关于多跑道机场噪声优化预测方法

发布时间：2018-05-24 03:11

本文选题：机场噪声预测 + 航迹聚类　；参考：《计算机仿真》2016年12期

【摘要】：多跑道机场噪声预测是机场规划和改扩建的重要基础,随着机场跑道数量的增加,航班、跑道、飞行程序之间组合更加多样化,机场噪声预测问题更加复杂。为了得到可靠性高的预测结果,根据不同跑道的航迹聚类和机型聚类,把聚类结果的每簇中心航迹和代表机型数据组合导入INM(Intergrated Noise Models)计算噪声值构成噪声数据库,通过贝叶斯分类算法构建了一个采用贝叶斯分类的多跑道机场噪声预测模型。输入航班号、机型、航迹、目的地、出港点等基础数据即可快速确定航迹、机型所属类别和跑道号,然后查询噪声数据库得到噪声预测结果。实验结果表明,上述模型能够在一定误差范围内方便快捷地预测出机场周围敏感点的噪声,从而验证了预测模型的合理性和有效性。
[Abstract]:Noise prediction of multi-runway airports is an important basis for airport planning and expansion. With the increase of the number of airport runways, the combination of flight, runway and flight procedures is more diversified, and the problem of airport noise prediction becomes more complex. In order to obtain reliable prediction results, according to track clustering and model clustering of different runways, each cluster center track and representative model data of clustering result are combined into INM(Intergrated Noise models to calculate the noise value to form noise database. The Bayesian classification algorithm is used to construct a noise prediction model for multi-runway airports using Bayesian classification. The flight number, aircraft type, track, destination, departure point and other basic data can be input to quickly determine the flight path, the type of the aircraft and the runway number, and then the noise prediction results can be obtained by querying the noise database. The experimental results show that the proposed model can conveniently and quickly predict the noise of sensitive points around the airport within a certain range of errors, thus validating the rationality and validity of the prediction model.
【作者单位】：中国民航大学计算机科学与技术学院;中国民航信息技术科研基地;
【基金】：国家科技支撑计划课题(2014BAJ04B02) 中央高校基本科研业务费专项基金(3122013P013)
【分类号】：V351

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