Validating Social Force based Models with Comprehensive Real World Motion Data

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. The authors 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. The 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. The authors demonstrate the 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. The authors validate the Social Force model of Helbing and Molnar (1995), Helbing and Johansson (2009) and Rudloff et al. (2011).