Agent-Based Modeling Simulation of Uncrewed Aerial Vehicle Placement Influence on Traffic Patterns

This paper presents a lightweight simulation framework for rapidly assessing the risk and traffic impact of integrating uncrewed aerial vehicles (UAVs) with various trajectories into the National Airspace System. The framework combines agent-based modeling and Monte Carlo simulation with real air traffic data to generate realistic traffic patterns and quantify integration risk through loss of separation events. We validate the methodology by applying it to three representative scenarios: a terminal area near an airport, a rural region, and a state with diverse traffic densities. These case studies demonstrate the framework’s capability to provide fast, computationally efficient risk estimates for UAV operations. While we initially designed this approach for weather data collection missions, the methodology applies broadly to any planned UAV operation requiring airspace integration assessment. The framework offers aviation planners and regulators a practical tool for evaluating the safety implications of adding new actors to the airspace system.