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
Main Document Checksum:
[+]
urn:sha256:81a4d59e92ed87851407f96984fff39508209f2347921f88502e5a24b34cc008
Download URL:
https://rosap.ntl.bts.gov/view/dot/8938/dot_8938_DS1.pdf
File Type:

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