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Spatiotemporal exploration of the non-linear impacts of accessibility on metro ridership

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  • Du, Qiang
  • Zhou, Yuqing
  • Huang, Youdan
  • Wang, Yalei
  • Bai, Libiao

Abstract

Identifying the determinants of metro ridership is essential for metro planning and passenger flow management. However, few studies to date have empirically examined how accessibility affects metro ridership and even fewer have emphasized the non-linear impacts from a spatiotemporal perspective. This study demarcates station areas via the network-distance method and precisely quantifies the accessibility of metro stations both internally and externally. This is combined with a gradient boosting regression trees (GBRT) model and a Shapley additive explanations (SHAP) model to understand the non-linear impacts of accessibility on metro ridership from a spatiotemporal perspective. The results show that accessibility indicators collectively contribute more than 60% of the predictive power for metro ridership at different times and the external accessibility has a greater impact on metro ridership than internal accessibility. Some indicators, such as the shortest path and population density show threshold effects on metro ridership. More importantly, the results demonstrate significant spatial heterogeneity in the effects of accessibility indicators on metro ridership and geographic trends generally from urban to suburban areas. The findings are expected to help planning departments and transit agencies improve the coordinated development of metro systems.

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  • 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).
    • Handle: RePEc:eee:jotrge:v:102:y:2022:i:c:s096669232200103x
    DOI: 10.1016/j.jtrangeo.2022.103380
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    Cited by:

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    3. Zhenbao Wang & Shihao Li & Yushuo Zhang & Xiao Wang & Shuyue Liu & Dong Liu, 2024. "Built Environment Renewal Strategies Aimed at Improving Metro Station Vitality via the Interpretable Machine Learning Method: A Case Study of Beijing," Sustainability, MDPI, vol. 16(3), pages 1-26, January.
    4. Gao, Fan & Han, Chunyang & Yang, Linchuan & Liang, Jian & He, Xuan & Li, Fan, 2024. "Analyzing spatiotemporal distribution patterns of metro ridership: Comparison between common-class and business-class carriage service," Journal of Transport Geography, Elsevier, vol. 115(C).
    5. Hong Zhu & Jin Li & Zhenjie Yuan & Jie Li, 2023. "Bibliometric Analysis of Spatial Accessibility from 1999–2022," Sustainability, MDPI, vol. 15(18), pages 1-17, September.
    6. Lv, Huitao & Li, Haojie & Chen, Yanlu & Feng, Tao, 2023. "An origin-destination level analysis on the competitiveness of bike-sharing to underground using explainable machine learning," Journal of Transport Geography, Elsevier, vol. 113(C).
    7. 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).
    8. Gao, Kun & Yang, Ying & Gil, Jorge & Qu, Xiaobo, 2023. "Data-driven interpretation on interactive and nonlinear effects of the correlated built environment on shared mobility," Journal of Transport Geography, Elsevier, vol. 110(C).
    9. 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).
    10. Lan Wu & Xiaorui Yuan & Chaoyin Yin & Ming Yang & Hongjian Ouyang, 2023. "Car Ownership Behavior Model Considering Nonlinear Impacts of Multi-Scale Built Environment Characteristics," Sustainability, MDPI, vol. 15(12), pages 1-14, June.
    11. Ermagun, Alireza & Witlox, Frank, 2024. "Transit access effectiveness in American metropolitan areas," Journal of Transport Geography, Elsevier, vol. 116(C).

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