Route sector traffic probability density prediction method

Abstract

The invention relates to a route sector traffic probability density prediction method. The method comprises the following steps of selecting the traffic flow of a route sector in preset time as a sample, and analyzing sample data; and according to sample data analysis, combining model parameter selection to probabilistically predicting a route sector traffic demand, and acquiring a first prediction result. The route sector traffic probability density prediction method is used to predict based on route sector traffic flow historical data which can be obtained in an existing system. Through combining a neural network and a quantile regression method, the several quantiles of the continuous traffic demand data of a certain day in the future are obtained. And then, the continuous conditional quantiles are used to acquire the probability density function and the probability density curve graph of the continuous traffic demands of the certain day in the future through a nuclear density estimation method. A specific point prediction value and a variation interval can be obtained, and the probability of each value of a route sector traffic demand prediction change interval can also be obtained. And the accurate point prediction value of the day is acquired.

Description

technical field [0001] The invention relates to the field of aviation, in particular to a method for predicting the probability density of airway sector traffic. Background technique [0002] With the rapid development of China's air transport industry, air traffic congestion has become increasingly prominent, and it continues to spread from the terminal area to the air route network. In order to alleviate the increasingly frequent airway congestion, it is necessary to implement scientific congestion management methods, and one of the prerequisites is to accurately and objectively predict traffic demand. According to the actual operation of current airspace congestion management, it is mainly realized through the demand forecasting method based on flight path reckoning, that is, the trajectory of each aircraft is determined based on the aircraft motion equation, and the position of each aircraft is predicted for a period of time in the future. The number of aircraft passing...

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Problems solved by technology

[0003] Although this deterministic prediction result can meet the needs of airspace congestion management to a certain extent, it has several shortcomings: First, although the influence of many uncertain factors in the process of aircraft operation on the prediction result may be considered in the prediction process (such as , unplanned flight cancellations, changes in arrival and departure times, and other random events that cause deviations in aircraft operating time, and unplanned changes in flight paths or altitudes caused by weather, etc.), the expression of this deterministic prediction result is To a certain extent, it cannot fully reflect the actual influence and extent of uncertain factors; in addition, due to objective reasons such as prediction models, input data, and inherent defects in the system, the accuracy of deterministic results will decrease accordingly, so this The degree of loss of accuracy cannot be reflected in the forecast results

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