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AST-GCN: Attribute-Augmented Spatiotemporal Graph Convolutional Network for Traffic Forecasting | IEEE Journals & Magazine | IEEE Xplore

AST-GCN: Attribute-Augmented Spatiotemporal Graph Convolutional Network for Traffic Forecasting


The framework of the AST-GCN model. By integrating the spatiotemporal graph convolution network and the attribute augmentation unit, we propose a traffic prediction model...

Abstract:

Traffic forecasting is a fundamental and challenging task in the field of intelligent transportation. Accurate forecasting not only depends on the historical traffic flow...Show More

Abstract:

Traffic forecasting is a fundamental and challenging task in the field of intelligent transportation. Accurate forecasting not only depends on the historical traffic flow information but also needs to consider the influence of a variety of external factors, such as weather conditions and surrounding POI distribution. Recently, spatiotemporal models integrating graph convolutional networks and recurrent neural networks have become traffic forecasting research hotspots and have made significant progress. However, few works integrate external factors. Therefore, based on the assumption that introducing external factors can enhance the spatiotemporal accuracy in predicting traffic and improving interpretability, we propose an attribute-augmented spatiotemporal graph convolutional network (AST-GCN). We model the external factors as dynamic attributes and static attributes and design an attribute-augmented unit to encode and integrate those factors into the spatiotemporal graph convolution model. Experiments on real datasets show the effectiveness of considering external information on traffic speed forecasting tasks when compared with traditional traffic prediction methods. Moreover, under different attribute-augmented schemes and prediction horizon settings, the forecasting accuracy of the AST-GCN is higher than that of the baselines. The source code of the AST-GCN is available at https://github.com/lehaifeng/T-GCN/AST-GCN.
The framework of the AST-GCN model. By integrating the spatiotemporal graph convolution network and the attribute augmentation unit, we propose a traffic prediction model...
Published in: IEEE Access ( Volume: 9)
Page(s): 35973 - 35983
Date of Publication: 24 February 2021
Electronic ISSN: 2169-3536

Funding Agency:


References

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