Supporting runway planning by visualizing capacity balances of arriving aircraft streams

Air traffic control uses arrival managers (AMANs) to schedule an inbound stream of aircraft. Most state-of-the-art AMANs require the operator to analyse and integrate the schedule data to optimize the use of runways. None of the currently operational systems explicitly visualize inefficient use of runway capacity or the imbalance between available capacity and demand. Rather, these decisions are based on predicted arrival times and require an initial planning to determine potential delay in the active runway configuration. The research presented in this paper focuses on supporting the runway planning decision-making process using an experimental ecological AMAN by visualizing imbalances between capacity and demand. An indepth analysis of the arrival management process has been conducted and a concept interface is proposed. A human-in-the-loop experiment was conducted to investigate the effect of visualizing performance deviation information on the quality of runway planning. No significant differences in planning performance have been found between these two conditions, most likely due to the nature of the various traffic scenarios and the high overall workload during the experiment. However, more consistent performance has been observed when the performance deviation information was available to the participants and more improvements of the runway planning closer to landing were visible. Both trends are desired effects of visualizing performance deviation and warrant further research.