Residual spatial-temporal graph convolutional neural network for on-street parking availability prediction

Abstract

Smart cities can provide people with a wealth of information to make their lives more convenient. Among many other benefits, effective parking availability prediction is essential as it can improve the overall efficiency of parking and significantly reduce city congestion and pollution. In this paper, we propose a novel model for parking availability prediction, i.e., the residual spatial-temporal graph convolutional neural network, which enhances the accuracy and efficiency of the prediction process. The model utilises graph neural networks and temporal convolutional networks to capture the spatial and temporal features, respectively, fusing through a residual structure called the residual spatial-temporal convolutional block. We conducted experiments using real-world datasets to compare the performance of the proposed model with that of the baseline models. The experimental results demonstrate that our model outperforms the baseline models in predicting the long-term parking occupancy rate and achieves the fastest prediction speed.