Strategic Aircraft Trajectory Prediction Uncertainty and Statistical Sector Traffic Load Modeling

Mueller, Tysen; Sorensen, John A.; Couluris, George J

doi:10.2514/6.2002-4765

Cited by 28 publications

(20 citation statements)

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“…Such errors, for instance, may arise from surveillance errors or other aircraft initial state uncertainties such as aircraft weight and departure time. [15] And finally, simulation approximation errors arise, for instance, from inexact solution to the differential equations describing the trajectory (including finite step size effects), approximate Earth model, simplified wind and temperature models, [16] and so forth.…”

Section: Trajectory Prediction Accuracymentioning

confidence: 99%

Preliminary investigation of trajectory prediction impact on decision support automation

Hunter¹,

Vivona

Garcia-Avello

2009

2009 IEEE/AIAA 28th Digital Avionics Systems Conference

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Section: Trajectory Prediction Accuracymentioning

confidence: 99%

Preliminary investigation of trajectory prediction impact on decision support automation

Hunter¹,

Vivona

Garcia-Avello

2009

2009 IEEE/AIAA 28th Digital Avionics Systems Conference

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“…In the area of trajectory prediction for trafficflow management (see [20]), range, altitude and true airspeed error are required as a function of look-ahead time. This is applied to obtain the probability that an aircraft will be present in a sector at a future time.…”

Section: Prior Accuracy Measuresmentioning

confidence: 99%

Development of Key Performance Indicators for Trajectory Prediction Accuracy

Mondoloni¹

2006

2006 Ieee/Aiaa 25TH Digital Avionics Systems Conference

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Key Performance Indicators (KPI) for trajectory prediction accuracy were developed by applying factor analysis to a wider set of accuracy metrics obtained from a literature search. A MonteCarlo simulation was conducted under operationally-representative conditions to provide a data set for the analysis. It is shown that the derived KPI can be linearly combined to estimate the larger set of metrics. These estimates provide good rank correlation with the actual metrics computed. KPIs can describe both the accuracy of trajectory prediction in addition to the quality of the input data supplied to a trajectory predictor. Various applications of these KPI are discussed including the specification of requirements on prediction performance. While certain KPI are described in this study, various values could have been selected. It is argued that TP KPIs should be made consistent with measurements used to express vertical and longitudinal RNP as those get defined.

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“…Both solutions, DAC and FCA, rely on traffic flow complexity assessment processes [7], methods [8][9][10][11][12][13][14][15] and metrics [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32], and integrate predicted workload function and confidence index. Despite the number of methodologies and metrics developed to measure complexity, there is no single agreed definition and several definitions of complexity could be used.…”

Section: Introductionmentioning

confidence: 99%

Impact of Trajectories’ Uncertainty in Existing ATC Complexity Methodologies and Metrics for DAC and FCA SESAR Concepts

et al. 2019

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The most relevant SESAR 2020 solutions dealing with future Capacity Management processes are Dynamic Airspace Configuration (DAC) and Flight Centric ATC (FCA). Both concepts, DAC and FCA, rely on traffic flow complexity assessment. For this reason, complexity assessments processes, methods and metrics, become one of the main constraints to deal with the growing demand and increasing airspace capacity. The aim of this work is to identify the influence of trajectories’ uncertainty in the quality of the predictions of complexity of traffic demand and the effectiveness of Demand Capacity Balance (DCB) airspace management processes, in order to overcome the limitations of existing complexity assessment approaches to support Capacity Management processes in a Trajectory-Based Operations (TBO) environment. This paper presents research conducted within COTTON project, sponsored by the SESAR Joint Undertaking and EU’s Horizon 2020 research and innovation program. The main objective is to deliver innovative solutions to maximize the performance of the Capacity Management procedures based on information in a TBO environment.

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Strategic Aircraft Trajectory Prediction Uncertainty and Statistical Sector Traffic Load Modeling

Cited by 28 publications

References 0 publications

Preliminary investigation of trajectory prediction impact on decision support automation

Preliminary investigation of trajectory prediction impact on decision support automation

Development of Key Performance Indicators for Trajectory Prediction Accuracy

Impact of Trajectories’ Uncertainty in Existing ATC Complexity Methodologies and Metrics for DAC and FCA SESAR Concepts

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