Predicting Public Transport Passenger Trips Using Automated Fare Collection Data: A Case Study in Fortaleza

Urban mobility in large cities faces increasing pressure due to population growth and congestion. Automated Fare Collection (AFC) systems offer a rich source of data for understanding public transport usage and informing data-driven improvements. This paper presents a case study in Fortaleza, Brazil, where we explore AFC smart card data to predict users’ next bus trips and travel volume. We develop a machine learning pipeline combining feature engineering and classification/regression models. A comparative evaluation of algorithms, including Random Forest, XGBoost, and Support Vector Machines, shows that decision-tree-based models achieve the best performance, particularly in handling noisy and imbalanced data. Our approach considers both user-level predictions and cluster-based analyses to improve model generalizability across user types. The results demonstrate the potential of AFC data to enhance transit planning, reduce overcrowding, and personalize mobility services. This study contributes to the growing body of research on smart mobility analytics in developing urban contexts.