Graph neural network in traffic forecasting: a review

Abstract

Traffic Forecasting is an important and challenging problem. The recent developed deep learning models are becoming dominant in this area. Especially, graph neural networks (GNNs) are being applied in traffic forecasting in recent years. In this paper, I give a review of the related work and the applications of GNNs in different traffic forecasting problems, e.g., bike sharing, metro flow, road traffic flow prediction, etc. I find that GNNs are only applied in recent years, and there is still a great research potential for this direction.