基于Hurst指数的空中交通流长相关性实证分析
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摘要
空中交通系统是复杂的非线性动力系统,空中交通流呈现明显的非线性分形特征,长相关性是分形的核心特征之一。为了对空中交通流进行精确的建模、预测和管控,利用管制扇区实测运行数据构建交通流时间序列,采用重标极差法计算时间序列的Hurst指数,对交通流长相关性进行了实证分析。结果表明:终端区交通流时间序列的全局和局部Hurst指数均大于0.5,说明该时间序列是非随机序列,且具有正相关性,交通流过去的状态对当前和未来都有影响;进一步验证了空中交通流具有分形特征,也为利用分形理论进行交通流预测提供了科学判据。
Air traffic system is a complex nonlinear dynamic system, air traffic flow shows obvious nonlinear fractal characteristics with long phase correlation as one of the key features of which. For accurate modeling, prediction and control of air traffic flow, air traffic flow time series are constructed using operation data in ATM sectors,calculating the Hurst exponent by rescaled range method and conducting empirical analysis on traffic flow long phase correlation. Results show that the local and global Hurst exponent of traffic flow time series are both more than 0.5, indicating that these time series are non-random and have a positive correlation, meanwhile, the past state of traffic flow has an influence on the current and future states, further verifying the fractal characteristics of air traffic flow, providing scientific criteria for traffic flow prediction based on fractal theory.
Air traffic system is a complex nonlinear dynamic system, air traffic flow shows obvious nonlinear fractal characteristics with long phase correlation as one of the key features of which. For accurate modeling, prediction and control of air traffic flow, air traffic flow time series are constructed using operation data in ATM sectors,calculating the Hurst exponent by rescaled range method and conducting empirical analysis on traffic flow long phase correlation. Results show that the local and global Hurst exponent of traffic flow time series are both more than 0.5, indicating that these time series are non-random and have a positive correlation, meanwhile, the past state of traffic flow has an influence on the current and future states, further verifying the fractal characteristics of air traffic flow, providing scientific criteria for traffic flow prediction based on fractal theory.
引文
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[2]张洪海,胡勇,杨磊,等.多机场终端区微观交通流建模与仿真分析[J].西南交通大学学报,2015,50(2):368-374.
[3]许炎,张洪海,杨磊,等.基于实测数据的终端区空中交通流特性分析[J].交通运输系统工程与信息,2015,15(1):205-211.
[4]张洪海,杨磊,别翌荟,等.终端区进场交通流广义跟驰行为与复杂相变分析[J].航空学报,2015,36(3):949-961.
[5]王斯文.基于CTM的终端区进场交通流特性研究[D].天津:中国民航大学,2016.
[6]LI SHANMEI,XU XIAOHAO,MENG LINGHANG.Flight conflict forecasting based on chaotic timeseries[J].Transactions of Nanjing University of Aeronautics&Astronautics,2012,29(4):388-394.
[7]CONG WEI,HU MINGHUA.Chaotic characteristics analysis of air traffic system[J].Transactions of Nanjing University of Aeronautics&Astronautics,2014,31(6):636-642.
[8]郑旭芳.空中交通流非线性特征研究[D].天津:中国民航大学,2016.
[9]王超,郑旭芳,王蕾.交汇航路空中交通流的非线性特征研究[J].西南交通大学学报,2017,52(1):171-178.
[10]杨阳,王超.空中交通流扇区内飞行流量优化预测管理[J].计算机仿真,2017,34(9):74-78.
[11]贺国光,冯蔚东.基于R/S分析研究交通流的长程相关性[J].系统工程学报,2004,19(2):166-169.