Abstract
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 so- lutions 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.
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