IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i7p5900-d1110036.html
   My bibliography  Save this article

Transit Travel Community Detection and Evolutionary Analysis: A Case Study of Shenzhen

Author

Listed:
  • Jingjing Yan

    (Research Institute for Smart Cities, School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China
    Guangdong-Hong Kong-Macau Joint Laboratory for Smart Cities, Shenzhen 518060, China
    Shenzhen Key Laboratory of Urban Digital Twin Technology, Shenzhen 518060, China)

  • Zhengdong Huang

    (Research Institute for Smart Cities, School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China
    Guangdong-Hong Kong-Macau Joint Laboratory for Smart Cities, Shenzhen 518060, China
    Shenzhen Key Laboratory of Urban Digital Twin Technology, Shenzhen 518060, China)

  • Tianhong Zhao

    (Research Institute for Smart Cities, School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China
    Guangdong-Hong Kong-Macau Joint Laboratory for Smart Cities, Shenzhen 518060, China
    Shenzhen Key Laboratory of Urban Digital Twin Technology, Shenzhen 518060, China)

  • Ying Zhang

    (Research Institute for Smart Cities, School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China
    Guangdong-Hong Kong-Macau Joint Laboratory for Smart Cities, Shenzhen 518060, China
    Shenzhen Key Laboratory of Urban Digital Twin Technology, Shenzhen 518060, China)

  • Fei Chang

    (Research Institute for Smart Cities, School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China
    Guangdong-Hong Kong-Macau Joint Laboratory for Smart Cities, Shenzhen 518060, China
    Shenzhen Key Laboratory of Urban Digital Twin Technology, Shenzhen 518060, China)

Abstract

Community detection can reveal specific urban spatial structures related to human activities, and is achieved using mobility data from various sources. In the existing research, less attention has been devoted to communities related to urban transit travel. As public transit is a key component of the urban transport system, it is important to understand how transit communities are organized and how they evolve. This research proposes an approach to urban transit travel community detection using transit travel data and examines how the communities have evolved over time. The results in Shenzhen from 2015 to 2017 showed that the transit travel network had an obvious community structure, and the components (TAZs in this case) of the communities changed over time. During the three years, the western part of Shenzhen experienced more component changes on weekdays, and the central part of the city underwent more component changes on weekdays. In addition, the transit travel communities had a significant coupling relationship with urban administrative divisions. Exploring transit travel communities provides insight for improving public transit systems and enriches the research genealogy of urban spatial structure.

Suggested Citation

  • Jingjing Yan & Zhengdong Huang & Tianhong Zhao & Ying Zhang & Fei Chang, 2023. "Transit Travel Community Detection and Evolutionary Analysis: A Case Study of Shenzhen," Sustainability, MDPI, vol. 15(7), pages 1-17, March.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:7:p:5900-:d:1110036
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/7/5900/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/7/5900/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Danhao Zhu & Dongbo Wang & Saeed-Ul Hassan & Peter Haddawy, 2013. "Small-world phenomenon of keywords network based on complex network," Scientometrics, Springer;Akadémiai Kiadó, vol. 97(2), pages 435-442, November.
    2. Duncan J. Watts & Steven H. Strogatz, 1998. "Collective dynamics of ‘small-world’ networks," Nature, Nature, vol. 393(6684), pages 440-442, June.
    3. Mu Lin & Zhengdong Huang & Tianhong Zhao & Ying Zhang & Heyi Wei, 2022. "Spatiotemporal Evolution of Travel Pattern Using Smart Card Data," Sustainability, MDPI, vol. 14(15), pages 1-16, August.
    4. Rui Ding & Jun Fu & Yiming Du & Linyu Du & Tao Zhou & Yilin Zhang & Siwei Shen & Yuqi Zhu & Shihui Chen, 2022. "Structural Evolution and Community Detection of China Rail Transit Route Network," Sustainability, MDPI, vol. 14(19), pages 1-19, September.
    5. Carlo Ratti & Stanislav Sobolevsky & Francesco Calabrese & Clio Andris & Jonathan Reades & Mauro Martino & Rob Claxton & Steven H Strogatz, 2010. "Redrawing the Map of Great Britain from a Network of Human Interactions," PLOS ONE, Public Library of Science, vol. 5(12), pages 1-6, December.
    6. Liu, Xi & Gong, Li & Gong, Yongxi & Liu, Yu, 2015. "Revealing travel patterns and city structure with taxi trip data," Journal of Transport Geography, Elsevier, vol. 43(C), pages 78-90.
    Full references (including those not matched with items on IDEAS)

    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. Fan, Jijian & Friedman, Daniel & Gair, Jonathan & Iyer, Sriya & Redlicki, Bartosz & Velu, Chander, 2021. "A simulation study of how religious fundamentalism takes root," Journal of Economic Behavior & Organization, Elsevier, vol. 192(C), pages 465-481.
    2. He, Zhengbing, 2020. "Spatial-temporal fractal of urban agglomeration travel demand," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
    3. Shuaiming Chen & Ximing Ji & Haipeng Shao, 2024. "Revealing the Community Structure of Urban Bus Networks: a Multi-view Graph Learning Approach," Networks and Spatial Economics, Springer, vol. 24(3), pages 589-619, September.
    4. Huan Wang & Chuang Ma & Han-Shuang Chen & Ying-Cheng Lai & Hai-Feng Zhang, 2022. "Full reconstruction of simplicial complexes from binary contagion and Ising data," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    5. Vinayak, & Raghuvanshi, Adarsh & kshitij, Avinash, 2023. "Signatures of capacity development through research collaborations in artificial intelligence and machine learning," Journal of Informetrics, Elsevier, vol. 17(1).
    6. Gao, Qiang & Liang, Zhentao & Wang, Ping & Hou, Jingrui & Chen, Xiuxiu & Liu, Manman, 2021. "Potential index: Revealing the future impact of research topics based on current knowledge networks," Journal of Informetrics, Elsevier, vol. 15(3).
    7. Supriya Tiwari & Pallavi Basu, 2024. "Quasi-randomization tests for network interference," Papers 2403.16673, arXiv.org, revised Oct 2024.
    8. Anzhi Sheng & Qi Su & Aming Li & Long Wang & Joshua B. Plotkin, 2023. "Constructing temporal networks with bursty activity patterns," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    9. Emanuele Strano & Matheus Viana & Luciano da Fontoura Costa & Alessio Cardillo & Sergio Porta & Vito Latora, 2013. "Urban Street Networks, a Comparative Analysis of Ten European Cities," Environment and Planning B, , vol. 40(6), pages 1071-1086, December.
    10. Benjamin Davies & David C. Maré, 2020. "Delineating functional labour market areas with estimable classification stabilities," Working Papers 20_08, Motu Economic and Public Policy Research.
    11. Samrachana Adhikari & Beau Dabbs, 2018. "Social Network Analysis in R: A Software Review," Journal of Educational and Behavioral Statistics, , vol. 43(2), pages 225-253, April.
    12. Wang, Xiaojie & Slamu, Wushour & Guo, Wenqiang & Wang, Sixiu & Ren, Yan, 2022. "A novel semi local measure of identifying influential nodes in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 158(C).
    13. He, Yifan & Zhao, Chen & Zeng, An, 2022. "Ranking locations in a city via the collective home-work relations in human mobility data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P1).
    14. Lin, Dan & Wu, Jiajing & Xuan, Qi & Tse, Chi K., 2022. "Ethereum transaction tracking: Inferring evolution of transaction networks via link prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).
    15. Ferreira, D.S.R. & Ribeiro, J. & Oliveira, P.S.L. & Pimenta, A.R. & Freitas, R.P. & Dutra, R.S. & Papa, A.R.R. & Mendes, J.F.F., 2022. "Spatiotemporal analysis of earthquake occurrence in synthetic and worldwide data," Chaos, Solitons & Fractals, Elsevier, vol. 165(P2).
    16. Mario V. Tomasello & Mauro Napoletano & Antonios Garas & Frank Schweitzer, 2017. "The rise and fall of R&D networks," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 26(4), pages 617-646.
    17. Qinghu Liao & Wenwen Dong & Boxin Zhao, 2023. "A New Strategy to Solve “the Tragedy of the Commons” in Sustainable Grassland Ecological Compensation: Experience from Inner Mongolia, China," Sustainability, MDPI, vol. 15(12), pages 1-24, June.
    18. Xinyu Huang & Dongming Chen & Dongqi Wang & Tao Ren, 2020. "MINE: Identifying Top- k Vital Nodes in Complex Networks via Maximum Influential Neighbors Expansion," Mathematics, MDPI, vol. 8(9), pages 1-25, August.
    19. Xueguo Xu & Chen Xu & Wenxin Zhang, 2022. "Research on the Destruction Resistance of Giant Urban Rail Transit Network from the Perspective of Vulnerability," Sustainability, MDPI, vol. 14(12), pages 1-26, June.
    20. Jianhong Chen & Hongcai Ma & Shan Yang, 2023. "SEIOR Rumor Propagation Model Considering Hesitating Mechanism and Different Rumor-Refuting Ways in Complex Networks," Mathematics, MDPI, vol. 11(2), pages 1-22, January.

    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:gam:jsusta:v:15:y:2023:i:7:p:5900-:d:1110036. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.