Connectivity Contribution to Urban Hub Network Based on Super Network Theory – Case Study of Beijing

  • Guang Yuan Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing, China
  • Dewen Kong Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing, China
  • Lishan Sun Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing, China
  • Wei Luo Beijing University of Civil Engineering and Architecture, Beijing, China
  • Yan Xu Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing, China
Keywords: super network, super-edge, transportation hub, connectivity

Abstract

With the rapid development of urbanization in China, the number of travel modes and urban passenger transportation hubs has been increasing, gradually forming multi-level and multi-attribute transport hub networks in the cities. At the same time, Super Network Theory (SNT) has advantages in displaying the multi-layer transport hubs. The aim of this paper is to provide a new perspective to study connectivity contribution of potential hubs. Urban transport hubs are ranked through topological features based on Hub Super Network (HSN). This paper proposes two indexes based on Super-Edge (SE), Zero Hub Degree of SE (ZHDoSE) and a number of shared SEes (NSSE), respectively. Then, a case study was conducted in Beijing, which considers four combinations to study the influence of transport modes and subway lines on connectivity. The results show that no-normalization strengthens the contribution of transport modes and subway lines on connectivity. Besides, the transport mode contributes a lot to the connectivity. However, elements normalization strengthens the subway lines under ZHDoSE reciprocal. In addition, various weights of ZHDoSE and NSSE have different influences on the recognition results of SEes in HSN.

 

References

Shen RG, Pei YL. A Parking Demand Forecasting Method for Urban Comprehensive Passenger Transport Hub Oriented High-Speed Rail. Adv Mater Res. 2012;524-527: 828-31. DOI: 10.4028/www.scientific.net/AMR.524-527.828

Paredes R, Dueñas-Osorio L, Meel KS, Vardi MY. Principled Network Reliability Approximation: A Counting-Based Approach. Reliab Eng Syst Saf. 2019;191: 106472. DOI: 10.1016/j.ress.2019.04.025

Li Z, Jin C, Hu P, Wang C. Resilience-based transportation network recovery strategy during emergency recovery phase under uncertainty. Reliab Eng Syst Saf. 2019;188: 503-514. DOI: 10.1016/j.ress.2019.03.052

Leobons CM, Gouvêa Campos VB, De Mello Bandeira RA. Assessing Urban Transportation Systems Resilience: A Proposal of Indicators. Transp Res Procedia. 2019;37: 322-329. DOI: 10.1016/j.trpro.2018.12.199

Sun L, Huang Y, Chen Y, Yao L. Vulnerability assessment of urban rail transit based on multi-static weighted method in Beijing, China. Transp Res Part A Policy Pract. 2018;108: 12-24. DOI: 10.1016/j.tra.2017.12.008

Takebayashi M. Managing airport charges under the multiple hub network with high speed rail: Considering capacity and gateway function. Transp Res Part A Policy Pract. 2018;112: 108-123. DOI: 10.1016/j.tra.2018.01.011

Xiao X-M, Jia L-M, Wang Y-H. Correlation between heterogeneity and vulnerability of subway networks based on passenger flow. J Rail Transp Plan Manag. 2018;8: 145-157. DOI: 10.1016/j.jrtpm.2018.03.004

Sun D, Guan S. Measuring vulnerability of urban metro network from line operation perspective. Transp Res Part A Policy Pract. 2016;94: 348-359.

Strano E, Shai S, Dobson S, Barthelemy M. Multiplex networks in metropolitan areas: Generic features and local effects. J R Soc Interface. 2015;12(111): 20150651. DOI: 10.1098/rsif.2015.0651

Chen D, Yang Z. Systematic optimization of port clusters along the Maritime Silk Road in the context of industry transfer and production capacity constraints. Transp Res Part E Logist Transp Rev. 2018;109: 174-189. DOI: 10.1016/j.tre.2017.11.007

Acer UG, Giaccone P, Hay D, Neglia G, Tarapiah S. Timely data delivery in a realistic bus network. IEEE Trans Veh Technol. 2012;61(3): 1251-1265. DOI: 10.1109/TVT.2011.2179072

Rodríguez-Núñez E, García-Palomares JC. Measuring the vulnerability of public transport networks. J Transp Geogr. 2014;35: 50-63. DOI: 10.1016/j.jtrangeo.2014.01.008

Mattsson LG, Jenelius E. Vulnerability and resilience of transport systems - A discussion of recent research. Transp Res Part A Policy Pract. 2015;81: 16-34. DOI: 10.1016/j.tra.2015.06.002

Reggiani A, Nijkamp P, Lanzi D. Transport resilience and vulnerability: The role of connectivity. Transp Res Part A Policy Pract. 2015;81: 4-15. DOI: 10.1016/j.tra.2014.12.012

Ortega A, Stegmann T, Benet L. Robustness of optimal transport in disordered interacting many-body networks. Phys Rev E. 2018;arXiv:1803.05974. DOI: 10.1103/PhysRevE.98.012141

Corman F, Quaglietta E, Goverde RMP. Automated real-time railway traffic control: An experimental analysis of reliability, resilience and robustness. Transp Plan Technol. 2018;41(4): 421-447. DOI: 10.1080/03081060.2018.1453916

Boonzajer Flaes DE, Stopka J, Turtaev S, De Boer JF, Tyc T, Čižmár T. Robustness of Light-Transport Processes to Bending Deformations in Graded-Index Multimode Waveguides. Phys Rev Lett. 2018;120(23). DOI: 10.1103/PhysRevLett.120.233901

M’Cleod L, Vecsler R, Shi Y, Levitskaya E, Kulkarni S, Malinchik S, Sobolevsky S. Vulnerability of Transportation Networks: The New York City Subway System under Simultaneous Disruptive Events. Procedia Comput Sci. 2017;119: 42-50. DOI: 10.1016/j.procs.2017.11.158

Sheffi Y. Urban transportation networks: Equilibrium analysis with mathematical programming methods. Prentice-Hall; 1984.

Nagurney A, Wakolbinger T. Supernetworks: An Introduction to the Concept and its Applications with a Specific Focus on Knowledge Supernetworks. Int J Knowledge, Cult Chang Manag Annu Rev. 2005;4(1). DOI: 10.18848/1447-9524/cgp/v04/50227

Ma N, Liu Y. SuperedgeRank algorithm and its application in identifying opinion leader of online public opinion supernetwork. Expert Syst Appl. 2014;41: 1357-1368. DOI: 10.1016/j.eswa.2013.08.033

Feng HU. An evolving hypernetwork model and its properties. Sci Sin. 2013;43(1): 16-22. DOI: 10.1360/132012-87

Wang G, Liu Y, Li J, Tang X, Wang H. Superedge coupling algorithm and its application in coupling mechanism analysis of online public opinion supernetwork. Expert Syst Appl. 2015;42: 2808-2823. DOI: 10.1016/j.eswa.2014.11.026

Zhao L, Zhang H, Wu W. Knowledge service decision making in business incubators based on the supernetwork model. Phys A Stat Mech Its Appl. 2017;479: 249-264. DOI: 10.1016/j.physa.2017.03.013

Yamada T, Febri Z. Freight transport network design using particle swarm optimisation in supply chain-transport supernetwork equilibrium. Transp Res Part E Logist Transp Rev. 2015;75: 164-187. DOI: 10.1016/j.tre.2015.01.001

Feng Z, Wang Z, Chen Y. The equilibrium of closed-loop supply chain supernetwork with time-dependent parameters. Transp Res Part E Logist Transp Rev. 2014;64: 1-11. DOI: 10.1016/j.tre.2014.01.009.

Zhang X. Multilayer Networks Science: Concepts,Theories and Data. Complex Syst Complex Sci. 2015;12: 103-107.

Fang J. From a single network to "network of networks" development process: Some discussion on the exploration of the maltilayer supernetwork model and challenges. Complex Systems and Complexity Science. 2016;13(1): 40-47.

Liu Q, Fang J, Li Y. Research on hierarchical hyper network model based on unified hybrid network theory framework. Complex Systems and Complexity Science. 2016;13(1): 84-90.

Ma J, Wang S, Jian R, Sun L. Using Point of Interest Data from Electronic Map to Predict Transit Station Rideship. Transportation Research Board 93rd Annual Meeting, 12-16 Jan. 2014, Washington DC, USA; 2014.

Beijing Transport Institute. 2019 Beijing Transportation Annual Report. Beijing; 2019.

Liu Y, Li Q, Tang X, Ma N, Tian R. Superedge prediction: What opinions will be mined based on an opinion supernetwork model?. Decis Support Syst. 2014;64: 118-129. DOI: 10.1016/j.dss.2014.05.011

Mamun SA, Lownes NE. Access and connectivity trade-offs in transit stop location. Transp Res Rec. 2014;2466: 1-11. DOI: 10.3141/2466-01

Wei P, Chen L, Sun D. Algebraic connectivity maximization of an air transportation network: The flight routes’ addition/deletion problem. Transp Res Part E Logist Transp Rev. 2014;61: 13-27. DOI: 10.1016/j.tre.2013.10.008

Spiers G, Wei P, Sun D. Algebraic connectivity optimization of the air transportation network. 2012 American Control Conference (ACC), 27-29 June 2012, Montreal, QC, Canada; 2012. p. 1702-1707. DOI: 10.1109/acc.2012.6314649

Gao YL, Chen SM, Nie S, Ma F, Guan JJ, Robustness analysis of interdependent networks under multiple-attacking strategies. Phys A Stat Mech Its Appl. 2018;496: 495-504. DOI: 10.1016/j.physa.2017.12.085

Published
2021-02-01
How to Cite
1.
Yuan G, Kong D, Sun L, Luo W, Xu Y. Connectivity Contribution to Urban Hub Network Based on Super Network Theory – Case Study of Beijing. Promet - Traffic&Transportation. 2021;33(1):35-7. DOI: 10.7307/ptt.v33i1.3536
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Articles