Traffic Situation Analysis In Terminal Area Based On ADS-B Data

The identification and prediction of air traffic situation is an important guiding factor for the controller to conduct the aircraft to take off and land.With the rapid development of air transport industry,the airport traffic flow is increasing,and the traffic jam in the terminal area is becoming more and more serious.It is urgent to establish an air traffic congestion discrimination and prediction system to improve the efficiency of airspace operation.Based on the ADS-B data of terminal area,the identification and prediction of traffic congestion in terminal area is in-depth studied.Firstly,by analyzing the causes of traffic congestion,the traffic characteristic indexes related to the traffic situation in the terminal area are determined and extracted from ADS-B data.Secondly,considering the complexity of terminal area traffic situation,a Fuzzy C-Means algorithm based on information entropy optimization is proposed to identify terminal area traffic situation.Compared with the original FCM algorithm,the doubt rate is reduced by 23.1%.Then,based on the prediction of fuzzy soft set theory,the combined prediction model of terminal area traffic situation is established.Considering the diversity,periodicity and uncertainty of the characteristic values of the traffic situation,three basic prediction models of historical average,clustering and BP neural network are used to make the prediction.Further more,the fuzzy soft set is constructed with three predicted values for combined prediction,and mathematical statistics and intelligent prediction methods are combined to obtain more accurate predicted traffic situation values,which improved the prediction accuracy to84.42%.Finally,the terminal area traffic situation is showing in the Geographic Information System,providing decision support for terminal area traffic operation optimization and control.