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Vulnerability analysis of urban rail transit based on complex network theory: a case study of Shanghai Metro

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Abstract

With increasing passenger flows and construction scale, metro systems in metropolises have entered a new era of networking operation and become the most effective way to alleviate and decrease traffic congestion. However, frequent occurrence of random failures and malicious attacks pose a serious threat to metro security and reliability. Thus, it is necessary to quantitatively evaluate the vulnerability of the metro network to different failures or attacks from a networking perspective. Based on the complex network theory, this study took the Shanghai Metro Network (SMN) as an example to investigate vulnerability of a weighted metro network in responding to random failures as well as malicious attacks. In particular, compared to topological networks, the vulnerability of weighted networks was analyzed to investigate how traffic and spatial constraints influence the transport system’s vulnerability, since topological features of complex networks are often associated with the weights of the edges and spatial constraints. Simulation results show that the SMN is robust against random failures but fragile for malicious attacks. The vulnerability analysis of weighted properties shows that all targeted attacks are capable to shatter the network’s communication or transport properties at a very low level of removed nodes and the highest betweenness attack strategy is the most effective mode to cause destructive effects on SMN among five attack or failure strategies. The inclusion of passenger flows provides evidence for the view that topological networks cannot convey all the information of a real-world network and traffic flow in the network should be considered as one of the key features in the finding and development of defensive strategies. Our results provide a richer view on complex weighted networks in real-world and possibilities of risk analysis and policy decisions for the metro operation department.

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(Source: The website of Shanghai Shentong Metro Company)

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References

  • Albert R, Barabási A-L (2002) Statistical mechanics of complex networks. Rev Mod Phys 74:47–97

    Article  Google Scholar 

  • Angeloudis P, Fisk D (2006) Large subway systems as complex networks. Physica A 367:553–558

    Article  Google Scholar 

  • Berche B, von Ferber C, Holovatch T, Holovatch Y (2009) Resilience of public transport networks against attacks. Eur Phys J B Condens Matter Complex Syst 71(1):125–137

    Article  Google Scholar 

  • Berche B, von Ferber C, Holovatch T, Holovatch Y (2010) Public transport networks under random failure and directed attack. Dyn Socio-Econ Syst 2(2):42–54

    Google Scholar 

  • Berdica K (2002) An introduction to road vulnerability: what has been done, is done and should be done. Transp Policy 9:117–127

    Article  Google Scholar 

  • Berdica K, Mattsson LG (2007) Vulnerability: A Model-Based Case Study of the Road Network in Stockholm. In: Murray AT, Grubesic TH (eds) Critical Infrastructure. Advances in Spatial Science. Springer, Berlin, Heidelberg, pp 81–106

  • Boccaletti S, Latora V, Moreno Y, Chavez M, Hwang DU (2006) Complex networks: structure and dynamics. Phys Rep 424(4–5):175–308

    Article  Google Scholar 

  • Bruyelle JL, O’Neill C, El-Koursi EM, Hamelin F, Sartori N, Khoudour L (2014) Improving the resilience of metro vehicle and passengers for an effective emergency response to terrorist attacks. Saf Sci 62:37–45

    Article  Google Scholar 

  • Cats O, Jenelius E (2012) Vulnerability analysis of public transport networks: a dynamic approach and case study for Stockholm. In: The international symposium on transportation network reliability

  • Cats O, Yap M, Oort NV (2015) Exposing the role of exposure: identifying and evaluating critical links in public transport networks. In: The international symposium on transportation network reliability

  • Chopra SS, Dillon T, Bilec MM, Khanna V (2016) A network-based framework for assessing infrastructure resilience: a case study of the London metro system. J R Soc Interface 13(118):20160113

    Article  Google Scholar 

  • Crucitti P, Latora V, Marchiori M, Rapisarda A (2003) Efficiency of scale-free networks: error and attack tolerance. Physica A 320:622–642

    Article  Google Scholar 

  • Dall’Asta L, Barrat A, Vespignani L (2006) Vulnerability of weighted networks. J Stat Mech: Theory Exp 25(04):04006

    Article  Google Scholar 

  • De-Los-Santos A, Laporte G, Mesa JA, Perea F (2012) Evaluating passenger robustness in a rail transit network. Transport Res Part C Emerg Technol 20(1):34–46

    Article  Google Scholar 

  • Derrible S, Kennedy C (2010) The complexity and robustness of metro networks. Physica A 389(17):3678–3691

    Article  Google Scholar 

  • Ghedini CG, Ribeiro CH (2011) Rethinking failure and attack tolerance assessment in complex networks. Physica A 390:4684–4691

    Article  Google Scholar 

  • Guimerá R, Amaral LAN (2004) Modeling the world-wide airport network. Eur Phys J B Condens Matter Complex Syst 38(2):381–385

    Article  Google Scholar 

  • Han Y, Cheng H, Zhao X, Xue X (2012) Theoretic structure of urban mass transit operation safety based on vulnerability. Urban Mass Transit 15:15–19

    Google Scholar 

  • Kyriakidis M, Hirsch R, Majumdar A (2012) Metro railway safety: an analysis of accident precursors. Saf Sci 50:1535–1548

    Article  Google Scholar 

  • Kyriakidis M, Hirsch R, Majumdar A (2014) A global safety analysis and best practice for metro railways. Soc Sci Electron Publ 166(6):362–374

    Google Scholar 

  • Laporte G, Mesa JA, Perea F (2010) A game theoretic framework for the robust railway transit network design problem. Transport Res Part B Methodol 44(4):447–459

    Article  Google Scholar 

  • Luathep P, Sumalee A, Ho HW, Kurauchi F (2011) Large-scale road network vulnerability analysis: a sensitivity analysis based approach. Transportation 38(5):799–817

    Article  Google Scholar 

  • Mattsson LG, Jenelius E (2015) Vulnerability and resilience of transport systems—a discussion of recent research. Transport Res Part A Policy Pract 81:16–34

    Article  Google Scholar 

  • Motter AE, Lai Y-C (2003) Cascade-based attacks on complex networks. Phys Rev E: Stat Nonlinear Soft Matter Phys 66(6):114–129

    Google Scholar 

  • Nawrath C (2006) Unraveling the complex network of cuticular structure and function. Curr Opin Plant Biol 9(3):281–287

    Article  Google Scholar 

  • Newman ME, Strogatz SH, Watts DJ (2001) Random graphs with arbitrary degree distributions and their applications. Phys Rev E 64:026118

    Article  Google Scholar 

  • Ouyang M, Zhao L, Hong L, Pan Z (2014) Comparisons of complex network based models and real train flow model to analyze Chinese railway vulnerability. Reliab Eng Syst Saf 123(3):38–46

    Article  Google Scholar 

  • Perea F, Puerto J (2013) Revisiting a game theoretic framework for the robust railway network design against intentional attacks. Eur J Oper Res 226(2):286–292

    Article  Google Scholar 

  • Petter H, Beom Jun K, No YC, Seung Kee H (2002) Attack vulnerability of complex networks. Phys Rev E 65(5):056109

    Article  Google Scholar 

  • Riedel HU (2014) Chinese metro boom shows no sign of abating. Int Railw J 54(11):46–48

    Google Scholar 

  • Rodríguez-Núñez E, García-Palomares JC (2014) Measuring the vulnerability of public transport networks. J Transp Geogr 35:50–63

    Article  Google Scholar 

  • Sienkiewicz J, Hołyst JA (2005) Statistical analysis of 22 public transport networks in Poland. Phys Rev E Stat Nonlin Soft Matter Phys 72(4 Pt 2):046127

    Article  Google Scholar 

  • Taylor MAP, D’Este GM (2007) Transport network vulnerability: a method for diagnosis of critical locations in transport infrastructure systems. Critical infrastructure. Springer, Berlin, pp 9–30

    Google Scholar 

  • von Ferber C, Holovatch T, Holovatch Y, Palchykov V (2007) Network harness: metropolis public transport. Physica A 380(7):585–591

    Article  Google Scholar 

  • Wang J (2013) Robustness of complex networks with the local protection strategy against cascading failures. Saf Sci 53:219–225

    Article  Google Scholar 

  • Wang J, Fang W (2014) A structured method for the traffic dispatcher error behavior analysis in metro accident investigation. Saf Sci 70:339–347

    Article  Google Scholar 

  • Wang J, Mo H, Wang F, Jin F (2011) Exploring the network structure and nodal centrality of China’s air transport network: a complex network approach. J Transp Geogr 19(4):712–721

    Article  Google Scholar 

  • Wang H, Huang J, Xu X, Xiao Y (2014) Damage attack on complex networks. Physica A 408:134–148

    Article  Google Scholar 

  • Xu Z, Sui DZ (2007) Small-world characteristics on transportation networks: a perspective from network autocorrelation. J Geogr Syst 9(2):189–205

    Article  Google Scholar 

  • Xu X, Hu J, Liu F (2007a) Scaling and correlations in three bus-transport networks of China. Physica A 374(1):441–448

    Article  Google Scholar 

  • Xu X, Hu J, Liu F (2007b) Empirical analysis of the ship-transport network of China. Chaos 17(2):471–516

    Google Scholar 

  • Yang Y, Liu Y, Zhou M, Li F, Sun C (2015) Robustness assessment of urban rail transit based on complex network theory: a case study of the Beijing subway. Saf Sci 79:149–162

    Article  Google Scholar 

  • Yuan J, Li Q, Jia R, Wang Z (2012) Analysis of operation vulnerabilities of urban metro network system. China Saf Sci J 22:92–98

    Google Scholar 

  • Zhang J, Xu X, Hong L, Wang S, Fei Q (2011) Networked analysis of the Shanghai subway network, in China. Physica A 390(23):4562–4570

    Article  Google Scholar 

  • Zhang J, Xu X, Hong L, Wang S, Fei Q (2012) Attack vulnerability of self-organizing networks. Saf Sci 50:443–447

    Article  Google Scholar 

  • Zhou Z, Irizarry J, Li Q (2014) Using network theory to explore the complexity of subway construction accident network (SCAN) for promoting safety management. Saf Sci 64:127–136

    Article  Google Scholar 

Download references

Acknowledgements

The authors thank the Shanghai Shentong Metro Company, for providing valuable information about the SMN for academic and research activities. In addition, the authors acknowledge the financial support of Project 71671127 by the National Natural Science Foundation of China.

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Authors and Affiliations

  1. School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, People’s Republic of China

    Yingying Xing

  2. College of Transportation Engineering, Tongji University, 4800 Cao’an Road, Shanghai, People’s Republic of China

    Jian Lu

  3. College of Transport and Communication, Shanghai Maritime University, 1550 Haigang Ave, Shanghai, People’s Republic of China

    Shengdi Chen

  4. Department of Civil Engineering, Kansas State University, 2118 Fiedler Hall, Manhattan, Kansas, 66506, USA

    Sunanda Dissanayake

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  1. Yingying Xing
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  2. Jian Lu
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  3. Shengdi Chen
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  4. Sunanda Dissanayake
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Correspondence to Jian Lu.

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Xing, Y., Lu, J., Chen, S. et al. Vulnerability analysis of urban rail transit based on complex network theory: a case study of Shanghai Metro. Public Transp 9, 501–525 (2017). https://doi.org/10.1007/s12469-017-0170-2

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  • DOI: https://doi.org/10.1007/s12469-017-0170-2

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