Short-time Traffic Flow Forecasting Based on Multi-adjacent Graph and Multi-head Attention Mechanism

Abstract

Traffic flow forecasting is the cornerstone of many applications in transportation which has a great importance in smart city system.The difficulty of this task is how to effectively model the temporal and spatial dependence.Existing methods usually use GNN to model temporal correlation and CNN or RNN to model temporal correlation.When modeling the spatial correlation,only the adjacency matrix is applied to model local relationships while ignoring global spatial information.However,there are some roads in the entire road network whose surrounding structures are similar,and these roads carry similar functions in the road network.Therefore,the characteristics of these similar roads can also be used as the basis for traffic prediction.This paper proposes a traffic flow forecasting model based on multi-adjacent matrix and multi-head attention mechanism.It includes:1)the node2vec algorithm is applied to calculate the vector representation of the road in road network,and the similarity matrix is calculated through the threshold for graph convolution operation to extract global spatial information;2)the multi-channel self-attention mechanism is used to mine the spatial and temporal features of the model.Experiments on public datasets PEMS04 and PEMS08 demonstrate the proposed model’s effectiveness.Its accuracy is improved compared with the mainstream models.

Keywords

• traffic forecasting|dilated convolution|spatial-temporal network|attention mechanism|node embedding