Monitoring and Alerting Congestion at Airports and Sectors under Uncertainty in Traffic Demand Predictions.

Gilbo, Eugene; Smith, Scott; John A. Volpe National Transportation Systems Center (U.S.)

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Monitoring and Alerting Congestion at Airports and Sectors under Uncertainty in Traffic Demand Predictions.

2011-04-01
By Gilbo, Eugene ; Smith, Scott

[PDF-2.97 MB]

English

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Creators:

Gilbo, Eugene ; Smith, Scott

Gilbo, Eugene ; Smith, Scott Less -
Corporate Creators:

John A. Volpe National Transportation Systems Center (U.S.)
Subject/TRT Terms:

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Advanced Automation System (Air Traffic Control) Aircraft Separation Airport Traffic Air Traffic Control Arrivals And Departures Enroute Traffic Control Flow Control (Air Traffic Control) Linear Regression Analysis Mathematical Prediction Regression Analysis
Resource Type:

Journal Article
Abstract:

Important functions of the Traffic Flow Management System (TFMS) include prediction air traffic demand for National Air Space (NAS) elements (airports, fixes and enroute sectors) for several hours into the future, and using these predictions to alert traffic flow management (TFM) specialists to potential congestion when predicted demand exceeds available capacity. The current TFMS Monitor/Alert functionality uses deterministic predictions, neglecting their stochastic nature. This paper focuses on improving the accuracy and stability of traffic demand predictions for airports and sectors by considering the uncertainity in aggregate demand count predictions. The emphasis is on uncertainty caused by errors inherenct in TFMS during processing flight data not affected by future air traffic control. We proppose a constructive approach for improving aggregate demand predictions under uncertainty based on linear regression that includes TFMS demand counts for several adjacent time intervals within a sliding time window. Numerical examples based on TFMS data showed that the regression models produce more accurate (up to 22% reduction in the standard deviation of errors in demand predictions) and more stable (fewer crossings of the Monitor/Alert threshold) predictions than current TFMS predictions. For airports, regression significantly reduced the total number of missed alerts (21%) with a small increse in the total number of false alerts (3%). For sectors, the reduction in missed alerts was 22% with an increase in false alerts of 8%.
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PDF
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Volpe Center
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urn:sha256:8f0748c2622e0a5ddd684f099d55ea2551413c8013e6bece2d0a8decfc702f22
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https://rosap.ntl.bts.gov/view/dot/9471/dot_9471_DS1.pdf
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Monitoring and Alerting Congestion at Airports and Sectors under Uncertainty in Traffic Demand Predictions.

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