Data driven learning to enhance a kinetic model of distressed crowd dynamics

TL;DR

This paper introduces a data-driven approach to estimate stress levels in a kinetic crowd model, enabling better simulation of crowd behavior during emergencies by solving an inverse problem with synthetic data.

Contribution

It presents a novel inverse problem formulation for estimating stress parameters in a kinetic crowd model, along with a numerical solution method.

Findings

Preliminary results demonstrate the feasibility of estimating stress levels from synthetic crowd data.

The approach allows for improved modeling of adaptive crowd behaviors in emergency scenarios.

Abstract

The mathematical modeling of crowds is complicated by the fact that crowds possess the behavioral ability to develop and adapt moving strategies in response to the context. For example, in emergency situations, people tend to alter their walking strategy in response to fear. To be able to simulate these situations, we consider a kinetic model of crowd dynamics that features the level of stress as a parameter and propose to estimate this key parameter by solving an inverse crowd dynamics problem. This paper states the mathematical problem and presents a method for its numerical solution. We show some preliminary results based on a synthetic data set, i.e., test cases where the exact stress level is known and the crowd density data are generated numerically by solving a forward crowd dynamics problem.