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Mathematical Biosciences and Engineering (MBE)
 

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

Pages: 337 - 356, Volume 12, Issue 2, April 2015      doi:10.3934/mbe.2015.12.337

 
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Alessandro Corbetta - 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 (email)
Adrian Muntean - 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, Netherlands (email)
Kiamars Vafayi - 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 (email)

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.

Keywords:  Crowd dynamics, parameter estimation, Bayes theorem, models classification, data analysis.
Mathematics Subject Classification:  Primary: 35R30, 91D10; Secondary: 62F15, 91C99.

Received: April 2014;      Accepted: October 2014;      Available Online: December 2014.

 References