基于FCM-粗糙集的多扇区交通拥挤识别方法研究

通过分析管制扇区交通时空拥挤特征,基于雷达航迹数据建立了多扇区交通拥挤识别模型.建立当量交通量、接近度、饱和度、交通密度4个多扇区拥挤特征指标,采用FCM(模糊C均值聚类算法)和粗糙集理论,对扇区拥挤程度进行划分和识别,并以中南地区区域管制扇区数据进行了实例验证.实验结果表明,扇区的拥挤态势受扇区多种宏观和微观特征的共同影响,且拥挤识别模型计算可行、识别效率较高.多扇区交通拥挤识别对空域规划、空管辅助决策、空中交通流量管理具有一定的应用价值.

Multi-sector Traffic Congestion Identification Method Based on FCM-rough Sets

Based on the temporal and spatial characteristics of traffic congestion in ATM sector, a multisector traffic congestion identification model is established with the radar trajectory data. We establish four multi- sector congestion indicators which are the equivalent traffic volume, the degree of proximity, the saturation degree and the traffic density. The sector congestion level is classified and identified with fuzzy clustering algorithm (FCM) and rough set theory, and verified with the observed data from area control sectors of Central South Air Traffic Control Bureau. The result shows that the sector congestion level is affected by the macro and micro characteristics of the sector, and congestion identification model is feasible and efficient. The method of multi-sector traffic congestion identification model has certain application value in airspace planning, ATC assistant decision and air traffic flow management.