Abstract
The concept of 4D trajectory management relies on the prediction of aircraft trajectories in time and space. Due to changes in atmospheric conditions and complexity of the air traffic itself, the reliable prediction of system states is an ongoing challenge. The emerging uncertainties have to be modeled properly and considered in decision support tools for efficient air traffic flow management. Therefore, the subjacent causes for uncertainties, their effects on the aircraft trajectory and their dependencies to each other must be understood in detail. Besides the atmospheric conditions as the main external cause, the aircraft itself induces uncertainties to its trajectory. In this study, a cause-and-effect model is introduced, which deals with multiple interdependent uncertainties with different stochastic behavior and their impact on trajectory prediction. The approach is applied to typical uncertainties in trajectory prediction, such as the actual take-off mass, non-constant true air speeds, and uncertain weather conditions. The continuous climb profiles of those disturbed trajectories are successfully predicted. In general, our approach is applicable to all sources of quantifiable interdependent uncertainties. Therewith, ground-based trajectory prediction can be improved and a successful implementation of trajectory-based operations in the European air traffic system can be advanced.
Highlights
The currently ongoing evolution of the Air Traffic Management (ATM) system through large-scale research programs, such as Single European Sky ATM Research (SESAR) [1], promote the introduction of so-called Trajectory-based Operations (TBO), where all flights are represented by 4D trajectories, composed of future aircraft positions relative to the flight time
At fixes along the flight path, as proven with flight tests in Seattle [2]. This will sacrifice the potential for optimization [3,4,5], which is an imperative for the efficiency and ecological targets defined by SESAR [1]
Full trajectories are calculated with the simulation environment TOMATO (Toolchain for Multi-Criteria Trajectory Optimization [7]), which uses the precise and mostly analytical flight performance model COALA (Compromised Aircraft performance model with Limited Accuracy [8]). These simulation runs provide the required data to prove a correlation between input probability density functions (PDF) and the resulting position uncertainty statistically, as well as the statistical behavior of the resulting position uncertainty depending on the Look-Ahead Time (LAT)
Summary
The currently ongoing evolution of the Air Traffic Management (ATM) system through large-scale research programs, such as Single European Sky ATM Research (SESAR) [1], promote the introduction of so-called Trajectory-based Operations (TBO), where all flights are represented by 4D trajectories, composed of future aircraft positions relative to the flight time. Full trajectories are calculated with the simulation environment TOMATO (Toolchain for Multi-Criteria Trajectory Optimization [7]), which uses the precise and mostly analytical flight performance model COALA (Compromised Aircraft performance model with Limited Accuracy [8]) These simulation runs provide the required data to prove a correlation between input PDF and the resulting position uncertainty statistically, as well as the statistical behavior of the resulting position uncertainty depending on the Look-Ahead Time (LAT). The most probable ATOM is estimated based on current aircraft behavior and previous knowledge generated for the cause-and-effect model.
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