关键词: 交通流预测, 时空依赖, 图神经网络, 不确定性建模, 多尺度分析

Abstract: The accuracy of traffic flow prediction directly affects the dispatching efficiency of intelligent transportation systems and indirectly determines the quality of travel experience. In response to the deficiencies of existing models in complex spatio-temporal dependency modeling, dynamic road network relationship processing, and handling of uncertain disturbances, a Multiscale Uncertianty-aware Dual-Graph Collaborative Learning(MUDGCL) is proposed. Firstly, the traffic sequence is decoupled into trend and disturbance terms through wavelet decomposition, and multi-scale features are captured by time attention mechanism and temporal convolutional network respectively, with adaptive fusion achieved through gated units. Meanwhile, a dual-graph collaborative framework is constructed, combining the probabilistic modeling of variational graph generator and the dynamic graph structure of periodic time embedding to jointly model stable topology and time-varying spatial correlations. Additionally, node importance masks and Kullback-Leibler Divergence(KL) constraints are introduced to enhance model robustness. Experimental results on PEMS04, PEMS08, METR-LA, and PEMS-BAY datasets demonstrate that MUDGCL achieved reductions of 1.57% in Mean Absolute Error(MAE) and 3.07% in Root Mean Square Error (RMSE) compared to Multi-scale Time-Enhanced Graph Convolutional Recurrent Network(MTEGCRN) on PEMS04. On PEMS08, decreases of 5.98% in MAE, 5.64% in RMSE, and 2.61% in Mean Absolute Percentage Error(MAPE) were observed relative to Temporal-Frequency Self-supervised Guided Graph Learning(TFSGGL). For multi-step prediction tasks on METR-LA (3/6/12-step), MAE was reduced by 1.47%, 2.59%, and 1.45% respectively compared to Random Graph Diffusion Attention Network(RGDAN). On PEMS-BAY, MAE decreased by 2.99%, 2.41%, and 3.55% respectively relative to Wavelet Decomposition and Structure-Enhanced Graph Attention Method(WD-SEGAM). Furthermore, MUDGCL outperforms the existing mainstream models in all evaluation metrics, effectively improving the ability to model complex spatio-temporal dependencies, the accuracy of uncertainty quantification in prediction, and generalization performance.

Key words: traffic flow prediction, spatiotemporal dependency, graph neural network, uncertainty modeling, multiscale analysis