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Evolution assessment of Shanghai Urban Rail Transit Network

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  • Yang, Zhijie
  • Chen, Xiaolong

Abstract

There has been extensive research on complex network theory in regards to transportation applications in recent years, but relatively few studies on network evolution. In this study, we assessed the evolution of the Shanghai Urban Rail Transit Network (SURTN) from 1993 to 2020 to identify future trends in the network. Machine learning theory is successfully utilized to split the evolution process into two stages based on the topological data. The evolution process is then analyzed based on six typical topology indicators, the relevance between the different indicators is determined, and notable regularities are identified: clustering nodes have tendency to scatter in a circular line, the importance of nodes decreases to form a democratic distribution, and the importance of nodes evolves toward polarization over the course of the network evolution process. A clear growth trend is seen that the network evolves in an orderly and stable direction with the network evolution. We then simulate the network growth trend by network densification and extension to represent SURTN’s dynamic performance and potential. We hope that this analysis represents not only a truly comprehensive explanation of SURTN, but also empirical guidance for future growth as transit managers continue to establish and maintain URTNs.

Suggested Citation

  • Yang, Zhijie & Chen, Xiaolong, 2018. "Evolution assessment of Shanghai Urban Rail Transit Network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 1263-1274.
    • Handle: RePEc:eee:phsmap:v:503:y:2018:i:c:p:1263-1274
    DOI: 10.1016/j.physa.2018.08.099
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