Structural Evolution and Community Detection of China Rail Transit Route Network

Rui Ding, Jun Fu, Yiming Du, Linyu Du, Tao Zhou, Yilin Zhang, Siwei Shen, Yuqi Zhu and Shihui Chen ()
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Rui Ding: Guizhou Key Laboratory of Big Data Statistical Analysis, Guizhou University of Finance and Economics, Guiyang 550025, China
Jun Fu: College of Big Data Application and Economics (Guiyang College of Big Data Finance), Guizhou University of Finance and Economics, Guiyang 550025, China
Yiming Du: College of Big Data Application and Economics (Guiyang College of Big Data Finance), Guizhou University of Finance and Economics, Guiyang 550025, China
Linyu Du: College of Big Data Application and Economics (Guiyang College of Big Data Finance), Guizhou University of Finance and Economics, Guiyang 550025, China
Tao Zhou: College of Big Data Application and Economics (Guiyang College of Big Data Finance), Guizhou University of Finance and Economics, Guiyang 550025, China
Yilin Zhang: College of Big Data Application and Economics (Guiyang College of Big Data Finance), Guizhou University of Finance and Economics, Guiyang 550025, China
Siwei Shen: College of Big Data Application and Economics (Guiyang College of Big Data Finance), Guizhou University of Finance and Economics, Guiyang 550025, China
Yuqi Zhu: College of Big Data Application and Economics (Guiyang College of Big Data Finance), Guizhou University of Finance and Economics, Guiyang 550025, China
Shihui Chen: College of Big Data Application and Economics (Guiyang College of Big Data Finance), Guizhou University of Finance and Economics, Guiyang 550025, China

Sustainability, 2022, vol. 14, issue 19, 1-19

Abstract: How to improve the partial or overall performance of rail transit route network, strengthen the connection between different rail network stations, and form corresponding communities to resist the impact of sudden or long-term external factors has earned a lot of attention recently. However, the corresponding research studies are mostly based on the rail network structure, and the analysis and exploration of the community formed by the stations and its robustness are not enough. In this article, the evolution of the China rail transit route network (CRTRN) from 2009 to 2022 is taken as the research object, and its complex network characteristics, BGLL model-based community division, and multi disturbance strategies for network robustness are analyzed in depth to better understand and optimize the rail network structure to further effectively improve the efficiency of the public transport system. It is found that CRTRN is gradually expanding following the southwest direction (with the migration distance of nearly 200 km), the distribution of routes is more balanced, and the number of network communities is steadily decreasing (it dropped from 30 communities in 2009 to 25 in 2019), making various regions become closely connected. However, it can also be found that during the COVID-19 pandemic, the CRTRN is strongly affected, and the network structure becomes relatively loose and chaotic (the number of communities became 30). To protect the railway networks, the CRTRN system should pay more attention to stations with high node degree values; if they get disturbed, more areas will be affected. The corresponding research conclusions can provide some theoretical and practical support for the construction of the rail transit network in China.

Keywords: structural evolution; complex network; community detection; rail transit route network (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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