Analysis of Flight Punctuality Rate Based on Complex Network

Aviation service plays an indispensable role in the process of social and economic development.In this process,the problem of flight punctuality becomes more and more serious.Flight delay will bring a variety of implicit and explicit losses to individual passengers and airlines,so it is necessary to analyze the influencing factors of flight punctuality rate.Complex network can be used to study various objects with complex relations,obtain the relations of these objects and calculate the influence of different indicators on the objects.This article mainly has carried on the three aspects:Firstly,get the flight data from the Internet,and use the knowledge of the complex network to obtain the data to build into a directed network.Secondly,to analyze the building of directed network,computing network centricity index,study the influence of different centricity index for flight punctuality.Thirdly,get the statistics of airport indicators including network indicators and calculate the correlation of different indicators and punctuality.Through the above three points,the degree of influence of different influencing factors on flight on-time rate is studied,which is the positive influence or the negative influence.The study of these factors will provide some references for the practical application of the improvement of flight punctuality.

Flight Delay Prediction Using Gradient Boosting Machine Learning Classifiers

With the increasing of civil aviation business,flight delay has become a key problem in civil aviation field in recent years,which has brought a considerable economic impact to airlines and related industries.The delay prediction of specific flights is very important for airlines’plan,airport resource allocation,insurance company strategy and personal arrangement.The influence factors of flight delay have high complexity and non-linear relationship.The different situations of various regions and airports,and even the deviation of airport or airline arrangement all have certain influence on flight delay,which makes the prediction more difficult.In view of the limitations of the existing delay prediction models,this paper proposes a flight delay prediction model with more generalization ability and corresponding machine learning classification algorithm.This model fully exploits temporal and spatial characteristics of higher dimensions,such as the influence of preceding flights,the situation of departure and landing airports,and the overall situation of flights on the same route.In the process of machine learning,the model is trained with historical data and tested with the latest actual data.The test result shows that the model and this machine learning algorithm can provide an effective method for the prediction of flight delay.