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Risk analysis of crowd gathering on metro platforms during large passenger flow
Highlights Proposing a 7-phase model for crowd gathering risk on metro platforms. Utilizing smart card data to estimate the number of passenger gathered. Dividing the travel process of metro passengers into 7 components. Identifying high-risk periods and stations in the metro network. Analyzing the spatial and temporal distribution of crowd gathering risk.
Abstract With the continuous development of urbanization, metro systems have become an essential means of travel for urban residents. However, the increase of passengers using the metro system also increases the risk of occupant instability and stampede, which is especially significant on metro platforms considering the mixed passenger flow of boarding, waiting and getting off. How to grasp the overcrowding of passengers and gathering risk on the metro platform in time is the critical problem of the current research. To solve the difficulties in acquiring the number of passengers at metro platforms, a 7-phase model is constructed to estimate the risk of crowd gathering in each platform according to the Origin-Destination (OD) data. The proposed model divides a travel process into 7 components in detail and evaluates the crowd-gathering risk accurately to the platform. The travel time of each passenger is calculated by the station structure parameters and pedestrian parameters. Then, the travel stages of passengers are acquired by the travel time. Thirdly, the number of passengers on any platform and at any time is calculated. Finally, the temporal and spatial distribution of crowd-gathering risk on all metro platforms of a metro network is analyzed. The constructed passenger travel model and the crowd-gathering risk analysis method can effectively identify the high-risk platform and high-risk periods, which provides method and data support for metro operation safety improvement and crowd accident prevention.
Risk analysis of crowd gathering on metro platforms during large passenger flow
Highlights Proposing a 7-phase model for crowd gathering risk on metro platforms. Utilizing smart card data to estimate the number of passenger gathered. Dividing the travel process of metro passengers into 7 components. Identifying high-risk periods and stations in the metro network. Analyzing the spatial and temporal distribution of crowd gathering risk.
Abstract With the continuous development of urbanization, metro systems have become an essential means of travel for urban residents. However, the increase of passengers using the metro system also increases the risk of occupant instability and stampede, which is especially significant on metro platforms considering the mixed passenger flow of boarding, waiting and getting off. How to grasp the overcrowding of passengers and gathering risk on the metro platform in time is the critical problem of the current research. To solve the difficulties in acquiring the number of passengers at metro platforms, a 7-phase model is constructed to estimate the risk of crowd gathering in each platform according to the Origin-Destination (OD) data. The proposed model divides a travel process into 7 components in detail and evaluates the crowd-gathering risk accurately to the platform. The travel time of each passenger is calculated by the station structure parameters and pedestrian parameters. Then, the travel stages of passengers are acquired by the travel time. Thirdly, the number of passengers on any platform and at any time is calculated. Finally, the temporal and spatial distribution of crowd-gathering risk on all metro platforms of a metro network is analyzed. The constructed passenger travel model and the crowd-gathering risk analysis method can effectively identify the high-risk platform and high-risk periods, which provides method and data support for metro operation safety improvement and crowd accident prevention.
Risk analysis of crowd gathering on metro platforms during large passenger flow
Zhou, Yiqi (author) / Chen, Junfeng (author) / Zhong, Maohua (author) / Li, Zhongwen (author) / Zhou, Wei (author) / Zhou, Zhijie (author)
2023-09-18
Article (Journal)
Electronic Resource
English
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