Simulation Model of Crowd Following Phenomenon in Emergencies

Authors

Fang Hao, Faculty of Information Science and Technology, Ningbo University, Ningbo 315211, China;
Liu Zhen, Faculty of Information Science and Technology, Ningbo University, Ningbo 315211, China;
Lu Tao, Faculty of Information Science and Technology, Ningbo University, Ningbo 315211, China;
Liangping Liu, Faculty of Information Science and Technology, Ningbo University, Ningbo 315211, China;
Tingting Liu, Faculty of Information Science and Technology, Ningbo University, Ningbo 315211, China;
Cuijuan Liu, Faculty of Information Science and Technology, Ningbo University, Ningbo 315211, China;

Abstract

Abstract: Researches on crowd behavior in emergent events are important practical significance for emergency management, most of the current crowd models are collision detection, motion plan and so on. According to the emergency scene analysis of surveillance video, following behaviors of crowd are obviously, modeling following behaviors is an interesting topic in crowd simulation. A following model was proposed based on classical social force model and OCEAN personlity theory, the concept of panic was introduced,, and following conditions of individual was set up, the speed formulas of individual were constructed. Experimental results show that the following model can closely simulate the collective behavior of emergent crowd.

Recommended Citation

Hao, Fang; Zhen, Liu; Tao, Lu; Liu, Liangping; Liu, Tingting; and Liu, Cuijuan (2020) "Simulation Model of Crowd Following Phenomenon in Emergencies," Journal of System Simulation: Vol. 27: Iss. 10, Article 31.
Available at: https://dc-china-simulation.researchcommons.org/journal/vol27/iss10/31

First Page

2432

Revised Date

2015-07-28

DOI Link

https://doi.org/

Last Page

2438

CLC

TP391.9

Recommended Citation

Fang Hao, Liu Zhen, Lu Tao, Liu Liangping, Liu Tingting, Liu Cuijuan. Simulation Model of Crowd Following Phenomenon in Emergencies[J]. Journal of System Simulation, 2015, 27(10): 2432-2438.