Human Error Identification for Air Traffic Controller in Remote Tower Apron Operation

Remote towers are increasingly deployed at small-to-medium airports globally for cost efficiency, yet safety optimization for large airport remote apron control remains underexplored. This study proposes a human error identification framework for air traffic controllers (ATCOs) in large airport remote apron operations. Using hierarchical task analysis (HTA), a cognitive-behavioral model, and the technique for retrospective analysis of cognitive errors (TRACEr), we analyzed error probability and severity through field research. Key findings reveal critical divergences. Memory functions showed the highest error probability, while perception errors caused the most severe outcomes. Working memory errors were most prevalent, but visual detection errors were most severe. Attention deficits were most frequent, while spatial confusion and information integration failures exceeded severity thresholds. Personal factors dominated performance-shaping factors, with low vigilance and equipment unavailability as primary high-risk conditions. This research provides an error identification checklist and analysis methodology to enhance human performance and aviation safety in remote apron control.