Optimization-based pedestrian model calibration for evaluation

The evaluation and comparison of crowd simulation algorithms (complex, high-dimensional, multi-scale systems) is an important question. "Realism" being dependent on target applications, comparisons with real measurements are not easy. Promising solutions have been suggested for such evaluations (Guy et al. (2012)). Here, we address estimating simulation parameters before evaluating: what do evaluation results mean if the assessed model is not performing at its best? We propose an optimization-based approach encompassing: reference data, metrics, simulation algorithms and optimization techniques. We demonstrate finding good parameter values setting simulation results as close as possible to reference data, enabling fair and meaningful comparisons. (C) 2014 Published by Elsevier B.V.