The Estimation Of Flight Delay And Propagation Based On Bayesian Networks

In domestic and overseas, flight delay is one of the problems that the staffs inindustry devote themselves to research. In recent years, the delays of flights indomestic become more and more serious, and have effected on the developing of thecivil aviation industry. Flight delay status needs to be researched and improvedimmediately. The flight delays are often happened in those busy hub-airports whichare important in flight chains. Many flights turn around in hub-airports. Therefore,when the delay happened in a busy hub-airport, the delay propagation will beinevitable. Relieving the delay status of busy hub-airports will lighten the pressure ofwhole flight chain, even the whole civil aviation system. Therefore, focusing on oneof those busy hub-airports named AIRPORTP, arrival/departure delays and delaypropagation have been discussed in this paper separately.Orienting to the hub-airports and flight trains, many algorithms and methods areadvanced based on Bayesian Network:Firstly, orienting to the busy hub-airports, Bayesian Network parameter learningalgorithm is used in the field of flight delay estimation. In order to solve the problemof high-dimension, large-computing-scale in the network constructed by experts'experiments, two methods are raised. One is named Independent Attribute TakingOut,which separates the network based on analyzing of the correlation between allattributes. This method is suit for dealing with huge data set since its speed incalculating is high. The other is named Optimized Model. The nodes with lowcorrelation are eliminated after regressive analyzing. The correct rate of estimatingwith this method is high. This method is suit for dealing with small data set since itsspeed in calculating is slower than the first method stated above. Then the dimensionof model's structure is reduced. After analyzing and modeling, the same nodes andstructure are discovered in the arrival and departure delays models, therefore asymmetrical model named PMofA is established for the delay propagation based onOptimized Model.Secondly, orienting to the delay propagation in flight chains, after modelingarrival delay and departure delay separately based on the Bayesian structure learningalgorithm, MSPmodel is established bya Mixed Learning Method. Then an improvedK2 structure learning algorithm named TFK2 is raised. It is proved to be more suitable to model and estimate the flight delays than the traditional K2 algorithm bothin learning speed and correct rate. Based on TFK2, a Negotiating Structure Learningalgorithm is raised. The network's structure built by this Negotiate Structure Learningincludes redundancy nodes. The nodes change their working states as competition orredundancy in different conditions to further enhance the correct rate and the speed incalculating ulteriorly.Thirdly, a Self-feedback Ensemble-learning Flight-delay Estimating System(SEFS) is raised based on Ensemble Learning Method and TFK2 algorithm. Thesystem includes three sub-learners. After trained,the system can be used to estimateflight delays. And according to the estimated result of SEFS, the delay of specificflight can be pre-warned with color and probability, to make pre-warning morehumanity.Finally, pre-warning is issued for a busy hub-airport, based on the estimatednumber of delayed flights during a period time in AIRPORTP by the SEFS System.And pre-warning is issued for air companies and guests, based on the estimated delaytime of specific flights by the SEFS.