中国民航大学学报 ›› 2026, Vol. 44 ›› Issue (3): 80-85.

• 航空运输管理 • 上一篇    下一篇

基于多特征融合的终端区交通流分布识别方法

  

  1. (民航局空管局运行管理中心,北京 100022)
  • 收稿日期:2025-11-16 修回日期:2026-01-07 出版日期:2026-06-30 发布日期:2026-07-04
  • 作者简介:郭宇帅(1995— ),男,陕西西安人,工程师,硕士,研究方向为交通运输规划与管理. 
  • 基金资助:
    国家自然科学基金民航联合基金项目(U2033203)

Identification method for traffic flow distribution in the terminal area based on multi-feature fusion

  1. (Operation Management Center, Air Traffic Management Bureau, CAAC, Beijing 100022, China)
  • Received:2025-11-16 Revised:2026-01-07 Online:2026-06-30 Published:2026-07-04

摘要: 针对终端区空域运行环境复杂、标准航路难以动态反映实际空中交通管制指挥情况的问题,为精细刻画实 际交通态势并有效评估管制复杂性,本文提出一种基于多特征融合的终端区交通流分布识别方法,该方法 旨在解决航迹数据长度不均、特征维度单一及传统聚类精度不足等问题。通过重采样将原始航迹规范为 等长序列,融合位置、航向与速度特征,构建多特征融合的相似度模型,并结合改进的层次聚类方法提取平 均交通流,以此构建实际运行条件下的终端区航路拓扑图。以昆明长水国际机场的进离场数据进行验证, 结果表明,该方法能有效识别各走廊口交通流分布,所得结果较标准航图更能体现管制动态引导的特征, 可为终端区精细化管理与管制行为分析提供依据。

关键词: 交通流分布, 终端区空域, 多特征融合, 平均交通流

Abstract: Aiming at the problems that the airspace operation environment in the terminal area is complex and standard routes are difficult to dynamically reflect the actual air traffic control (ATC) command scenarios, this paper pro鄄 poses a terminal area traffic flow distribution identification method based on multi-feature fusion to accurately describe the real traffic situation and effectively evaluate the complexity of ATC. This method aims to solve the problems of uneven trajectory data length, single feature dimension and insufficient accuracy of traditional clus鄄 tering algorithms. Through resampling, the original trajectories are standardized into equal-length sequences; by fusing position, heading and velocity features, a similarity model with multi-feature fusion is constructed. Com鄄 bined with the improved hierarchical clustering algorithm, the average traffic flow is extracted to construct the terminal area route topology map under actual operating conditions. Verified by the arrival and departure data from Kunming Changshui International Airport, the results show that this method can effectively identify the traf鄄 fic flow distribution at each corridor entrance. The results obtained can better reflect the characteristics of dy鄄 namic ATC guidance than the standard aeronautical chart, which can provide a basis for the refined management of the terminal area and the analysis of ATC behaviors.

Key words:  , traffic flow distribution, terminal area airspace, multi-feature fusion, average traffic flow

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