TSTA-GCN: trend spatio-temporal traffic flow prediction using adaptive graph convolution network

Balancing the need to satisfy both long-term and short-term requirements and comprehensively considering spatial and temporal dependencies are key challenges in metro passenger prediction. A trend spatio-temporal adaptive graph convolution network (TSTA-GCN) model for metro passenger flow prediction is presented in this paper. A trend convolutional self-attention model is designed to learn long-term and short-term trends. Adaptive graph is utilized to capture the complex relationships between stations and an adaptive graph convolutional recurrent unit module is proposed to capture local spatial and dynamic spatio-temporal correlations. In order to simulate the spatio-temporal heterogeneity implied in traffic flow, a spatio-temporal interaction module is used to fuse the heterogeneity in space and time. Extensive experiments are carried out on two metro traffic flow datasets and the experimental results show that the TSTA-GCN model outperforms the state-of-the-art baseline methods and is able to effectively predict long-term and short-term metro passenger flow.