# Cyber-Secure IoT and Machine Learning Framework for Optimal Emergency Ambulance Allocation
## 4 Results
### Simulation Application and Visualization Results  
To enhance the practical utility of the ambulance deployment prediction model developed in this study, a simulation-based visualization system was implemented. This system enables users to intuitively assess model outputs and explore various policy scenarios by adjusting key input variables. Rather than serving merely as a static prediction display, the tool is designed as a dynamic decision-support platform for policymakers and field-level emergency management personnel, supporting real-time strategic planning. The system comprises two main simulation modules. The first module estimates ambulance demand based on user-specified input values. These include the total population within the jurisdiction, the number of food establishments, the number of critical patient transfers received from other EMS centers, the average dispatch duration, the current month's dispatch volume, and the number of traffic accidents. The system applies a regression formula derived from Lasso analysis to calculate the appropriate number of ambulances for deployment. Notably, the model was trained using data from 100 EMS centers identified by the National Fire Agency as exemplifying optimal deployment. These reference centers serve as a benchmark to ensure the reliability and practical relevance of the simulation results. The second module predicts expected dispatch duration, incorporating four primary input variables: proportion of residents aged 80 and older, number of ambulances assigned to the center, total population, and local alcohol consumption rate. This model captures region-specific characteristics that contribute to delays--such as aging populations and alcohol-related incidents--and facilitates realistic scenario analysis for resource allocation. By reflecting the nuanced operational challenges associated with local demographic and behavioral patterns, this simulation tool supports the formulation of more context-sensitive emergency response strategies. The simulation system was implemented as a web-based interface, allowing users to select a specific region or EMS center and interactively adjust key input variables to obtain real-time predictions. As illustrated in Figure 7, Section 1 provides an overview of current operational indicators, such as the jurisdiction's total population, daily number of dispatches, proportion of high-acuity patients, and average on-scene arrival time. Section 2 displays the outputs of both the ambulance demand simulation and the dispatch time prediction module. Users can modify simulation inputs corresponding to the six and four variables described in Section 4.1, using either the plus/minus buttons or direct numeric entry in Section 5. Adjustments are immediately reflected in the simulation outputs. Predicted values are presented not only as numerical results but also through geospatial visualization overlaid on a nationwide map. This enables spatial comparison and the identification of inter-regional disparities. The map interface integrates additional layers of contextual information, including the locations of EMS centers, jurisdictional boundaries, and nearby medical facilities. These features collectively support comprehensive spatial analysis of the emergency response network, facilitating more informed and efficient resource deployment strategies. Furthermore, the system provides supplementary information, such as time-series trends in dispatch frequency, average hospital transport durations, and current hospital capacity and equipment availability (Figure 8). These additional data layers enable the platform to serve not only as a tool for short-term simulation, but also as a robust environment for long-term demand forecasting and strategic investment planning. For instance, if a region exhibits both a growing elderly population and an increasing trend in emergency calls, the model will flag a projected rise in ambulance demand.