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3 months ago

GCT-TTE: Graph Convolutional Transformer for Travel Time Estimation

Vladimir Mashurov Vaagn Chopurian Vadim Porvatov Arseny Ivanov Natalia Semenova

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 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.

Code Repositories

eighonet/gct-tte
Official
pytorch
Mentioned in GitHub

Benchmarks

BenchmarkMethodologyMetrics
travel-time-estimation-on-tte-a-oWDR
Root mean square error (RMSE): 190.09
mean absolute error: 97.22
travel-time-estimation-on-tte-a-oDeepI2T
Root mean square error (RMSE): 201.33
mean absolute error: 97.99
travel-time-estimation-on-tte-a-oDeepTTE
Root mean square error (RMSE): 174.56
mean absolute error: 111.03
travel-time-estimation-on-tte-a-oDeepIST
Root mean square error (RMSE): 241.29
mean absolute error: 153.88
travel-time-estimation-on-tte-a-oGCT-TTE
Root mean square error (RMSE): 147.89
mean absolute error: 92.26

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GCT-TTE: Graph Convolutional Transformer for Travel Time Estimation | Papers | HyperAI