A Spatio-Temporal Decision-Support Framework for Station-Level Rail Transit Ridership Forecasting Using XGBoost

Advances in artificial intelligence have led to the widespread use of complicated nonlinear models in various disciplines, including transportation. These models frequently outperform conventional linear models in terms of prediction accuracy. Machine learning and deep learning models have shown great potential for estimating public transit ridership. Consequently, station-pair passenger flow forecast modelling has become crucial to address emerging needs in expanding megacities, providing backbone support for proactive resource allocation, fleet and infrastructure planning, and emergency crowd management. However, traditional statistical models and baseline machine learning algorithms often face critical bottlenecks in capturing highly non-linear passenger dynamics, complex network topologies, and sudden external operational variations simultaneously. In this study, we proposed a versatile methodology using the extreme gradient boosting tree ensemble (XGBoost) to investigate the relationship between built environment factors in station areas and ridership, focusing on the complex spatial-temporal feature engineering frameworks to capture both deep sequential historical trends and non-linear interactions. This article examines real-world passenger flow data collected between January and December 2025. The dataset comprises over 135,000 dense operational records, enabling an exploratory investigation of passenger commuting patterns, the generation of interactive ridership intensity heatmaps via spatial coordinates, and the implementation of an optimised XGBoost model tailored for rail transit ridership. Evaluating the framework against a 20% hold-out validation matrix yielded exceptional predictive synchronisation, achieving an R score of 0.9083, an MAE of 1,236.48 passengers, and an RMSE of 2,388.85 passengers. The framework's underlying decision hierarchy indicates that structural station demand dynamics (45.24%), regional/demographic dynamics (38.12%), and corridor alignment effects (15.15%) overwhelmingly dictate network variance over minor temporal cycles. To bridge the gap between statistical outputs and operational planning, this research implements an integrated prediction-and-visualisation pipeline that transforms complex data distributions into actionable visual analytics, such as interpretable station-to-station demand heatmaps via interactive GIS Folium layers. Ultimately, this framework effectively addresses the non-linear effects of the built environment and temporal heterogeneity on rail transit ridership, providing an operationally robust framework to support smart-city transportation management and build more sustainable urban transit systems.