Parameter Estimation of Social Forces in Crowd Dynamics Models via a   Probabilistic Method
  Focusing on a specific crowd dynamics situation, including real life experiments and measurements, our paper targets a twofold aim: (1) we present a Bayesian probabilistic method to estimate the value and the uncertainty (in the form of a probability density function) of parameters in crowd dynamic models from the experimental data; and (2) we introduce a fitness measure for the models to classify a couple of model structures (forces) according to their fitness to the experimental data, preparing the stage for a more general model-selection and validation strategy inspired by probabilistic data analysis. Finally, we review the essential aspects of our experimental setup and measurement technique. 
Alessandro Corbetta, Adrian Muntean, Federico Toschi, Kiamars Vafayi
physics.data-an cs.SI math.PR math.ST physics.soc-ph stat.TH