Air traffic control is advancing digitalization by developing advanced decision-support systems, where the way information is presented to operators plays a central role in shaping performance. However, the effects of different visual representations within these systems on human decision-making remain not fully understood. In this study, we compared two Conflict Detection and Resolution (CD&R) tools: the Highly Interactive Problem Solver (HIPS) and the Solution Space Diagram (SSD). Although both systems are grounded in the same control problem, they differ in how they represent the control constraints that define conflict conditions and feasible responses. Through a human-in-the-loop experiment under low-and high-traffic conditions, we analyzed how these differences influence decision-making. Results showed that, particularly in low-density traffic, HIPS enabled quicker responses, fewer commands, and smaller safety margins, whereas SSD, despite receiving more favorable subjective ratings, led to greater variability in actions. These findings suggest that visualization significantly impacts decision-making consistency and efficiency. However, in highly complex environments, overall effectiveness may depend more on operators' ability to shift and adapt decision-making patterns facilitated by the interface than on specific visual elements.

Air Traffic Controllers (ATCOs) ensure safe and efficient operations by scanning radar displays to identify flights needing clearances. They then compare flight parameters to assess the impact of potential actions on sector safety. With global air traffic expected to rise, comparing flight labels will become more time-consuming, increasing workload and response delays. To ease this cognitive burden, a flight filtering mechanism is introduced, focusing on flights with spatio-temporal proximities to a selected flight of interest. Based on data from a previous study involving five professional controllers, filter parameters and their thresholds have been selected and tuned. Results indicate that filtering by consolidated state- and intent-based interaction parameters yield the best match to controllers’ judgements about relevant flights relative to a flight of interest. It is anticipated that the filter, outputting a list of relevant flights, can serve as an operational support tool by fading non-relevant flights, reducing cognitive effort in visual searches, and could aid Flight-Centric Air Traffic Control (ATC) allocation models that are based on predicting flight-centric complexity.