突发事件下大规模空中交通流量管理的组合优化模型

1. 中国民航大学 天津市空管运行规划与安全技术重点实验室, 天津 300300
• 收稿日期:2019-01-10 修回日期:2019-04-02 出版日期:2019-08-15 发布日期:2019-05-29
• 通讯作者: 王莉莉 E-mail:llwang@cauc.edu.cn
• 基金资助:
国家自然科学基金与中国民用航空局联合资助（U1633124）

Combined optimization method for large-scale air traffic flow management under emergencies

WANG Lili, WANG Hangchen

1. ATM Operation Planning and Safety Techniques Key Lab of Tianjin, Civil Aviation University of China, Tianjin 300300, China
• Received:2019-01-10 Revised:2019-04-02 Online:2019-08-15 Published:2019-05-29
• Supported by:
Jointly Fund of National Natural Science Foundation of China and Civil Aviation Administration of China (U1633124)

Abstract: To address the disturbance like extreme weather conditions and air traffic control for military activities, a dynamic network flow method is proposed. Firstly, combined with the characteristics of civil aviation control in China, the impact of emergencies on traffic management is analyzed. Also, this paper uses the graph theory to establish a mathematical model for en-route network and flight level. Then the aircraft is divided into large, medium, and small traffic flows, introducing the necessity of using the multi-commodity flow method. Secondly, considering the condition of network congestion varies with time and traffic flow, the random changes of dangerous weather, and the characteristics of air holding and ground holding cost, three optimization objectives are defined, then the multi-objective mathematical model is constructed with the constraint of airport capacity, sector capacity, flight continuity, and sector continuity. Thirdly, to deal with the demand conflict with military activities and the defects of high time complexity in the multi-commodity flow model that can not meet the demand of short-term flow management, the progressive tolerance constraint method is improved, and a phased solution approximation algorithm is designed. Finally, a part of the airspace of the Southwest Air Traffic Control Administration is used for simulation in three scenarios. Simulation results show that the proposed model and algorithm can effectively solve the short-term traffic management problem under emergencies. The algorithm is more efficient than the traditional algorithm under large traffic flows.