Uncertainty-Aware Temporal Graph Convolutional Network for Traffic Speed Forecasting

Traffic speed forecasting has been a very active research area as it is essential for Intelligent Transportation Systems. Although a plethora of deep learning methods have been proposed for traffic speed forecasting, the majority of them can only make point-wise prediction, which may not provide eno...

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Vydáno v:IEEE transactions on intelligent transportation systems Ročník 25; číslo 8; s. 8578 - 8590
Hlavní autoři: Qian, Weizhu, Nielsen, Thomas Dyhre, Zhao, Yan, Larsen, Kim Guldstrand, Yu, James Jianqiao
Médium: Journal Article
Jazyk:angličtina
Vydáno: IEEE 01.08.2024
Témata:
Convolutional neural networks
Data models
Deep learning
Forecasting
gated recurrent unit
graph convolutional network
Predictive models
Roads
spatio-temporal model
Traffic speed forecasting
Uncertainty
uncertainty quantification
ISSN:1524-9050, 1558-0016
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Shrnutí:Traffic speed forecasting has been a very active research area as it is essential for Intelligent Transportation Systems. Although a plethora of deep learning methods have been proposed for traffic speed forecasting, the majority of them can only make point-wise prediction, which may not provide enough information for critical real-world scenarios where prediction confidence also need to be estimated, e.g., route planning for ambulances and rescue vehicles. To address this issue, we propose a novel uncertainty-aware deep learning method coined Uncertainty-Aware Temporal Graph Convolutional Network (UAT-GCN). UAT-GCN employs a Graph Convolutional Network and Gated Recurrent Unit based architecture to capture spatio-temporal dependencies. In addition, UAT-GCN consists of a specialized regressor for estimating both epistemic (model-related) and aleatoric (data-related) uncertainty. In particular, UAT-GCN utilizes Monte Carlo dropout and predictive variances to estimate epistemic and aleatoric uncertainty, respectively. In addition, we also consider the recursive dependency between predictions to further improve the forecasting performance. An extensive empirical study with real datasets offers evidence that the proposed model is capable of advancing current state-of-the-arts in terms of point-wise forecasting and quantifying prediction uncertainty with high reliability. The obtained results suggest that, compared to existing methods, the RMSE and MAE of the proposed model on the SZ-taxi dataset are reduced by 2.15% and 7.23%, respectively; the RMSE and MAE of the proposed model on the Los-loop dataset are reduced by 4.17% and 8.53%, respectively.
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2024.3365721