mlsanityData to 7 Oct 2026

GCT-TTE: Graph Convolutional Transformer for Travel Time Estimation

arXiv 2306.04324 ↗First posted 7 Jun 2023cs.AIcited by 2

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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
  1. 2019Travel Time Estimation without Road Networks: An Urban Morphological Layout Representation Approachcited by 8
  2. 2020Road Network Metric Learning for Estimated Time of Arrivalcited by 4
  3. 2021Hybrid Graph Embedding Techniques in Estimated Time of Arrival Taskcited by 3
  4. 2022Logistics, Graphs, and Transformers: Towards improving Travel Time Estimationcited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.