Evolution assessment of Shanghai Urban Rail Transit Network

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.

[1]  Vito Latora,et al.  Elementary processes governing the evolution of road networks , 2012, Scientific Reports.

[2]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[3]  Li Zhu,et al.  The Evolution Analysis of Guangzhou Subway Network by Complex Network Theory , 2016 .

[4]  V Latora,et al.  Efficient behavior of small-world networks. , 2001, Physical review letters.

[5]  Panagiotis Angeloudis,et al.  Large subway systems as complex networks , 2006 .

[6]  Zhang Xiong,et al.  Evaluating the evolution of subway networks: Evidence from Beijing Subway Network , 2014 .

[7]  Yifang Ban,et al.  The evolving network structure of US airline system during 1990–2010 , 2014 .

[8]  Wei Huang,et al.  Effective strategy of adding nodes and links for maximizing the traffic capacity of scale-free network. , 2010, Chaos.

[9]  Han Baoming,et al.  Characteristic analysis of basic unit and complex network for urban rail transit , 2010 .

[10]  Arnab Chatterjee,et al.  Small-world properties of the Indian railway network. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[11]  Ginestra Bianconi,et al.  Weighted Multiplex Networks , 2013, PloS one.

[12]  Leonard M. Freeman,et al.  A set of measures of centrality based upon betweenness , 1977 .

[13]  Rui Ding,et al.  Complex Network Theory Applied to the Growth of Kuala Lumpur’s Public Urban Rail Transit Network , 2015, PloS one.

[14]  Guo Shi-jin.  An Analysis of Complexity of Public Transportation Network in Shanghai , 2007 .

[15]  Xuan Li.  Research on the Topological Structure Description of Urban Rail Transit Network , 2016 .

[16]  Sybil Derrible,et al.  The complexity and robustness of metro networks , 2010 .

[17]  Tarek Sayed,et al.  Safety models incorporating graph theory based transit indicators. , 2013, Accident; analysis and prevention.

[18]  César Ducruet,et al.  Multilayer dynamics of complex spatial networks: The case of global maritime flows (1977–2008) , 2017 .

[19]  Shuliang Wang,et al.  Networked analysis of the Shanghai subway network, in China , 2011 .

[20]  Keumsook Lee,et al.  Statistical analysis of the Metropolitan Seoul Subway System: Network structure and passenger flows , 2008, 0805.1712.

[21]  K. Seaton,et al.  Stations, trains and small-world networks , 2003, cond-mat/0311254.

[22]  Michael Batty,et al.  A long-time limit of world subway networks , 2011, 1105.5294.

[23]  J. Hołyst,et al.  Statistical analysis of 22 public transport networks in Poland. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[24]  Pan Di,et al.  Weighted complex network analysis of travel routes on the Singapore public transportation system , 2010 .

[25]  Oded Cats,et al.  Topological evolution of a metropolitan rail transport network: The case of Stockholm , 2017 .

[26]  Purnima Bholowalia,et al.  EBK-Means: A Clustering Technique based on Elbow Method and K-Means in WSN , 2014 .

[27]  Jianhua Zhang,et al.  Networked characteristics of the urban rail transit networks , 2013 .

[28]  Shujing Li,et al.  Space evolution model and empirical analysis of an urban public transport network , 2012 .

[29]  Hyun Kim,et al.  Examining Accessibility and Reliability in the Evolution of Subway Systems , 2015 .

[30]  T. Thevenin,et al.  Measuring one century of railway accessibility and population change in France. A historical GIS approach , 2016 .