New method for probabilistic traffic demand predictions for en route sectors based on uncertain predictions of individual flight events.

Details

• Creators:
  Gilbo, Eugene P ; Smith, Scott B.
• Corporate Creators:
  John A. Volpe National Transportation Systems Center (U.S.)
• Subject/TRT Terms:
  • Air traffic control
  • Advanced automation system (Air traffic control)
  • Flow control (Air traffic control)
  • Decision making
  • Mathematical prediction
  • Arrivals and departures
• Resource Type:
  Proceedings
• Geographical Coverage:
  United States
• Corporate Publisher:
  United States. Department of Transportation. Federal Aviation Administration
• Abstract:
  This paper presents a novel analytical approach to and techniques for translating characteristics of uncertainty in predicting sector entry times and times in sector for individual flights into characteristics of uncertainty in predicting one-minute sector demand counts. The paper shows that expected one-minute sector demand predictions are determined by a probabilistically weighted average of one-minute sector entry demand predictions for several consecutive one-minute intervals within a sliding time window. The width of the window is determined depending on probability distributions of errors in flights’ sector entry time predictions. Expected one-minute sector demands along with standard deviations of demand counts are expressed via probabilistic averaging of series of one-minute deterministic predictions of number of flights entering a sector. The results of the paper contribute to probabilistic predictions of congestion in airspace. These analytical results can also be used to evaluate the impact of improved accuracy in flight timing predictions on reducing uncertainty in traffic demand predictions, hence leading to better identification of congestion in airspace.
• Format:
  PDF
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/9209/dot_9209_DS1.pdf
• File Type:
  [PDF - 259.71 KB]
• Collection(s):
  Volpe Center
• Main Document Checksum:
  urn:sha-512:59cb1a39adeeea6d67d22b52bbfc79e7c83f45e7f6b41206e0e11d40ccf87f175d52c3879f718968020b6493769f5689b003c7ac84517317889df7ccee845e73