# Cyber-Secure IoT and Machine Learning Framework for Optimal Emergency Ambulance Allocation
## 3 Methodology  
### Experimental Settings  
This study aims to derive an optimal ambulance deployment strategy for 119 emergency centers through artificial intelligence-based predictive modeling, adopting a quantitative empirical research design. The research framework consists of predictive modeling and simulation analysis using observation-based panel data, structured to enable multivariate analysis that comprehensively incorporates a variety of factors influencing ambulance allocation. The dataset integrates emergency dispatch records with a wide range of contextual information to reflect both static and dynamic variables relevant to deployment decisions. As summarized in Table 1, data were collected over a 9-month period, from January to September 2024, covering all 229 emergency centers nationwide. This yielded a total of 2061 center-month observations (229 centers \(\times\) 9 months), forming a panel data structure that captures both temporal and cross-sectional variations. Such a structure enhances the generalizability of the model and enables control for regional heterogeneity. The dataset includes variables such as ambulance dispatch frequency, patient transport times, patient severity levels, center-level operational statistics, population density, traffic congestion, hospital locations and capacities, and road infrastructure conditions. These data were compiled from administrative records of the National Fire Agency, Statistics Korea, the Ministry of Health and Welfare, and national traffic information systems. To address discrepancies in data formats across multiple sources, the collected datasets underwent a comprehensive integration and standardization process. Reverse geocoding was applied using a normalized address system aligned with administrative district boundaries. Following the integration of all sources, preprocessing procedures were conducted to manage outliers and missing values. Data consistency and structural validity were further verified through spatial comparative analysis at the city and county (Si/Gun/Gu) level. Throughout the dataset construction process, administrative district codes were employed as the primary mapping key to reduce mismatches and minimize data loss across heterogeneous regional datasets. The model development framework was structured around three major analytical pillars (Figure 2). First, a demand estimation phase was implemented to quantitatively predict regional emergency demand based on sociodemographic variables and location-specific factors, including population density, aging rates, floating population size, and accident occurrence rates. Second, an accessibility-based hospital connectivity analysis was conducted, evaluating the practical accessibility to emergency medical institutions by considering geographic distribution, patient transport distances, traffic flow conditions, and hospital bed capacities. Third, a dispatch and deployment optimization phase was developed to predict ambulance demand for each emergency center and to optimize the allocation of ambulances and dispatch patterns within their jurisdictions, based on historical dispatch records and patient transport data.