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- Author: Scott B Smith
- Publisher: CreateSpace Independent Publishing Platform
- Year: 2008
- Pages: 58
- ISBN-10: 1495416194
- ISBN-13: 9781495416194
- Format: 21.6 x 28 x 0.3 cm, softcover
- Language: English
- SAVE -10% with code: EXTRA
Characterization of Uncertainty in ETMS Flight Events Predictions and its Effect on Traffic Demand Predictions (e-book) (used book) | bookbook.eu
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Description
This report presents the results of analysis and characterization of uncertainty in traffic demand predictions using Enhanced Traffic Management System (ETMS) data and probabilistic representation of the predictions. Our previous research, described in two prior reports, was focused on analysis of aggregate 15-minute traffic demand predictions in ETMS, on improving the accuracy of these predictions and increasing the stability of the ETMS monitor/alert function, while not explicitly considering the uncertainty in predictions of flight events for individual flights. This study continues the previous one. It also focuses on uncertainty in traffic demand predictions, but, unlike the previous one, it explicitly considers uncertainty in individual flights' predictions for estimation of uncertainty in aggregate demand count predictions at National Airspace System (NAS) elements and for probabilistic representation of those predictions.
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- Author: Scott B Smith
- Publisher: CreateSpace Independent Publishing Platform
- Year: 2008
- Pages: 58
- ISBN-10: 1495416194
- ISBN-13: 9781495416194
- Format: 21.6 x 28 x 0.3 cm, softcover
- Language: English English
This report presents the results of analysis and characterization of uncertainty in traffic demand predictions using Enhanced Traffic Management System (ETMS) data and probabilistic representation of the predictions. Our previous research, described in two prior reports, was focused on analysis of aggregate 15-minute traffic demand predictions in ETMS, on improving the accuracy of these predictions and increasing the stability of the ETMS monitor/alert function, while not explicitly considering the uncertainty in predictions of flight events for individual flights. This study continues the previous one. It also focuses on uncertainty in traffic demand predictions, but, unlike the previous one, it explicitly considers uncertainty in individual flights' predictions for estimation of uncertainty in aggregate demand count predictions at National Airspace System (NAS) elements and for probabilistic representation of those predictions.
Reviews