This chapter introduces hybrid models that integrate both local and global spatial correlations, modeling the dynamic changes in traffic flow across different spatial scales. The approaches presented focus on combining local feature extraction with global spatial characteristics through hybrid graph convolution structures to achieve more accurate predictions of connected traffic speed and flow. The key methods discussed include the hybrid model integrating local and global spatial correlation, urban road network connected traffic speed prediction model based on global spatiotemporal characteristics, and the perturbation learning enhanced U-shaped multi-graph convolutional network (PLU-MCN). By effectively combining multi-level spatial information, these methods improve the models’ ability to capture both macro and micro features, resulting in more flexible and precise predictions.