A Graph Convolutional Stacked Bidirectional Unidirectional-LSTM Neural Network for Metro Ridership Prediction

Abstract

Timely precise metro ridership forecasting is helpful to reveal real-time traffic demand, which is a crucial but challenging task in modern traffic management. Given the complex spatial correlation and temporal variation of riding behaviour in a metro system, deep learning algorithms have been widely applied owing to their superior performance in capturing spatio-temporal features. However, current deep learning models utilize regular convolutional operations, which can barely provide satisfactory accuracy due to either the ignorance of realistic topology of a traffic network or insufficiency in capturing representative spatiotemporal patterns. To further improve the accuracy in metro ridership prediction, this study proposes a parallel-structured deep learning model that consists of a Graph Convolution Network and a stacked Bidirectional unidirectional Long short-term Memory network (GCN-SBULSTM). The GCN module regards a metro network as a structured graph, and a K-hop matrix, which integrates the travel distance, population flow, and adjacency, is introduced to capture the dynamic spatial correlation among metro stations. The SBULSTM module considers both backward and forward states of ridership time series and can learn complex temporal features with stacked recurrent layers. Experiments are conducted on three real-life metro ridership datasets to demonstrate the effectiveness of the proposed model. Compared with state-of-the-art prediction models, GCN-SBULSTM presents better performance in multiple scenarios and largely enhances the efficiencies of training processes.