###### Abstract  
In high-speed railway systems, the management of security resources at high-speed train stations is crucial for ensuring passenger safety and improving service efficiency. Effective security resource management enables the quick and efficient handling of large volumes of passengers, reduces queuing times, and ensures that safety measures are strictly enforced. However, current management practices often rely on a fixed-shift system, which lacks a dynamic correlation between the number of open security lanes and real-time passenger flow. This mismatch leads to resource shortages during peak times and resource wastage during off-peak periods. To address these challenges, this study introduces the Multi-Head Attention Long Short-Term Memory Network and Model Predictive Control (MHALSTM-MPC) model to improve security resource management at high-speed railway stations. The MHALSTM component predicts passenger flow by capturing trends and patterns, while the MPC component formulates an optimization problem that minimizes waiting times and operational costs by repeatedly solving it within a finite time horizon based on predicted passenger flow. This approach ensures real-time adjustments to security checkpoint configurations and staff allocation, achieving optimal resource utilization in response to forecasted demand. Experimental results based on real passenger flow data from Z City East Station demonstrate that the MHALSTM-MPC model reduces the average waiting time per passenger by 18.79% compared to the fixed-shift model and by 13.59% compared to the static scheduling model. Additionally, it achieves a 4.82% reduction in total human-hours compared to the fixed-shift model and a 2.65% reduction compared to the static scheduling model, highlighting its effectiveness in optimizing resource allocation and improving operational efficiency. high-speed railway station operations; dynamic resource scheduling; passenger flow prediction; Multi-Head Attention Long Short-Term Memory Network; Model Predictive Control; security resource management +
Footnote †: journal: applied sciences  
by predetermined shift plans rather than real-time passenger data. This experience-based scheduling method results in several challenges:  
1. It leads to resource mismatch during peak periods: When passenger arrivals surge rapidly, particularly during daytime peak periods, the fixed number of security lanes often fails to accommodate the increased demand. This results in long queues at security checkpoints, reducing both security efficiency and overall service quality. These challenges highlight the inability of the current scheduling system to effectively adapt to sudden fluctuations in passenger volume. 2. It leads to resource surpluses and cost wastage during off-peak periods: When passenger flow decreases during off-peak times, the use of an excessive number of predetermined security lanes leads to resources being idle. The lack of real-time adjustment mechanisms results in resource and cost wastage. Idle security lanes contribute to increased equipment depreciation and maintenance costs, further reducing overall resource utilization. 3. It leads to the static nature of the scheduling system: The absence of integration with real-time passenger flow data prevents the system from adapting flexibly to fluctuations in passenger traffic. This limitation hinders high-speed train stations' ability to ensure passenger safety and enhance service quality [3, 4]. Furthermore, two major research gaps remain in the field. First, existing models for predicting passenger flow at high-speed train stations often lack the accuracy needed to capture detailed trends and fluctuations. Second, the disconnect between passenger flow prediction and scheduling methods results in reactive resource management, which fails to adapt proactively to real-time changes. Addressing these gaps requires a comprehensive solution that combines advanced prediction techniques with dynamic scheduling strategies.