Cabras, StefanoSunhe, FlorUniversidad Carlos III de Madrid. Departamento de Estadística2021-12-162021-12-162021-12-162387-0303https://hdl.handle.net/10016/33787This work focuses on predicting metro passenger flow at Beijing Metro stations and assessing uncertainty using a Bayesian Spatio-temporal model. Forecasting is essential for Metro operation management, such as automatically adjusting train operation diagrams or crowd regulation planning measures. Different from another approach, the proposed model can provide prediction uncertainty conditionally on available data, a critical feature that makes this algorithm different from usual machine learning prediction algorithms. The Bayesian Spatio-temporal model for areal Poisson counts includes random effects for stations and days. The fitted model on a test set provides a prediction accuracy that meets the standards of the Beijing Metro enterprise.engAtribución-NoComercial-SinDerivadas 3.0 EspañaBayesian ModellingIntegrated Nested Laplace ApproximationSpatio-Temporal ModellingPoisson Countsworking paperDT/0000001952