Age-sensitive urban rail passenger demand forecasting and uncertainty-driven anomaly detection using a hybrid SAINT + CatBoost ensemble.

ABSTRACT: The rapid evolution of urban demographics necessitates advanced predictive modeling to optimize rail transit capacity and reliability. This study presents a novel age-sensitive demand forecasting and anomaly detection framework for Istanbul's urban rail network, utilizing a comprehensive dataset of 721,328 passenger-trip records collected between 2021 and 2023. By engineering eleven spatiotemporal and transactional features, passengers are classified into four distinct age cohorts (< 20, 20-30, 30-60, 60+) to capture diverse mobility behaviors. The methodological approach benchmarks four classical linear classifiers, three gradient-boosting decision trees, and a tabular deep learning model (SAINT) against a proposed two-stage hybrid ensemble. This hybrid architecture integrates the deep re

SUBMITTER: Akcay MT 

PROVIDER: S-EPMC12827967 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature