基于动态扇区的空域与飞行流量两阶段协同规划模型及算法 -- 西北工业大学学报,2016,34(4):549-557
论文:2016,Vol:34,Issue(4):549-557
引用本文:
姚頔, 王瑛. 基于动态扇区的空域与飞行流量两阶段协同规划模型及算法[J]. 西北工业大学学报
Yao Di, Wang Ying. Two-Stage Model and Algorithm for Airspace and Traffic Flow Collaborative Programming Based on Dynamic Sectorization[J]. Northwestern polytechnical university

基于动态扇区的空域与飞行流量两阶段协同规划模型及算法
姚頔1,2, 王瑛1
1. 空军工程大学 装备管理与安全工程学院, 陕西 西安 710051;
2. 国家飞行流量监控中心, 北京 100094
摘要:
针对管制扇区动态规划与飞行流量时空调配的耦合问题,考虑运行容量、效率等目标,建立了两阶段协同规划模型及求解框架。第一阶段根据自然航路点和流量分布,结合Voronoi图与图论模型构建有限元加权图拓扑抽象,以均衡管制负荷和减少协调移交负荷为目标,基于遗传算法适应性生成扇区结构;第二阶段综合等待和改航策略,以缓解区域总延误和该区域造成的区域外延误为目标,同时兼顾均摊延误和减少延误架次,在区域内容量约束和其他区域对该区域的流控约束下,基于NSGA-II进行流量时空优化。按照优先级顺序实施策略流程,为缓解空中交通拥堵探索综合施策框架。仿真结果表明,所提出的模型算法可为提升空管运行品质提供辅助决策支持。
关键词:    飞行流量管理    空域管理    空域动态配置    动态扇区    两阶段协同规划   
Two-Stage Model and Algorithm for Airspace and Traffic Flow Collaborative Programming Based on Dynamic Sectorization
Yao Di1,2, Wang Ying1
1. College of Equipment Management & Safety Engineering, Air Force Engineering University, Xi'an 710051, China;
2. State Air Traffic Flow Management Center, Beijing 100094, China
Abstract:
Aiming at the coupling interaction of dynamic airspace sectorization and the space-time allocation of air traffic flow, we establish a two-stage collaborative programming model and solution framework to maximize the operational capacity and efficiency. The first stage begins with the construction of a Voronoi cells based weighted graph model combined with Voronoi diagram and graph theory for the topological structure of given airspace and air traffic distribution. Then, the sector re-partitioning problem is solved based on genetic algorithm to balance the sector workloads and minimize the coordination workloads. In the second stage, we built air traffic flow network optimization model to reduce the total delay fairly, the ground delay out of the area fairly, the total number of delayed fights and the number of ground-delayed fights out of the area, with the airspace capacity constraint in the area and the minutes-in-trail restriction out of the area. Then, the multi-objective optimization problem is solved based on the non-dominated sorting genetic algorithm II (NSGA-II) for a combination of flow management actions, including ground holding, airborne holding, and rerouting. We explore the comprehensive framework for alleviating the traffic congestion according to the priority order of strategies described above. Simulation results show that: the proposed model can provide supporting decision-making for improving the operational quality of air traffic management.
Key words:    air traffic management    air traffic flow management    airspace management    algorithms    computer simulation    decision making    dynamic airspace configuration    dynamic airspace sectorization    efficiency    genetic algorithms    mathematical models    NSGA II (non-dominated sorting genetic algorithm II)    optimization    topology    two-stage collaborative programming    Voronoi cells    Voronoi diagram   
收稿日期: 2015-12-01     修回日期:
DOI:
基金项目: 国家自然科学基金(71171199)与国家空管“十二五”科研专项课题(GKG201401003)资助
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作者简介: 姚頔(1984-),空军工程大学博士研究生,主要从事信息系统工程与智能决策、空域与飞行流量管理的研究。
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