Formulating a Crowd State Prediction Problem for Application to Crowd Control
Primary Investigator:
Philip Paré
Brooks A. Butler, Philip E Paré, Mark K. Transtrum, Sean Warnick
Abstract
Crowd violence and the repression of free speech have become increasingly relevant concerns in recent years. This paper considers a new application of crowd control, namely, keeping the public safe during large scale demonstrations. This problem is difficult for a variety of reasons, including limited access to informative sensing and effective actuation mechanisms, as well as limited understanding of crowd psychology and dynamics. This paper takes a first step towards solving this problem by formulating a crowd state prediction problem in consideration of recent work involving crowd behavior identification, crowd movement modeling, crowd psychology modeling. We build a non-linear crowd behavior model incorporating components of personality modeling, human emotion modeling, group opinion dynamics, and group movement modeling. This model is then linearized and used to build a state observer whose effectiveness is then tested on system outputs from both non-linear and linearized models. We conclude that crowd emotion prediction is achievable if given the equilibrium point of the crowd system. Additionally, we find that zero error convergence is achievable even when crowd personality parameters are assumed to be homogeneous. Directions for future work are discussed.