## 3 Methodology
### Model Implementation
Therefore, among the statistical approaches, the local spatial regression models have better forecasting performance than the global regression models, \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{4}{c}{**Evaluation Metrics**} \\  & **MAE** & **MAPE** & **RMSE** & **R\({}^{2}\)** \\ \hline MLR & 2.26 & 0.477 & 2.91 & 0.645 \\ GPR & 6.579 & 0.646 & 8.666 & 0.552 \\ GWR & 1.589 & 0.251 & 1.852 & 0.89 \\ RF & 2.019 & 0.366 & 2.487 & 0.764 \\ LSTM & 1.772 & 0.281 & 2.247 & 0.806 \\ Joint-IF & 1.567 & 0.197 & 1.836 & 0.911 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance evaluation of all models for predicting the complete transfer passenger flows. Figure 7: Measured metrics of the Joint-IF model for forecasting transfer passenger flow under various missing cases: (**a**) 50%, RM; (**b**) 50%, NM; (**c**) 70%, RM; (**d**) 70%, NM. and their performance is significantly improved after taking spatial location information into account. Additionally, when comparing the results of the statistical approaches, ML and DL methods in predicting complete transfer passenger flow, it was found that the ML and DL methods outperformed other statistical methods except for GWR. The LSTM model has good forecasting performance for complete transfer passenger flow. However, compared with LSTM, GWR has a smaller prediction error and better prediction performance, and GWR takes less time and has higher computational efficiency than LSTM. Further, GWR can be improved to obtain more accurate prediction results for transfer passenger flow. Overall, the Joint-IF performed better than baselines in predicting complete transfer passenger flow and the comprehensive results also conclude that Joint-IF is capable of better forecasting transfer passenger flow with missing values between metro and bus. ### Visualization Analysis

Except for the entire prediction performance as mentioned above, we have visualized and analyzed the predicted transfer passenger flow from all models with their estimations in Figures 8-11. As seen in Figures 8-11, we can find that the distribution of transfer flow has obvious variability at different intervals and metro stations; the daily distribution of transfer flow shows significant morning and evening peaks, with typical cyclical properties. Meanwhile, there are obvious differences in the outputs of different forecasting models. Among these models, the forecasting values of Joint-IF are most similar to the corresponding true values with the smallest variations. Additionally, for different metro stations, Joint-IF can accurately prediction the periodicities and trends of the transfer passenger flow. When comparing Figures 8-11, it can be seen that the transfer flow of different stations has a large variability. The transfer flow of Shenzhen North metro station and Wuhe metro station was larger than of the Laojie station. When each model forecasted the transfer volume of Shenzhen North Station, the predictions fit better with the real data and can better adapt to the changing trend of transfer flow. However, at Laojie and Wuhe station, only Joint-IF obtained a better fit and other methods had significant variability with those true values. Thus, this result shows that Joint-IF can remain good forecasting performance for different stations and different orders of magnitude of transfer flow, and also forecast the changing trend of transfer flows at different scales. This is also corroborated by the predicted results in Figure 11. In other baselines, the predicted values of GWR also have better fitting performance with the true values, and its performance is closer to the Joint-IF model. Although Joint-IF has the best forecasting performance among these methods, it does not explain the effects of each external factor on the transfer passenger flow, which is just remedied by those traditional statistical models (e.g., MLR, GPR, GWR).