UG-STNN: A Spatial-Temporal Neural Network Based on Unsupervised Graph Representation Module for Traffic Flow Prediction

Accurate and efficient traffic flow prediction helps to build an intelligent transportation system and improve the travel experience in daily life. In this study, a new Spatial-Temporal Neural Network Based on Unsupervised Graph Representation Module (UG-STNN) is proposed to improve the graph convolution module, which uses unsupervised learning to extract features in spatial dimensions, and it can learn the structural and feature information in the graph better. Our UG-STNN uses fewer convolutional layers to reduce the number of parameters, decrease the complexity of the model, and improve performance and accuracy. From the experimental results of UG-STNN on different test datasets, the model can approach or even achieve better prediction results compared with other models, which well illustrates the accuracy and stability of the UG-STNN model.