| PHYSICS AND ENGINEERING PHYSICS |
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Risk analysis of metro station passenger flow based on passenger flow patterns |
| LI Zihao1, TIAN Xiangliang1, LI Zhongwen2, ZHOU Wei2, ZHOU Zhijie2, ZHONG Maohua1 |
1. Institute of Public Safety Research, Department of Engineering Physics, Tsinghua University, Beijing 100084, China;
2. Shenzhen Metro Co., Ltd, Shenzhen 518026, China |
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Abstract The safe operation and management of metro systems are related to the metro station design and the passenger flow rates into and out of stations and between stations. This study examines the passenger flow rates in and out of each station at various times on various days by filtering and standardizing the automatic fare collection (AFC) system data of the Shenzhen metro system. The data was analyzed using a principle component analysis (PCA) and a Gaussian mixture model (GMM) to cluster passenger movement data on weekdays and weekends to simplify analysis of the passenger flow patterns. The results were then used to identify time periods and types of metro stations having greater risks as a passenger flow risk analysis method for metro stations. This big data analysis can identify risk conditions for large passenger volumes to avoid mass incidents like stampedes so as to protect passenger safety.
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| Keywords
metro stations
big data
clustering
travel patterns
risk analyses
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Issue Date: 14 October 2019
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2019,
Vol. 59
