Evaluating Air Traffic Complexity via Human-In-The-Loop Simulation Experiments: Novel Metrics for Air Traffic Controllers

Abstract

Quantitative methods are used alongside qualitative methods in Human-in-the-Loop experiments for operational feasibility evaluation of a novel air traffic control operations. In this study, we developed a complexity index that captures traffic flow characteristics. The developed index is calculated solely from traffic data, with the ability to express temporal and spatial complexity changes and variation in a given experiment. The utility of this metric was examined in a simulation experiment concerning an additional area control tool for Tokyo Haneda arrivals. The results show that the application of an en route arrival manager reduces the average value of the proposed complexity index based on the air traffic control characteristics of the arriving traffic flow by up to 16% and 9% on average. This was cross-confirmed by insights given by typical methods, such as the number of instructions given by controllers and qualitative feedback from controllers. Our proposed new indicators complemented the qualitative and subjective feedback from controllers, and quantified what controllers were unaware of over a wide range and long-term, enabled visual and objective discussions.