An adaptive traffic flow prediction model based on spatiotemporal graph neural network

摘要

The traffic flow prediction task is essential to the urban intelligent transportation system. Due to the complex correlation of traffic flow data, insufficient use of spatiotemporal features will often lead to significant deviations in prediction results. This paper proposes an adaptive traffic flow prediction model AD-GNN based on spatiotemporal graph neural network. The gated temporal convolutional network captures the temporal dependence between layers. Moreover, the diffusion graph convolutional network simulates the spatial relationship between nodes. Then, the parameterized adjacency matrix is used to construct an adaptive convolutional network to adaptively mine the implicit global deep spatial dependence. The experimental results show that the model has good prediction performance on three real public datasets and can sufficiently meet real needs.