Local XGBoost Ensemble for Station-Level Ridership Prediction: Modeling Built Environment and Spatial Heterogeneity

Abstract

Accurate metro ridership prediction is crucial for transit planning, particularly as urban rail networks expand and travel patterns vary across different travel scenarios. Existing models often rely on historical ridership data and may have limited adaptability to temporal and spatial heterogeneity. This study proposes a spatial proximity weighted eXtreme gradient boosting (XGBoost) ensemble to predict daily metro ridership under holidays, weekdays, and weekends. By integrating built environment variables and spatial adjacency, the proposed framework constructs local subdata sets using a nearest-neighbor approach and independently trains submodels. The model reduces dependence on continuous historical ridership sequences and is suitable for limited-data conditions. Using urban rail transit (URT) data from Nanjing, the proposed framework is compared with multiscale geographically weighted regression (MGWR) and global XGBoost. The results show that it achieves higher prediction accuracy in terms of R 2 , mean absolute error (MAE), and root mean squared error (RMSE). Feature importance analysis confirms the model’s interpretability, revealing temporal differences in the effects of variables. The proposed method effectively captures spatiotemporal heterogeneity in ridership dynamics, providing strong generalizability and practical value for urban transit planning and demand forecasting.