Spatiotemporal exploration of the non-linear impacts of accessibility on metro ridership
mode - subway/metro, land use - impacts, land use - planning, land use - urban density, ridership - modelling, planning - methods
Accessibility, Metro ridership, Spatiotemporal analysis, Non-linear impacts, Gradient boosting regression trees (GBRT), Shapley additive explanations (SHAP)
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.
Permission to publish the abstract has been given by Elsevier, copyright remains with them.
Du, Q., Zhou, Y., Huang, Y., Wang, Y., & Bai, L. (2022). Spatiotemporal exploration of the non-linear impacts of accessibility on metro ridership. Journal of Transport Geography, Vol. 102, 103380.
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