摘要
Abstract
Traffic prediction faces three primary challenges:traditional spatiotemporal modeling methods struggle to capture long-range dependencies effectively,fixed time-window mechanisms cannot adapt to dynamic temporal patterns,and conventional statistical-based models exhibit limitations in modeling complex topological relationships.To address these issues,this study proposes a Temporal-enhanced Efficient Graph Attention Network(T-EGT).First,an Efficient Multi-head Self-Attention(EMSA)mechanism is designed,employing parameter sharing and sparse computation strategies to reduce the computational complexity of attention heads from O(N2)to O(N logaN).Second,a linear temporal extension module is developed,extending the temporal perception range from fixed K steps to an elastic window of K+△ through learnable temporal convolution kernels,where △∈[0,12]serves as an adaptive adjustment parameter.Finally,a dynamic graph inference architecture is constructed by utilizing the neighborhood aggregation characteristics of Graph Neural Networks(GNNs)to automatically generate topological relationship matrixes containing 83 traffic elements at each time step.Experiments on five benchmark datasets,including PeMSD4 and METR-LA,demonstrate that T-EGAT significantly outperforms 16 baseline models(including Diffusion Convolutional Recurrent Neural Network(DCRNN),GraphWaveNet,and Attention Based Spatial-Temporal Graph Convolutional Network(ASTGCN)),achieving a 2.77％-5.97％reduction in Mean Absolute Error(MAE),3.12％-6.44％improvement in Root Mean Square Error(RMSE),and 1.41％-2.3％decrease in single-step prediction time.Ablation studies quantify the module contributions:EMSA accounts for a 42％accuracy improvement,the temporal extension module reduces long-term prediction errors by 17％,and the dynamic graph generation mechanism enhances the topological modeling accuracy by 29％.The model demonstrates enhanced robustness in sudden traffic accident scenarios,achieving an anomaly detection F1 value of 0.873,indicating a 21.5％improvement over conventional methods.These findings provide a new technical framework for real-time traffic management systems with an elastic temporal modeling mechanism and efficient attention architecture,offering universal solutions for spatiotemporal prediction tasks.关键词
智能交通/交通预测模型/图神经网络/交通流/多头自注意力机制/人工智能决策Key words
intelligent traffic/traffic prediction model/Graph Neural Network(GNN)/traffic flow/multi-head self-attention mechanism/artificial intelligence decision-making分类
信息技术与安全科学
