## 4 Experiment
### Experimental Results and Discussion  
As shown in Table 1, the evaluation criteria among the different models were compared to predict the bike-sharing rental and return demand. The RMSE and MAE of each model at various prediction horizons are the average of 10 times the training results. The smaller the value of the MAE and RMSE, the better the performance of the model. ANN, XGBoost, and SVR have worse performance than the GCN- and LSTM-based neural networks in each prediction horizon, because these three models cannot capture the hidden temporal information and hidden spatial relationships among stations. LSTM and GCN have better performance than those three models, because LSTM can capture the hidden temporal information from the data and GCN has good ability to capture spatial attributes among data, especially for graph structure data. Besides, the comparison between GCN and LSTM can tell that the influence of spatial information contributes more to prediction accuracy than temporal information at a large time granularity. At a small time granularity, LSTM have more temporal information to capture, which makes the LSTM performance better than GCN at this situation. Combining GCN and LSTM, GC-LSTM performs better than GCN and LSTM because it integrates the attribute of those two models. STGA-LSTM has the best performance in each prediction horizon and under all criteria, followed by GC-LSTM in all the prediction models. After adding the attention mechanism with GC-LSTM, STGA-LSTM enhances the ability of capturing temporal and spatial information. When comparing STGA-LSTM with GC-LSTM, among the four different prediction horizons, the largest improvement occurs in the 15-min interval, with an increase of 4.49% in RMSE and 6.43% in MAE for rental demand prediction and an increase of 4.49% in RMSE and 6.43% in MAE for return demand prediction, respectively. This is because, given the total amount of data, the shorter the prediction horizon is, the longer the length of time sequence demand data will be. It will contain more detail when having a longer sequence demand data, which can help to improve the performance. The results explain why the RMSE and MAE grow larger as the prediction horizon turns larger. Figure 4 shows the comparison between the real demand curve and predicted demand curve in the 60-min prediction horizon at station No. 125. The datasets were drawn based on the training in the 60-min prediction horizon. This prediction horizon is a reasonable value for bike rebalancing [52]. We chose this station because it recorded the largest passenger flows among all stations, as the work in Pandya [33] did. The prediction is implemented by using the STGA-LSTM model with the test dataset. The test dataset has a time span of five days, consisting of 120 intervals. The predictions of rental demand and return demand are shown in Figure 4a and Figure 4b, respectively. We can see that both the rental and return demand prediction fit the real demand well, with very small offsets at the peak point. When predicting the peak demand, especially for those time that the demand suddenly raises up, the model loses some accuracy compared with the prediction accuracy in other periods. As for demand valleys, the proposed model performs well on both types