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

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dc.contributor.author Cabras, Stefano
dc.contributor.author Sunhe, Flor
dc.contributor.editor Universidad Carlos III de Madrid. Departamento de Estadística
dc.date.accessioned 2021-12-16T19:29:34Z
dc.date.available 2021-12-16T19:29:34Z
dc.date.issued 2021-12-16
dc.identifier.issn 2387-0303
dc.identifier.uri http://hdl.handle.net/10016/33787
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.
dc.language.iso eng
dc.relation.ispartofseries Working paper Statistics and Econometrics
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
dc.subject.other Integrated Nested Laplace Approximation
dc.subject.other Spatio-Temporal Modelling
dc.subject.other Poisson Counts
dc.title A Bayesian Spatio-temporal model for predicting passengers' occupancy at Beijing Metro
dc.type workingPaper
dc.identifier.uxxi DT/0000001952
dc.affiliation.dpto UC3M. Departamento de Estadística
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