The paper reports that a GCN+GRU model achieves MAE 2.01, RMSE 4.12, and R2 0.956 on Abilene network traffic, outperforming four baselines.
Network traffic prediction model considering road traffic parameters using artificial intelligence methods in VANET,
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Joint Graph Convolution and Sequential Modeling for Scalable Network Traffic Estimation
The paper reports that a GCN+GRU model achieves MAE 2.01, RMSE 4.12, and R2 0.956 on Abilene network traffic, outperforming four baselines.