A spatiotemporal graph convolution gated recurrent unit model for short-term passenger flow estimation

Abstract

Accurate estimation of short-term passenger flow is of great significance for metro managers to organize passenger flow and allocate capacity resources high-efficiently. In this paper, we propose a spatiotemporal graph convolution gated recurrent unit neural network (GCGRUA) combined with attention mechanism to predict short-term passenger flow in metro systems. Graph convolutional network is applied to extract spatial feature of passenger flow in the metro network. Gated recurrent unit is introduced to extract temporal feature of passenger flow. Attention mechanism is proposed to identify the more relevant time step inputs to improve the performance in temporal estimation. The proposed model can handle spatiotemporal correlation of passenger flow estimation in large-scale metro network. A case study of Beijing metro system is illustrated to verify the performance of the proposed model. The results show that the proposed model can well deal with the spatial-temporal relationship of passenger flow in metro networks and is superior to other baseline models.