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Capturing Local and Global Spatial-Temporal Correlations of Spatial-Temporal Graph Data for Traffic Flow Prediction
conference contribution
posted on 2023-02-01, 03:15 authored by S Cao, L Wu, R Zhang, Jianxin LiJianxin Li, D WuTraffic flow prediction is a challenging task due to complex spatial-temporal correlations. Most existing methods leverage graph convolutional network (GCN) to capture spatial correlations. However, GCN has limited ability in mining global spatial correlations. Multi-layer GCN for aggregating multi-order neighbor information will result in high-degree nodes being prone to over-smoothing. To this end, we develop a graph convolutional recurrent attention network (GCRAN) for traffic flow prediction. Specifically, we take the advantage of Gated Recurrent Units (GRU) and Attention to explore local and global temporal correlations. Moreover, we design a novel local context aware spatial attention to extract local and global spatial correlations simultaneously. Experiments on two public real-world traffic datasets demonstrate that GCRAN outperform state-of-the-art baselines.
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2022-JulyPagination
1-8Publisher DOI
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2022-07-18End date
2022-07-23ISBN-13
9781728186719Title of proceedings
Proceedings of the International Joint Conference on Neural NetworksEvent
2022 International Joint Conference on Neural Networks (IJCNN)Publisher
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