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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
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    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. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
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