Abstract
Crowd merging is a complex process, and any sudden external or internal disturbance will destroy the stability of the crowd. The occurrence of abnormal behavior will affect the crowd flow process and inevitably affect the stability of the crowd flow system. The position information of the joint points is obtained through the OpenPose algorithm, and the kinematics characteristics of each node are studied. It is judged whether the number of pedestrians in the crowd and the scale of the building scene are greater than the empirical setting value based on engineering statistical data and expert experience. When the number of pedestrians is more than 2,000 and the total area of the passage is more than 2,000 square meters, the appropriate macro-dynamic model is selected. The Aw-Rascle (AR) fluid dynamics model is selected in this study. The joint point information obtained through the OpenPose is combined with the macroscopic fluid dynamics model to construct a macroscopic crowd flow dynamics model based on the pedestrian's abnormal posture.
Highlights
The abnormal behaviour of pedestrians in public places will cause a series of problems such as crowd instability
Considering the calculation time and training complexity, 18 joint points are selected for research; OpenPose uses a non-parametric representation called Part Affinity Fields (PAF) to learn how to associate body parts with individuals
The study judges whether the number of pedestrians in the crowd and the scale of the building scene are greater than the empirical setting value to select the appropriate dynamic model under different merging scenarios
Summary
The abnormal behaviour of pedestrians in public places will cause a series of problems such as crowd instability. The occurrence of abnormal behaviour will affect the crowd merging process. It will disturb the crowd's movement direction and forward speed and other factors, causing an instantaneous density surge, which inevitably affects the stability of the crowd flow system. There are few studies on the impact of crowd movement and group behaviour caused by emergencies in group scenarios. This is of scientific significance and realistic demand for detecting abnormal events in time and avoiding risks
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