Abstract: Accurate traffic flow prediction is an indispensable part of intelligent transportation system.In recent years,graph neural networks have generated effective results in traffic flow prediction tasks.However,the information transfer of graph neural network is discontinuous latent state propagation,and there is an over-smoothing problem as the number of network layers increases,which limits the ability of the model to capture the spatial dependencies of distant nodes.At the same time,when representing the spatial relationship of the road network,most of the existing methods only use the predefined graph constructed by prior knowledge or the adaptive graph constructed only by the road network conditions,ignoring the combination of those two graphs.Aiming at solving the above problems,this paper proposes a traffic flow prediction model based on a dual prior adaptive graph neural ordinary differential equation.Temporal convolutional network are utilized to capture the temporal correlation of sequences,a priori adaptive graph fusion module is used to represent the road network,and complex spatio-temporal features are propagated in a continuous manner through tensor multiplication-based nerual ODEs.Finally,experiments are carried out on four public data sets of highway traffic in California,USA.Experimental results show that the prediction performance of the model is better than that of the existing ten methods.

Key words: Traffic forecasting, Prior adaptive graph, Graph convolutional network, Neural ordinary differential equations, Tensor multiplication