A new model to improve aggregregate air traffic demand predictions

Details

• Creators:
  Gilbo, Eugene
• Corporate Creators:
  John A. Volpe National Transportation Systems Center (U.S.)
• Subject/TRT Terms:
  • Regression analysis
  • Arrivals and departures
  • Flow control (Air traffic control)
  • Traffic forecasting
• Publication/ Report Number:
  AIAA 2007-6450
• Resource Type:
  Proceedings
• Geographical Coverage:
  United States
• 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 predictions of traffic demand and ; available capacity at various National Airspace System (NAS) elements such as airports, ; fixes and en-route sectors. The FAA uses the Enhanced Traffic Management System ; (ETMS) to predict traffic demand and available capacity, identify congestion and alert NAS ; elements when the predicted demand exceeds capacity. Although predicted demands and ; capacities are uncertain, ETMS treats them deterministically and does not take into account ; the errors in subsequent prediction updates. This paper proposes a regression model for ; improving aggregate traffic demand predictions in ETMS. This approach acknowledges the ; uncertainty in these predictions, and uses ETMS demand count data in a novel way to make ; improved predictions in terms of both accuracy and stability. The proposed linear regression ; model includes predicted demand counts for a time interval of interest along with the ; demand predictions for two immediately adjacent intervals: the preceding and the following ; ones. The model was calibrated and validated using data from 9 airports and 13 en-route ; sectors. Numerical results are presented that illustrate the potential benefits of using the ; proposed model.
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• Format:
  PDF
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/8938/dot_8938_DS1.pdf
• File Type:
  [PDF - 287.24 KB]
• Collection(s):
  Volpe Center
• Main Document Checksum:
  urn:sha-512:987d58b49bafa814a0c4c0611a1c91119abbdd5acfe9e33cedc270ff3b50f114a5224e825f9cb65cd3ec3dd9b43c0c1e0b2b7da408329bb255eee16dfbfdb6e9