Multi-Step Parking Demand Prediction Model Based on Multi-Graph Convolutional Transformer - Yixiong Zhou; Xiaofei Ye; Xingchen Yan; Tao Wang; Jun Chen (Systems)
The increase in motorized vehicles in cities and the inefficient use of parking spaces have exacerbated parking difficulties in cities. To effectively improve the utilization rate of parking spaces, it is necessary to accurately predict future parking demand. This paper proposes a deep learning model based on multi-graph convolutional Transformer, which captures geographic spatial features through a Multi-Graph Convolutional Network (MGCN) module and mines temporal feature patterns using a Transformer module to accurately predict future multi-step parking demand. The model was validated using historical parking transaction volume data from all on-street parking lots in Nanshan District, Shenzhen, from September 2018 to March 2019, and its superiority was verified through comparative experiments with benchmark models. The results show that the MGCN–Transformer model has a MAE, RMSE, and R2 error index of 0.26, 0.42, and 95.93%, respectively, in the multi-step prediction task of parking demand, demonstrating its superior predictive accuracy compared to other benchmark models.