IDEAS home Printed from https://ideas.repec.org/a/hin/complx/4860531.html
   My bibliography  Save this article

Establishment and Analysis of the Supernetwork Model for Nanjing Metro Transportation System

Author

Listed:
  • Yu Wei
  • Sun Ning

Abstract

In recent years, many researchers have applied complex network theory to urban public transport network to construct complex network and analyze its network performance. The original analysis method generally uses the Space L and Space R model to establish a simple link between public sites but ignores the organic link between the overall network system and the line subsystem. As an important part of urban public transport system, subway plays an important role in alleviating traffic pressure. In this paper, a supernetwork model of Nanjing metro network is established by using the supernetwork method. Three parameters, node-hyperedge degree, hyperedge-node degree, and hyperedge degree, are proposed to describe the model. The model is compared with the traditional Space L and Space P models. The study on the supernetwork model of Nanjing metro complex network shows that the network density, network centrality, and network clustering coefficient are large, and the average network distance is small, which meets the requirements of traffic planning and design. In this study, the subway line is considered as a subsystem and further simplified as a node, so that the complex network analysis method can be applied to the new supernetwork model, expanding the thinking of complex network research.

Suggested Citation

  • Yu Wei & Sun Ning, 2018. "Establishment and Analysis of the Supernetwork Model for Nanjing Metro Transportation System," Complexity, Hindawi, vol. 2018, pages 1-11, December.
    • Handle: RePEc:hin:complx:4860531
    DOI: 10.1155/2018/4860531
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/8503/2018/4860531.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/8503/2018/4860531.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2018/4860531?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Zhao, Liming & Zhang, Haihong & Wu, Wenqing, 2017. "Knowledge service decision making in business incubators based on the supernetwork model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 249-264.
    2. Rui Ding & Norsidah Ujang & Hussain bin Hamid & Jianjun Wu, 2015. "Complex Network Theory Applied to the Growth of Kuala Lumpur’s Public Urban Rail Transit Network," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-22, October.
    3. Zhang, Jianhua & Wang, Shuliang & Wang, Xiaoyuan, 2018. "Comparison analysis on vulnerability of metro networks based on complex network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 72-78.
    4. Yamada, Tadashi & Febri, Zukhruf, 2015. "Freight transport network design using particle swarm optimisation in supply chain–transport supernetwork equilibrium," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 75(C), pages 164-187.
    5. Latora, Vito & Marchiori, Massimo, 2002. "Is the Boston subway a small-world network?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 314(1), pages 109-113.
    6. Ouyang, Min & Zhao, Lijing & Hong, Liu & Pan, Zhezhe, 2014. "Comparisons of complex network based models and real train flow model to analyze Chinese railway vulnerability," Reliability Engineering and System Safety, Elsevier, vol. 123(C), pages 38-46.
    7. An, Xin-lei & Zhang, Li & Li, Yin-zhen & Zhang, Jian-gang, 2014. "Synchronization analysis of complex networks with multi-weights and its application in public traffic network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 412(C), pages 149-156.
    8. Wang, Jiang-Pan & Guo, Qiang & Yang, Guang-Yong & Liu, Jian-Guo, 2015. "Improved knowledge diffusion model based on the collaboration hypernetwork," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 428(C), pages 250-256.
    9. Cats, Oded & Koppenol, Gert-Jaap & Warnier, Martijn, 2017. "Robustness assessment of link capacity reduction for complex networks: Application for public transport systems," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 544-553.
    10. Liu, Zhong & Huang, Jincai & Cheng, Guangquan, 2016. "Community detection in hypernetwork via Density-Ordered Tree partitionAuthor-Name: Cheng, Qing," Applied Mathematics and Computation, Elsevier, vol. 276(C), pages 384-393.
    11. Juliane Manitz & Jonas Harbering & Marie Schmidt & Thomas Kneib & Anita Schöbel, 2017. "Source estimation for propagation processes on complex networks with an application to delays in public transportation systems," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(3), pages 521-536, April.
    12. Yang, Guang-Yong & Hu, Zhao-Long & Liu, Jian-Guo, 2015. "Knowledge diffusion in the collaboration hypernetwork," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 429-436.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ming Li & Wei Yu & Jun Zhang, 2023. "Clustering Analysis of Multilayer Complex Network of Nanjing Metro Based on Traffic Line and Passenger Flow Big Data," Sustainability, MDPI, vol. 15(12), pages 1-17, June.
    2. Wei Yu & Tao Wang & Yujie Xiao & Jun Chen & Xingchen Yan, 2020. "A Carbon Emission Measurement Method for Individual Travel Based on Transportation Big Data: The Case of Nanjing Metro," IJERPH, MDPI, vol. 17(16), pages 1-15, August.
    3. Wei Yu & Xiaofei Ye & Jun Chen & Xingchen Yan & Tao Wang, 2020. "Evaluation Indexes and Correlation Analysis of Origination–Destination Travel Time of Nanjing Metro Based on Complex Network Method," Sustainability, MDPI, vol. 12(3), pages 1-21, February.
    4. Chen, Junlan & Pu, Ziyuan & Guo, Xiucheng & Cao, Jieyu & Zhang, Fang, 2023. "Multiperiod metro timetable optimization based on the complex network and dynamic travel demand," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 611(C).
    5. Zhiru Wang & Wubin Ma & Albert Chan, 2020. "Exploring the Relationships between the Topological Characteristics of Subway Networks and Service Disruption Impact," Sustainability, MDPI, vol. 12(10), pages 1-18, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wei Yu & Jun Chen & Xingchen Yan, 2019. "Space‒Time Evolution Analysis of the Nanjing Metro Network Based on a Complex Network," Sustainability, MDPI, vol. 11(2), pages 1-17, January.
    2. Hong, Liu & Ye, Bowen & Yan, Han & Zhang, Hui & Ouyang, Min & (Sean) He, Xiaozheng, 2019. "Spatiotemporal vulnerability analysis of railway systems with heterogeneous train flows," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 725-744.
    3. Evangelos Ioannidis & Nikos Varsakelis & Ioannis Antoniou, 2021. "Intelligent Agents in Co-Evolving Knowledge Networks," Mathematics, MDPI, vol. 9(1), pages 1-17, January.
    4. Zhang, Mengyao & Huang, Tao & Guo, Zhaoxia & He, Zhenggang, 2022. "Complex-network-based traffic network analysis and dynamics: A comprehensive review," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    5. Rui Ding & Norsidah Ujang & Hussain Bin Hamid & Mohd Shahrudin Abd Manan & Rong Li & Safwan Subhi Mousa Albadareen & Ashkan Nochian & Jianjun Wu, 2019. "Application of Complex Networks Theory in Urban Traffic Network Researches," Networks and Spatial Economics, Springer, vol. 19(4), pages 1281-1317, December.
    6. Lin Zhang & Jian Lu & Bai-bai Fu & Shu-bin Li, 2018. "A Review and Prospect for the Complexity and Resilience of Urban Public Transit Network Based on Complex Network Theory," Complexity, Hindawi, vol. 2018, pages 1-36, December.
    7. Liming Zhao & Haihong Zhang & Wenqing Wu, 2019. "Cooperative knowledge creation in an uncertain network environment based on a dynamic knowledge supernetwork," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(2), pages 657-685, May.
    8. Hui Xu & Liudan Jiao & Shulin Chen & Milan Deng & Ningxin Shen, 2018. "An Innovative Approach to Determining High-Risk Nodes in a Complex Urban Rail Transit Station: A Perspective of Promoting Urban Sustainability," Sustainability, MDPI, vol. 10(7), pages 1-17, July.
    9. Zhao, Liming & Zhang, Haihong & Wu, Wenqing, 2017. "Knowledge service decision making in business incubators based on the supernetwork model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 249-264.
    10. Wang, Jiang-Pan & Guo, Qiang & Zhou, Lei & Liu, Jian-Guo, 2019. "Dynamic credit allocation for researchers," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 520(C), pages 208-216.
    11. Chi, Yuxue & Tang, Xianyi & Liu, Yijun, 2022. "Exploring the “awakening effect” in knowledge diffusion: a case study of publications in the library and information science domain," Journal of Informetrics, Elsevier, vol. 16(4).
    12. Yue, Zenghui & Xu, Haiyun & Yuan, Guoting & Pang, Hongshen, 2019. "Modeling study of knowledge diffusion in scientific collaboration networks based on differential dynamics: A case study in graphene field," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 524(C), pages 375-391.
    13. Li, Tao & Rong, Lili, 2021. "Impacts of service feature on vulnerability analysis of high-speed rail network," Transport Policy, Elsevier, vol. 110(C), pages 238-253.
    14. Shen, Ai-Zhong & Guo, Jin-Li & Wu, Guo-Lin & Jia, Shu-Wei, 2018. "The agglomeration phenomenon influence on the scaling law of the scientific collaboration system," Chaos, Solitons & Fractals, Elsevier, vol. 114(C), pages 461-467.
    15. Ioannidis, Evangelos & Varsakelis, Nikos & Antoniou, Ioannis, 2018. "Experts in Knowledge Networks: Central Positioning and Intelligent Selections," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 890-905.
    16. Junhong Hu & Mingshu Yang & Yunzhu Zhen, 2024. "A Review of Resilience Assessment and Recovery Strategies of Urban Rail Transit Networks," Sustainability, MDPI, vol. 16(15), pages 1-16, July.
    17. Xiao Liao & Guangyu Ye & Juan Yu & Yunjiang Xi, 2021. "Identifying lead users in online user innovation communities based on supernetwork," Annals of Operations Research, Springer, vol. 300(2), pages 515-543, May.
    18. Yang, Jinqing & Bu, Yi & Lu, Wei & Huang, Yong & Hu, Jiming & Huang, Shengzhi & Zhang, Li, 2022. "Identifying keyword sleeping beauties: A perspective on the knowledge diffusion process," Journal of Informetrics, Elsevier, vol. 16(1).
    19. Tang, Jinjun & Li, Zhitao & Gao, Fan & Zong, Fang, 2021. "Identifying critical metro stations in multiplex network based on D–S evidence theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 574(C).
    20. Zhang, Haihong & Wu, Wenqing & Zhao, Liming, 2016. "A study of knowledge supernetworks and network robustness in different business incubators," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 447(C), pages 545-560.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hin:complx:4860531. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.