A new model to improve aggregate air traffic demand predictions

Details

• Creators:
  Gilbo, Eugene
• Corporate Creators:
  John A. Volpe National Transportation Systems Center (U.S.)
• Subject/TRT Terms:
  Arrivals And Departures Flow Control (Air Traffic Control) Regression Analysis Traffic Forecasting
• Publication/ Report Number:
  AIAA 2007-6450
• Resource Type:
  Proceedings
• Corporate Publisher:
  American Institute of Aeronautics and Astronautics
• NTL Classification:
  NTL-AVIATION-AVIATION ; NTL-AVIATION-Air Traffic Control ; NTL-AVIATION-Aviation Planning and Policy
• 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.
  
  
• Format:
  PDF
• Collection(s):
  Volpe Center
• Main Document Checksum:
  urn:sha256:81a4d59e92ed87851407f96984fff39508209f2347921f88502e5a24b34cc008
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/8938/dot_8938_DS1.pdf
• File Type:
  [PDF - 287.24 KB ]