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- W4385324103 abstract "Recently deep learning-based models have performed remarkably performance in traffic flow prediction in intelligent traffic system. To capture the complex spatial-temporal features better, this work proposes a novel multi-scale dual-attention feature fusion model, abbreviated as MDAFF-Net, for long-term traffic prediction. First, to address the lack of information capture of graph-based traffic data of the previous methods, the input data are fused after modeling by splitting the local and global attention into two paths separately, which enhances the ability to capture spatial-temporal correlations. Then, to handle temporal series better, a multi-scale temporal convolutional module is constructed by using a one-dimensional convolutional layer for extracting features in the temporal dimension, which boosts the generalization ability of the spatial-temporal convolutional layer. Extensive experiments are conducted on two public transportation datasets, i.e., METR-LA and PEMS-BAY, and the experiment results indicate that the proposed method achieves the best performance compared with competitive baselines." @default.
- W4385324103 created "2023-07-28" @default.
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- W4385324103 date "2022-12-01" @default.
- W4385324103 modified "2023-09-23" @default.
- W4385324103 title "MDAFF-Net: A Multi-scale Dual-attention Feature Fusion Model for Long-term Traffic Prediction" @default.
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- W4385324103 doi "https://doi.org/10.1109/smartworld-uic-atc-scalcom-digitaltwin-pricomp-metaverse56740.2022.00096" @default.
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