RT Generic
T1 A Bayesian Spatio-temporal model for predicting passengers' occupancy at Beijing Metro
A1 Cabras, Stefano
A1 Sunhe, Flor
A2 Universidad Carlos III de Madrid. Departamento de Estadística, 
AB This 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 thatmeets the standards of the Beijing Metro enterprise.
SN 2387-0303
YR 2021
FD 2021-12-16
LK https://hdl.handle.net/10016/33787
UL https://hdl.handle.net/10016/33787
LA eng
DS e-Archivo
RD 27 jul. 2024