Data-driven Aircraft Performance Factor Calculation for Flight Planning

This paper proposes a data-driven methodology that leverages high-resolution Quick Access Recorder (QAR) data—now routinely transmitted wirelessly and stored in modern airline databases—to more accurately characterize the performance factor under varying flight conditions. By exploiting this accessible data source, the proposed approach paves the way for more dynamic and operationally resilient flight planning applications. The performance factor is a critical parameter used by airlines to estimate trip fuel; however, traditional monthly updates rely on crude approximations, often leading to misestimations. In contrast, we introduce a standardized post flight QAR stable point detection algorithm that offers configurable parameters to meet specific airline requirements and operational differences. A key advantage of this methodology is its universal applicability, allowing for seamless implementation across diverse aircraft types within a fleet. The experimental results are promising, demonstrating that the proposed method effectively mitigates prediction inaccuracies, reducing trip fuel errors below 1%.