Abstract: 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.

Key words: air traffic management, air traffic flow, fractal theory, long phase correlation, Hurst exponent