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.
| Task | Dataset | Metric | Value | Model |
|---|---|---|---|---|
| regression | TTE-A&O | Root mean square error (RMSE) | 147.89 | GCT-TTE |
| regression | TTE-A&O | mean absolute error | 92.26 | GCT-TTE |
| regression | TTE-A&O | Root mean square error (RMSE) | 174.56 | DeepTTE |
| regression | TTE-A&O | mean absolute error | 111.03 | DeepTTE |
| regression | TTE-A&O | Root mean square error (RMSE) | 190.09 | WDR |
| regression | TTE-A&O | mean absolute error | 97.22 | WDR |
| regression | TTE-A&O | Root mean square error (RMSE) | 201.33 | DeepI2T |
| regression | TTE-A&O | mean absolute error | 97.99 | DeepI2T |
| regression | TTE-A&O | Root mean square error (RMSE) | 241.29 | DeepIST |
| regression | TTE-A&O | mean absolute error | 153.88 | DeepIST |