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Spatial-Temporal Position-Aware Graph Convolution Networks for Traffic Flow Forecasting
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DOI：10.1109/TITS.2022.3220089.png)
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摘要
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Recent works demonstrate that capturing correlations between road network nodes is crucial to improving traffic flow forecasting accuracy. In general, there are spatial, temporal, and joint spatial-temporal correlations between two nodes, whose strength is related to spatial and temporal position factors. For example, traffic congestion that occurs at a traffic hub has a wider and stronger impact than that at a branch road. Moreover, the above impacts can vary with temporal position. Although spatial-temporal graph convolution networks have become a popular paradigm for modeling those correlations, there are still three problems with existing models: (i) failing to effectively model joint spatial-temporal correlations; (ii) ignoring spatial and temporal position factors when modeling the aforementioned correlations; and (iii) failing to capture distinct spatial-temporal patterns of each node. To cope with the above issues, this paper proposes a novel Spatial-Temporal Position-aware Graph Convolution Network (STPGCN) for traffic flow forecasting. Specifically, a trainable embedding module is constructed to represent the spatial and temporal positions of the nodes. Subsequently, a spatial-temporal position-aware relation inference module is proposed to adaptively infer the correlation weights of the three important spatial-temporal relations. Based on this, the generated spatial-temporal relations are integrated into a graph convolution layer for aggregating and updating node features. Finally, we design a spatial-temporal position-aware gated activation unit in the graph convolution, to capture the node-specific pattern features under the guidance of position embedding. Extensive experiments on six real-world datasets demonstrate the superiority of our model in terms of prediction performance and computational efficiency.
Keyword:
Correlation
Convolution
Forecasting
Predictive models
Feature extraction
Roads
Data models
Traffic flow forecasting
spatial-temporal correlations
spatial-temporal position
graph convolution networks
期刊
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8.4
论文数:
9.7K
被引数:
6.3W
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