Short-term Inbound Passenger Flow Forecasting for Urban Rail Transit Based on Deep Ensemble Neural Network

; Accurate and reliable short-term passenger flow forecasting for urban rail transit is an important component of a smart metro. Most of the existing short-term passenger flow prediction models, proposed under normal conditions, can hardly obtain satisfactory prediction accuracy under abnormal conditions. In this paper, a model was proposed based on Deep Ensemble Neural Network (DENN) for short-term inbound passenger flow forecasting. The model models and integrates external environmental factors such as weather, time of day and special events, the time dependence of inbound passenger flows in the recent period, and the correlation of outbound passenger flows, with a high degree of flexibility and scalability. Specifically, in DENN, firstly, a Gated Recurrent Unit (GRU) network was embedded to extract the time-dependency of inbound passenger data for the most recent time period. Secondly, a Transformer network was introduced to adaptively capture the time period that has the greatest impact on inbound passenger flow to extract the correlation of outbound passenger flows. Finally, fully connected networks were applied to encode external environmental factors as well as to achieve feature fusion and prediction. In addition, numerical experiments at Xujing East Station and Shanghai Stadium Station of Shanghai Metro show that the proposed method can achieve high prediction accuracy under common conditions. © 2023 Science Press. All rights reserved.