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Spatial-temporal gated graph convolutional network: a new deep learning framework for long-term traffic speed forecasting.
- Source :
-
Journal of Intelligent & Fuzzy Systems . 2023, Vol. 44 Issue 6, p10437-10450. 14p. - Publication Year :
- 2023
-
Abstract
- The key to solving traffic congestion is the accurate traffic speed forecasting. However, this is difficult owing to the intricate spatial-temporal correlation of traffic networks. Most existing studies either ignore the correlations among distant sensors, or ignore the time-varying spatial features, resulting in the inability to extract accurate and reliable spatial-temporal features. To overcome these shortcomings, this study proposes a new deep learning framework named spatial-temporal gated graph convolutional network for long-term traffic speed forecasting. Firstly, a new spatial graph generation method is proposed, which uses the adjacency matrix to generate a global spatial graph with more comprehensive spatial features. Then, a new spatial-temporal gated recurrent unit is proposed to extract the comprehensive spatial-temporal features from traffic data by embedding a new graph convolution operation into gated recurrent unit. Finally, a new self-attention block is proposed to extract global features from the traffic data. The evaluation on two real-world traffic speed datasets demonstrates the proposed model can accurately forecast the long-term traffic speed, and outperforms the baseline models in most evaluation metrics. [ABSTRACT FROM AUTHOR]
- Subjects :
- *TRAFFIC speed
*TRAFFIC estimation
*DEEP learning
*TRAFFIC congestion
Subjects
Details
- Language :
- English
- ISSN :
- 10641246
- Volume :
- 44
- Issue :
- 6
- Database :
- Academic Search Index
- Journal :
- Journal of Intelligent & Fuzzy Systems
- Publication Type :
- Academic Journal
- Accession number :
- 167307003
- Full Text :
- https://doi.org/10.3233/JIFS-224285