Spatial-Temporal Graph Convolutional Networks for Parking Space Prediction in Smart Cities

In smart cities, on-street parking space prediction is the key yet difficult point in smart parking system. However, conventional prediction methods generally neglect spatial and temporal dependencies and cannot predict long-term parking events accurately. To this end, we propose a parking space prediction scheme based on the spatial-temporal graph convolution networks (STGCN). We first consider the instantaneous status of the parking to calculate the on-street parking occupancy rate (POR). Then, based on the POR, we exploit a time convolution module and a graph convolution module to extract spatial and temporal dependencies of the parking spaces, respectively. Next, we design the parameters of the STGCN to predict the POR of all the parking spaces based on the spatial and temporal dependencies. Finally, based on the real-world data sets, we compare the proposed scheme with the benchmark models. The experimental results show that the proposed scheme has the best performance in predicting the POR.