Traffic Flow Prediction Model Based on Temporal-aware Attention and Congestion-driven Graph Convolutional Network
Current traffic flow prediction models typically rely on spatiotemporal graph neural networks(GNN)to extract spatiotemporal features.However,GNN generally constructs graph structures based on road network connectivity and the distance between sensors,often overlooking congestion information in road segments.To fully capture the dynamic spatiotemporal dependencies in traffic flow data,this paper proposes a spatiotemporal traffic flow prediction model based on Temporal-Aware Attention and Congestion-Driven Graph Convolutional Network(TCGCN).In the spatial dimension,TCGCN employs two metrics to measure traffic congestion,which are utilized to construct graph structures that reveal dynamic spatial dependencies between road segments.In the temporal dimension,TCGCN designs a time-gated causal convolutional multi-head self-attention mechanism to enhance the model's temporal awareness and local trend perception capabilities.Experimental results on two real-world traffic datasets demonstrate that TCGCN outperforms the optimal baseline model,achieving average reductions of 5.22%,5.06%,and 2.90%in the evaluation metrics of MAE,MAPE,and RMSE,respectively.
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