A probability density prediction system applied to airway sector traffic

Abstract

The invention relates to a system for predicting the traffic probability density of a route sector. The system comprises a sample acquisition module suitable for acquiring the traffic flow of the route sector within a preset time as a sample; the sample analysis module is suitable for sample data analysis; the first prediction result prediction module carries out probabilistic prediction on the traffic demand of the route sector according to sample data analysis in combination with model parameter selection, and obtains a first prediction result. A neural network is combined with a quantile regression method, so that a plurality of quantiles of continuous traffic demand data of a certain day in the future are obtained. Then, the continuous conditional quantiles are utilized, and a probability density function and a probability density curve graph of traffic demand continuity in a certain day in the future are obtained through a kernel density estimation method. Therefore, the specificpoint prediction value and the change interval thereof can be obtained, the probability of each value of the traffic demand prediction change interval of the route sector can be obtained, and the accurate point prediction value of the day can be obtained.

Description

technical field [0001] The invention relates to the field of aviation, in particular to a traffic probability density prediction system for air route sectors. 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 th...

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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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