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Vladimir Mashurov Vaagn Chopurian Vadim Porvatov Arseny Ivanov Natalia Semenova

Abstract
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 Repositories
Benchmarks
| Benchmark | Methodology | Metrics |
|---|---|---|
| travel-time-estimation-on-tte-a-o | WDR | Root mean square error (RMSE): 190.09 mean absolute error: 97.22 |
| travel-time-estimation-on-tte-a-o | DeepI2T | Root mean square error (RMSE): 201.33 mean absolute error: 97.99 |
| travel-time-estimation-on-tte-a-o | DeepTTE | Root mean square error (RMSE): 174.56 mean absolute error: 111.03 |
| travel-time-estimation-on-tte-a-o | DeepIST | Root mean square error (RMSE): 241.29 mean absolute error: 153.88 |
| travel-time-estimation-on-tte-a-o | GCT-TTE | Root mean square error (RMSE): 147.89 mean absolute error: 92.26 |
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