Reduced Taxi Delays With Intelligent Departure Metering Advisory Tool

Airport taxi delays pose significant challenges to airlines, passengers, and environmental sustainability. Addressing these delays requires synchronizing uncertain airside operations, including pushback sequencing, taxiway movements, and runway take-offs. This article presents the intelligent departure metering advisory tool (I-MATE), an artificial intelligence (AI)-based decision support system that recommends pushback timings to air traffic controllers (ATCOs) to reduce taxi delays while balancing downstream runway throughput. I-MATE's recommendation outcomes unfold over a longer time horizon compared to other AI-driven decision support systems in air traffic control, such as conflict resolution tools, posing a greater cognitive challenge for ATCOs. We conducted validation experiments to assess the efficacy and acceptability of I-MATE in assisting ATCOs to manage airside traffic. The study revealed a spectrum of compliance with I-MATE recommendations among ATCOs, highlighting the complex interplay between human behavior and AI-driven decision support systems. While ATCOs rated I-MATE highly for usefulness and reliability, concerns regarding transparency and explainability emerged. ATCO feedback also emphasized the importance of considering individual differences and human factors in the design of such systems. This research underscores the value of AI-based decision support systems in complex, dynamic environments, particularly for addressing the cognitive challenge of balancing long-term outcomes with immediate actions.