GCT-TTE: Graph Convolutional Transformer for Travel Time Estimation
This addresses travel time prediction for navigation systems, but it appears incremental as it builds on existing transformer and graph convolutional methods.
The paper tackled travel time estimation by introducing a transformer-based model that uses multiple data modalities, and it outperformed state-of-the-art models on two datasets.
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.