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.
@article{arxiv.2306.04324,
title = {GCT-TTE: Graph Convolutional Transformer for Travel Time Estimation},
author = {Vladimir Mashurov and Vaagn Chopurian and Vadim Porvatov and Arseny Ivanov and Natalia Semenova},
journal= {arXiv preprint arXiv:2306.04324},
year = {2023}
}
Comments
17 pages, 7 figures, 4 tables; supplementary included; accepted in Journal of Big Data