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

The growing population density in cities requires urban trans-portation to meet the travel needs of citizens fast and accurately. Therefore, the correct prediction of daily passenger flow in ur-ban subway transportation is of great practical importance for rationalizing the traffic arrangement and safely responding to unexpected passenger flow. This work builds a Bayesian spatial- temporal model for predicting station occupancy. The proposed one provides point estimations of daily passenger flow, a reliable assessment of their uncertainty, and the possibility of under-standing traffic features. It also provides a prediction accuracy that meets the standards of the Beijing Metro enterprise. The model discussed in this paper is in force at Beijing Metro Group Ltd to programme daily train schedules.(c) 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Automated summary

The growing population density in cities requires fast and accurate urban transportation to meet citizens' travel needs. This study proposes a Bayesian spatial-temporal model for predicting station occupancy in urban subway transportation. The model provides point estimations of daily passenger flow, reliable assessment of uncertainty, and understanding of traffic features. It also meets the prediction accuracy standards of the Beijing Metro enterprise. The discussed model is currently implemented at Beijing Metro Group Ltd for daily train scheduling.