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

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  • 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
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    References listed on IDEAS

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    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. 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.
    3. 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.
    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. Duncan J. Watts & Steven H. Strogatz, 1998. "Collective dynamics of ‘small-world’ networks," Nature, Nature, vol. 393(6684), pages 440-442, June.
    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.
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