# Cyber-Secure IoT and Machine Learning Framework for Optimal Emergency Ambulance Allocation
## 6 Conclusions
As a result, both central policymakers and local administrators can effectively utilize the platform, promoting decentralized and evidence-based EMS policy planning. Fourth, the simulation framework aligns with broader digital transformation strategies within the emergency medical system. As the system is expanded to include features such as time-series forecasting, spatial optimization, and hospital capacity integration, it holds the potential to serve as a scientific decision-support tool in large-scale crisis situations, such as pandemics or natural disasters. Finally, the performance indicator proposed in this study--namely, the workload equity index based on the coefficient of variation across EMS centers--has potential as a long-term policy evaluation metric. Beyond simply measuring supply-demand balance, it offers a standardized measure to monitor equity and quality of emergency medical services across regions. Collectively, these policy insights contribute not only to short-term improvements in ambulance allocation efficiency but also to the establishment of a long-term strategic framework for protecting public health and safety through more equitable and data-driven EMS policy development. Despite the strengths of the proposed simulation system, it is important to acknowledge several limitations. First, the current system produces estimates based on static input variables at a fixed point in time, without incorporating real-time traffic flow or the occurrence of emergent events. Future development plans include the integration of traffic APIs and real-time incident prediction models to enhance the system's responsiveness under dynamic conditions. Second, the temporal scope of the dataset imposes constraints on the model's ability to capture longer-term patterns. This study utilized data collected from January to September 2024, which may not fully reflect seasonal trends or structural shifts across years. Incorporating extended longitudinal datasets in future studies would help improve the model's temporal robustness and generalizability. Third, the modeling framework is currently limited to predicting ambulance demand and dispatch time. While these are critical aspects of EMS efficiency, other factors such as hospital capacity, post-transport treatment time, and inter-agency coordination also significantly influence the overall effectiveness of emergency care. Expanding the model to include hospital-side variables, such as emergency department occupancy rates and specialist availability, is recommended for a more comprehensive optimization framework. Fourth, the variable selection process did not account for certain real-world constraints. For example, ambulance deployment may be restricted in specific regions due to legal, institutional, or political factors. However, this study's approach relied solely on demand-driven predictive modeling. Future work should consider integrating operational constraints such as regulatory limitations, budget allocations, and staffing availability into a constraint-based optimization model. Fifth, while multicollinearity among variables was partially mitigated through Lasso regression, the model does not fully account for all interaction effects or complex nonlinear relationships. Variables such as the proportion of socially vulnerable populations or seasonal tourist influxes may exert influence in nonlinear ways. Incorporating advanced modeling techniques, including nonlinear regression or neural network-based architectures, could improve prediction accuracy and interpretability in such cases. Lastly, this study did not include comprehensive validation of the system's real-world policy impact. The simulation outputs were assessed through quantitative evaluation without incorporating user feedback from system stakeholders such as EMS field commanders or policymakers. Future research should adopt mixed-method approaches to assess the system's usability, acceptability, and effectiveness in operational environments, including pilot testing in real administrative settings.