Volume 121, Number 4, February 2018
|Number of page(s)||6|
|Section||Interdisciplinary Physics and Related Areas of Science and Technology|
|Published online||17 April 2018|
Cyclic subway networks are less risky in metropolises
1 Guangdong HUST Industrial Technology Research Institute, Guangdong Province Key Lab of Digital Manufacturing Equipment - Dongguan, 523000, China
2 Key Laboratory of Image Processing and Intelligent Control, School of Automation, State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Techonology, Wuhan, 430074, China
3 Department of Electronic Engineering, City University of Hong Kong - Kowloon, Hong Kong SAR, China
Received: 9 February 2018
Accepted: 27 March 2018
Subways are crucial in modern transportation systems of metropolises. To quantitatively evaluate the potential risks of subway networks suffered from natural disasters or deliberate attacks, real data from seven Chinese subway systems are collected and their population distributions and anti-risk capabilities are analyzed. Counterintuitively, it is found that transfer stations with large numbers of connections are not the most crucial, but the stations and lines with large betweenness centrality are essential, if subway networks are being attacked. It is also found that cycles reduce such correlations due to the existence of alternative paths. To simulate the data-based observations, a network model is proposed to characterize the dynamics of subway systems under various intensities of attacks on stations and lines. This study sheds some light onto risk assessment of subway networks in metropolitan cities.
PACS: 89.75.-k – Complex systems / 89.75.Kd – Patterns / 89.75.Fb – Structures and organization in complex systems
© EPLA, 2018
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.