Air traffic complexity evaluation method based on probabilistic trajectory prediction

Predicting air traffic complexity accurately for a long period is the key factor for the air traffic management system to configure resources dynamically. Bring attention to the problem of insufficient accuracy of the previous complexity predicting methods for a long predicted period, an air traffic complexity evaluation method based on probabilistic trajectory prediction is proposed. The proposed method uses stochastic linear hybrid system theory to model the aircraft motion and the influence of external uncertain factors, in combination with the flight intent information, to realize the accurate prediction of the flight trajectory. According to the definition of first-order and second-order complexity mapping, the issue of air traffic complexity prediction is transformed into the calculation of quadratic form of Gaussian random variables, and a Laurent series expansion algorithm is adopted to calculate the air traffic complexity mapping. A numerical simulation example is provided to illustrate the proposed method.