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

Alessandro Corbetta; Adrian Muntean; Kiamars Vafayi

doi:10.3934/mbe.2015.12.337

Article Contents

2015, Volume 12, Issue 2: 337-356. Doi: 10.3934/mbe.2015.12.337 open access

This issue Previous Article Stability and optimization in structured population models on graphs Next Article Modelling with measures: Approximation of a mass-emitting object by a point source

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

1.
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
2.
CASA- Centre for Analysis, Scientific computing and Applications, ICMS - Institute for Complex Molecular Systems, Department of Mathematics and Computer Science, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven

Received: April 2014

Revised: October 2014

Published: November 2014

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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.

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Mathematics Subject Classification: Primary: 35R30, 91D10; Secondary: 62F15, 91C99.

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