# Cyber-Secure IoT and Machine Learning Framework for Optimal Emergency Ambulance Allocation
## 4 Results
### Simulation Application and Visualization Results
Policymakers can then use this information to reprioritize resource allocations or secure funding in anticipation of future needs. In doing so, the system facilitates a shift from traditional qualitative judgment to quantitative, data-driven decision-making. The simulation module is designed to support proactive resource allocation strategies, moving beyond reactive emergency response. It is particularly useful in scenarios involving large-scale public events or predictable seasonal surges in demand, where advance simulations can guide temporary ambulance reinforcements or pre-emptive adjustments in hospital transport routes in anticipation of capacity constraints. Such analytics-driven simulation not only enhances operational readiness but also contributes to mitigating regional disparities in emergency medical resources. In addition to serving as a predictive model, the developed simulation system was designed to contribute to the strategic objective of reducing inter-regional disparities in parametric workload across EMS centers. To this end, the National Fire Agency of Korea has adopted the Load Equity Index (LEI) as a key performance indicator (KPI) for policy evaluation. The LEI quantifies the level of workload distribution equity by calculating the coefficient of variation (CV) of the average number of daily dispatches per ambulance across all centers. This metric allows policymakers to assess the degree of imbalance in operational burden and supports evidence-based adjustments to resource allocation. The Load Equity Index (LEI) is defined as follows:  
\[=\left(1-\frac{\sum\sqrt{\frac{1}{N}\sum_{i=1}^{N}(H_{i}-\overline{H})}}{N \cdot\overline{H}}\right)\times 100\]  
The variable \(H_{i}\) denotes the workload intensity at EMS center \(i\), calculated by dividing the average number of daily dispatches by the number of ambulances in operation at that center. The term \(\overline{H}\) refers to the overall mean workload intensity across all centers, while  
Figure 7: UI for simulating ambulance demand and dispatch time based on regional parameters. \(N\) indicates the total number of EMS centers. This index standardizes the variation in workload across regions and expresses it as a coefficient of variation (CV), where a higher value reflects a more equitable distribution of resources among centers. The Load Equity Index (LEI) is computed annually based on operational data collected from January 1 to December 31, with the final value reported in early December. As of 2024, the LEI stands at 25.04%, and the National Fire Agency has outlined progressive improvement targets, aiming to increase the index to 55% by 2025, 60% by 2026, and 65% by 2027. The simulation system developed in this study serves as a practical tool for forecasting the effects of policy interventions on LEI outcomes. By enabling real-time estimation of changes in workload equity based on regional variable adjustments, the platform provides empirical support for improving ambulance allocation efficiency, enhancing the working conditions of EMS personnel, and ultimately increasing patient survival rates. In conclusion, this simulation-based visualization system bridges the gap between predictive model outputs and actionable field-level insights. It functions as a core infrastructure for policy formulation and resource optimization in emergency medical services. Furthermore, the system holds the potential to evolve into a comprehensive smart EMS platform by integrating additional functionalities such as real-time hospital bed availability, traffic API connections, and predictive incident detection modules. Figure 8: Visualization interface of emergency patient transfers and medical resource distribution in the jurisdictional area.