Source
Journal of Big Data
DATE OF PUBLICATION
01/13/2024
Authors
Vadim Porvatov
Natalia Semenova
Vladimir Mashurov
Vaagn Chopuryan
Arseny Ivanov
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Gct-TTE: graph convolutional transformer for travel time estimation
Machine learning,
Graph convolutional networks,
Transformers,
Geospatial data,
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 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.
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