A probabilistic model based on the peak-over-threshold approach for risk assessment of airport controllers' performance

Airport tower control plays an instrumental role in ensuring airport safety. However, obtaining objective, quantitative safety evaluations is challenging due to the unavailability of pertinent human operation data. This study introduces a probabilistic model that combines aircraft dynamics and the peak-over-threshold (POT) approach to assess the safety performance of airport controllers. We applied the POT approach to model reaction times extracted from a radiotelephony dataset via a voice event detection algorithm. The model couples the risks of tower control and aircraft operation to analyze the influence of human factors. Using data from radiotelephony communications and the Base of Aircraft Data (BADA) database, we compared risk levels across scenarios. Our findings revealed heightened airport control risks under low demand (0.374) compared to typical conditions (0.197). Furthermore, the risks associated with coupling under low demand exceeded those under typical demand, with the final approach stage presenting the highest risk ($4.929\times {10}^{-7}$). Our model underscores the significance of human factors and the implications of mental disconnects between pilots and controllers for safety risks. Collectively, these consistent findings affirm the reliability of our probabilistic model as an evaluative tool for evaluating the safety performance of airport tower controllers. The results also illuminate the path toward quantitative real-time safety evaluations for airport controllers within the industry. We recommend that airport regulators focus on the performance of airport controllers, particularly during the final approach stage.