## 4 Experiments
### Performance Comparison  
Table 1 shows the results of our proposed model, the improved spatio-temporal residual network (ISTR-Net) and its two variants compared with four baseline models. From the table, we can see that our model achieves the lowest RMSE and MAE, which proves that our proposed method can more effectively predict bus traffic flow than the baseline methods. More specifically, we can see that the predicted result of historical average (HA) is worst, which simply calculates the average value of historical flow in some previous relative time interval, for example, using all previous historical flow of 8:00 to 8:30 to predict 8:00 to 8:30, thus it cannot capture the temporal correlation of traffic flow effectively. Autoregressive integrated moving average (ARIMA) performs poorly when it just considers the temporal correlation of traffic flow, and overlooks the spatial correlation of traffic flow, such as the fact that traffic flow in one region may affect its adjacent region. For DeepST and ST-ResNet, we used the same hyperparameters with our proposed methods, we can see that DeepST and ST-ResNet, which use convolution neural networks to capture the spatio-temporal correlation of bus traffic flow, achieve better results than the traditional time-series method such as HA and ARIMA. However, these methods have not considered the bus scenario patterns. Compared with ST-ResNet, our proposed method uses improved residual blocks to model the spatio-temporal correlation of bus traffic flow effectively, while at the same time considering bus scenario patterns. Consequently, our proposed method outperforms those methods. Figure 7: Warm-up training on Beijing transportation smart card data. **Left**: The two curves denote training loss and validation loss, respectively. **Right**: The two curves denote training normalization root mean square error (RMSE) and validation normalization RMSE, respectively. We also compare variants of our proposed method. There are two variants. In the first variant, we did not use the bus scenario patterns component which feeds nothing to the bus scenario patterns component, marked as none in the table. In the second variant, we encoded the metadata of weekday and weekend into one-hot values, and then fed the one-hot values instead of the flow sum value to the bus scenario patterns component to capture the scenario patterns, which is marked as metadata in the table. From the table, we can see that without the bus scenario patterns component, it performs poorly compared with our original method, which proves the effectiveness of the bus scenario patterns component. We also find that feeding one-hot encoded metadata of weekday and weekend cannot capture the bus scenario patterns, and it may lead to overfitting compared with the first variant, which also performs poorly. However, these two variants are all outperformed by the four baseline methods, which shows the effectiveness of our improved residual block. Therefore, we can see that it is important to capture both spatio-temporal correlation and its scenario patterns for bus traffic flow prediction, and our proposed method can synthetically capture spatio-temporal correlation and bus specific scenario patterns of bus traffic flow. We further compared our proposed method with ST-ResNet during the rush hour period and off-peak period of the last four test days, that is from 27 August 2015 to 30 August 2015. As a result of the space limitation, we only show RMSE here, though we got the same conclusion of MAE. Figure 8 shows RMSE between ST-ResNet and our proposed method in rush hour (7:30-9:30), evening rush hour (17:00-19:00), and the off-peak period (12:00-14:00), respectively. From the figure, we can see that our proposed method has a lower RMSE compared with ST-ResNet, which shows that our proposed method has a better ability for bus traffic flow prediction in different periods. Moreover, we can see that prediction during rush hour is generally worse than the off-peak period.