Short-term prediction of passenger volume for urban rail systems: A deep learning approach based on smart-card data

Abstract

Short-term prediction of passenger volume is a complex but critical task to urban rail companies, which desire prediction methods with high accuracy, time efficiency and good practicality. Good prediction results of the outbound passenger volume at urban rail stations are important to the organization of passenger flow, and helpful to the arrangement of shuttles, especially in large transit junctions. The application of automatic fare collection (AFC) devices in urban rail transit systems helps to collect large amounts of historical data of completed journeys, which can be used by metro operators to construct a database of the urban rail passenger volume. Based on deep learning techniques and big data, this paper develops an improved spatiotemporal long short-term memory model (Sp-LSTM) to forecast short-term outbound passenger volume at urban rail stations. The proposed model predicts the outbound passenger volume on the basis of the historical data of the spatial-temporal passenger volume, station origin–destination (OD) matrix and the operation data of the rail transit network. Finally, based on actual data of the Beijing Metro Airport Line, a case study is carried out to compare the proposed Sp-LSTM with other prediction methods, i.e., the general long short-term memory model (LSTM), the autoregressive integrated moving average model (ARIMA), and the non-linear autoregressive neural network model (NAR), and the results show that the proposed method outperforms the others.