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中文摘要：
终端区交通态势日益拥挤，严重制约了航空运输业的快速健康发展。为了科学评估空中交通状态，提高空域资源的利用率，对终端区交通态势进行了研究。提取影响终端区的属性指标，运用模糊c-均值聚类（FCM）对数据进行分类离散化，采用粗糙集理论分析各属性的权重，并结合模糊关系矩阵构建终端区交通态势识别模型。采用FCM和FCM-粗糙集两种识别方法对终端区交通态势进行识别。结果表明FCM-粗糙集模型既可避免人为因素干扰，还可解决数据多属性冗余问题，使交通状态判断更加准确可信；终端区交通态势分四类时，效果最好。该模型为终端区交通态势识别提供了新的研究方法。
英文摘要：
Traffic situation of terminal area, which becoming increasingly crowded, has seriously hampered the rapid and healthy development of the air transport industry. The terminal area traffic situation was studied, in order to assessing it scientifically and improving the utilization of airspace resources. The identification model is what combines the rough set theory aimed to analyse the index weights with the fuzzy relationship matrix before extracting the indicators of the terminal area and using the fuzzy C-means clustering for data classificatory discretization. Using FCM and FCM-rough set two kinds of identification methods identify the terminal area traffic situation. The results show that FCM-rough set identification method not only avoid man-made interference, but also solve the problem of redundant data in multi-attribute, make sure the traffic state judgement more accurate and reliable; terminal area traffic situation works the best when divided into four categories. The FCM-rough set identification method is a new research method for traffic situation of terminal area.
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