Abstract: In view of the fact that the existing flight delay prediction methods are rarely analyzed from the perspective of the de-signated flight plan delay prediction,a prediction method to study the delay situation of a specified flight plan in the departure flight plan is proposed.First,analyzing the intrinsic characteristics of a large number of historical flight data mining data.Secondly,this research employs Dynamic Bayesian Network inference as the main modeling method to obtain the probability distribution under different conditions of flight delay.By studying the Dynamic Bayesian Network inference process and simulation,this paper presents a new method for the construction of the flight delay prediction model which is to establish Hidden Markov flight delay prediction model based on the real flight data.Using the Viterbi algorithm of Hidden Markov model decoding problem to predict the flight delay time.Finally,taking an airline's full-year flight operation data as an example for example simulation and verification,the results show that this method improves the accuracy of flight delay prediction objects.

Key words: Bayesian networks, Data mining, Delay prediction, Designated flight schedule, Hidden Markov model