Data-Driven Trajectory Uncertainty Quantification For Climbing Aircraft To Improve Ground-Based Trajectory Prediction

Year 2017, Volume: 18 Issue: 2, 323 - 345, 30.06.2017

Mevlüt Uzun Emre Koyuncu

https://doi.org/10.18038/aubtda.270074

Cited By: 2

Abstract

Efficient trajectory prediction tools will be the crucial functions in future trajectory-based
operations (TBO). In addition to win and controller actions, uncertainties in climbing flights
are major components of prediction errors in a flight trajectory. Due to the operational
concerns, aircraft take-off weight and climb speed intent, which are key performance
parameters that define climb profiles, is not entirely available to round-based trajectory
prediction infrastructure. In the scope of air traffic flow management, sector entry and exit
times, including where the climb ends and descending starts, are the main inputs for demand-
capacity balancing processes. In this work, we have focused on uncertainties over climb
trajectory to quantify and analyze their impact on climb times to cruise altitudes. We have used
model-driven data statistical approaches through aircraft flight record data sets (i.e. QAR). As
result of this analyze, probabilistic definitions are generated for aircraft take-off weight and
speed intent. The regression between these climb parameters and flight distance is acquired to
reduce the uncertainty at strategical level. Moreover, reducing climb uncertainty through
adaptive uncertainty reduction is also demonstrated at the tactical level of flight. Through the
simulations, the impact of reducing the uncertainty in aircraft mass on climb time is illustrated. 

Keywords

Flight Trajectory Uncertainty, Aircraft Climb, Uncertainty Reduction, Aircraft Performance

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