A Bayesian Spatio-temporal model for predicting passengers' occupancy at Beijing Metro

Cabras, Stefano; Sunhe, Flor

Publication:
A Bayesian Spatio-temporal model for predicting passengers' occupancy at Beijing Metro

dc.affiliation.dpto	UC3M. Departamento de Estadística	es
dc.contributor.author	Cabras, Stefano
dc.contributor.author	Sunhe, Flor
dc.contributor.editor	Universidad Carlos III de Madrid. Departamento de Estadística	es
dc.date.accessioned	2021-12-16T19:29:34Z
dc.date.available	2021-12-16T19:29:34Z
dc.date.issued	2021-12-16
dc.description.abstract	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 that meets the standards of the Beijing Metro enterprise.	en
dc.identifier.issn	2387-0303	es
dc.identifier.uri	https://hdl.handle.net/10016/33787
dc.identifier.uxxi	DT/0000001952	es
dc.language.iso	eng	es
dc.relation.ispartofseries	Working paper Statistics and Econometrics	en
dc.relation.ispartofseries	21-10
dc.rights	Atribución-NoComercial-SinDerivadas 3.0 España	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/es/	*
dc.subject.other	Bayesian Modelling	en
dc.subject.other	Integrated Nested Laplace Approximation	en
dc.subject.other	Spatio-Temporal Modelling	en
dc.subject.other	Poisson Counts	en
dc.title	A Bayesian Spatio-temporal model for predicting passengers' occupancy at Beijing Metro	en
dc.type	working paper	*
dspace.entity.type	Publication