title: Parameter estimation of social forces in pedestrian dynamics models via a probabilistic method.
abstract: 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.
authors: CASA- Centre for Analysis, Scientific computing and Applications, Department of Mathematics and Computer Science, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, Netherlands. a.corbetta@tue.nl.
authors: Alessandro
authors: A
authors: Corbetta
authors: Adrian
authors: A
authors: Muntean
authors: Kiamars
authors: K
authors: Vafayi
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D001696:Biomechanical Phenomena; D003198:Computer Simulation; D003441:Crowding; D006801:Humans; D015233:Models, Statistical; D016011:Normal Distribution; D000069636:Pedestrians; D011336:Probability; D012919:Social Behavior; D016138:Walking