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Probabilistic prediction of aggregate traffic demand using uncertainty in individual flight predictions.

File Language:
English


Details

  • Creators:
    Gilbo, Eugene ; Smith, Scott ORCID ICON IMG
  • Corporate Creators:
    John A. Volpe National Transportation Systems Center (U.S.)
  • Subject/TRT Terms:
  • Publication/ Report Number:
    AIAA 2009-6194
  • Resource Type:
    Research Paper
  • Geographical Coverage:
    United States
  • Edition:
    Aug. 10-13, 2009
  • Corporate Publisher:
    American Institute of Aeronautics and Astronautics
  • 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.
  • Format:
    PDF
  • Download URL:
    https://rosap.ntl.bts.gov/view/dot/8565/dot_8565_DS1.pdf
  • File Type:
    Filetype[PDF - 260.41 KB]
  • Collection(s):
    Volpe Center
  • Main Document Checksum:
    urn:sha-512:ed1e21ee9e1257de23ffc6ed1d56ca174fb7e0e0049e5b585c6e8cc17a665543b6157887d281cca96dbef461be97b0f5a1984592934f49496f37c75741a2eb0a
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