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The Influence of the Built Environment on School Children’s Metro Ridership: An Exploration Using Geographically Weighted Poisson Regression Models

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  • Yang Liu

    (Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, Dongnandaxue Road 2, Nanjing 211189, China)

  • Yanjie Ji

    (Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, Dongnandaxue Road 2, Nanjing 211189, China)

  • Zhuangbin Shi

    (Intelligent Transportation System Research Center, Southeast University, Dongnandaxue Road 2, Nanjing 211189, China)

  • Liangpeng Gao

    (Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, Dongnandaxue Road 2, Nanjing 211189, China)

Abstract

Long-distance school commuting is a key aspect of students’ choice of car travel. For cities lacking school buses, the metro and car are the main travel modes used by students who have a long travel distance between home and school. Therefore, encouraging students to commute using the metro can effectively reduce household car use caused by long-distance commuting to school. This paper explores metro ridership at the station level for trips to school and return trips to home in Nanjing, China by using smart card data. In particular, a global Poisson regression model and geographically weighted Poisson regression (GWPR) models were used to examine the effects of the built environment on students’ metro ridership. The results indicate that the GWPR models provide superior performance for both trips to school and return trips to home. Spatial variations exist in the relationship between the built environment and students’ metro ridership across metro stations. Built environments around metro stations, including commercial-oriented land use; the density of roads, parking lots, and bus stations; the number of docks at bikeshare stations; and the shortest distance between bike stations and metro stations have different impacts on students’ metro ridership. The results have important implications for proposing relevant policies to guide students who are being driven to school to travel by metro instead.

Suggested Citation

  • Yang Liu & Yanjie Ji & Zhuangbin Shi & Liangpeng Gao, 2018. "The Influence of the Built Environment on School Children’s Metro Ridership: An Exploration Using Geographically Weighted Poisson Regression Models," Sustainability, MDPI, vol. 10(12), pages 1-16, December.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:12:p:4684-:d:189180
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    References listed on IDEAS

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    Cited by:

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    2. Yani Quarta Mondiana & Henny Pramoedyo & Atiek Iriany & Marjono, 2024. "Exploring Geographical Variability in Sugarcane Yields: A Geographically Weighted Panel Regression Approach with MM Estimation," Advances in Decision Sciences, Asia University, Taiwan, vol. 28(2), pages 35-65, June.
    3. Marques, Samuel de França & Pitombo, Cira Souza, 2023. "Local modeling as a solution to the lack of stop-level ridership data," Journal of Transport Geography, Elsevier, vol. 112(C).
    4. 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).
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