数据驱动下的国际航空货运不定期航线优化  

Data-driven Optimization of Nonscheduled International Air Cargo Routes

作　　者：陈华群[1] 黄方玮 杨伟超 CHEN Huaqun;HUANG Fangwei;YANG Weichao(College of Air Traffic Management,Civil Aviation Flight University of China,Guanghan 618307,Sichuan,China)

机构地区：[1]中国民用航空飞行学院空中交通管理学院,四川广汉618307

出　　处：《科技和产业》2024年第24期308-315,共8页Science Technology and Industry

基　　金：四川省科技计划(23ZDYF0586);中国民用航空局安全能力建设项目(2022237);中国民用航空飞行学院大学生创新创业项目(2024106204028)。

摘　　要：为了解决传统人工经验进行航线规划缺乏系统性和效率低下的问题,考虑了机场和航路点限制条件建立基于优化理论的航线规划数学模型。引入动态规划算法思想对传统A*算法进行改进,实现了智能化的航线规划。通过数据挖掘、清洗、坐标点转换,实现对航线规划及全球历史航线数据的可视化输出。基于Python仿真平台对2022年全球航线和14 110个机场数据进行训练,分别采用改进A*算法与贪心算法对洲际航线与短途国际航线进行规划对比。实验结果表明,改进A*算法能够减少节点的数量,缩短航线距离,洲际长航线总航程缩短37.66%,短航线总航程缩短4.36%,提高了航线规划的效率和准确性。In the context of intelligent civil aviation construction,considering the inefficiency and lack of systematization in traditional manual route planning,a mathematical model was established for route planning based on optimization theory,considering airport and route point restrictions.The traditional A*algorithm was improved through dynamic programming for intelligent route planning.Visual output of route planning and global historical route data was realized through data mining,cleaning,and coordinate point transformation.Using the Python simulation platform with 2022 global route data and 14,110 airports,the improved A*algorithm was compared with the classic greedy algorithm for long-haul and short-haul routes.The results show the improved A*algorithm reduces node count and route length,with a 37.66%reduction for long-haul routes and a 4.36%reduction for short-haul routes,enhancing planning efficiency and accuracy.

关 键 词：数据驱动 国际航空货运 不定期航线规划 改进A~*算法 航线可视化 

分 类 号：V37[航空宇航科学与技术—航空宇航推进理论与工程]