Logistics, Graphs, and Transformers: Towards Improving Travel Time Estimation
Abstract
The problem of travel time estimation is widely considered as the fundamental challenge of modern logistics. The complex nature of interconnections between spatial aspects of roads and temporal dynamics of ground transport still preserves an area to experiment with. However, the total volume of currently accumulated data encourages the construction of the learning models which have the perspective to significantly outperform earlier solutions. In order to address the problems of travel time estimation, we propose a new method based on transformer architecture – TransTTE.
Cite
Text
Semenova et al. "Logistics, Graphs, and Transformers: Towards Improving Travel Time Estimation." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2022. doi:10.1007/978-3-031-26422-1_36Markdown
[Semenova et al. "Logistics, Graphs, and Transformers: Towards Improving Travel Time Estimation." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2022.](https://mlanthology.org/ecmlpkdd/2022/semenova2022ecmlpkdd-logistics/) doi:10.1007/978-3-031-26422-1_36BibTeX
@inproceedings{semenova2022ecmlpkdd-logistics,
title = {{Logistics, Graphs, and Transformers: Towards Improving Travel Time Estimation}},
author = {Semenova, Natalia and Porvatov, Vadim and Tishin, Vladislav and Sosedka, Artyom and Zamkovoy, Vladislav},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2022},
pages = {589-593},
doi = {10.1007/978-3-031-26422-1_36},
url = {https://mlanthology.org/ecmlpkdd/2022/semenova2022ecmlpkdd-logistics/}
}