Data-driven predictive modelling of stop-level public transit patterns

Abstract There is a growing emphasis in urban centres on promoting sustainable mobility modes, particularly public transit systems. This highlights the critical need for predictive modelling frameworks that capture the local spatiotemporal dynamics of public transit to inform policy and planning decisions. This study develops a horizon-agnostic modelling framework using automated passenger count (APC) data from a public bus transit system, integrating machine learning (ML) and deep learning (DL) algorithms to forecast stop-level passenger counts and operational factors. We assess APC data quality, implement a feature-space optimisation pipeline to enhance algorithm-data fit, and use SHAP values to analyse feature attributions for model interpretability. Our analyses reveal a weak but asymmetric relationship between boarding and alighting passenger counts. Tree-based ML algorithms outperform DL algorithms due to the high proportion of categorical features, with Extreme Gradient Boosting (XGBoost) achieving the best performance. Furthermore, incorporating non-mobility data (weather, terrain, demographics, land use) improved modelling of passenger dynamics. However, stop-level modelling lacks inductive biases on the spatial structure of transit networks. The proposed framework provides policymakers and planners with data-driven tools to understand the local spatiotemporal dynamics of public transit under external influences, supporting resource allocation for stop placement, line routing, and bus scheduling. By predicting outcomes based on input feature combinations rather than specific temporal horizons, the framework enables scenario analysis for planning applications and can be embedded in digital twins and mobility dashboards to support informed commuting decisions by urban residents.