Research On Evaluation And Prediction Technology Of Air Traffic Congestion In Airport Terminal Area

With the continuous growth of air transportation demand in China,air traffic congestion is becoming increasingly serious,which has adversely affected the development of air transportation industry in China.The airport terminal area is a key node of air traffic network.Once air traffic congestion occurs,it will directly lead to a large area of flight delay and aggravate the congestion situation of air traffic network.Therefore,the accurate evaluation of congestion situation and the accurate prediction of congestion level in airport terminal area can provide a basis for the macro decisionmaking of air traffic control department,which is of great significance for improving flight operation efficiency,alleviating airspace congestion and reducing the workload of control personnel.Taking the airport terminal area as the research object,the congestion measurement indexes were established according to the influence factors of air traffic congestion,two indicators of transit time and congestion were proposed,and the flight delay indicator was optimized.The evaluation index system of air traffic congestion in airport terminal area was constructed from three aspects: congestion phenomenon,congestion reason and congestion consequence.An index weight calculation method based on the rough set condition information entropy was formed.Each index value of the matter-element to be evaluated was mapped to the corresponding level according to the evaluation index limit,and the corresponding level values were used to complete the weight calculation of the rough set condition information entropy.Based on the extension cloud theory,the evaluation method of congestion level in airport terminal area was established.It is verified by an example that the method can solve the problems of fuzziness and randomness in the evaluation of air traffic congestion,and has better suitability than traditional methods.A prediction model of air traffic congestion level in airport terminal area based on improved particle swarm optimization BP neural network was proposed.By improving the learning factors and inertial weights of the particle swarm optimization algorithm,the accuracy of prediction results was further improved,and training and test data sets were constructed according to the evaluation results.The training data set was imported into the model for training,and then the trained model was used to predict the level of air traffic congestion in the airport terminal area.The prediction accuracy rate reaches 90%.Figure 20;Table 11;Reference 78...