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.
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Acknowledgments
This work was funded by the National Natural Science Foundation of China (71571186) and the Postgraduate Research Innovation Project of Hunan Province, China (CX20200058).
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Zhu, M., Zhu, X., Zhu, C. (2021). STGATP: A Spatio-Temporal Graph Attention Network for Long-Term Traffic Prediction. In: Farkaš, I., Masulli, P., Otte, S., Wermter, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2021. ICANN 2021. Lecture Notes in Computer Science(), vol 12893. Springer, Cham. https://doi.org/10.1007/978-3-030-86365-4_21
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