Research On Identification And Mitigation Of Traffic Congestion At Large Airport Surface

Identifying and predicting the traffic congestion on the airport surface helps controllers to grasp the overall operational situation on the surface in time,and to predict in advance the possible areas of future congestion and its impact range.At the same time,based on the daily operation of the field congestion situation,from the perspective of optimizing the overall spatial distribution of traffic flow on the field,the scientific taxiway operation plan is designed to achieve the strategic level of field operation congestion relief,which helps to reduce field congestion and delays,reduce flight operation costs and negative environmental impact.Because of this,this paper investigates the airport field traffic congestion state identification and its propagation law,predicts the field traffic congestion state,and designs taxiway operation schemes to achieve field traffic congestion mitigation at the strategic level.(1)An optimized "node-roadway" modeling method is completed for the maneuvering area class of large airports.Firstly,the modeling of each module of the traffic system is completed,and the basic features of each module are assigned according to the actual needs of the field operation.Finally,the field modeling of Chengdu Tianfu International Airport is completed with the actual operation scenarios.Through reasonable modeling and basic feature setting,the model can accurately express the actual operation process and fully reflect the real field operation process.(2)Complete the research on the identification of traffic congestion on the field surface of large airports.First,the simulation of the airport traffic operation scenario is completed and the operation data is extracted according to the actual operation scheme and the flight plan with equal proportional augmentation.Then,the traffic congestion evaluation index is selected and combined with a fuzzy clustering algorithm to identify the traffic congestion status of the airport taxiway.Finally,the propagation law of the traffic congestion state is analyzed based on the traffic congestion state-level data to realize the overall perception of the airport traffic congestion state.(3)Complete the research on traffic congestion prediction at large airports.Firstly,we combine convolutional neural networks and long and short-term memory neural networks to build a prediction model of traffic congestion state based on improved long and short-term memory neural network algorithm.Then,the prediction of the overall traffic congestion state of the field is completed by the predicted field operation data and the field traffic congestion state identification method.The short-term prediction of traffic congestion on the field can help field controllers to predict the time of traffic congestion in advance and adjust the control measures in time to improve the field traffic operation efficiency.(4)Complete the study of congestion mitigation at large airports.Firstly,the principles and corresponding design paradigms for the design of taxiway operation schemes in the maneuvering area of large airports are proposed.Then,a set of generalized evaluation and selection methods for large airports are established.Finally,the optimization design and simulation of the taxiway operation scheme of Chengdu Tianfu International Airport are carried out to verify the effectiveness of the given optimization method.The paper constructs a congestion identification and prediction model for the airport surface and improves the efficiency and safety of the airport surface operation by optimizing the taxiway operation scheme in the maneuvering area of large airports.The results obtained in this paper can provide theoretical support for the congestion management of airport surface operation.