面向动态交通流量预测的自适应图注意Transformer
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刘宇轩,刘毅志,廖祝华,邹正标,汤璟昕
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Adaptive graph attention Transformer for dynamic traffic flow prediction
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Yuxuan LIU,Yizhi LIU,Zhuhua LIAO,Zhengbiao ZOU,Jingxin TANG
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| 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 |
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| 模型 | Tp=15 min | | Tp=30 min | | Tp=45 min | | Tp=60 min | | RMSE | MAE | | RMSE | MAE | | RMSE | MAE | | RMSE | MAE | | STGCN | 26.98 | 19.77 | | 29.47 | 21.52 | | 32.87 | 24.14 | | 34.59 | 25.97 | | T-GCN | 25.34 | 20.22 | | 28.74 | 22.41 | | 33.05 | 25.32 | | 35.15 | 27.64 | | DCRNN | 24.46 | 17.93 | | 27.59 | 20.37 | | 31.49 | 23.78 | | 32.92 | 25.13 | | DMSTGCN | 24.01 | 17.58 | | 26.89 | 19.84 | | 29.34 | 22.08 | | 31.73 | 24.68 | | Trendformer | 23.71 | 17.33 | | 26.15 | 19.54 | | 28.84 | 21.85 | | 30.58 | 24.06 | | STTN | 22.78 | 16.95 | | 25.48 | 19.16 | | 27.38 | 20.92 | | 29.04 | 22.87 | | ATST-GCN | 23.58 | 17.26 | | 25.58 | 19.28 | | 27.16 | 20.79 | | 28.78 | 22.67 | | MSAGAFormer | 21.87 | 16.05 | | 23.87 | 17.26 | | 25.13 | 17.76 | | 26.63 | 19.14 |
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