@article{ref1,
title="Parameter estimation of social forces in pedestrian dynamics models via a probabilistic method",
journal="Mathematical biosciences and engineering",
year="2015",
author="Corbetta, Alessandro and Muntean, Adrian and Vafayi, Kiamars",
volume="12",
number="2",
pages="337-356",
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.<p /> <p>Language: en</p>",
language="en",
issn="1547-1063",
doi="",
url="http://dx.doi.org/"
}