Short-term passenger flow prediction during station closures in subway systems

Abstract

Passenger flow prediction is critical for subway managers to efficiently organize passenger flow and assign capacity resources. Station closures caused by economic conferences and political events alter the topology of subway networks, resulting in large-scale passenger flows with irregular trends. Thus, predicting subway passenger flow during station closures is a challenging task. In this study, we propose a hybrid model for predicting short-term passenger flow during subway station closures. First, a spatiotemporal ensemble prediction model is used to capture the temporal characteristics of passenger flow at each station and the spatial characteristics of passenger flow among distinct groups of stations. Then, to address the effects of station closures, a support vector regression method is devised to investigate the residual time series from the ensemble model. Finally, a case study of Beijing subway is examined to validate the performance of the proposed model. The results indicate that the proposed model is superior to other state-of-the-art models in terms of accuracy and reliability in estimating the effects of station closures. Furthermore, the residuals from station closures are thoroughly converted into white noise and the proposed model is more interpretable than those in previous studies.