Papers › Logistics, Graphs, and Transformers: Towards improving Travel Time Estimation
Logistics, Graphs, and Transformers: Towards improving Travel Time Estimation
Natalia Semenova, Vadim Porvatov, Vladislav Tishin, Artyom Sosedka, Vladislav Zamkovoy
The problem of travel time estimation is widely considered as the fundamental challenge of modern logistics. The complex nature of interconnections between spatial aspects of roads and temporal dynamics of ground transport still preserves an area to experiment with. However, the total volume of currently accumulated data encourages the construction of the learning models which have the perspective to significantly outperform earlier solutions. In order to address the problems of travel time estimation, we propose a new method based on transformer architecture - TransTTE.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Travel Time Estimation | TTE-A&O | TransTTE | Root mean square error (RMSE) | 168.421 | #2 of 6 | Archive leaderboard | report |
| Travel Time Estimation | TTE-A&O | TransTTE | mean absolute error | 83.616 | #2 of 6 | Archive leaderboard | report |
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