Using mobile technology and a participatory sensing approach for crowd monitoring and management during large-scale mass gatherings

Abstract

Understanding the behavior of pedestrian crowds in physical spaces is important in many areas ranging from urban planning, policy making at community and state level as well as design, and management of pedestrian facilities and transportation systems. Therefore, in the past few years, efforts have increased to study how human crowds form and how specific collective behavior patterns among the involved individuals emerge. To do so, simulation tools have been used to study the self-organizing effects of large groups of pedestrians. Different models exist to simulate pedestrian dynamics. The most popular models include: Physical models that model pedestrians based on the analogy to gases or fluids; the social force model together with its extensions [1]; and cellular automata [2]. To perform a calibration of the model parameters, experiments under controlled conditions can be performed [3], [4] or video footage capturing pedestrian dynamics can be evaluated [5]