Developing a complexity-based flight allocator for a shared human-automation air traffic control environment

Abstract

The continued growth of air traffic demand is placing increasing pressure on current air traffic control (ATC) systems, prompting the need for alternative ATC strategies. A promising approach is a shared ATC environment between a human controller and an automated controller, where basic, low-complexity traffic is delegated to automation while complex traffic remains under human control. This concept requires a reliable method for predicting the operational complexity of individual flights. This research presents the design and evaluation of a complexity-based flight allocation algorithm for an en-route shared human–automation ATC environment. The allocator classifies incoming aircraft based primarily on the predicted number of interactions along their trajectories, using a flight-filtering mechanism derived from existing models. Additional allocation metrics include the expected number of interactions between human and automation-directed flights and a minimum number of flights controlled by each controller. The allocator was evaluated using offline simulations with real traffic data, followed by a human-in-the-loop experiment with two professional air traffic controllers. Results show that the allocator can consistently assign more complex flights to the human controller while maintaining a balanced workload distribution. The human-in-the-loop experiment saw substantial manual re-allocation and revealed low trust in both the allocator and the automation, indicating the need for further refinement and closer integration with automation capabilities.