A lightweight spatiotemporal graph dilated convolutional network for urban sensor state prediction

Spatiotemporal prediction is one attractive research topic in urban computing, which is of great significance to urban planning and management. At present, there are many attempts to predict the spatiotemporal state of systems using various deep learning models. However, most existing models tend to improve prediction accuracy with larger parameter scale and time consumption, but ignoring ease of use in practice. To overcome this question, we propose a lightweight spatiotemporal graph dilated convolutional network called STGDN with satisfactory prediction accuracy and lower model complexity. More specifically, we propose a novel dilated convolution operator and integrate it into traditional causal convolutional networks and graph convolutional networks to greatly improve the efficiency of prediction. The proposed dilated convolution operator can significantly reduce the depth of the model, thereby reducing the parameter scale and improving the computational efficiency of the model. We conducted on multi experiments on three real-world spatiotemporal datasets (traffic dataset, PM2.5 dataset, and temperature dataset) to prove the effectiveness and advantage of our proposed STGDN. The experimental results show that the proposed STGDN model outperforms or achieves comparable prediction accuracy of the existing nine baselines with higher operational efficiency and fewer model parameters. Codes are available at anonymous private link on https://doi.org/10.6084/m9.figshare.23935683.