基于贝叶斯网的航班过站时间动态估计

U8; 一架飞机每天要执行多个航班,从而形成航班链.前序航班进港后,若估计出飞机在机场的过站时间,后续航班的离港时间便可较准确给出.文中选取了对航班过站时间影响较为显著的几个因素,运用历史数据,采用最大似然估计进行贝叶斯网参数学习并获得不同情况下过站时间的估计值.同时,利用贝叶斯网增量学习的特性,运用航班增量数据基于贝叶斯估计修正贝叶斯网参数,并用新的学习结果更新过站时间估计值.实验数据表明,所提出的方法能较好地对飞机过站时间进行估计.最后,对影响过站时间的各因素进行了灵敏度分析对比....

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Published in南京航空航天大学学报 Vol. 47; no. 4; pp. 517 - 524
Main Authors 丁建立, 赵键涛, 曹卫东
Format Journal Article
LanguageChinese
Published 中国民航大学计算机科学与技术学院,天津,300300 2015
Subjects
过站时间估计
灵敏度分析
turnaround time estimation
incremental learning
sensitivity analysis
贝叶斯网
air transport
航空运输
增量学习
Bayesian network
Online AccessGet full text
ISSN1005-2615
DOI10.16356/j.1005-2615.2015.04.007

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Abstract U8; 一架飞机每天要执行多个航班,从而形成航班链.前序航班进港后,若估计出飞机在机场的过站时间,后续航班的离港时间便可较准确给出.文中选取了对航班过站时间影响较为显著的几个因素,运用历史数据,采用最大似然估计进行贝叶斯网参数学习并获得不同情况下过站时间的估计值.同时,利用贝叶斯网增量学习的特性,运用航班增量数据基于贝叶斯估计修正贝叶斯网参数,并用新的学习结果更新过站时间估计值.实验数据表明,所提出的方法能较好地对飞机过站时间进行估计.最后,对影响过站时间的各因素进行了灵敏度分析对比.
AbstractList U8; 一架飞机每天要执行多个航班,从而形成航班链.前序航班进港后,若估计出飞机在机场的过站时间,后续航班的离港时间便可较准确给出.文中选取了对航班过站时间影响较为显著的几个因素,运用历史数据,采用最大似然估计进行贝叶斯网参数学习并获得不同情况下过站时间的估计值.同时,利用贝叶斯网增量学习的特性,运用航班增量数据基于贝叶斯估计修正贝叶斯网参数,并用新的学习结果更新过站时间估计值.实验数据表明,所提出的方法能较好地对飞机过站时间进行估计.最后,对影响过站时间的各因素进行了灵敏度分析对比.
Author 丁建立
曹卫东
赵键涛
AuthorAffiliation 中国民航大学计算机科学与技术学院,天津,300300
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Ding Jianli
Cao Weidong
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DocumentTitle_FL Dynamic Estimation About Turnaround Time of Flight Based on Bayesian Network
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Keywords 过站时间估计
灵敏度分析
turnaround time estimation
incremental learning
sensitivity analysis
贝叶斯网
air transport
航空运输
增量学习
Bayesian network
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Snippet U8; 一架飞机每天要执行多个航班,从而形成航班链.前序航班进港后,若估计出飞机在机场的过站时间,后续航班的离港时间便可较准确给出.文中选取了对航班过站时间影响较为显著...
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Title 基于贝叶斯网的航班过站时间动态估计
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