This chapter explores the application of spatial-temporal graph convolutional networks (STGCNs) for connected traffic flow prediction. By integrating spatiotemporal dependencies with graph structures, STGCNs are effective in capturing complex static and dynamic spatial correlations within traffic flow data while accounting for historical and multi-factor dynamic changes. This chapter covers various methods, including the static and dynamic spatial correlation neural network (SDSCNN), spatial-temporal complex graph convolution network, prior knowledge enhanced time-varying graph convolution network, and residual attention-enhanced multi-factor graph convolutional network. Each approach aims to improve prediction accuracy by leveraging distinct mechanisms for capturing spatiotemporal features and addressing the heterogeneity introduced by multi-dimensional factors.