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

Thumbnail Image
Identifiers
URI: https://hdl.handle.net/10016/33787
ISSN: 2387-0303
UXXI: DT/0000001952
Files
ws202110.pdf (2.69 MB)
Publication date
2021-12-16
Defense date
Authors
Advisors
Tutors
Journal Title
Journal ISSN
Volume Title
Publisher
Impact
Google Scholar
Export
Research Projects
Organizational Units
Journal Issue
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.
Description
Keywords
Bayesian Modelling, Integrated Nested Laplace Approximation, Spatio-Temporal Modelling, Poisson Counts
Bibliographic citation
Collections
DES - Working Papers. Statistics and Econometrics. WS