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Probabilistic prediction of aggregate traffic demand using uncertainty in individual flight predictions.
  • Published Date:
    2009-08-01
  • Language:
    English
Filetype[PDF-260.41 KB]


Details:
  • Publication/ Report Number:
    AIAA 2009-6194
  • Resource Type:
  • Geographical Coverage:
  • Edition:
    Aug. 10-13, 2009
  • NTL Classification:
    NTL-AVIATION-Air Traffic Control ; NTL-AVIATION-Aviation Planning and Policy ; NTL-AVIATION-Aviation Safety/AirworthinessNTL-PLANNING AND POLICY-Aviation Planning and Policy ; NTL-SAFETY AND SECURITY-Aviation Safety/Airworthiness ;
  • Format:
  • Abstract:
    Federal Aviation Administration (FAA) air traffic flow management (TFM)

    decision-making is based primarily on a comparison of deterministic predictions of demand

    and capacity at National Airspace System (NAS) elements such as airports, fixes and enroute

    sectors. The current Traffic Flow Management System (TFMS) and its decisionsupport

    tools ignore the stochastic nature of the predictions. Taking into account

    uncertainty in predictions and moving from deterministic to probabilistic TFM is an

    important part of the NextGen program that will help TFM specialists make better and

    more realistic decisions. This paper uses current TFMS data to analyze how uncertainty in

    prediction of arrival times for individual flights translates into uncertainty in prediction of

    aggregate traffic demand counts at arrival airports. A methodology was developed for

    probabilistic prediction of aggregate 15-minute demand counts by using the probability

    distributions of arrival time predictions for individual flights. A key element of the

    methodology is that the aggregate demand counts are predicted from extended sets of flights

    with the estimated times of arrival (ETAs) in both the interval of interest and several

    adjacent intervals. Numerical examples are presented that illustrate the difference between

    deterministic and probabilistic traffic demand predictions.

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