Abstract—Although airspace congestion is becoming more and more serious with the increase of the air traffic flow, there have been still no mature and effective methods and models developed for measuring the uncertainty of the air traffic flow, so that the air traffic prediction is lack of accuracy. Thus, in this paper we extract the numerical characteristics of the random variables during the flight process, and then establish the probability density functions and en-route sector demand prediction model based on the probability distributions. Through comparing the actual operation data and the prediction data of the aircraft, the variation of the sector traffic flow demand and its probability can be obtained based on the model proposed in the paper. The model in this paper remedies the insufficiency of the traditional flow prediction methods which merely provide static prediction results, and thus can be a useful decision support tool for the air traffic flow managers to dynamically know about the sector traffic demand and its accuracy in the future.
Index Terms—Air traffic management, en-route sector demand, probabilistic prediction, uncertianty measuring.
Wen Tian, Ying Zhang, and Yinfeng Li are with College of Civil Aviation, Nanjing University of Aeronautics and Astronautics Nanjing, China (e-mail: tianwen0665@qq.com, yoyozhying@163.com, li070320217@126.com).
Huili Zhang is with Central and Southern Regional Air Traffic Management Bureau, Guangzhou Area Control Centre, Guangzhou, China (e-mail: zhl.83@163.net).
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Cite:Wen Tian, Ying Zhang, Yinfeng Li, and Huili Zhang, "Probabilistic Demand Prediction Model for En-Route Sector," International Journal of Computer Theory and Engineering vol. 8, no. 6, pp. 495-499, 2016.
