SIMULATION OF UAV TRAFFIC IN URBAN AERIAL NETWORKS USING MONTE-CARLO AND DISCRETE-EVENT SIMULATION MODELS

The research focuses on developing an integrated simulation model for managing unmanned aerial vehicle (UAV) traffic in urban airspace. By combining Monte Carlo and discrete-event modeling (DEM) methods, the study aims to optimize UAV route planning under high air traffic density conditions, considering spatial constraints and stochastic influences. The primary objective is to create an efficient UAV rerouting algorithm that minimizes collision risks, reduces delays, and improves the overall utilization of urban airspace. The research methodology incorporates Monte-Carlo techniques to model stochastic events such as weather changes, emergencies, and fluctuating traffic patterns, while DEM is used to analyze UAV traffic flow dynamics and optimize routing strategies based on real-time conditions. The proposed adaptive rerouting algorithm dynamically adjusts UAV trajectories based on real-time parameters, including flight speed, route configuration, and traffic density within specific zones. This ensures a quick response to evolving air traffic conditions, enabling more effective and safer UAV operations while minimizing fuel consumption and maximizing mission efficiency. Simulation results indicate that implementing the proposed model reduces the average UAV waiting time in congested zones by 20-30%, leading to more balanced airspace utilization and more efficient allocation. The adaptive redirection strategy prevents local traffic overloads, enhancing flight safety, lowers the probability of mid-air conflicts, and optimizing UAV navigation paths. The results confirm that integrating Monte-Carlo and DEM approaches is a robust method for modeling urban UAV traffic and assessing the impact of dynamic constraints. The developed framework can be employed in urban air traffic management systems, particularly in logistics, emergency response, infrastructure monitoring, and smart city applications. Future research can focus on enhancing real-time decision-making mechanisms, incorporating and improving UAV autonomy for large-scale deployment in complex urban environments. Additionally, further refinements of the model could include multi-agent reinforcement learning techniques to improve decision-making in high-density scenarios, ensuring even more efficient UAV coordination and airspace optimization.