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Smart city article details
| Title | Stn-Gcn: Spatial And Temporal Normalization Graph Convolutional Neural Networks For Traffic Flow Forecasting |
|---|---|
| ID_Doc | 53045 |
| Authors | Wang C.; Wang L.; Wei S.; Sun Y.; Liu B.; Yan L. |
| Year | 2023 |
| Published | Electronics (Switzerland), 12, 14 |
| DOI | http://dx.doi.org/10.3390/electronics12143158 |
| Abstract | In recent years, traffic forecasting has gradually become a core component of smart cities. Due to the complex spatial-temporal correlation of traffic data, traffic flow prediction is highly challenging. Existing studies are mainly focused on graphical modeling of fixed road structures. However, this fixed graphical structure cannot accurately capture the relationship between different roads, affecting the accuracy of long-term traffic flow prediction. In order to address this problem, this paper proposes a modeling framework STN-GCN for spatial-temporal normalized graphical convolutional neural networks. In terms of temporal dependence, spatial-temporal normalization was used to divide the data into high-frequency and low-frequency parts, allowing the model to extract more distinct features. In addition, fine data input to the temporal convolutional network (TCN) was used in this module to conduct more detailed temporal feature extraction so as to ensure the accuracy of long-term sequence extraction. In addition, the transformer module was added to the model, which captured the real-time state of traffic flow by extracting spatial dependencies and dynamically establishing spatial correlations through a self-attention mechanism. During the training process, a curriculum learning (CL) method was adopted, which provided optimized target sequences. Learning from easier targets can help avoid getting trapped in local minima and yields better generalization performance to more accurately approximate global minima. As shown by experimental results the model performed well on two real-world public transportation datasets, METR-LA and PEMS-BAY. © 2023 by the authors. |
| Author Keywords | graph neural network; self-attention mechanism; spatial-temporal correlation; spatial-temporal normalization; transformer |
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