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随机GHP模型中机场容量混合聚类算法

, PP. 64-68

Keywords: 空中交通管理,机场容量评估,流量管理,混合聚类算法,随机GHP模型,典型容量样本

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Abstract:

为了有效利用机场容量资源,克服现有随机GHP模型中容量预测存在的人为误差,研究了机场容量混合聚类算法。将每天的容量按照30min间隔划分为多个区间,每个区间对应着1个容量值,这样每天的容量就作为1个容量样本。采集国内某机场半年的容量样本,采用k-means和SOM神经网络的混合聚类算法,确定机场典型容量样本,计算相应的概率,建立典型容量样本树,并应用于随机GHP的静态和动态模型。仿真结果表明与不执行GHP相比,静态和动态模型的总延误损失分别减少了32.7%和52.7%,验证了混合聚类算法的可行性以及典型容量样本树的实用性。

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