A new model to improve aggregate air traffic demand predictions
Advanced Search
Select up to three search categories and corresponding keywords using the fields to the right. Refer to the Help section for more detailed instructions.

Search our Collections & Repository

For very narrow results

When looking for a specific result

Best used for discovery & interchangable words

Recommended to be used in conjunction with other fields

Dates

to

Document Data
Library
People
Clear All
Clear All

For additional assistance using the Custom Query please check out our Help Page

i

A new model to improve aggregate air traffic demand predictions

Filetype[PDF-287.24 KB]


English
Details You May Also Like

Details:

  • Creators:
    Gilbo, Eugene
  • Corporate Creators:
    John A. Volpe National Transportation Systems Center (U.S.)
  • Subject/TRT Terms:
  • 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):
  • Main Document Checksum:
  • Download URL:
  • File Type:

Supporting Files

  • No Additional Files
More +

You May Also Like

Checkout today's featured content at rosap.ntl.bts.gov