Chapter
Jan 13, 2020
Sixth International Conference on Transportation Engineering

Vulnerable Stations Identification of Urban Rail Transit Network: A Case Study of the Shenzhen Metro

Publication: ICTE 2019

ABSTRACT

Rail transit in major cities in China has developed rapidly and become an essential part of urban transport. However, safety is a significant issue that cannot be ignored. Combined the network structure and passenger flow distribution, the vulnerability of urban rail transit network are analyzed, and the hub stations in the network by their vulnerability are identified. The characteristics of urban rail transit network structure are analyzed by complex network theory. From the perspective of operation, an urban rail transit network model is established considering the load factor. The damage to the station varies according to different emergencies. Attack strategies are classified into random attacks and deliberate attacks which include static and dynamic ones. The consequences of an attack are divided into two cases: full failure and semi-failure. The overall network efficiency loss is used to evaluate the vulnerability of urban rail transit network on the node attacked. Ultimately, the Shenzhen Metro is taken as an example to analyze and evaluate the vulnerability and identify the vulnerable stations, which provide scientific reference and basis for the safety management of urban rail transit networks.

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ACKNOWLEDGEMENT

This research was supported by the Open Research Fund for National Engineering Laboratory of Integrated Transportation Big Data Application Technology (NO. CTBDAT201910). The authors appreciate the data support from Shenzhen Urban Planning & Land Resource Research Center.

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Published In

Go to ICTE 2019
ICTE 2019
Pages: 549 - 559
Editors: Xiaobo Liu, Ph.D., Southwest Jiaotong University, Qiyuan Peng, Ph.D., Southwest Jiaotong University, and Kelvin C. P. Wang, Ph.D., Oklahoma State University
ISBN (Online): 978-0-7844-8274-2

History

Published online: Jan 13, 2020

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Authors

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School of Transportation and Logistics, Southwest Jiaotong Univ., Chengdu, Sichuan 611756, China; Dept. of Engineering, Univ. of Yamanashi, Japan; National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Chengdu, Sichuan 611756, China. E-mail: [email protected]
Hezhou Qu
School of Transportation and Logistics, Southwest Jiaotong Univ., Chengdu, Sichuan 611756, China; National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Chengdu, Sichuan 611756, China
School of Transportation and Logistics, Southwest Jiaotong Univ., Dept. of Engineering, Chengdu, Sichuan 611756, China; National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Chengdu, Sichuan 611756, China (corresponding author). E-mail: [email protected]

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