基于K-means的空中交通流模式识别

Air Traffic Flow Pattern Recognition Based on K-means

空域系统容量与空中交通态势密切相关。对空中交通态势的精确把握可有效降低当前大流量情况下的空域系统压力、提高管制员有效决策水平。提出了一种基于K-means聚类方法的空中交通流识别方法,有助于清晰把握空中交通整体态势。首先分析了空中交通流特性,然后根据雷达数据统计了主要航段在15 min内飞机架次,聚类识别为多种交通流模式,最后通过计算时段内所有飞机在扇区的总飞行时间,验证了该方法的有效性。

Airspace system capacity is closely related to the air traffic situation. Accurate grasp of the situation of air traffic airspace system can effectively reduce the airspace system pressure under the current large flow situation,and improve the effective decision-making level of controllers. A method of air traffic flow identification method based on K-means clustering is presented,helping to grasp the overall situation of air traffic clearly. The air traffic flow characteristics firstly is analyzed. Then count the aircraft sorties on the main leg within 15 minutes according to radar data,clustering and identified as a variety of traffic patterns. Finally the effectiveness of this method by calculating the total flight time of all aircraft in the sector is verified.