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Parameter estimation of social forces in pedestrian dynamics models via a probabilistic method

Alessandro Corbetta Adrian Muntean Kiamars Vafayi

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MBE2015,2,337doi:10.3934/mbe.2015.12.337

Focusing on a specific crowd dynamics situation, including real lifeexperiments and measurements, our paper targets a twofold aim: (1) wepresent a Bayesian probabilistic method to estimate the value and theuncertainty (in the form of a probability density function) ofparameters in crowd dynamic models from the experimental data; and (2)we introduce a fitness measure for the models to classify acouple of model structures (forces) according to their fitness to theexperimental data, preparing the stage for a more generalmodel-selection and validation strategy inspired by probabilistic dataanalysis. Finally, we review the essential aspects of our experimentalsetup and measurement technique.

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