Abstract

This project 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 project 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, and 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 show that knowledge of the crowd emotion equilibrium is necessary for zero-error convergence; however, other parameters, such as individual personality parameters of crowd agents, may be approximated and zero-error convergence still achieved given the crowd equilibrium point and sign of agent opinions. We conclude that using this model class to predict live crowd emotion may be impractical due to the need for knowledge of individual agent personality parameters to simulate the crowd equilibrium. Directions for future work are discussed.

Degree

MS

College and Department

Physical and Mathematical Sciences

Rights

https://lib.byu.edu/about/copyright/

Date Submitted

2020-11-16

Document Type

Thesis

Handle

http://hdl.lib.byu.edu/1877/etd11462

Keywords

Networks, estimation, contagion, emotion, modeling

Language

English

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