基于贝叶斯网的航班过站时间动态估计

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。

Dynamic Estimation About Turnaround Time of Flight Based on Bayesian Network

An aircraft needs to perform several flights one day, thus forming a flight chai n. After the former flight arrives the estimate departure time of next flight could be obtained if the approximate turnaround time is acquired. This paper selects several notable factors which affect the turnaround time. Firstly, the Bayesian network is used to acquire estimate turnaround time by learning the parameters through maximum likehood estimation based on historical data. Secondly, the incremental lerning property of Bayesian network is used to revise the parameters of the model based on Bayesian estimation using the increased flight data and the turnaround time is updated by the new results. The experimental data indicate that the proposed me thod has good performance on estimating the turnaround time. Finally, the s ensitivity analysis and comparison of the factors influencing turnaround time are carried out.