Predicting electric vehicle charging demand using a heterogeneous spatio-temporal graph convolutional network

•Propose a spatial–temporal graph conventional network to predict charging demand;•Train the model with a large private electric vehicle trajectory dataset;•Our model outperforms recently popular spatio-temporal prediction methods;•Incorporating the demand and geographic graphs helps improve model performance.