CAJ | 학술논문

　　对机场拥挤机理进行分析；从后果类指标入手，提出基于饱和度的拥挤等级评价方法，建立机场拥挤5色预警等级，从原因类指标入手，提取出分别刻画机场容量和需求的5个拥挤特征指标；提出了基于聚类的神经网络分类算法；利用ATL机场实际航班数据进行实例验证，拥挤等级预测的准确度达到80%，预测效果优于BP神经网络。结果表明，提出的方法预测效果较好，具有一定的实用性。
　　대궤장옹제궤리진행분석；종후과류지표입수，제출기우포화도적옹제등급평개방법，건립궤장옹제5색예경등급，종원인류지표입수，제취출분별각화궤장용량화수구적5개옹제특정지표；제출료기우취류적신경망락분류산법；이용ATL궤장실제항반수거진행실례험증，옹제등급예측적준학도체도80%，예측효과우우BP신경망락。결과표명，제출적방법예측효과교호，구유일정적실용성。
The mechanism of airport congestion is analyzed. Evaluation method of congestion level is established based on satu-ration from result indicators. So the five-color warning level of airport congestion is established. 5 features are extracted for depict-ing airport demand and airport capacity from reason indicators. Neural network classifier algorithm based on cluster is proposed. Real flight data of ATL airport is used to verify this method. The accuracy is up to 80%. The results are proved to be super to the method of BP neural network. Thus the proposed method leads to better forecasting, is applicable for the real condition.