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- W3199464873 abstract "Traffic prediction is essential to public transportation management in cities. However, long-term traffic prediction involves complex spatio-temporal correlations changing dynamically, which is highly challenging to capture in road networks. We focus on these dynamic correlations and propose a spatio-temporal graph modeling method to solve the long-term traffic prediction problem. Our proposed method builds a Spatio-Temporal Graph Attention network for Traffic Prediction (STGATP), exploring and capturing the complex spatial-temporal nature in traffic networks. We apply dilated causal convolution with a gated fusion in the temporal modeling block, and diffusion convolution with the attention mechanism in the spatial modeling block. This results in that STGATP can simultaneously capture spatial dependencies and temporal dependencies in road networks. Finally, we conduct the experiments on public traffic datasets METR-LA and PEMS-BAY, and our method reaches superior performance. In particular, STGATP surpasses state-of-the-art methods by up to 11% improvement of RMSE measure on the PEMS-BAY datasets." @default.
- W3199464873 created "2021-09-27" @default.
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- W3199464873 date "2021-01-01" @default.
- W3199464873 modified "2023-10-16" @default.
- W3199464873 title "STGATP: A Spatio-Temporal Graph Attention Network for Long-Term Traffic Prediction" @default.
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- W3199464873 doi "https://doi.org/10.1007/978-3-030-86365-4_21" @default.
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