IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i10p7944-d1145580.html
   My bibliography  Save this article

Unraveling the Impact of Spatial Configuration on TOD Function Mix Use and Spatial Intensity: An Analysis of 47 Morning Top-Flow Stations in Beijing (2018–2020)

Author

Listed:
  • Bo Wan

    (School of Architecture and Design, Beijing Jiaotong University, Beijing 100044, China)

  • Xudan Zhao

    (School of Architecture and Design, Beijing Jiaotong University, Beijing 100044, China)

  • Yuhan Sun

    (School of Architecture and Design, Beijing Jiaotong University, Beijing 100044, China)

  • Tao Yang

    (School of Architecture, Tsinghua University, Beijing 100084, China)

Abstract

Urban rail transportation is an important public transportation network for realizing the dual carbon strategy and sustainable transportation development. A top-stream station is an important node in this network. This paper focuses on 47 top-stream station sites during the peak hours of workdays from 2018 to 2020 in Beijing (26 inbound and 22 outbound, with Beijing South Station as a double-stream station) and collects and analyzes information such as station area spatial texture, spatial organization efficiency, land use function, land use mix, POI (Point of Interest) distribution and functional mix, construction intensity, and population heat distribution. Through an analysis of the network topological structure characteristics of each station area, comparison of spatial efficiency differences, analysis of land use function composition and mix characteristics, and distribution of spatial construction intensity, this paper discusses the relationship between the spatial structure, spatial function, spatial intensity characteristics and key indicators of the built environment of station areas from the perspective of urban design. The conclusion shows that there is a close relationship between the function, structure, and strength of the overall built environment of the station domain at the theoretical level. The regression test, to some extent, confirms the close relationship between key indicators and expands the indicator system for measuring the fit relationship. The comparison between general station sites and headstream station sites shows that the fit relationship of indicators for headstream station sites is not completely the same as that for general station sites, indicating that the influencing factors for headstream station sites are diverse. This reminds investment, design, construction, and management teams in practice that the measurement and planning of the built environment space structure of the station domain should be based on local conditions and be closely related to the topological structure of the station domain’s rail network and road network structure. At the same time, whether the stability of the indicator system and the specific R-squared value have differences in various cities requires further verification. This paper explores and tries to raise questions about the research methodology of the built environment space structure, the measurement of the station domain, and the concepts of station-city coordination and development.

Suggested Citation

  • Bo Wan & Xudan Zhao & Yuhan Sun & Tao Yang, 2023. "Unraveling the Impact of Spatial Configuration on TOD Function Mix Use and Spatial Intensity: An Analysis of 47 Morning Top-Flow Stations in Beijing (2018–2020)," Sustainability, MDPI, vol. 15(10), pages 1-27, May.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:10:p:7944-:d:1145580
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/10/7944/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/10/7944/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jinkyung Choi & Yong Lee & Taewan Kim & Keemin Sohn, 2012. "An analysis of Metro ridership at the station-to-station level in Seoul," Transportation, Springer, vol. 39(3), pages 705-722, May.
    2. Cervero, Robert, 1994. "Transit-based housing in California: evidence on ridership impacts," Transport Policy, Elsevier, vol. 1(3), pages 174-183, June.
    3. Jun, Myung-Jin & Choi, Keechoo & Jeong, Ji-Eun & Kwon, Ki-Hyun & Kim, Hee-Jae, 2015. "Land use characteristics of subway catchment areas and their influence on subway ridership in Seoul," Journal of Transport Geography, Elsevier, vol. 48(C), pages 30-40.
    4. Kwoka, Gregory J. & Boschmann, E. Eric & Goetz, Andrew R., 2015. "The impact of transit station areas on the travel behaviors of workers in Denver, Colorado," Transportation Research Part A: Policy and Practice, Elsevier, vol. 80(C), pages 277-287.
    5. Shao, Qifan & Zhang, Wenjia & Cao, Xinyu & Yang, Jiawen & Yin, Jie, 2020. "Threshold and moderating effects of land use on metro ridership in Shenzhen: Implications for TOD planning," Journal of Transport Geography, Elsevier, vol. 89(C).
    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. Vergel-Tovar, C. Erik & Rodriguez, Daniel A., 2018. "The ridership performance of the built environment for BRT systems: Evidence from Latin America," Journal of Transport Geography, Elsevier, vol. 73(C), pages 172-184.
    2. Su, Shiliang & Zhao, Chong & Zhou, Hao & Li, Bozhao & Kang, Mengjun, 2022. "Unraveling the relative contribution of TOD structural factors to metro ridership: A novel localized modeling approach with implications on spatial planning," Journal of Transport Geography, Elsevier, vol. 100(C).
    3. Christian Martin Mützel & Joachim Scheiner, 2022. "Investigating spatio-temporal mobility patterns and changes in metro usage under the impact of COVID-19 using Taipei Metro smart card data," Public Transport, Springer, vol. 14(2), pages 343-366, June.
    4. Li, Mengya & Kwan, Mei-Po & Hu, Wenyan & Li, Rui & Wang, Jun, 2023. "Examining the effects of station-level factors on metro ridership using multiscale geographically weighted regression," Journal of Transport Geography, Elsevier, vol. 113(C).
    5. Ingvardson, Jesper Bláfoss & Nielsen, Otto Anker, 2018. "How urban density, network topology and socio-economy influence public transport ridership: Empirical evidence from 48 European metropolitan areas," Journal of Transport Geography, Elsevier, vol. 72(C), pages 50-63.
    6. Su, Shiliang & Wang, Zhuolun & Li, Bozhao & Kang, Mengjun, 2022. "Deciphering the influence of TOD on metro ridership: An integrated approach of extended node-place model and interpretable machine learning with planning implications," Journal of Transport Geography, Elsevier, vol. 104(C).
    7. Peikun Li & Quantao Yang & Wenbo Lu & Shu Xi & Hao Wang, 2024. "An Improved Machine Learning Framework Considering Spatiotemporal Heterogeneity for Analyzing the Relationship Between Subway Station-Level Passenger Flow Resilience and Land Use-Related Built Environ," Land, MDPI, vol. 13(11), pages 1-20, November.
    8. Ding, Chuan & Cao, Xinyu & Liu, Chao, 2019. "How does the station-area built environment influence Metrorail ridership? Using gradient boosting decision trees to identify non-linear thresholds," Journal of Transport Geography, Elsevier, vol. 77(C), pages 70-78.
    9. Liu, Xiang & Chen, Xiaohong & Tian, Mingshu & De Vos, Jonas, 2023. "Effects of buffer size on associations between the built environment and metro ridership: A machine learning-based sensitive analysis," Journal of Transport Geography, Elsevier, vol. 113(C).
    10. Yuxin He & Yang Zhao & Kwok Leung Tsui, 2021. "An adapted geographically weighted LASSO (Ada-GWL) model for predicting subway ridership," Transportation, Springer, vol. 48(3), pages 1185-1216, June.
    11. Sung, Hyungun & Choi, Keechoo & Lee, Sugie & Cheon, SangHyun, 2014. "Exploring the impacts of land use by service coverage and station-level accessibility on rail transit ridership," Journal of Transport Geography, Elsevier, vol. 36(C), pages 134-140.
    12. 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.
    13. Cao, Xinyu Jason, 2019. "Examining the effect of the Hiawatha LRT on auto use in the Twin Cities," Transport Policy, Elsevier, vol. 81(C), pages 284-292.
    14. Zhang, Xiaojian & Zhao, Xilei, 2022. "Machine learning approach for spatial modeling of ridesourcing demand," Journal of Transport Geography, Elsevier, vol. 100(C).
    15. Yadi Zhu & Feng Chen & Zijia Wang & Jin Deng, 2019. "Spatio-temporal analysis of rail station ridership determinants in the built environment," Transportation, Springer, vol. 46(6), pages 2269-2289, December.
    16. Du, Qiang & Zhou, Yuqing & Huang, Youdan & Wang, Yalei & Bai, Libiao, 2022. "Spatiotemporal exploration of the non-linear impacts of accessibility on metro ridership," Journal of Transport Geography, Elsevier, vol. 102(C).
    17. 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).
    18. Andersson, David Emanuel & Shyr, Oliver F. & Yang, Jimmy, 2021. "Neighbourhood effects on station-level transit use: Evidence from the Taipei metro," Journal of Transport Geography, Elsevier, vol. 94(C).
    19. Wu, Hao & Lee, Jinwoo (Brian) & Levinson, David, 2023. "The node-place model, accessibility, and station level transit ridership," Journal of Transport Geography, Elsevier, vol. 113(C).
    20. Karnberger, Stephan & Antoniou, Constantinos, 2020. "Network–wide prediction of public transportation ridership using spatio–temporal link–level information," Journal of Transport Geography, Elsevier, vol. 82(C).

    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:10:p:7944-:d:1145580. 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.