A Methodology for Quantifying Response Times for Deconfliction Actions Through ATC Communications

Abstract

The reaction time to a deconfliction situation refers to the interval between detecting a potential loss of separation and taking corrective action. For air traffic controllers, it represents the lead time between identifying a conflict and issuing a deconfliction instruction to a pilot. For pilots, it corresponds to the delay between receiving an ATC clearance and initiating the associated maneuver. Because both processes are influenced by human factors, these response times constitute a significant source of uncertainty in Air Traffic Management. While the controller’s reaction time is particularly difficult to estimate since the exact moment at which a conflict is cognitively detected cannot be directly inferred from operational data, this paper focuses on quantifying pilot response times. To this end, we propose a methodology that combines Natural Language Processing techniques with surveillance and flight plan data. ATC–pilot voice communications are transcribed using a fine-tuned Automatic Speech Recognition model, and aircraft callsigns are identified through Named Entity Recognition. The transcriptions are then matched with corresponding flights in ADS-B surveillance data. Using flight plans, we identify lateral deconfliction maneuvers and align them temporally with the preceding ATC clearances to estimate the elapsed time between instruction and execution. Because the approach depends on a sequence of emerging algorithms whose robustness is still evolving, the study focuses on identifying the conditions under which the methodology performs reliably, highlighting its current limitations and associated data challenges, and proposing ways to overcome them.