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Examining the effects of station-level factors on metro ridership using multiscale geographically weighted regression

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  • Li, Mengya
  • Kwan, Mei-Po
  • Hu, Wenyan
  • Li, Rui
  • Wang, Jun

Abstract

Metro systems provide a mass and sustainable mobility option for urban populations. Drawing on the ridership data in 2019 released by the Shanghai Municipal Transportation Commission, we examine the spatially varying effects of station-level factors measured with different metro-station catchments (MSCs) on the daily and hourly ridership through multiscale geographically weighted regression (MGWR) models. The results indicate that independent variables measured with relatively small and non-overlapping MSCs can better explain the spatial variations in metro ridership. Regarding the important determinants, land use intensity (LUI) demonstrates positive effects diminishing from the core to the peripheral areas, transfer lines (TL) also exhibits positive effects that decrease from southwest to northeast. The effects of road density (RD) and building density (BD) are negative in central urban areas but positive in suburbs, which is the opposite of bus lines (BL). Additionally, the explanatory variables have significantly different ranges of influence, and the range of influence for a single variable also varies across models that use ridership at different times (e.g., weekdays vs. non-weekdays, the morning/evening peak vs. non-peak hours) as dependent variables. This study expands the knowledge of the spatial variations in the relationship between metro ridership and the geographical determinants. The findings can provide empirical evidence and implications for urban planners in formulating context-specific policies to improve the usage of metro systems.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:jotrge:v:113:y:2023:i:c:s0966692323001928
    DOI: 10.1016/j.jtrangeo.2023.103720
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    as
    1. Long, Alfie & Carney, Ffion & Kandt, Jens, 2023. "Who is returning to public transport for non-work trips after COVID-19? Evidence from older citizens' smart cards in the UK's second largest city region," Journal of Transport Geography, Elsevier, vol. 107(C).
    2. Hengyu Gu & Hanchen Yu & Mehak Sachdeva & Ye Liu, 2021. "Analyzing the distribution of researchers in China: An approach using multiscale geographically weighted regression," Growth and Change, Wiley Blackwell, vol. 52(1), pages 443-459, March.
    3. 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).
    4. 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.
    5. Caset, Freke & Blainey, Simon & Derudder, Ben & Boussauw, Kobe & Witlox, Frank, 2020. "Integrating node-place and trip end models to explore drivers of rail ridership in Flanders, Belgium," Journal of Transport Geography, Elsevier, vol. 87(C).
    6. Chakour, Vincent & Eluru, Naveen, 2016. "Examining the influence of stop level infrastructure and built environment on bus ridership in Montreal," Journal of Transport Geography, Elsevier, vol. 51(C), pages 205-217.
    7. Wei, Ming, 2022. "How does the weather affect public transit ridership? A model with weather-passenger variations," Journal of Transport Geography, Elsevier, vol. 98(C).
    8. 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.
    9. Liu, Yan & Wang, Siqin & Xie, Bin, 2019. "Evaluating the effects of public transport fare policy change together with built and non-built environment features on ridership: The case in South East Queensland, Australia," Transport Policy, Elsevier, vol. 76(C), pages 78-89.
    10. Adnan, Muhammad & Nahmias Biran, Bat-hen & Baburajan, Vishnu & Basak, Kakali & Ben-Akiva, Moshe, 2020. "Examining impacts of time-based pricing strategies in public transportation: A study of Singapore," Transportation Research Part A: Policy and Practice, Elsevier, vol. 140(C), pages 127-141.
    11. Steven Farber & Antonio Páez, 2007. "A systematic investigation of cross-validation in GWR model estimation: empirical analysis and Monte Carlo simulations," Journal of Geographical Systems, Springer, vol. 9(4), pages 371-396, December.
    12. Tu, Wei & Cao, Rui & Yue, Yang & Zhou, Baoding & Li, Qiuping & Li, Qingquan, 2018. "Spatial variations in urban public ridership derived from GPS trajectories and smart card data," Journal of Transport Geography, Elsevier, vol. 69(C), pages 45-57.
    13. Muhammad Zudhy Irawan & Prawira Fajarindra Belgiawan & Ari Krisna Mawira Tarigan & Fajar Wijanarko, 2020. "To compete or not compete: exploring the relationships between motorcycle-based ride-sourcing, motorcycle taxis, and public transport in the Jakarta metropolitan area," Transportation, Springer, vol. 47(5), pages 2367-2389, October.
    14. A. Stewart Fotheringham & Taylor M. Oshan, 2016. "Geographically weighted regression and multicollinearity: dispelling the myth," Journal of Geographical Systems, Springer, vol. 18(4), pages 303-329, October.
    15. Gao, Fan & Yang, Linchuan & Han, Chunyang & Tang, Jinjun & Li, Zhitao, 2022. "A network-distance-based geographically weighted regression model to examine spatiotemporal effects of station-level built environments on metro ridership," Journal of Transport Geography, Elsevier, vol. 105(C).
    16. Hasnine, Md Sami & Hawkins, Jason & Habib, Khandker Nurul, 2021. "Effects of built environment and weather on demands for transportation network company trips," Transportation Research Part A: Policy and Practice, Elsevier, vol. 150(C), pages 171-185.
    17. A. Stewart Fotheringham & Wenbai Yang & Wei Kang, 2017. "Multiscale Geographically Weighted Regression (MGWR)," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 107(6), pages 1247-1265, November.
    18. Zhang, Xiaohu & Xu, Yang & Tu, Wei & Ratti, Carlo, 2018. "Do different datasets tell the same story about urban mobility — A comparative study of public transit and taxi usage," Journal of Transport Geography, Elsevier, vol. 70(C), pages 78-90.
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