Traditional traffic flow prediction models often fail to effectively account for temporal delays across regions and time periods， and struggle to capture both short-term fluctuations and long-term trends in traffic flow. To address these limitations， a Wavelet Transform and Attention-based Latency-Aware long short Graph Neural Network （WTA-LAGNN） was proposed. Firstly， wavelet decomposition was applied to separate traffic flow data into long-term trend and short-term fluctuation patterns. Key features in the short-term fluctuation pattern were enhanced using a feature enhancement module， improving the model's sensitivity to short-term variations. For the long-term trend， a sequence-enhanced multi-head self-attention was designed to capture sustained changes in flow. To address temporal delay effects， a time delay-aware layer was designed to optimize spatio-temporal dependencies in traffic flow propagation between regions. Finally， the fusion layer outputted the prediction results. Experiments were conducted on real highway traffic datasets including PeMS03， PeMS04， PeMS07 and PeMS08 for 60-minute flow prediction. The results showed that compared to Spatio-Temporal Graph Neural Controlled Differential Equation（STG-NCDE）， WTA-LAGNN reduced the Mean Absolute Error （MAE） and Root Mean Square Error （RMSE） by 5.14% and 2.69%， as well as 5.80% and 2.69% on the PeMS03 and PeMS07 datasets， respectively； compared to Traffic Flow Matrix-based Graph Convolutional Attention Mechanism （TFM-GCAM）， WTA-LAGNN reduced the MAE and RMSE by 9.28% and 3.32% on PeMS08； compared to Spatio-Temporal Fusion Graph Convolutional Network（STFGCN）， WTA-LAGNN reduced the MAE and RMSE by 3.53% and 2.72% on PeMS04， respectively. These results demonstrate that WTA-LAGNN outperforms comparison baseline models， and can effectively capture spatio-temporal dependencies， thereby improving traffic flow prediction precision.