arXiv subjects

Oscar Dufour

Publications and source records attributed to Oscar Dufour.

3 recordsLinked to original sources

arXiv2026

LEMONS: An open-source platform to generate non-circuLar, anthropometry-based pEdestrian shapes and simulate their Mechanical interactiONS in two dimensions

To model dense crowds, the usual recourse to oversimplified (circular) pedestrian shapes and contact forces shows limitations. To help modellers overcome these limitations, we propose an open-source numerical tool. It consists of a Python library that generates 2D and 3D pedestrian crowds based on anthropometric data, and a C++ library that computes mechanical contacts with other agents and with obstacles, and evolves the crowd's configuration. Additionally, we provide an online platform with a user-friendly graphical interface for the Python library, and scripts to call the C++ library from Python. The tool enables users to implement their own decisional layer, i.e., to control the agents' choices of desired velocities.

cond-mat.soft↗

arXiv2025

Noise-induced transition to stop-and-go waves in single-file traffic rationalized by an analogy with Kapitza's inverted pendulum

Stop-and-go waves in vehicular traffic are commonly explained as a linear collective instability induced by e.g. response delays. We explore an alternative mechanism that more faithfully mirrors oscillation formation in dense single-file traffic. Stochastic noise plays a key role in this model; as it is increased, the base (uniform) flow abruptly switches to stop-and-go dynamics despite its unconditional linear stability. We elucidate the instability mechanism and rationalize it quantitatively by likening the system to a cyclically driven Kapitza pendulum.

physics.soc-ph↗

arXiv2024

Dense Crowd Dynamics and Pedestrian Trajectories: A Multiscale Field Study at the Fête des Lumières in Lyon

We present one of the first comprehensive field datasets capturing dense pedestrian dynamics across multiple scales, ranging from macroscopic crowd flows over distances of several hundred meters to microscopic individual trajectories, including approximately 7,000 recorded trajectories. The dataset also includes a sample of GPS traces, statistics on contact and push interactions, as well as a catalog of non-standard crowd phenomena observed in video recordings. Data were collected during the 2022 Festival of Lights in Lyon, France, within the framework of the French-German MADRAS project, covering pedestrian densities up to 4 individuals per square meter.

physics.soc-ph↗