Crowd Stability Analysis Based on Pedestrian Abnormal Postures. Issue 1 (1st April 2022)

• Record Type: Journal Article
• Authors: Zhu, Wenjie; Zhao, Rongyong; Zhang, Hao; Jia, Ping; Wang, Yan; Li, Cuiling; Ma, Yunlong
• Is Part Of: Journal of physics. Volume 2224:Issue 1(2022)
• Publication Date: 2022-04-01
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/2224/1/012062
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: Abnormal behaviors of pedestrians in crowd gathering public places are important factors affecting the stability of crowd flow. Pedestrian abnormal postures are important manifestation of abnormal behaviors, which often leads to local turbulence, disturbance and density-speed fluctuations. It is urgent to discover the disturbance mechanism of abnormal pedestrian posture on the stability of crowd flow. This study intends to establish machine vision, kinematics, dynamic models and crowd confluence dynamic models for typical abnormal pedestrian postures in public places. We mainly use computer vision related technology based to recognize abnormal postures of pedestrians in videos, constructs a network matrix of key posture nodes, and studys the kinematics characteristics of abnormal posture nodes. Considering the number of pedestrians and the characteristics of the architectural scenes, we design a workflow to select the appropriate macro or micro dynamic model to build the crowd flow model. To validate the propuesd model, case in Shanghai Hongqiao railway station is studied.