Parameter Estimation of Social Forces in Crowd Dynamics Models via a Probabilistic Method

TL;DR

This paper introduces a Bayesian probabilistic approach to estimate parameters and their uncertainties in crowd dynamics models from experimental data, and proposes a fitness measure for model classification and validation.

Contribution

It presents a novel probabilistic method for parameter estimation in crowd models and a fitness measure for model comparison based on experimental data.

Findings

Successful estimation of model parameters with uncertainty quantification

Effective classification of model structures based on fitness measures

Framework for model validation and selection in crowd dynamics

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.