A physically-based particle model of emergent crowd behaviors

TL;DR

This paper introduces a physically-based particle model to simulate emergent crowd behaviors in ancient Greek settings, capturing phenomena like jamming and flowing through nonlinear interactions, enhancing realism and understanding of collective human dynamics.

Contribution

It presents a novel particle-based modeling approach using nonlinear interactions and differential automata to simulate emergent crowd phenomena with realistic visualizations.

Findings

Successfully models jamming and flowing behaviors

Produces realistic visualizations of crowd dynamics

Demonstrates the importance of nonlinear interactions

Abstract

This paper presents a modeling process in order to produce a realistic simulation of crowds in the ancient Greek agora of Argos. This place was a social theater in which two kinds of collective phenomena took place: interpersonal interactions (small group discussion and negotiation, etc.) and global collective phenomena, such as flowing and jamming. In this paper, we focus on the second type of collective human phenomena, called non-deliberative emergent crowd phenomena. This is a typical case of collective emergent self-organization. When a great number of individuals move within a confined environment and under a common fate, collective structures appear spontaneously: jamming with inner collapses, organized flowing with queues, curls, and vortices, propagation effects, etc. These are particularly relevant features to enhance the realism - more precisely the "truthfulness" - of models of this kind of collective phenomena. We assume that this truthfulness is strongly associated with the concept of emergence: evolutions are not predetermined by the individual characters, but emerge from the interaction of numerous characters. The evolutions are not repetitive, and evolve on the basis of small changes. This paper demonstrates that the physically-based interacting particles system is an adequate candidate to model emergent crowd effects: it associates a large number of elementary dynamic actors via elementary non-linear dynamic interactions. Our model of the scene is regulated as a large, dynamically coupled network of second order differential automata. We take advantage of symbolic non-photorealistic and efficient visualization to render the style of the person, rather than the person itself. As an artistic representation, NPR reinforces the symbolic acceptance of the scene by the observer, triggering an immediate and intuitive recognition of the scene as a plausible scene from ancient Greece.