福建师范大学学报（自然科学版）2025，Vol.41Issue(1)：1-10,10.DOI:10.12046/j.issn.1000-5277.2024060035
基于时间感知注意力与拥塞驱动图卷积的交通流量预测
Traffic Flow Prediction Model Based on Temporal-aware Attention and Congestion-driven Graph Convolutional Network
摘要
Abstract
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.关键词
交通流量预测/交通拥塞/注意力机制/时空图神经网络Key words
traffic flow prediction/traffic congestion/attention mechanism/GNN分类
计算机与自动化引用本文复制引用
王小军,王兴,林羽,金彪,熊金波..基于时间感知注意力与拥塞驱动图卷积的交通流量预测[J].福建师范大学学报（自然科学版）,2025,41(1):1-10,10.基金项目
国家自然科学基金项目(62272102) （62272102）
福建省自然科学基金项目(2024J01070) （2024J01070）
