Detection of Primitive Collective Behaviours in a Crowd Panic Simulation Based on Multi-Agent Approach

Detection of Primitive Collective Behaviours in a Crowd Panic Simulation Based on Multi-Agent Approach

Jérémy Patrix, Abdel-Illah Mouaddib, Sylvain Gatepaille
Copyright: © 2012 |Pages: 16
DOI: 10.4018/jsir.2012070104
OnDemand:
(Individual Articles)
Available
$37.50
Current Special Offers
No Current Special Offers
TOTAL SAVINGS: $37.50

Abstract

In case of emergency and evacuation, it is often impossible to interpret manually the complex behaviour of a crowd, essentially due to the lack of staff and time needed to understand a situation. In the literature, a monitored system using data fusion methods makes it possible to perform automatic situation awareness. Using Swarm Intelligence domain, the authors propose an approach based on multi-agent system to simulate and detect primitive collective behaviours emerging from a crowd panic. It enables anticipating collective behaviours in real-time as well as their anomalies according to specific scenarios. Detection is the possibility to learn, recognize and anticipate different behaviours by a probabilistic model. The collective behaviour detection of a crowd panic in real-time is based on a learning method on an extended model of Hidden Markov Model. This paper presents experiments of simulation and detection using an implementation of a virtual environment.
Article Preview
Top

Background

To model a crowd panic behaviour, we present a study of crowd behaviours by a simulated multi-agent system. Agent, multi-agent system and swarm intelligence concepts lead in literature to several kind of behaviour modeling. We have used some of these methods which we will present in this part.

Crowd Behaviour Simulation

Some ways are available to model and generate collective movements. We were interested first of all in their origin, that is to say individual behaviours.

Complete Article List

Search this Journal:
Reset
Volume 15: 1 Issue (2024)
Volume 14: 3 Issues (2023)
Volume 13: 4 Issues (2022)
Volume 12: 4 Issues (2021)
Volume 11: 4 Issues (2020)
Volume 10: 4 Issues (2019)
Volume 9: 4 Issues (2018)
Volume 8: 4 Issues (2017)
Volume 7: 4 Issues (2016)
Volume 6: 4 Issues (2015)
Volume 5: 4 Issues (2014)
Volume 4: 4 Issues (2013)
Volume 3: 4 Issues (2012)
Volume 2: 4 Issues (2011)
Volume 1: 4 Issues (2010)
View Complete Journal Contents Listing