Probabilistic Predictions of Traffic Demand for En Route Sectors Based on Individual Flight Data

Details

• Creators:
  Gilbo, Eugene ; Smith, Scott
• Corporate Creators:
  John A. Volpe National Transportation Systems Center (U.S.)
• Corporate Contributors:
  United States. Department of Transportation. Federal Aviation Administration
• Subject/TRT Terms:
  • Air traffic control
  • Advanced automation system (Air traffic control)
  • Flow control (Air traffic control)
  • Decision support systems
  • Mathematical prediction
  • Uncertainty
  • Empirical methods
• Publication/ Report Number:
  DOT-VNTSC-TFM-10-01
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Corporate Publisher:
  John A. Volpe National Transportation Systems Center (U.S.)
• Abstract:
  The Traffic Flow Management System (TFMS) predicts the demand for each sector, and traffic managers use these predictions to spot possible congestion and to take measures to prevent it. These predictions of sector demand, however, are currently made in a deterministic way with no regard for the uncertainty in the predictions. The purpose of this report is to discuss how these deterministic predictions can be misleading, how probabilistic predictions of aggregate traffic demand counts can be made that take account of the uncertainty in predicting timing events for individual flights, and how this can lead to better information on which traffic managers can base their decisions.
• Format:
  PDF
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/10633/dot_10633_DS1.pdf
• File Type:
  [PDF - 882.19 KB]
• Collection(s):
  Volpe Center
• Main Document Checksum:
  urn:sha-512:728e439a24425661f3dad0f41537de50c15267cac17cba2a67b3a426db6eb1041301f182e2bf96675bfc782c41e44aa11795819d9b46e3eadb08c8e43fb6e9b6