Identifying critical nodes in metro network considering topological potential: A case study in Shenzhen city—China

Abstract How to identify critical nodes in metro networks is still an open and vital topic in complex networks, which has been a key issue in analyzing the structural organization of a network, especially in public transportation. Many effective measures have been developed to solve this problem in undirected or unweighted network. However, statistical indicators of static networks cannot reflect the spatial–temporal characteristics of passenger flow in the metro network. Furthermore, a single measure in critical nodes identification has its own shortcomings causing inaccurate estimation results. In this paper, a novel method for node significance on metro network based on Improved Topological Potential model considering Entropy (ITPE) is proposed. ITPE is utilized to aggregate the multi-measure by considering several different centrality measures to conduct the evaluation of node importance. In order to fully reflect the influence of nodes, topological entropy is adopted and applied to identify the weights of different centrality measures. In addition, invulnerability measurement is used to demonstrate the effectiveness of the proposed node identification method. Finally, the metro transit system in Shenzhen City, China was used as a case study to demonstrate the feasibility of the proposed method. It is found that ITPE method could effectively identify nodes or stations which are crucial both on network structure and passenger flow mobility while traditional undirected and unweighted network cannot completely identify. Accordingly, all the nodes estimated from ITPE method are ranked by the significance, and invulnerability is further used to test the rationality of the identification for critical nodes.

[1]  Yong Qi,et al.  Spatio-temporal modeling of destination choice behavior through the Bayesian hierarchical approach , 2018, Physica A: Statistical Mechanics and its Applications.

[2]  M. Jong,et al.  Introducing public–private partnerships for metropolitan subways in China: what is the evidence? , 2010 .

[3]  Jianhua Zhang,et al.  Comparison analysis on vulnerability of metro networks based on complex network , 2018 .

[4]  Massimo Marchiori,et al.  Is the Boston subway a small-world network? , 2002 .

[5]  Xiao Sun,et al.  Understanding parking decisions with structural equation modeling , 2019, Physica A: Statistical Mechanics and its Applications.

[6]  R. Knowles.  Transport impacts of greater Manchester's metrolink light rail system , 1996 .

[7]  Beom Jun Kim,et al.  Attack vulnerability of complex networks. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[8]  Sebastián Raveau,et al.  A topological route choice model for metro , 2011 .

[9]  Daniel Sun,et al.  Vulnerability Analysis of Urban Rail Transit Networks: A Case Study of Shanghai, China , 2015 .

[10]  R. Cervero,et al.  Assessing the impacts of urban rail transit on local real estate markets using quasi-experimental comparisons , 1993 .

[11]  Özgür Yalçinkaya,et al.  Modelling and optimization of average travel time for a metro line by simulation and response surface methodology , 2009, Eur. J. Oper. Res..

[12]  Shanshan Zhao,et al.  Two dimensional accessibility analysis of metro stations in Xi’an, China , 2017 .

[13]  Shenghua Liu,et al.  ANALYSIS OF THE IMPACT OF THE MRT SYSTEM ON ACCESSIBILITY IN SINGAPORE USING AN INTEGRATED GIS TOOL , 2004 .

[14]  Jian Lu,et al.  Vulnerability analysis of urban rail transit based on complex network theory: a case study of Shanghai Metro , 2017, Public Transp..

[15]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

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

[17]  Liya Yao,et al.  Vulnerability assessment of urban rail transit based on multi-static weighted method in Beijing, China , 2018 .

[18]  John Golias,et al.  Analysis of traffic corridor impacts from the introduction of the new Athens Metro system , 2002 .

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

[20]  Tschangho John Kim.  EFFECTS OF SUBWAYS ON URBAN FORM AND STRUCTURE , 1978 .

[21]  Devon McAslan,et al.  Walking and Transit Use Behavior in Walkable Urban Neighborhoods , 2017 .

[22]  Josep Barberillo,et al.  Navigation in large subway networks: An informational approach , 2011 .

[23]  Vinod Mishra,et al.  The Environment and Well-Being in Urban China , 2008, Ecological Economics.

[24]  Xiaofei Wang,et al.  Understanding characteristics in multivariate traffic flow time series from complex network structure , 2017 .

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

[26]  S. Phang.  Urban rail transit PPPs: Survey and risk assessment of recent strategies , 2007 .

[27]  Fang Liu,et al.  Inferring driving trajectories based on probabilistic model from large scale taxi GPS data , 2018, Physica A: Statistical Mechanics and its Applications.

[28]  L. Freeman.  Centrality in social networks conceptual clarification , 1978 .

[29]  Jinjun Tang,et al.  Traffic flow prediction based on combination of support vector machine and data denoising schemes , 2019, Physica A: Statistical Mechanics and its Applications.

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

[31]  Yicheng Zhang,et al.  Identifying influential nodes in complex networks , 2012 .

[32]  Y. Zou,et al.  Taxi trips distribution modeling based on Entropy-Maximizing theory: A case study in Harbin city—China , 2018 .

[33]  G. Laporte,et al.  Planning rapid transit networks , 2010 .

[34]  Graham Crampton.  Urban Economic Structure and the Optimal Rail System , 2000 .

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

[36]  John Skvoretz,et al.  Node centrality in weighted networks: Generalizing degree and shortest paths , 2010, Soc. Networks.

[37]  Jinjun Tang,et al.  Trip destination prediction based on multi-day GPS data , 2019, Physica A: Statistical Mechanics and its Applications.

[38]  Marc Schlossberg,et al.  Comparing Transit-Oriented Development Sites by Walkability Indicators , 2004 .

[39]  Gan Wen.  Community Discovery Method in Networks Based on Topological Potential , 2009 .

[40]  U. Brandes.  A faster algorithm for betweenness centrality , 2001 .

[41]  S. Mahadevan,et al.  A new method of identifying influential nodes in complex networks based on TOPSIS , 2014 .

[42]  Zhijie Yang,et al.  Evolution assessment of Shanghai Urban Rail Transit Network , 2018, Physica A: Statistical Mechanics and its Applications.

[43]  Corinne Mulley,et al.  The short-term land value impacts of urban rail transit: Quantitative evidence from Sunderland, UK , 2007 .

[44]  Cai Jun.  Evaluating the Accessibility about Beijing′s Subways in 2008 Based on Spatial Syntax , 2007 .

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

[46]  Juan P. Fernández Palacios,et al.  A reliability analysis of Double-Ring topologies with Dual Attachment using p-cycles for optical metro networks , 2010, Comput. Networks.

[47]  Jen-Jia Lin,et al.  A TOD planning model to review the regulation of allowable development densities around subway stations , 2006 .