A Neural Network Based on Spatial Decoupling and Patterns Diverging for Urban Rail Transit Ridership Prediction

Urban rail transit (URT) is an essential part of urban public transportation. Accurate ridership prediction is increasingly important for the safe operation and efficient management of URT. However, existing studies regard the URT stations with different intersecting subway lines as a whole, which ignores the internal spatial connections within the stations. In fact, URT stations are embodiments of spatial coupling between subway lines. Additionally, the intrinsic patterns of ridership are also neglected. To further improve the prediction accuracy, this study proposes a deep learning model based on graph convolutional network (GCN) and bidirectional long short-term memory network (Bi-LSTM) with a non-parallel structure (D-BLGCN). At the beginning, this study decouples the URT stations according to the intersecting subway lines. On the basis of spatial decoupling, different patterns of ridership are diverged into tributaries. Then, a non-parallel structure in the proposed model is designed to capture the intrinsic spatio-temporal correlations of ridership. To the best of our knowledge, this is the first time that the integration of internal spatial connections and ridership diverging is employed for URT ridership prediction. Extensive experiments are conducted on Beijing URT ridership data with different time granularities. The results demonstrate that the proposed model achieves better prediction performance compared with baselines.

Automated summary

This study proposes a deep learning model based on graph convolutional network (GCN) and bidirectional long short-term memory network (Bi-LSTM) with a non-parallel structure (D-BLGCN) to improve the prediction accuracy of urban rail transit (URT) ridership. The URT stations are decoupled according to intersecting subway lines, and different patterns of ridership are diverged into tributaries. A non-parallel structure is designed to capture the intrinsic spatio-temporal correlations of ridership. The experimental results demonstrate that the proposed model achieves better prediction performance compared with baselines.