Abstract
In public places, it is significant to analyze the stability of the crowd which can support the crowd management and control, and protect the evacuees safely and effectively. The numerical analysis method of system stability based on Lyapunov theory suffers problems that it is difficult to avoid random errors in the initialization of pedestrian density and velocity, as well as cumulative errors due to time increasing, limiting its application. This study adopts a complementary model of theoretical numerical analysis and machine vision with a parallel convolutional neural network (CNN) model. It proposes an approach of stability analysis and closed-loop verification for crowd merging systems. Thereby, this research provides theoretical and methodological support for planning of the functional layout of crowd flow in public crowd-gathering places and the control measures for stable crowd flow.
Highlights
In recent years, the crowd flows in large-scale public places such as high-speed rail stations and airports in many parts of the world are increasing
The numerical analysis method of system stability based on Lyapunov theory can realize rapid prediction of crowd stability
In view of the existing Lyapunov theory analysis error defects, this study introduces the convolutional neural network (CNN) model in the field of machine vision, collects the crowd data in the video surveillance image information, studies the crowd count and density estimation, and uses the density judgment method to analyze the crowd stability Sex [6]-[7]
Summary
The crowd flows in large-scale public places such as high-speed rail stations and airports in many parts of the world are increasing. Most public places cover large areas with complex structures. They can accommodate thousands of tourists or pedestrians in a single crowd. More scientific and effective methods to detect crowd density and maintain the stability of crowd movement are urgently needed, prevent excessive crowd density in public places, and prevent and avoid stampede accidents.
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