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In recent years, deep learning has been a hot research topic in traffic flow prediction. Graph convolutional networks outperform traditional convolutional neural networks in spatial feature modeling, in view of their powerful capabilities in processing non-Euclidean data such as topological map, distance map and flow similarity map. Therefore, graph convolutional network and its variants have become a research hotspot in traffic flow prediction, and many attractive research results have been obtained. This article classifies and summarizes traffic flow prediction models based on graph convolutional networks in recent years. First, the graph convolution is elaborated by combining the definitions of spatial convolution and spectral convolution. Second, in view of the network structure of the prediction model, the graph convolutional network based traffic flow prediction models are divided into two major categories of combined type and improved type, each of which are analyzed and discussed in detail with representative model structures. In addition, typical datasets commonly used in traffic flow prediction for model performance comparison are reviewed, and a simulation test is conducted using one real dataset to demonstrate the prediction performance of four traffic flow prediction models based on graph convolutional networks. Finally,the future research hotspots and challenges in traffic flow prediction based on graph convolutional networks are prospected [ABSTRACT FROM AUTHOR]

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