IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v11y2022i12p2189-d991654.html
   My bibliography  Save this article

The Impact of Land Use on Time-Varying Passenger Flow Based on Site Classification

Author

Listed:
  • Kexin Lei

    (School of Architecture, Chang’an University, Xi’an 710061, China)

  • Quanhua Hou

    (School of Architecture, Chang’an University, Xi’an 710061, China)

  • Weijia Li

    (The Engineering Design Academy of Chang’an University Co., Ltd., Xi’an 710064, China)

  • Meng Zhao

    (School of Architecture, Chang’an University, Xi’an 710061, China)

  • Jizhe Zhou

    (School of Architecture, Chang’an University, Xi’an 710061, China)

  • Lingda Zhang

    (School of Architecture, Chang’an University, Xi’an 710061, China)

  • Shihan Chen

    (School of Architecture, Chang’an University, Xi’an 710061, China)

  • Yaqiong Duan

    (School of Architecture, Chang’an University, Xi’an 710061, China)

Abstract

During the different periods of a day, the imbalanced distribution of inbound ridership, that is related to land use, results in extreme flow, which makes metro management challenging. The causes of imbalanced passenger flow from the perspective of land use in metro station areas are studied in this paper. More specifically, based on site classification, the impact of land use, including the floor area ratio and gross floor area on passenger flow, was explored by using a multiple linear regression model. The results first indicate that the impact intensities of the floor area ratio on peak hourly flow were 0.41, 0.21, and 0.20 around business, residential, and mixed sites, respectively. Second, for the abovementioned sites, the types with the greatest impact intensities of gross floor area on peak hourly flow were commercial and business facilities (B), residential (R), as well as administration and public services (A), which were 0.73, 0.32, and 0.87, respectively. Finally, for the land-development-control schemes for business, residential, and mixed sites, the maximum values of the floor area ratio were roughly 7.2, 5.3, and 8.2, respectively. The results presented in this study provide guidance for land development in metro station areas and contribute to avoiding the emergence of extreme passenger flow.

Suggested Citation

  • Kexin Lei & Quanhua Hou & Weijia Li & Meng Zhao & Jizhe Zhou & Lingda Zhang & Shihan Chen & Yaqiong Duan, 2022. "The Impact of Land Use on Time-Varying Passenger Flow Based on Site Classification," Land, MDPI, vol. 11(12), pages 1-19, December.
  • Handle: RePEc:gam:jlands:v:11:y:2022:i:12:p:2189-:d:991654
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/11/12/2189/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2073-445X/11/12/2189/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kuby, Michael & Barranda, Anthony & Upchurch, Christopher, 2004. "Factors influencing light-rail station boardings in the United States," Transportation Research Part A: Policy and Practice, Elsevier, vol. 38(3), pages 223-247, March.
    2. Xi Chen, 2021. "Driving Factors Analysis on Urban Vibrancy: A Case Study of Chongqing Main Area," Springer Books, in: Xinhai Lu & Zuo Zhang & Weisheng Lu & Yi Peng (ed.), Proceedings of the 25th International Symposium on Advancement of Construction Management and Real Estate, pages 1137-1147, Springer.
    3. 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.
    4. Heungsoon Kim & Jaehyeong Nam, 2013. "The size of the station influence area in Seoul, Korea: based on the survey of users of seven stations," International Journal of Urban Sciences, Taylor & Francis Journals, vol. 17(3), pages 331-349, November.
    5. Ke Wang & Jianjun Zhang & Di Zhang & Xia Wu, 2022. "A Priority in Land Supply for Sustainable Transportation of Chinese Cities: An Empirical Study from Perception, Discrimination, Linkage to Decision," Land, MDPI, vol. 11(1), pages 1-19, January.
    6. 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.
    7. Zegras, P. Christopher, 2010. "Transport and Land Use in China," The Journal of Transport and Land Use, Center for Transportation Studies, University of Minnesota, vol. 3(3), pages 1-3.
    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. 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.
    2. Aston, Laura & Currie, Graham & Kamruzzaman, Md. & Delbosc, Alexa & Teller, David, 2020. "Study design impacts on built environment and transit use research," Journal of Transport Geography, Elsevier, vol. 82(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. 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).
    5. Kepaptsoglou, Konstantinos & Stathopoulos, Antony & Karlaftis, Matthew G., 2017. "Ridership estimation of a new LRT system: Direct demand model approach," Journal of Transport Geography, Elsevier, vol. 58(C), pages 146-156.
    6. 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.
    7. Dohyung Kim & Yongjin Ahn & Simon Choi & Kwangkoo Kim, 2016. "Sustainable Mobility: Longitudinal Analysis of Built Environment on Transit Ridership," Sustainability, MDPI, vol. 8(10), pages 1-14, October.
    8. Lijie Yu & Yarong Cong & Kuanmin Chen, 2020. "Determination of the Peak Hour Ridership of Metro Stations in Xi’an, China Using Geographically-Weighted Regression," Sustainability, MDPI, vol. 12(6), pages 1-22, March.
    9. 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.
    10. Jinbao Zhao & Wei Deng & Yan Song & Yueran Zhu, 2014. "Analysis of Metro ridership at station level and station-to-station level in Nanjing: an approach based on direct demand models," Transportation, Springer, vol. 41(1), pages 133-155, January.
    11. 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.
    12. Meng Zhao & Haiyan Tong & Bo Li & Yaqiong Duan & Yubai Li & Jianpo Wang & Kexin Lei, 2022. "Analysis of Land Use Optimization of Metro Station Areas Based on Two-Way Balanced Ridership in Xi’an," Land, MDPI, vol. 11(8), pages 1-20, July.
    13. 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.
    14. Iseki, Hiroyuki & Liu, Chao & Knaap, Gerrit, 2018. "The determinants of travel demand between rail stations: A direct transit demand model using multilevel analysis for the Washington D.C. Metrorail system," Transportation Research Part A: Policy and Practice, Elsevier, vol. 116(C), pages 635-649.
    15. 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).
    16. Daeyoung Kwon & Sung Eun Sally Oh & Sangwon Choi & Brian H. S. Kim, 2023. "Viability of compact cities in the post-COVID-19 era: subway ridership variations in Seoul Korea," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 71(1), pages 175-203, August.
    17. Shao, Rui & Derudder, Ben & Yang, Yongchun & Witlox, Frank, 2023. "The association between transit accessibility and space-time flexibility of shopping travel: On the moderating role of ICT use," Journal of Transport Geography, Elsevier, vol. 111(C).
    18. Zhenjun Zhu & Jun Zeng & Xiaolin Gong & Yudong He & Shucheng Qiu, 2021. "Analyzing Influencing Factors of Transfer Passenger Flow of Urban Rail Transit: A New Approach Based on Nested Logit Model Considering Transfer Choices," IJERPH, MDPI, vol. 18(16), pages 1-14, August.
    19. 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).
    20. Zhuangbin Shi & Ning Zhang & Yang Liu & Wei Xu, 2018. "Exploring Spatiotemporal Variation in Hourly Metro Ridership at Station Level: The Influence of Built Environment and Topological Structure," Sustainability, MDPI, vol. 10(12), pages 1-16, December.

    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:jlands:v:11:y:2022:i:12:p:2189-:d:991654. 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.