## 5 Conclusions  
This study proposes an integrated model, MHALSTM-MPC, which combines a Long Short-Term Memory network with a multi-head attention mechanism and Model Predictive Control (MPC). By leveraging real-time passenger flow data, the model dynamically schedules security resources at high-speed rail stations, offering a data-driven solution to improve operational efficiency and reduce costs. The methodology enables proactive resource allocation during peak periods, minimizing waiting times and avoiding resource wastage during off-peak hours, thereby delivering both economic and operational benefits. Furthermore, the approach supports contingency planning for high-demand scenarios, such as holiday travel surges or extreme weather conditions, demonstrating its adaptability and robustness in handling diverse operational challenges.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metric** & **Plan B** & **Fixed-Shift Model** &
\begin{tabular}{c} **Static Scheduling** \\ **Model** \\ \end{tabular} \\ \hline Average waiting Time & & & \\ (minutes) & 4.45 & 5.48 & 5.15 \\ Total human-hours & 299.83 & 315 & 308 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Plan B vs. fixed-shift and static scheduling models.  
Beyond high-speed rail stations, the MHALSTM-MPC model holds promise for other application domains that require dynamic resource management based on time-series data. Potential fields include airport security, urban transit systems, logistics hubs, and even energy grid management, where accurate demand forecasting and adaptive resource allocation are critical. These applications highlight the broader utility of the proposed model across different industries.  
However, certain limitations should be acknowledged. The model primarily relies on passenger flow data and does not consider other influencing factors, such as extreme weather conditions or train schedules, that may affect traffic patterns. Additionally, its robustness under highly volatile conditions requires further enhancement. Finally, the model has not been deployed in real-world settings, and its effectiveness in practical applications remains unvalidated.  
Future research could address these limitations by incorporating additional factors into the prediction process, such as external disruptions and environmental conditions, in order to improve the model's comprehensiveness. Enhancing its robustness through hybrid approaches and implementing field tests in real-world scenarios would further validate its effectiveness. Exploring its application in other domains could also expand its impact, demonstrating its versatility as a data-driven resource management tool.  
Conceptualization, M.L. and C.X.; methodology, Y.S.; software, Y.S.; validation, C.D. and W.S.; formal analysis, M.L. and C.X.; writing--review and editing, Y.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Scientific Funding for China Academy of Railway Sciences Corporation Limited, China, grant number 2023 YJ125 and 2024 YJ114.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the requirements of superior management.  
Author [PERSON] was employed by the company China Academy of Railway Sciences Corporation Limited. 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.