随机GHP模型中机场容量混合聚类算法

王飞; 徐肖豪

doi:10.19818/j.cnki.1671-1637.2011.01.011

随机GHP模型中机场容量混合聚类算法

doi: 10.19818/j.cnki.1671-1637.2011.01.011

王飞,
徐肖豪

中国民航大学空中交通管理研究基地，天津 300300

基金项目:

国家自然科学基金项目 60972006

中央高校基本科研业务费专项资金项目 ZXH2009C005

天津市应用基础及前沿技术研究计划项目 09JCZDJC16800

详细信息

作者简介:
王飞(1982-), 男, 安徽凤阳人, 中国民航大学讲师, 工学博士, 从事空域规划和容量评估研究

中图分类号: V355
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出版历程
- 收稿日期: 2010-10-26
- 刊出日期: 2011-02-25

Mixed clustering algorithm of airport capacity in stochastic GHP model

Air Traffic Management Research Base, Civil Aviation University of China, Tianjin 300300, China

More Information

Author Bio:
WANG Fei(1982-), male, lecturer, PhD, +86-22-24092433, wangfei820815@hotmail.com

Article Text (Baidu Translation)

摘要

摘要: 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30 min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。
- 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
Abstract: In order to make full use of airport capacity resources and eliminate existing human prediction error in stochastic ground holding policy(GHP) model, a mixed clustering algorithm was researched. Daily capacity was divided into several intervals in accordance with 30 min, each interval corresponded to a certain capacity value, and the capacity of one day was a capacity scenario. The capacity scenarios of an airport in half a year were collected, typical capacity scenarios were produced by using self-organizing-maps(SOM) neural network and k-means clustering algorithm, and the probability of each capacity scenario was calculated. Typical capacity scenario tree was constructed and applied in stochastic static and dynamic GHP models. Simulation result shows that compared with no-GHP case, the total delay costs of static and dynamic GHP models reduce by 32.7% and 52.7% respectively. So the mixed algorithm is feasible, and the typical capacity scenario tree is practical.
- air traffic management /
- airport capacity evaluation /
- flow management /
- mixed clustering algorithm /
- stochastic GHP model /
- typical capacity scenario

HTML全文

图 1 T(k)变化趋势

Figure 1. Change trend of T(k)

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图 2 典型容量样本分布

Figure 2. Distributions of typical capacity scenarios

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图 3 典型容量样本

Figure 3. Typical capacity scenarios

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图 4 典型容量样本树

Figure 4. Typical capacity scenario tree

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表 1 计算结果对比

Table 1. Comparison of computing results

下载: 导出CSV

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