Understanding the built environment’s impact on metro ridership is essential for developing targeted strategies for built environment renewal. Taking into consideration the limitations of existing studies, such as not proposing targeted strategies, using unified pedestrian catchment areas (PCA), and not determining the model’s accuracy, Beijing was divided into three zones from inside to outside by the distribution pattern of metro stations. Three PCAs were assumed for each zone and a total of 27 PCA combinations. The study compared the accuracy of the Ordinary Least Square (OLS) and several machine learning models under each PCA combination to determine the model to be used in this study and the recommended PCA combination for the three zones. Under the recommended PCA combinations for the three zones, the model with the highest accuracy was used to explore the built environment’s impact on metro ridership. Finally, prioritized stations for renewal were identified based on ridership and the built environment’s impact on metro ridership. The results are as follows: (1) The eXtreme Gradient Boosting (XGBoost) model has a higher accuracy and was appropriate for this study. The recommended PCA combination for the three zones in Beijing was 1000 m_1200 m_1800 m. (2) During the morning peak hours, the density of office and apartment facilities greatly influenced the ridership, with a strong threshold effect and spatial heterogeneity. Our research framework also provides a new way for other cities to determine the scope of Transit-Oriented Development (TOD) and proposes a new decision-making method for improving the vibrancy of metro stations.

Incorporating the distance-decay effects of facility points into the analysis of metro ridership helps generate more precise and actionable strategies for station area renewal. The majority of existing studies, however, calculated the built environment variables based on the same pedestrian catchment areas (PCAs) of metro stations and failed to consider the impact of distance decay from POIs (points of interest) on the accuracy of metro station ridership regression models. The objective of this study is to determine which distance-decay function best improves the fit of the metro ridership regression model and investigate the effect of the built environment on ridership under the optimal distance-decay model. Based on the distribution density of metro stations in Beijing, the research area is divided into three zones with different PCAs. Built environment variables for all metro stations are aggregated according to the PCA scope. Various distance-decay functions are examined to determine how the accuracy of the Multi-scale Geographically Weighted Regression (MGWR) model is affected by built environment variables calculated from POI facilities (Gaussian distance decay, power distance decay, piecewise distance decay). Finally, under optimal distance decay, the MGWR model is used to investigate how the built environment influences metro ridership. The results show the following: (1) The Gaussian distance-decay function improves the goodness of fit of the regression model, resulting in an 11.25% increase in the R² value when compared to the model without a distance-decay function. (2) During morning peak hours, apartment and office density significantly impacts ridership. The proposed research framework is conducive to improving the accuracy of the metro station ridership regression model. Moreover, it facilitates the formulation of targeted strategies for the renewal of the built environment by government managers and planners.