A spatio-temporal graph neural network framework for predicting usage efficiency of e-scooter sharing services
This thesis focuses on predicting the usage efficiency of e-scooters through the application of a Spatio-Temporal Graph Neural Network (STGNN). The predictions made by the STGNN will be compared with classical machine learning (ML) methods, including Random Forest (RF), Artificial Neural Network (ANN), and Linear Regression (LR). To facilitate the prediction of usage efficiency, the Time to Bookin