Abstract
Accurate traffic prediction is a crucial aspect of intelligent transportation systems. However, existing methods typically rely on static graphs to learn correlations between different sensor in space, which ignores dynamic impact of latent factors on topology of the road network. To address this issue, we propose a traffic flow prediction method based on a dynamic spatial-temporal dual graph neural network that extracts deeper and finer-grained features from traffic data. Firstly, we propose a new data-driven strategy based on a dynamic spatial-temporal-aware graph to replace the commonly used predefined static graph in traditional graph convolutional networks. This strategy enables us to collect edge attributes (geographical proximity) and node attributes (spatial heterogeneity) between nodes. Secondly, we introduce the duality principle to construct the dual hypergraph of the traffic graph, which captures the correlations between edges of the traffic graph. In the process of dynamic graph convolutional iteration, we capture the dependencies between dynamic edge attributes and static node attributes on the basis of merging spatial relationships. Finally, an improved multi-head attention mechanism designed to represent dynamic spatial correlations. We conducted experiments on two real-world traffic prediction tasks, results demonstrate our method outperforms others.
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Acknowledgements
This work was supported by National Natural Science Foundation of China (No. 62176088), the Key Science and Technology Research Project of Henan Province of China (Grant No. 22102210067, 222102210022), and the Program for Science & Technology Development of Henan Province (No. 212102210412 and 202102310198).
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Wang, L. et al. (2024). Dynamic Spatial-Temporal Dual Graph Neural Networks for Urban Traffic Prediction. In: Liu, F., Sadanandan, A.A., Pham, D.N., Mursanto, P., Lukose, D. (eds) PRICAI 2023: Trends in Artificial Intelligence. PRICAI 2023. Lecture Notes in Computer Science(), vol 14325. Springer, Singapore. https://doi.org/10.1007/978-981-99-7019-3_34
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