## 3 Methodology
### Model Implementation
Nevertheless, our proposed Joint-IF model ultimately offers robust potentialities for ITS refinement because

Figure 10: Predicted and true value distribution of all models for transfer passenger flow at Shenzhen North station: (**a**) GPR; (**b**) MLR; (**c**) GWR; (**d**) LSTM; (**e**) RF; (**f**) Joint-IF. of its accurate prediction for future transfer passenger flow, such as improving service quality and supporting the transportation planning process [48]. ## 5 Conclusions

An accurate prediction of transfer passenger flow in public transportation systems is critical for passengers and transport managers. However, the available studies do not provide accessible approaches to transfer passenger flow prediction. Moreover, it is inevitable that transfer activities are greatly affected by varied factors (e.g., weather) due to exposure to the outdoors. To address these issues, we propose a Joint-IF model to forecast the transfer passenger flows of the public transportation system when considering multiple factors. After fulfilling the repair task for incomplete transfer passenger flow, we test and compare the forecasting performance of the Joint-IF, statistical-based and DL-based models, and our core conclusions and contributions are as follows:

1. The Joint-IF model is capable of effectively repairing the transfer passenger flow between metro and bus under various missing combinations. Especially for those cases with large missing rates, Joint-IF can still maintain excellent repair performance, which is helpful for subsequent task of transfer passenger flow forecasting. 2. The Joint-IF model can accurately predict the future transfer passenger flows from metro stations to all their nearby bus stops. Compared with the baselines (e.g., GPR, GWR and MLR), Joint-IF yields greater performance gains and smaller prediction errors. 3. Overall, this study provides a reliable Joint-IF model for both accurate imputation and prediction of transfer passenger flow by considering the synergistic effects of multiple facts. Moreover, the visual analysis results can further confirm and explain the advantages of the Joint-IF over baselines, especially for GWR. These not only

Figure 11: Predicted and true value distribution of all models for transfer passenger flow at all metro stations: (**a**) GPR; (**b**) MLR; (**c**) GWR; (**d**) LSTM; (**e**) RF; (**f**) Joint-IF. provide helpful insights for travelers and operators, but also provide a basis for later interpretable forecasting studies. In future research, we will study the prediction of short-term transfer passenger flow (e.g., 1, 5 and 10 min intervals). Additionally, we will apply more DL-based algorithms to optimize the residuals of Joint-IF and minimize its computational complexity. Lastly, we will further explore the predictive performance of Joint-IF in other domains, such as the transfer speed of passengers. Conceptualization, J.L. and P.W. ; methodology, J.L. and P.W. ; software, R.L. and H.G. ; resources, G.L. and L.X. ; writing--original draft preparation, J.L. ; writing--review and editing, J.L. and P.W. ; visualization, R.L. and H.G. ; supervision, P.W. and L.X. ; project administration, G.L. and L.X. ; funding acquisition, L.X. All authors have read and agreed to the published version of the manuscript. This study is supported by the projects of the National Natural Science Foundation of China, Youth Fund Project (No. 11702099) and the National Natural Science Foundation of China (No. 52072130). Not applicable. The authors thank all members of the research group for their technical support during the research activities. The authors thank the anonymous referees for their valuable comments. The authors declare no conflict of interest. ## References

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