T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction.

@inproceedings{Zhao2018TGCNAT,
  title={T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction.},
  author={Ling Zhao and Yujiao Song and Chao Zhang and Yu Liu and Pu Patrick Wang and Tao Lin and Min Deng and Haifeng Li},
  year={2018}
}
Accurate and real-time traffic forecasting plays an important role in the Intelligent Traffic System and is of great significance for urban traffic planning, traffic management, and traffic control. However, traffic forecasting has always been considered an open scientific issue, owing to the constraints of urban road network topological structure and the law of dynamic change with time, namely, spatial dependence and temporal dependence. To capture the spatial and temporal dependence… CONTINUE READING
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