Validation of an Air-Traffic Controller behavioral model for fast time simulation

Abstract

The US National Airspace System (NAS) is incredibly complex, and consists of many specific functions. Given predicted increases in air traffic, enhancement of the current system and development of the NextGEN system are critical to maintain safe and efficient operation. Each feature of the system needs to be carefully designed, developed, tested and validated. To this end, human-in-the-loop (HITL) simulations represent one of the most powerful and realistic testing tools. HITL simulations can provide valuable feedback on how new features influence the behavior of human operators. The drawbacks of HITL simulations include limited flexibility and scalability, and high cost. Computer simulations, which involve no direct human activity, can avoid some of these potential problems, and often represent an attractive alternative (or adjunct) to HITL simulation. A crucial question underlying the use of computer models is how well the model captures the human operator (in this case, the air traffic controller). This paper presents a validation of the AgentFly system, specifically a human en-route Air Traffic Controller (ATC) behavioral model. The ATC workload model is based on Multiple Resource Theory including visual scanning, radio emulation and different kinds of uncertainty. Validation of the AgentFly system was performed by comparing model output to HITL simulation data. The AgentFly system used simulated behavior of air traffic controllers and pilots to collect similar data. Both types of output data were processed and compared based on selected metrics.