GCT-TTE: Graph Convolutional Transformer for Travel Time Estimation

7 Jun 2023  ยท  Vladimir Mashurov, Vaagn Chopurian, Vadim Porvatov, Arseny Ivanov, Natalia Semenova ยท

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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Datasets


Introduced in the Paper:

TTE-A&O

Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Travel Time Estimation TTE-A&O GCT-TTE mean absolute error 92.26 # 2
Root mean square error (RMSE) 147.89 # 1
Travel Time Estimation TTE-A&O DeepIST mean absolute error 153.88 # 6
Root mean square error (RMSE) 241.29 # 6
Travel Time Estimation TTE-A&O WDR mean absolute error 97.22 # 3
Root mean square error (RMSE) 190.09 # 4
Travel Time Estimation TTE-A&O DeepI2T mean absolute error 97.99 # 4
Root mean square error (RMSE) 201.33 # 5
Travel Time Estimation TTE-A&O DeepTTE mean absolute error 111.03 # 5
Root mean square error (RMSE) 174.56 # 3

Methods


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