Parameter estimation for macroscopic pedestrian dynamics models using trajectory data

Add to your list(s) Download to your calendar using vCal

• Susana Gomes (University of Warwick)
• Monday 23 October 2023, 14:00-15:00
• Room 104, Arts Building (R16).

If you have a question about this talk, please contact Xiaocheng Shang.

In this talk I will present a framework for estimating parameters in macroscopic models for crowd dynamics using data from individual trajectories. I consider a model for the unidirectional flow of pedestrians in a corridor which consists of a coupling between a density dependent stochastic differential equation and a nonlinear partial differential equation for the density. In the stochastic differential equation for the trajectories, the velocity of a pedestrian decreases with the density according to the fundamental diagram. Although there is a general agreement on the basic shape of this dependence, its parametrization depends strongly on the measurement and averaging techniques used as well as the experimental setup considered. I will discuss identifiability of the parameters appearing in the fundamental diagram, introduce optimisation and Bayesian methods to perform the identification, and analyse the performance of the proposed methodology in various realistic situations. Finally, I discuss possible generalisations, including the effect of the form of the fundamental diagram and the use of experimental data.

This talk is part of the Data Science and Computational Statistics Seminar series.

This talk is included in these lists:

• Data Science and Computational Statistics Seminar
• Room 104, Arts Building (R16)
• School of Mathematics events

Note that ex-directory lists are not shown.