Characterization of uncertainty in ETMS flight events predictions and its effect on traffic demand predictions
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Characterization of uncertainty in ETMS flight events predictions and its effect on traffic demand predictions

  • Published Date:

    2008-07-11

  • Language:
    English
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  • NTL Classification:
    NTL-AVIATION-AVIATION ; NTL-AVIATION-Air Traffic Control ;
  • Abstract:
    This report presents the results of analysis and characterization of uncertainty in traffic demand predictions using ETMS data and probabilistic representation of the predictions. Our previous research, described in two prior reports, was focused on analysis of aggregate 15-minute traffic demand predictions in ETMS, on improving the accuracy of these predictions and increasing the stability of the ETMS monitor/alert function, while not explicitly considering the uncertainty in predictions of flight events for individual flights. This study continues the previous one. It also focuses on uncertainty in traffic demand predictions, but, unlike the previous one, it explicitly considers uncertainty in individual flights’ predictions for estimation of uncertainty in aggregate demand count predictions at NAS elements and for probabilistic representation of those predictions. The major steps in this study include: Characterization of uncertainty in predicting times for individual flights via statistical analysis of prediction errors; Estimation of predicted probabilities for individual flights to be in a NAS element at any time by using the probability distribution functions of prediction errors; Estimation of probability distributions of predicted 15-minute demand counts for NAS elements by using the predicted probabilities for individual flights to be in the NAS element (in this report, for airport arrivals); Probabilistic prediction of 15-minute traffic demand counts that includes expected value of traffic demand and a range of uncertainty around the expected demand with a certain probability for the predicted demand to be within this range; and Comparison of probabilistic and deterministic demand count predictions.
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