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

Flor Sunhe

DES - Working Papers. Statistics and Econometrics. WS from Universidad Carlos III de Madrid. Departamento de Estadística

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.

Keywords: Bayesian; Modelling; Integrated; Nested; Laplace; Approximation; Spatio-Temporal; Modelling; Poisson; Counts (search for similar items in EconPapers)
Date: 2021-12-16
New Economics Papers: this item is included in nep-big, nep-for and nep-tre
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Persistent link: https://EconPapers.repec.org/RePEc:cte:wsrepe:33787

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