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《Journal of Transportation Systems Engineering and Information Technology》 2016-01
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An En-route Sector Probability Traffic Demand Prediction Method

TIAN Wen;ZHANG Ying;DAI Xiao-xu;HU Bin;National Key Laboratory of Air Traffic Flow Management,Nanjing University of Aeronautics and Astronautics;  
With air traffic congestion spreading from terminal areas to upper en- route network, accurate prediction of the probabilistic en- route sector traffic demand changes is important for airspace congestion management, since the existing methods aren't suitable for our actual air traffic control data. To solve this problem, based on the existing air traffic data of aircraft passing point time, the data statistics method based on prediction error distribution characteristics is designed, and an en-route sector probability traffic demand prediction method is proposed. Combined with the typical operation data of south- middle area, the prediction error distribution of passing- point time in sectors is abstracted and verified, the sector traffic demand and its probabilistic distribution is obtained. It is founded that the accuracy of probability traffic demand prediction is improved comparing to classic methods. Thus, this method is more suitable to provide traffic demand prediction results for our upper en-route congestion management research.
【Fund】: 国家自然科学基金(71301074);; 国家自然科学基金委员会与中国民用航空局联合资助项目(U1333202);; 中央高校基本科研业务费专项资金资助(NJ20150029)~~
【CateGory Index】: V355
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