Mathematical Biosciences and Engineering, 2015, 12(2): 337-356. doi: 10.3934/mbe.2015.12.337.

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

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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

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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Keywords Bayes theorem; Crowd dynamics; models classification; data analysis.; parameter estimation

Citation: Alessandro Corbetta, Adrian Muntean, Kiamars Vafayi. Parameter estimation of social forces in pedestrian dynamics models via a probabilistic method. Mathematical Biosciences and Engineering, 2015, 12(2): 337-356. doi: 10.3934/mbe.2015.12.337

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