Current research on traffic flow prediction commonly employs graph convolutional networks(GCNs)to learn spatial graph structure.However,these approaches often lack the ability to retain important node features within the graph and overlook long-distance dependencies between time se-ries.To address these issues,a traffic flow prediction method that combines gated spatiotemporal graph neural network and temporal convolutional networks(TCNs)was proposed.First,the gated graph neural network(GGNN)was used in the model to learn the spatial graph structure while pre-serving key node feature information.Then,TCN was employed to capture long-distance dependen-cies between time series.Finally,comparison study,ablation study,and hyperparameter study were conducted on the publicly available PeMSD04 and PeMSD08 traffic flow datasets.Experimental re-sults show that the GGNN-TCN model significantly outperforms baseline models in terms of MAE,RMSE,and MAPE.The ablation study confirms that both the GGNN and TCN components contrib-ute positively to the overall model performance,while parameter study indicate that the model achieves optimal performance when the number of GGNN layers is set to 2.
关键词
空间图结构/长距离依赖/交通流预测/时间序列
Key words
spatial graph structure/long-distance dependency/traffic flow prediction/time series
维普
万方数据