Gct-TTE: graph convolutional transformer for travel time estimation

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 m...

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Bibliographic Details
Main Authors: Vladimir Mashurov, Vaagn Chopuryan, Vadim Porvatov, Arseny Ivanov, Natalia Semenova
Format: Article
Language:English
Published: SpringerOpen 2024-01-01
Series:Journal of Big Data
Subjects:
Online Access:https://doi.org/10.1186/s40537-023-00841-1
Description
Summary: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.
ISSN:2196-1115