DIEP seminar by Prof. Alessandro Corbetta
Achieving a quantitative understanding and modelling of the dynamics of pedestrian crowds is not only a primary societal need, but also an outstanding fundamental challenge connected with the statistical physics of active flowing matter. Developments in automated vision over the last 15 years have given us increasing possibilities to measure the crowd behaviour at larger and larger spatial scales and with finer and finer details. Data acquisition in real-life with 24/7/365 schedules in public places enables datasets including millions of trajectories, resolving common and rare events. These open big-data investigations aiming at statistical features emerging from universal physical properties of pedestrian dynamics.
Alessandro will briefly review the work conducted over the last decade and the general trend of the scientific community. Then, Prof. Corbetta will focus on the challenge of modeling crowd dynamics targeting quatitative accuracy in statistical sense, from dilute to dense. The discussion will then follow recent and in-development models hinging on (functional) Langevin equations and variational approaches whose stationary measures manage to reproduce the experimental ensembles. Lastly, the participants will learn about recent applications of generative autoregressive AI to extract n-body interactions from massive data.
A. Corbetta, F. Toschi. Physics of Human Crowds. Annu. Rev. Condens. Matter Phys. Vol. 14:311-333, 2023
A. Gabbana, F. Toschi, P. Ross, A. Haans, A. Corbetta. Fluctuations in pedestrian dynamics routing choices. PNAS Nexus. pgac169, 2022
K. Minartz, F. Hendriks, S. Koop, A. Corbetta, V. Menkovski. Discovering interaction mechanisms in crowds via deep generative surrogate experiments. Sci. Rep. 15, 10385, 2025
C. Pouw, G. vd Vleuten, A. Corbetta. F. Toschi. Data-driven physics-based modeling of pedestrian dynamics. Phys. Rev. E 110, 064102, 2024
If you wish to attend this seminar online, please send an email to r.lier@uva.nl to receive the zoom-link.