Graph-GAN: A spatial-temporal neural network for short-term passenger flow prediction in urban rail transit systems

Short-term passenger flow prediction plays an important role in better managing the urban rail transit (URT) systems. Emerging deep learning models provide good insights to improve short-term prediction accuracy. However, a large number of existing prediction models combine diverse neural network layers to improve accuracy, making their model structures extremely complex and difficult to be applied to the real world. Therefore, it is necessary to trade off between the model complexity and prediction performance from the perspective of real-world applications. To this end, we propose a deep learning-based Graph-GAN model with a simple structure and high prediction accuracy to predict short-term passenger flows of the URT network. The Graph-GAN consists of two major parts: (1) a simplified and static version of the graph convolution network (GCN) used to extract network topological information; (2) a generative adversarial network (GAN) used to predict passenger flows, with generators and discriminators in GAN just composed of simple fully connected neural networks. The Graph-GAN is tested on two large-scale real-world datasets from Beijing Subway. A comparison of the prediction performance of Graph-GAN with those of several state-of-the-art models illustrates its superiority and robustness. This study can provide critical experience in conducting short-term passenger flow predictions, especially from the perspective of real-world applications.