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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。
Optimizing Flight Flux Forecast in Air Traffic Flow Sector provides decision support information for the optimization control and management of air traffic flow and plays a guiding role in decision making effectiveness, optimization and accuracy. The traditional theory of reduction theory can not reflect the chaotic characteristics of air traffic flow and can not meet the accuracy requirements of air traffic flow forecasting. In order to solve the above problems, the construction of traffic flow time series in airspace sector is firstly realized based on the method of computational geometry. Then the phase space of traffic flow is reconstructed based on chaos theory, the time delay and embedding dimension are calculated by C-C method, and the maximum Lyapunov exponent is calculated by small data method to judge the chaotic characteristics of the air traffic flow time series. Finally, the maximum Lyapunov exponent is used to predict the chaotic time series. The experimental results show that the above algorithm can determine the chaotic characteristics of the time series of the traffic flow and the prediction accuracy is high.