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

Language:

Dates

Publication Date Range:

to

Document Data

Title:

Document Type:

Library

Collection:

Series:

People

Author:

Help
Clear All

Query Builder

Query box

Help
Clear All

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

i

Probabilistic prediction of aggregate traffic demand using uncertainty in individual flight predictions.

Filetype[PDF-260.41 KB]


  • English
    • Details You May Also Like

    Details:

    • Creators:
    • Corporate Creators:
      John A. Volpe National Transportation Systems Center (U.S.)
    • Subject/TRT Terms:
    • Publication/ Report Number:
      AIAA 2009-6194
    • Resource Type:
      Research Paper
    • Geographical Coverage:
      United States
    • Edition:
      Aug. 10-13, 2009
    • Corporate Publisher:
      American Institute of Aeronautics and Astronautics
    • NTL Classification:
      NTL-AVIATION-Air Traffic Control;NTL-AVIATION-Aviation Planning and Policy;NTL-AVIATION-Aviation Safety/Airworthiness;NTL-PLANNING AND POLICY-Aviation Planning and Policy;NTL-SAFETY AND SECURITY-Aviation Safety/Airworthiness;
    • Abstract:
      Federal Aviation Administration (FAA) air traffic flow management (TFM)

      decision-making is based primarily on a comparison of deterministic predictions of demand

      and capacity at National Airspace System (NAS) elements such as airports, fixes and enroute

      sectors. The current Traffic Flow Management System (TFMS) and its decisionsupport

      tools ignore the stochastic nature of the predictions. Taking into account

      uncertainty in predictions and moving from deterministic to probabilistic TFM is an

      important part of the NextGen program that will help TFM specialists make better and

      more realistic decisions. This paper uses current TFMS data to analyze how uncertainty in

      prediction of arrival times for individual flights translates into uncertainty in prediction of

      aggregate traffic demand counts at arrival airports. A methodology was developed for

      probabilistic prediction of aggregate 15-minute demand counts by using the probability

      distributions of arrival time predictions for individual flights. A key element of the

      methodology is that the aggregate demand counts are predicted from extended sets of flights

      with the estimated times of arrival (ETAs) in both the interval of interest and several

      adjacent intervals. Numerical examples are presented that illustrate the difference between

      deterministic and probabilistic traffic demand predictions.

    • 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

    Version 3.26