GCT-TTE: Graph Convolutional Transformer for Travel Time Estimation
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 16 of its 33 references that are in the map. First posted on arXiv 7 Jun 2023.
Builds on
16 of 33 references.
References · 16
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4 more
- 2019Travel Time Estimation without Road Networks: An Urban Morphological Layout Representation Approachcited by 8
- 2020Road Network Metric Learning for Estimated Time of Arrivalcited by 4
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.