# Cyber-Secure IoT and Machine Learning Framework for Optimal Emergency Ambulance Allocation
## 6 Conclusions  
This study developed an optimized ambulance deployment model that integrates a wide range of sociodemographic and environmental variables, targeting all 229 emergency medical service (EMS) centers across South Korea. To address the limitations of conventional deployment policies, which rely on static standards or single-variable heuristics, this study employed machine learning-based regression algorithms--including Random Forest, XGBoost, and LightGBM--to predict both the \"optimal number of ambulances per EMS center\" and \"dispatch-to-hospital travel time.\" The model was trained on a national-level dataset that combined diverse data sources such as emergency dispatch records, elderly population ratios, transportation indicators, and regional healthcare infrastructure. Dimensionality reduction and variable selection were performed using Lasso regression to mitigate overfitting while enhancing model interpretability. Among the evaluated models, the Random Forest algorithm demonstrated superior performance across key metrics, including mean squared error (MSE), mean absolute error (MAE), and root mean squared logarithmic error (RMSLE). Feature importance analysis further identified total dispatch volume, statistical variability in monthly dispatches, and six-month moving averages as primary predictors. Based on these findings, a real-time simulation system was developed that allows users to adjust input variables by administrative district and receive immediate predictions on optimal ambulance allocation and expected dispatch times. This tool serves as a practical decision-support interface for both on-site practitioners and policy planners. Beyond its predictive capabilities, the proposed framework offers structural support for achieving equitable distribution of EMS resources across regions. In particular, the incorporation of a performance indicator based on the coefficient of variation in workload intensity enables quantitative assessment of regional disparities, thereby supporting long-term infrastructure realignment and enhancement of emergency response systems. A user evaluation survey involving frontline EMS personnel and policy officials at the National Fire Agency revealed that 82% of respondents rated the simulation outputs as \"highly reflective of real-world conditions.\" Moreover, the predictive and visualization components of the system were considered significantly more effective than conventional Excel-based static analyses for operational decision-making. The findings of this study provide a robust foundation for quantitatively assessing and improving ambulance resource allocation strategies across South Korea's EMS network. Based on the results, the following policy implications can be drawn:  
First, this study facilitates dynamic ambulance redeployment strategies aimed at addressing regional disparities in resource distribution. While traditional allocation criteria have typically relied on single factors such as population size or administrative area, the proposed model enables customized deployment based on temporal and spatial variations in demand, driven by multivariate predictions. This approach enhances the responsiveness of high-risk areas by enabling more efficient utilization of limited EMS resources. Second, the ability to quantitatively evaluate disparities in workload provides critical evidence to support fairness and sustainability in EMS operations. By calculating workload intensity differentials between EMS centers through a coefficient of variation, the system offers an objective basis for decisions regarding personnel assignment, operating hours, and compensation adjustments. Third, the simulation-based prediction system serves as a practical tool to improve transparency and stakeholder participation in the policy formulation process. It allows users to interactively adjust input parameters and observe real-time changes in recommended ambulance deployment and dispatch times.