Abstract: With the construction of smart cities,urban traffic flow forecasting is crucial in intelligent traffic early warning and traffic management decision-making.Due to the complex temporal and spatial correlation,it is a challenge to effectively predict traffic flow.Most of the existing traffic flow prediction methods use machine learning algorithms or deep learning models,and the methods have their own advantages and disadvantages.If the two advantages can be combined,the accuracy of traffic flow prediction will be further improved.Aiming at the traffic spatio-temporal data,a dual autoregressive component traffic prediction model based on improved Graph WaveNet is proposed.First,the three time convolution layers are effectively fused through the gated three-branch time convolution network,which further improves the ability of capture time correlation.Second,the autoregression component is introduced for the first time to effectively fuse the autoregression component with the gated three-branch time convolution network and the convolution layer,so that the model can fully reflect the linear and non-linear relationship between space-time data.Through experiments on two real public transportation data sets of METR-LA and PEMS-BAY,the proposed model is compared with other traffic flow prediction benchmark models.The results show that whether it is a short-term or long-term forecast,the model proposed in the paper is better than the benchmark model in all indicators.

Key words: Autoregressive component, Intelligent transportation, Spatio-temporal data, Time convolution layer, Traffic flow forecasting