## 5 Conclusions  
This study focuses on optimizing the accuracy of airport express passenger flow prediction to adapt to the urbanization process and the development of airport express services. The proposed FINM skillfully combines the local feature extraction ability of the CNN, the global information capture ability of the BiLSTM, and the computational efficiency advantage of the GRU. This fusion strategy improves the model's comprehensive understanding and expression ability for input data features and ensures efficiency and stability in the training process. The experimental results show that it performs well in several key evaluation indicators. The Loss Value and MSE are as low as 0.0160, the MAE is 0.0947, the RMSE is 0.1255, the MAPE is 18.40, and the R-Squared is as high as 0.7788, which are better than the comparison algorithm. It has been proven that the FINM is efficient and accurate in processing complex time series data. There are limitations to this study. Although some factors have been considered in terms of the impact of airport express passenger flow, other potential factors such as accidents and special activities have not been fully included in the model. In the future, we will pay attention to, and explore, the impact of these factors and integrate them into the model to improve the prediction ability when the conditions are mature. In addition, in the actual traffic scene, the traffic management decision system interacts closely with the passenger flow, and the system will adjust the operation strategy according to the feedback in terms of passenger satisfaction, which in turn affects the passenger flow. Future research is expected to incorporate factors such as passenger satisfaction into the FINM so as to provide more accurate passenger flow prediction information for the traffic management decision-making system, help optimize the allocation of traffic resources, improve service quality, and promote efficient operation in the transportation sector.  
**Author Contributions:** Conceptualization, Y.L. and J.H.; methodology, J.H. and Y.C.; software, J.H. and Y.C. validation, J.H.; data curation, J.H. and Y.C.; writing--original draft preparation, Y.L., J.H. and Y.C.; writing--review and editing, Y.L., J.H. and Y.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is supported by National Natural Science Foundation of China [grant number 72361020], Gansu Province Education Department Project [grant number 2023A-037], and Gansu Provincial Science and Technology Department Program Projects [grant number 22 JR5 RA379], as well as Lanzhou Jiaotong University Youth Fund [grant number 2024063]. The authors appreciate the anonymous reviewers for their valuable suggestions and comments.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data in this paper are restricted from being disclosed for confidential reasons.  
**Conflicts of Interest:** Author [PERSON] was employed by CRRC P&D INSTITUTE Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.