空中交通流扇区内飞行流量优化预测管理  

Forecasting and Management of Flight Flow in Air Traffic Flow Sector

作　　者：杨阳 王超[1] 

机构地区：[1]中国民航大学空中交通管理学院,天津300300

出　　处：《计算机仿真》2017年第9期74-78,共5页Computer Simulation

基　　金：国家自然科学基金项目资助(61039001);国家自然科学基金;民航联合基金项目资助(U1533106)

摘　　要：空中交通流扇区内飞行流量优化预测为空中交通流优化控制与管理提供决策支持信息,对于决策的有效性、优化程度和准确性具有导向性作用。传统的还原论思想的流量预测理论模型不能体现空中交通流具有的混沌特性,亦难以满足空中交通流预测的精度要求。为解决上述问题,首先基于计算几何的方法,实现了空域扇区交通流量时间序列的构建。然后根据混沌理论对交通流时间序列进行相空间重构,利用C-C方法求得时间延迟和嵌入维度,通过小数据量法计算最大李雅普诺夫指数判断空中交通流时间序列的混沌特性。最后采用最大李雅普诺夫指数进行混沌时间序列预测。实验结果表明,上述算法能够判定扇区交通流时间序列的混沌特性且预测精度较高。The optimal traffic flow forecasting in the air traffic flow sector provides decision support information for air traffic flow optimization control and management, and it has a guiding effect on the effectiveness, optimization and accuracy of decision making. The traditional traffic prediction model based on the reduction theory can not reflect the chaotic characteristics of air traffic flow and cannot meet the accuracy requirements of air traffic flow forecasting. In order to solve the problem mentioned above, firstly, based on the method of computational geometry, a time series of airspace sector flow was built in the paper. Secondly, phase space reconstruction of time series of traffic flow was completed according to the theory of chaos. At the same time, the time delay and the embedding dimension were worked out based on the C-C method. Thirdly, with the largest Lyapunov exponent of time series of air traffic flow obtained through small data method, the analysis of chaos of time series of air traffic flow was realized. Finally, the chaotic time series forecasting based on the largest Lyapunov exponent was adopted. The experimental results show that the proposed algorithm can determine the chaotic characteristics of the traffic flow time series and the forecasting accuracy is high.

关 键 词：空中交通流 混沌 最大李雅普诺夫指数 时间序列预测 

分 类 号：V355[航空宇航科学与技术—人机与环境工程]