基于DTW的K-medoids扇区交通特征聚类研究

Clustering of Sector Traffic Characteristics Based on DTW k-Medoids

摘要: 随着航空运输需求的不断增加，交通运行模式日渐复杂，空管系统的运行压力与日俱增。如何在满足发展需求的基础上，提升空管系统的运行效能成为业内关注的热点。本文筛选扇区交通特征行为指标，统计不同指标的时间序列。针对时间序列的高维属性，选取了基于DTW的K-medoids聚类算法，识别了15个扇区样本在各扇区交通特征指标下的聚类结果。实例分析表明，基于时间序列的聚类方法可以识别多个扇区在单个交通特征下的分布模式，最终为交通行为多样化分析需求提供有效手段和依据。

Abstract: With the increasing demand for air transportation, the traffic operation mode is becoming more and more complicated, and the operation pressure of the air traffic control system is increasing day by day. How to improve the operating efficiency of the air traffic control system on the basis of meeting development needs has become a hot spot in the industry. This paper screens the traffic characteristics and behavior indicators of sectors and counts the time series of different indicators. For the high-dimensional attributes of the time series, the K-medoids clustering algorithm based on DTW was selected, and the clustering results of 15 sector samples under the traffic characteristic indicators of each sector were identified. The case analysis shows that the clustering method based on time series can identify the distribution pattern of multiple sectors under a single traffic characteristic, and finally provide an effective means and basis for the diversified analysis needs of traffic behavior.