (within a few centimeters) set of human movement trajectories obtained from real-world pedestrian traffic
with bidirectional flow using an automatic people tracking approach based on Kinect sensors. We validate the Social Force model