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Urban Rail Transit Station Type Identification Based on “Passenger Flow—Land Use—Job-Housing”

Author

Listed:
  • Hongxia Feng

    (School of Architecture, Xi’an University of Architecture and Technology, Xi’an 710055, China
    These authors contributed equally to this work.)

  • Yaotong Chen

    (School of Architecture, Xi’an University of Architecture and Technology, Xi’an 710055, China
    These authors contributed equally to this work.)

  • Jinyi Wu

    (School of Architecture, Xi’an University of Architecture and Technology, Xi’an 710055, China)

  • Zhenqian Zhao

    (Beijing Qinghua Tongheng Planning and Design Institute, Beijing 100080, China)

  • Yuanqing Wang

    (Key Laboratory of Transport Industry of Management, Control and Cycle Repair Technology for Traffic Network Facilities in Ecological Security Barrier Area, Chang’an University, Xi’an 710064, China)

  • Zhuoting Wang

    (School of Architecture, Xi’an University of Architecture and Technology, Xi’an 710055, China)

Abstract

Urban rail transit stations serve as pivotal hubs that facilitate the advancement of diverse economic activities. Based on different types of metro stations, the sustainable and coordinated development of public transport and land use can be achieved through rational land use planning and the rational allocation of urban infrastructure and public service facilities. Drawing upon mobile phone signaling data and land use data, this article presents a complex classification methodology for metro stations, employing the lens of “passenger flow behavior—land use structure—job-housing density” in the context of Xi’an. The stations are categorized into six distinct types, including employment-led stations with a job–housing density balance, as well as stations characterized by job–housing mismatch with a high residential density. The results indicate a low level of coupling between the passenger flow patterns of the stations and the spatial characteristics of the station areas. In addition, the spatial distributions of the stations demonstrate a significant aggregation effect in each station type, while the degree of integration between the different station types remains limited. These findings collectively suggest that the urban rail transit stations in Xi’an have not achieved complementary development, thereby reflecting a notable trend of cross-regional commuter flow in the city.

Suggested Citation

  • Hongxia Feng & Yaotong Chen & Jinyi Wu & Zhenqian Zhao & Yuanqing Wang & Zhuoting Wang, 2023. "Urban Rail Transit Station Type Identification Based on “Passenger Flow—Land Use—Job-Housing”," Sustainability, MDPI, vol. 15(20), pages 1-24, October.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:20:p:15103-:d:1264111
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    References listed on IDEAS

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    1. Camille Roth & Soong Moon Kang & Michael Batty & Marc Barthélemy, 2011. "Structure of Urban Movements: Polycentric Activity and Entangled Hierarchical Flows," PLOS ONE, Public Library of Science, vol. 6(1), pages 1-8, January.
    2. Yu, Zidong & Zhu, Xiaolin & Liu, Xintao, 2022. "Characterizing metro stations via urban function: Thematic evidence from transit-oriented development (TOD) in Hong Kong," Journal of Transport Geography, Elsevier, vol. 99(C).
    3. Li, Shaoying & Lyu, Dijiang & Huang, Guanping & Zhang, Xiaohu & Gao, Feng & Chen, Yuting & Liu, Xiaoping, 2020. "Spatially varying impacts of built environment factors on rail transit ridership at station level: A case study in Guangzhou, China," Journal of Transport Geography, Elsevier, vol. 82(C).
    4. Zhou, Yuyang & Zheng, Shuyan & Hu, Zhonghui & Chen, Yanyan, 2022. "Metro station risk classification based on smart card data: A case study in Beijing," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 594(C).
    5. Chen Zhong & Michael Batty & Ed Manley & Jiaqiu Wang & Zijia Wang & Feng Chen & Gerhard Schmitt, 2016. "Variability in Regularity: Mining Temporal Mobility Patterns in London, Singapore and Beijing Using Smart-Card Data," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-17, February.
    6. Lei Pang & Yuxiao Jiang & Jingjing Wang & Ning Qiu & Xiang Xu & Lijian Ren & Xinyu Han, 2023. "Research of Metro Stations with Varying Patterns of Ridership and Their Relationship with Built Environment, on the Example of Tianjin, China," Sustainability, MDPI, vol. 15(12), pages 1-18, June.
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