Papers › GCT-TTE: Graph Convolutional Transformer for Travel Time Estimation
GCT-TTE: Graph Convolutional Transformer for Travel Time Estimation
Vladimir Mashurov, Vaagn Chopurian, Vadim Porvatov, Arseny Ivanov, Natalia Semenova
This paper introduces a new transformer-based model for the problem of travel time estimation. The key feature of the proposed GCT-TTE architecture is the utilization of different data modalities capturing different properties of an input path. Along with the extensive study regarding the model configuration, we implemented and evaluated a sufficient number of actual baselines for path-aware and path-blind settings. The conducted computational experiments have confirmed the viability of our pipeline, which outperformed state-of-the-art models on both considered datasets. Additionally, GCT-TTE was deployed as a web service accessible for further experiments with user-defined routes.
Code
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Tasks
Datasets
Introduced by this paper, per the archive.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Travel Time Estimation | TTE-A&O | GCT-TTE | Root mean square error (RMSE) | 147.89 | #1 of 6 | Archive leaderboard | report |
| Travel Time Estimation | TTE-A&O | GCT-TTE | mean absolute error | 92.26 | #1 of 6 | Archive leaderboard | report |
| Travel Time Estimation | TTE-A&O | DeepTTE | Root mean square error (RMSE) | 174.56 | #3 of 6 | Archive leaderboard | report |
| Travel Time Estimation | TTE-A&O | DeepTTE | mean absolute error | 111.03 | #3 of 6 | Archive leaderboard | report |
| Travel Time Estimation | TTE-A&O | WDR | Root mean square error (RMSE) | 190.09 | #4 of 6 | Archive leaderboard | report |
| Travel Time Estimation | TTE-A&O | WDR | mean absolute error | 97.22 | #4 of 6 | Archive leaderboard | report |
| Travel Time Estimation | TTE-A&O | DeepI2T | Root mean square error (RMSE) | 201.33 | #5 of 6 | Archive leaderboard | report |
| Travel Time Estimation | TTE-A&O | DeepI2T | mean absolute error | 97.99 | #5 of 6 | Archive leaderboard | report |
| Travel Time Estimation | TTE-A&O | DeepIST | Root mean square error (RMSE) | 241.29 | #6 of 6 | Archive leaderboard | report |
| Travel Time Estimation | TTE-A&O | DeepIST | mean absolute error | 153.88 | #6 of 6 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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