交通运输系统工程与信息 ›› 2023, Vol. 23 ›› Issue (6): 120-132.DOI: 10.16097/j.cnki.1009-6744.2023.06.013

• 系统工程理论与方法 • 上一篇    下一篇

终端区交通拥堵识别及其时空演化特性分析

李善梅*1,王雨鑫1,雷青蕾2,宋思霓1,王超1   

  1. 1. 中国民航大学,空中交通管理学院,天津 300300;2. 浙江省通用航空产业发展有限公司, 低空飞行服务中心,杭州 311600
  • 收稿日期:2023-08-02 修回日期:2023-09-18 接受日期:2023-09-27 出版日期:2023-12-25 发布日期:2023-12-23
  • 作者简介:李善梅(1982- ),女,天津人,副教授,博士。
  • 基金资助:
    国家自然科学基金(78101215);天津市应用基础多元投入基金重点项目 (21JCZDJC00780);中央高校基本科研业务费专项资金(312202YY02)。

Identification and Spatiotemporal Evolution Analysis of Air Traffic Congestion in Terminal Area

LI Shan-mei*1,WANG Yu-xin1,LEI Qing-lei2,SONG Si-ni1,WANG Chao1   

  1. 1. College of Air Traffic Management, Civil Aviation University of China, Tianjin 300300, China; 2. Low-altitude Flight Service Center, Zhejiang Provincial General Aviation Industry Development Cooperation, Hangzhou 311600, China
  • Received:2023-08-02 Revised:2023-09-18 Accepted:2023-09-27 Online:2023-12-25 Published:2023-12-23
  • Supported by:
     National Natural Science Foundation of China (78101215); Key Program of Tianjin Science and Technology Plan (21JCZDJC00780);Fundamental Research Funds for the Central Universities of Ministry of Education of China (312202YY02)。

摘要: 空中交通拥堵识别大多依赖于空管人员的主观经验认知,而非源于空中交通拥堵演化的科学规律,从而影响拥堵识别的准确性。为提升管制员对空中交通态势把握的科学性与准确性,将交通数据与管制经验相结合,提出一套基于核密度估计和交通流基本图的空中交通流拥堵识别方法,并研究空中交通拥堵时空演化特性。考虑管制行为及航迹特点,提出基于基本图理论的空中交通流三参数识别方法,将交通流三参数指标数据和管制经验相结合,建立基于高斯核密度估计的交通流相态识别方法,采用北京终端区验证本文方法的有效性。结果表明:用于划分北京终端区交通状态的交通流相对速度阈值为6.5 km·min-1和9.8 km·min-1 ;拥堵状态下,管制员雷达引导行为较为明显,飞行时长、飞行距离和转向次数较高,呈现出较高的航迹复杂性;交通拥堵的时空分布具有较强的不均衡性,拥堵时段主要位于9:00,14:00和19:00左右,拥堵常发区域主要位于中部区域,并挖掘出不同区域易发生交通拥堵的时段。这些规律的挖掘可提高管制员对拥堵态势把握的准确性与科学性。

关键词: 航空运输, 时空演化特性, 核密度估计, 交通拥堵, 识别

Abstract: The identification of air traffic congestion in terminal areas has traditionally relied on the subjective judgment of air traffic controllers, lacking a scientific foundation to understand the evolution of congestion. This has often led to inaccuracies in congestion assessment. To enhance the accuracy of congestion assessment by controllers, this study proposes a methodology for identifying air traffic congestion based on Kernel density estimation and the traffic flow fundamental diagram, and investigates the spatiotemporal evolution characteristics of air traffic congestion. Considering the characteristics of air traffic control behavior and aircraft trajectories, a parameter identification method is proposed for determining air traffic flow parameters based on the fundamental diagram theory. By combining traffic flow parameter data with air traffic control experiences, a congestion recognition method is developed using Gaussian Kernel density estimation. The effectiveness of the proposed method is demonstrated using data from the Beijing terminal area. The results indicate that the relative speed thresholds for dividing traffic states are 6.5 km · min- 1 and 9.8 km·min-1 . In congested states, controllers exhibit more pronounced radar guidance behavior, and flights have longer durations, greater distances, and more turns, reflecting increased traffic complexity. The spatiotemporal analysis of air traffic congestion in the Beijing terminal area reveals a significant imbalance in congestion distribution. Congestion periods are mainly observed around 9:00 am, 2:00 pm, and 7:00 pm, with the central region experiencing the highest levels of congestion. These findings contribute to improving the precision and scientific basis for controllers' understanding of congestion scenarios.

Key words: air transportation, spatiotemporal evolution, Kernel density estimation, traffic congestion, identification

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