ABSTRACT
Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
Simulating the behavior of human crowds requires an understanding of the interaction between individuals, which may be complex and unpredictable. Crowds sometimes display spontaneous collective behavior, the emergence of which is formulated by social scientists using different theories such as contagion models or predisposition hypotheses. Crowd simulation research as well as industrial applications have also gained a new direction of modeling and visualizing different categories of collective crowd behavior [1-5].
