English

GCT-TTE: Graph Convolutional Transformer for Travel Time Estimation

Artificial Intelligence 2023-10-17 v2

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.

Keywords

Cite

@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

R2 v1 2026-06-28T10:58:40.962Z