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Flight Path Optimization Based on Air Traffic Complexity and Aircraft Emissions
| 更新时间：2026-04-30
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Flight Path Optimization Based on Air Traffic Complexity and Aircraft Emissions
- Journal of Nanjing University of Aeronautics & Astronautics（Natural Science Edition） Vol. 58, Issue 2, Pages: 412-424(2026)
- 作者机构：
中国民航大学空中交通管理学院，天津300300
- 作者简介：
- 基金信息：
DOI：10.16356/j.2097-6771.2026.02.017
CLC： V355;U8Received：03 June 2025，
Revised：2025-09-29，
Published：28 April 2026
- 稿件说明：
移动端阅览
王红勇，兰雯. 基于空中交通复杂性及航空器排放的航迹优化［J］. 南京航空航天大学学报（自然科学版），2026，58（2）：412⁃424.
WANG Hongyong， LAN Wen. Flight path optimization based on air traffic complexity and aircraft emissions［J］. Journal of Nanjing University of Aeronautics & Astronautics（Natural Science Edition），2026， 58（2）：412⁃424.
王红勇，兰雯. 基于空中交通复杂性及航空器排放的航迹优化［J］. 南京航空航天大学学报（自然科学版），2026，58（2）：412⁃424. DOI： 10.16356/j.2097-6771.2026.02.017.
WANG Hongyong， LAN Wen. Flight path optimization based on air traffic complexity and aircraft emissions［J］. Journal of Nanjing University of Aeronautics & Astronautics（Natural Science Edition），2026， 58（2）：412⁃424. DOI： 10.16356/j.2097-6771.2026.02.017.
Flight Path Optimization Based on Air Traffic Complexity and Aircraft Emissions
摘要
为解决现有航迹优化研究中在空域效率与环境效益协同优化方面存在的模型耦合不足、缺乏动态响应机制以及多污染物协同减排考虑不完善等问题，提出一种基于空域复杂度的动态航迹调整方法及污染物排放协同优化方法。首先，构建一个时空交互驱动的空中交通复杂度计算模型。其次，整合SAGE（System for assessing aviation’s global emissions）模型和改进指标模型，建立巡航阶段的多污染物排放计算体系，并设计一种改进的模拟退火遗传算法（Simulated annealing genetic algorithm， SAGA）。基于2023年6月某日数据的模拟实验表明，在不考虑空域限制及其他约束的理想情况下，通过调整26.18%的飞行高度，所提出的方法将
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的排放量分别减少0.75%、0.14%、0.47%、0.48%和0.33%，并且每个时段的平均复杂度平均降低了19.82%。全天的总体平均复杂度也得以下降。该方法通过高度层的动态调整实现多目标协同，在保证飞行安全的同时显著提升了环境效益，为空中交通管制系统的绿色转型提供了技术支持。
Abstract
In order to solve the problems of insufficient model coupling， lack of dynamic response mechanism and imperfect consideration of multi‑pollutant collaborative emission reduction in the co‑optimization of airspace efficiency and environmental bene
fits in the existing research on trajectory optimization， this paper proposes a dynamic trajectory adjustment method based on airspace complexity and collaborative optimization of pollutant emissions. Firstly， a spatio‑temporal interaction‑driven air traffic complexity calculation model is constructed. Secondly， the system for assessing aviation’s global emissions （SAGE） and modified index models are integrated to establish a multi‑pollutant emission calculation system during the cruising phase. Finally， an improved simulated annealing genetic algorithm （SAGA） is designed to solve the multi‑objective optimization problem. Simulation experiments based on navigation data on a certain day in June 2023 show that under the ideal situation of not considering airspace restrictions and other constraints， by adjusting 26.18% of the flight altitude， the proposed method reduces emissions of
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by 0.75%， 0.14%， 0.47%， 0.48%， 0.33%， respectively， and the av
erage complexity of each time period is reduced by 19.82% on average. The overall average complexity of the whole day is also reduced. This method realizes multi‑objective collaboration through dynamic adjustment of the height layer， which significantly improves the environmental benefits while ensuring operational safety， and provides technical support for the green transformation of the air traffic control system.
关键词
Keywords
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