面向动态交通流量预测的自适应图注意Transformer
刘宇轩,刘毅志,廖祝华,邹正标,汤璟昕

Adaptive graph attention Transformer for dynamic traffic flow prediction
Yuxuan LIU,Yizhi LIU,Zhuhua LIAO,Zhengbiao ZOU,Jingxin TANG
表 3 PEMS08数据集上不同模型的RMSE和MAE对比结果
Tab.3 Comparison results of RMSE and MAE for different models on PEMS08 dataset
模型Tp=15 minTp=30 minTp=45 minTp=60 min
RMSEMAERMSEMAERMSEMAERMSEMAE
STGCN26.9819.7729.4721.5232.8724.1434.5925.97
T-GCN25.3420.2228.7422.4133.0525.3235.1527.64
DCRNN24.4617.9327.5920.3731.4923.7832.9225.13
DMSTGCN24.0117.5826.8919.8429.3422.0831.7324.68
Trendformer23.7117.3326.1519.5428.8421.8530.5824.06
STTN22.7816.9525.4819.1627.3820.9229.0422.87
ATST-GCN23.5817.2625.5819.2827.1620.7928.7822.67
MSAGAFormer21.8716.0523.8717.2625.1317.7626.6319.14