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Bayesian network-based flight departure sliding time dynamic-prediction method

A taxi time and dynamic prediction technology, applied in the field of computer simulation, can solve problems such as the dynamic prediction method of flight departure taxi time that has not yet been discovered, and achieve the effect of high application value

Inactive Publication Date: 2018-11-20
CIVIL AVIATION UNIV OF CHINA
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Problems solved by technology

However, there is no dynamic prediction method for flight departure taxiing time based on Bayesian network

Method used

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  • Bayesian network-based flight departure sliding time dynamic-prediction method
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  • Bayesian network-based flight departure sliding time dynamic-prediction method

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Embodiment Construction

[0034] The Bayesian network-based flight departure taxiing time dynamic prediction method provided by the present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0035] Such as figure 1 As shown, the flight departure taxiing time dynamic prediction method based on Bayesian network provided by the invention comprises the following steps carried out in order:

[0036] 1) According to the surface operation mode of the target airport, analyze each field in the airport surface operation database, extract flight departure operation data from it, and use these data to form the airport flight departure operation data set; some flights in the airport flight departure operation data set An example of outbound operation data is shown in Table 1:

[0037] Table 1. Example of flight departure operation data

[0038]

[0039] The airport flight departure operation data set records in detail the flight execution date,...

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Abstract

The invention discloses a Bayesian network-based flight departure sliding time dynamic-prediction method. The method includes the steps such as: forming an airport flight departure running data set; obtaining a complete airport flight departure running data set; determining main influencing factors; obtaining a Bayesian-network flight departure variable-sliding-time dynamic-estimation model; and estimating flight departure variable-sliding-time. The Bayesian network-based flight departure sliding time dynamic-prediction method provided by the invention has the following advantages: the flightdeparture variable-sliding-time dynamic-estimation model combining machine learning technology and civil aviation expert knowledge is established, an airport scene situation of departing flight can beaccurately and quickly analyzed, and powerful decision support is provided for civil aviation airport flight releasing and management. The method has the characteristics of simplicity, high efficiency and high precision, reflects autonomy and individual difference of flight departure sliding in an airport traffic system, and also has higher application values for analysis and evaluation of airport scene traffic running situations.

Description

technical field [0001] The invention belongs to the technical field of computer simulation, in particular to a method for dynamically predicting flight departure taxiing time based on a Bayesian network. Background technique [0002] Airports are important hubs of the air transportation system. With the surge in air transportation business volume, major airports around the world have experienced capacity saturation to varying degrees, which has also led to flight delays and congestion at airports. How to use the dynamic prediction method to deduce, calculate and evaluate the scene situation faced by the flight departure, and take effective measures to improve the overall operation efficiency of the airport to reduce flight delays has become a difficult point in the field of computer simulation technology. [0003] Bayesian network is a directed acyclic graph (Directed Acyclic Graphs, DAG), in which each node has its own random variable, and the directed edges between nodes r...

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Application Information

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IPC IPC(8): G06Q10/04
CPCG06Q10/04
Inventor 邢志伟姜松岳李彪李斯朱慧蒋骏贤
Owner CIVIL AVIATION UNIV OF CHINA
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