摘要
国内外相关研究表明,航空器着陆跑道占用时间(aircraft arrive runway occupation time,AROT)是影响机场跑道容量的重要因素,对跑道占用时间的准确预测有利于更准确地评估跑道容量。由于着陆过程的动态性和复杂性,采用注重数据特征提取的卷积神经网络(convolutional neural networks,CNN)对AROT进行预测,针对CNN容易陷入局部最优等缺点,采用麻雀搜索算法(sparrow search algorithm,SSA)对CNN相关参数进行优化。数据采用航空器快速存取记录器(quick access recorder,QAR)的记录作为数据源,涵盖机场数目为34个。根据QAR数据分析AROT影响因素,构建了SSA-CNN预测模型。对QAR数据分析表明AROT与滑行距离、落地气温、跑道入口速度、快速脱离道数量、脱离速度关联性较强,与航空器重量、风速、风向、脱离道角度等影响因素关联性较低。根据影响因素的关联性采用CNN预测模型均方误差为18.35,而优化后的SSA-CNN预测模型均方误差为17.31,预测结果可以为机场评估跑道容量提供参考。
Domestic and foreign studies have shown that aircraft arrive runway occupation time(AROT)is an important factor affecting airport runway capacity,and accurate prediction of runway occupation time is beneficial for more accurate evaluation of runway capacity.Due to the dynamic and complex nature of the landing process,a convolutional neural networks(CNN)focusing on data feature extraction was used to predict AROT,and the sparrow search algorithm(SSA)was used to optimize CNN-related parameters to overcome the problem of CNN easily falling into local optima.The data source used was the quick access recorder(QAR)records of aircraft,covering a total of 34 airports.Based on QAR data analysis,the SSA-CNN prediction model was constructed to analyze the factors affecting AROT.The analysis of QAR data showed that AROT was strongly correlated with taxiing distance,landing temperature,runway entrance speed,the number of rapid exit taxiways,and exit speed,while it had a relatively low correlation with factors such as aircraft weight,wind speed,wind direction,and exit taxiway angle.Based on the correlation of influencing factors,the mean square error(MSE)of the CNN prediction model was 18.35,while that of the optimized SSA-CNN prediction model was 17.31.The predicted results can provide reference for airport runway capacity evaluation.
作者
陈亚青
李颖哲
赵瑞
高浩然
CHEN Ya-qing;LI Ying-zhe;ZHAO Rui;GAO Hao-ran(Civil Aviation Administration of China(CAAC)Academy of Flight Technology and Safety,Civil Aviation Flight University of China,Guanghan 618307,China;College of Air Traffic Management,Civil Aviation Flight University of China,Guanghan 618307,China;College of Office,Civil Aviation Flight University of China,Guanghan 618307,China)
出处
《科学技术与工程》
北大核心
2024年第7期2813-2820,共8页
Science Technology and Engineering
基金
民航局空管局委托项目(H2021-61)。
