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Exploring Spatiotemporal Variation in Hourly Metro Ridership at Station Level: The Influence of Built Environment and Topological Structure

Author

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  • Zhuangbin Shi

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

  • Ning Zhang

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

  • Yang Liu

    (School of Transportation, Southeast University, Southeast University Road 2, Nanjing 211189, China
    Urban Planning Group, Eindhoven University of Technology, 5600 MB Eindhoven, the Netherlands)

  • Wei Xu

    (School of Automation, Southeast University, Sipailou 2, Nanjing 210096, China)

Abstract

Reliable and accurate estimates of metro demand can provide metro authorities with insightful information for the planning of route alignment and station locations. Many existing studies focus on metro demand from daily or annual ridership profiles, but only a few concern the variation in hourly ridership. In this paper, a geographically and temporally weighted regression (GTWR) model was used to examine the spatial and temporal variation in the relationship between hourly ridership and factors related to the built environment and topological structure. Taking Nanjing, China as a case study, an empirical study was conducted with automatic fare collection (AFC) data in three weeks. With an analysis of variance (ANOVA), it was found that the GTWR model produced the best fit for hourly ridership data compared with traditional regression models. Four built-environment factors, namely residence, commerce, scenery, and parking, and two topological-structure factors, namely degree centrality and closeness centrality, were proven to be significantly related to station-level ridership. The spatial distribution pattern and temporal nonstationarity of these six variables were further analyzed. The result of this study confirmed that the GTWR model can provide more realistic and useful information by capturing spatiotemporal heterogeneity.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:12:p:4564-:d:187472
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    References listed on IDEAS

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    4. Wang, Jing & Wan, Feng & Dong, Chunjiao & Yin, Chaoying & Chen, Xiaoyu, 2023. "Spatiotemporal effects of built environment factors on varying rail transit station ridership patterns," Journal of Transport Geography, Elsevier, vol. 109(C).
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    6. 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.
    7. Andreas Piter & Philipp Otto & Hamza Alkhatib, 2022. "The Helsinki bike‐sharing system—Insights gained from a spatiotemporal functional model," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(3), pages 1294-1318, July.
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