This chapter focuses on the role of dynamic and multi-graph models in connected traffic prediction, emphasizing how multi-graph convolution networks aggregate various graph structures to model spatiotemporal correlations. Dynamic multi-graph approaches capture dependencies across different graph structures and temporal scales, reflecting the evolving nature of traffic flow and its complex spatial structures. Covered methods include the multi-sequential temporal convolution gated graph neural network, dynamic spatiotemporal correlation graph convolutional network, and dynamic multi-graph synchronous aggregation framework. By incorporating multi-graph convolution structures, these approaches efficiently handle multi-scale spatiotemporal data, enhancing the models’ adaptability and prediction stability in complex traffic environments.