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The prediction of flight delays based the analysis of Random flight points

机译:基于随机飞行点分析的飞行延迟预测

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The prediction of flight delays is heavily investigated in the last few decades. However, there is a relatively low level of study on Random flight point delays in these important problems. In this paper, we present an influence factor model of random flight points by series analysis on actual airline data, which is combined with BN (Bayesian Network) and GMM-EM(Gaussian mixture model-expectation maximization algorithm) algorithm. The creation of the initial parameters is based on the analysis for the continuous flights fly over the same flight point. The test data is offered by some Air Traffic Management Bureau. And the test result clearly demonstrates the value of Bayesian Network for analyzing the system-level effects arising from micro-level causes.
机译:在过去的几十年里,飞行延误的预测受到大量调查。然而,在这些重要问题中对随机飞行点延迟的研究水平相对较低。在本文中,我们通过实际航空公司数据序列分析介绍了随机飞行点的影响因子模型,其与BN(贝叶斯网络)和GMM-EM(高斯混合模型预期最大化算法)算法组合。初始参数的创建基于对同一飞行点飞行的连续航班的分析。某些航空交通管理局提供的测试数据。测试结果清楚地展示了贝叶斯网络的价值分析了微观原因产生的系统级效应。

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