Abstract: With the development of deep learning,neural network has a large number of applications in various fields,and intelligent transportation system is no exception.Traffic flow forecast is the cornerstone of intelligent traffic system and the core of the whole traffic forecast.In recent years,the use of the graph convolutional neural network has effectively improved the performance of traffic prediction.How to further improve the ability to capture the spatial and temporal characteristics of the graph will become a hot topic.In order to improve the accuracy of traffic prediction,this paper proposes a traffic prediction model based on the convolution network of dual path information spatial-temporal map.First of all,the traffic prediction model based on the graph convolution network has some shortcomings in long-distance dependence modeling,and has not fully mined the hidden relationship between the spatial-temporal diagram information and the missing information in the spatial-temporal diagram structure,so we propose a triple pooling attention mechanism to model the global context information.Based on the figure of each increase in parallel convolution layer and the time convolution triple pooling attention path,we construct a dual path information spatial-temporal convolution layer,enhance the generalization ability of convolution layer,improve the model's ability to capture long distance dependence,and spatial-temporal convolution layer can capture figure characteristics of space and time structure of spacetime,effectively improve the traffic prediction performance.Experimental results on two public transport data sets (METR-LA and PEMS-BAY) show that the proposed model has good performance.

Key words: Global context modeling, Graph convolutional neural network, Long distance dependence, Traffic forecast