Forecasting network-wide multi-step metro ridership with an attention-weighted multi-view graph to sequence learning approach

Abstract

The primary objective of this study is to forecast network-wide multi-step metro ridership with a novel attention-weighted multi-view graph to sequence learning approach (AW-MV-G2S). The developed AW-MV-G2S model employs multiple graph convolutional neural networks to capture spatial heterogeneous correlations between stations from geographic distance view, functional similarity view and demand pattern view, respectively. A bidirectional LSTM neural network and the attention mechanism is utilized to encode the long-range temporal dependencies in multiple time steps. A three-month trip record of 64 stations on four metro lines is collected from Nanjing Metro System to validate the model. The results indicate that the developed AW-MV-G2S model can fully encode the spatiotemporal characteristics in network-wide metro ridership data, and achieve better prediction accuracy and more robust performance than other compared models when making predictions across multiple look-ahead time steps for all three metro station types. Moreover, the model transferability result also reveals that the developed multi-view graph-to-sequence learning framework can be well transferred to other metro systems with various network structures. The results of this study can help the metro system authorities to dynamically modify the operation plans according to the fluctuation of passenger flow, such as adjusting the headway and train dispatching schedule to ensure the service quality of the entire metro system.