Abstract: Traffic prediction plays a crucial role in urban planning and traffic management.Traditional prediction methods based on machine learning and statistics are limited in their ability to capture complex nonlinear relationships and long-term dependencies,failing to capture the intricate spatiotemporal relationships within traffic networks.Existing models based on graph neural networks(GNN) mostly use preset static graphs,which cannot accurately reflect the actual topology of road networks,and almost all models simply consider the propagation process of traffic flow between different nodes,neglecting the traffic generation process at each node.To address these issues,we propose a decoupled adaptive dynamic graph convolutional network(DADGCN) model.This model effectively quantifies the dynamic correlations among different nodes through an adaptive dynamic graph mo-dule,thereby capturing the complex spatial dependencies in the traffic network.At the same time,it decouples the node traffic into propagated traffic and generated traffic in a data-driven manner.It utilizes a multi-head self-attention mechanism to process the decoupled signals,thus enhancing the model’s flexibility in handling complex traffic data and improving prediction accuracy.Experiments demonstrate that on the METR-LA andPEMS-BAY datasets,DADGCN achieves 7.78%,10.14% and 25.39%,21.19% improvement in MAE over 60 minutes compared to the diffusion convolution-based model DCRNN and Graph Wavenet.On the PEMS04 and PEMS08 datasets, DADGCN demonstrates significant improvements of 11.61% in MAPE and 3.90% in RMSE compared to the adaptive graph-based model MTGNN.This shows that the model is not only capable of more profoundly understanding the inherent dynamic features within traffic flows but also able to adapt to changes in various complex environments,providing more accurate and reliable data support for urban traffic management and planning.

Key words: Traffic prediction, Adaptive dynamic graph, Graph neural network, Model decoupling, Multi-head attention mechanism