Spatial-Temporal Interactive Dynamic Graph Convolution Network for Traffic Forecasting

• URL: http://arxiv.org/abs/2205.08689v2
• Date: Thu, 19 May 2022 00:38:10 GMT
• Title: Spatial-Temporal Interactive Dynamic Graph Convolution Network for Traffic Forecasting
• Authors: Aoyu Liu, Yaying Zhang
• Abstract summary: We propose a neural network-based Spatial-Temporal Interactive Dynamic Graph Convolutional Network (STIDGCN) for traffic forecasting in this paper. In STIDGCN, we propose an interactive dynamic graph convolution structure, which first divides the sequences at intervals and captures the spatial-temporal dependence of the traffic data simultaneously. Experiments on four real-world traffic flow datasets demonstrate that STIDGCN outperforms the state-of-the-art baseline.
• Score: 1.52292571922932
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate traffic forecasting is essential for smart cities to achieve traffic flow control, route planning, and detection. Although many spatial-temporal methods are currently proposed, these methods are deficient in capturing the spatial-temporal dependence of traffic data synchronously. In addition, most of the methods ignore the dynamically changing correlations between road network nodes that arise as traffic data changes. To address the above challenges, we propose a neural network-based Spatial-Temporal Interactive Dynamic Graph Convolutional Network (STIDGCN) for traffic forecasting in this paper. In STIDGCN, we propose an interactive dynamic graph convolution structure, which first divides the sequences at intervals and captures the spatial-temporal dependence of the traffic data simultaneously through an interactive learning strategy for effective long-term prediction. We propose a novel dynamic graph convolution module consisting of a graph generator, fusion graph convolution. The dynamic graph convolution module can use the input traffic data, pre-defined graph structure to generate a graph structure and fuse it with the defined adaptive adjacency matrix, which is used to achieve the filling of the pre-defined graph structure and simulate the generation of dynamic associations between nodes in the road network. Extensive experiments on four real-world traffic flow datasets demonstrate that STIDGCN outperforms the state-of-the-art baseline.

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