DocumentCode :
2155364
Title :
Probabilistic Methods for Airspace Sector Congestion Prediction
Author :
Wang Chao ; Yang Le
Author_Institution :
Civil Aviation Coll., Nanjing Univ. of Aeronaut. & Astronaut., Nanjing, China
fYear :
2010
fDate :
24-26 Aug. 2010
Firstpage :
1
Lastpage :
4
Abstract :
Abstract-In order to improve the accuracy of airspace sector congestion prediction, the probabilistic method for sector congestion prediction has been proposed. By analyzing the uncertainty of air traffic, on the basis of theoretical analysis about sector demand probabilistic forecasting, the sector demand probabilistic forecasting method and the sector congestion prediction method based on the Monte Carlo simulation have been proposed. The methods are easy to implement. The simulation results show that the methods reduce the uncertainty of the previous demand forecasting and improve the accuracy of sector congestion prediction.
Keywords :
Monte Carlo methods; air traffic control; probability; Monte Carlo simulation; airspace sector congestion prediction; sector demand probabilistic forecasting method; Air traffic control; Aircraft; Demand forecasting; Monte Carlo methods; Probabilistic logic; Uncertainty;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Management and Service Science (MASS), 2010 International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-5325-2
Electronic_ISBN :
978-1-4244-5326-9
Type :
conf
DOI :
10.1109/ICMSS.2010.5576472
Filename :
5576472
Link To Document :
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