一种航路扇区概率性交通需求预测方法

空中交通拥堵逐渐从终端区向高空航路网络蔓延，准确预测航路扇区交通需 求概率性变化成为科学实施拥堵管理的重要前提，而国外已有方法较难适用我国空管实 际数据条件.为解决该问题，本文基于空管现有航空器过点时间数据，设计了基于预测误 差分布特性的统计方法，提出了航路扇区概率性交通需求预测方法.结合中南地区典型运 行数据，提取并验证了各扇区过点时间的预测误差分布规律，获得了各扇区交通需求值 及其概率分布，发现所得概率性交通需求预测结果较之传统确定性交通需求预测方法更 准确，适合为我国高空航路拥堵管理研究提供需求预测依据.

An En-route Sector Probability Traffic Demand Prediction Method

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.