ru
ru
Source
Joint European Conference on Machine Learning and Knowledge Discovery in Databases
DATE OF PUBLICATION
03/18/2023
Authors
Vadim Porvatov Natalia Semenova Vladislav Tishin Artyom Sosedka Vladislav Zamkovoy
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Logistics, Graphs, and Transformers: Towards Improving Travel Time Estimation

Graph embedding, Travel time estimation , Geospatial linked data

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.

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