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

All these words:

For very narrow results

This exact word or phrase:

When looking for a specific result

Any of these words:

Best used for discovery & interchangable words

None of these words:

Recommended to be used in conjunction with other fields



Publication Date Range:


Document Data


Document Type:






Clear All

Query Builder

Query box

Clear All

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


A new model to improve aggregate air traffic demand predictions

Filetype[PDF-287.24 KB]

  • English

  • Details:

    • Creators:
    • Publication/ Report Number:
    • Resource Type:
    • 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:
    • Collection(s):
    • Main Document Checksum:
    • File Type:

    Supporting Files

    • No Additional Files

    More +

    You May Also Like

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

    Version 3.26