STAGCN: Spatial–Temporal Attention Graph Convolution Network for Traffic Forecasting

STAGCN: Spatial–Temporal Attention Graph Convolution Network for Traffic Forecasting

Traffic forecasting plays an important role in intelligent transportation systems. However, the prediction task is highly challenging due to the mixture of global and local spatiotemporal dependencies involved in traffic data. Existing graph neural networks (GNNs) typically capture spatial dependenc...

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Bibliographic Details
Main Authors: Yafeng Gu, Li Deng
Format: Article
Language:English
Published: MDPI AG 2022-05-01
Series:Mathematics
Subjects:
deep learning
traffic forecasting
graph convolution networks
attention mechanism
spatial–temporal graph data
Online Access:https://www.mdpi.com/2227-7390/10/9/1599

Internet

https://www.mdpi.com/2227-7390/10/9/1599

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