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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航班延误预测

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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:在过去的几十年中,对航班延误的预测进行了大量研究。但是,在这些重要问题中对随机飞行点延误的研究水平相对较低。本文通过对实际航空公司数据进行序列分析,提出了随机飞行点的影响因素模型,并与BN(贝叶斯网络)和GMM-EM(高斯混合模型期望最大化算法)算法相结合。初始参数的创建基于对相同飞行点上连续飞行的分析。测试数据由一些空中交通管理局提供。测试结果清楚地证明了贝叶斯网络在分析微观原因引起的系统级影响方面的价值。

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