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为了提高扇区流量预测准确度、减小扇区拥塞预测的虚警率,分析了影响空中交通的随机因素,建立了航空器进入扇区时刻、穿越扇区飞行时间和离开扇区时刻的概率分布模型.利用进入、离开扇区时刻的累积分布函数,计算航空器占用扇区的概率,并在此基础上,提出了基于Monte Carlo仿真的扇区拥塞预测概率方法.算例仿真结果表明:与确定型拥塞预测方法相比,采用概率预测方法可将拥塞时段比例的平均值从42%减少到33%.
In order to improve the accuracy of sector traffic prediction and reduce the false alarm rate of sector congestion prediction, the random factors affecting air traffic are analyzed, and the probability distribution of the time of aircraft entering the sector, the time of flight through the sector and the time of leaving the sector are established Model.According to the cumulative distribution function at the time of entering and leaving the sector and calculating the probability of occupying sectors of the aircraft, a method of predicting the probability of sector congestion based on Monte Carlo simulation is proposed.The simulation results show that: Compared with congestion prediction methods, the probability prediction method can reduce the average percentage of congestion periods from 42% to 33%.