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Travel Time Estimation 벤치마크

Travel Time Estimation on TTE-A&O

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Root mean square error (RMSE) 낮을수록 좋음

147.9 171.2 194.6 217.9 241.3 2022-07 2026-09 TransTTE — 168.421 (2022-07-12) GCT-TTE — 147.89 (2023-06-07) DeepTTE — 174.56 (2023-06-07) WDR — 190.09 (2023-06-07) DeepI2T — 201.33 (2023-06-07) DeepIST — 241.29 (2023-06-07) TransTTE — 168.421 (2022-07-12) GCT-TTE — 147.89 (2023-06-07)
RankModel Root mean square error (RMSE)mean absolute error PaperCodeYear
1 GCT-TTE 147.8992.26 GCT-TTE: Graph Convolutional Transformer for Travel Time Estimation eighonet/gct-tte 2023
2 TransTTE 168.42183.616 Logistics, Graphs, and Transformers: Towards improving Travel Time Estimation vloods/transtte_demo 2022
3 DeepTTE 174.56111.03 GCT-TTE: Graph Convolutional Transformer for Travel Time Estimation eighonet/gct-tte 2023
4 WDR 190.0997.22 GCT-TTE: Graph Convolutional Transformer for Travel Time Estimation eighonet/gct-tte 2023
5 DeepI2T 201.3397.99 GCT-TTE: Graph Convolutional Transformer for Travel Time Estimation eighonet/gct-tte 2023
6 DeepIST 241.29153.88 GCT-TTE: Graph Convolutional Transformer for Travel Time Estimation eighonet/gct-tte 2023
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