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Airport freight traffic prediction analysis method based on SARIMA and RBF neural network integration combination model

A neural network and combined model technology, applied in the field of transportation planning, design and management, can solve the problems that cannot meet the accuracy of the airport

Inactive Publication Date: 2015-10-21
THE SECOND RES INST OF CIVIL AVIATION ADMINISTRATION OF CHINA
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Problems solved by technology

However, the prediction results of the current combination model cannot meet the accuracy of the actual operation needs of the airport.

Method used

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  • Airport freight traffic prediction analysis method based on SARIMA and RBF neural network integration combination model
  • Airport freight traffic prediction analysis method based on SARIMA and RBF neural network integration combination model
  • Airport freight traffic prediction analysis method based on SARIMA and RBF neural network integration combination model

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Embodiment Construction

[0045] After the airport has been in operation for many years, the airport cargo volume is affected by both linear factors such as fixed customer groups and the number of routes, as well as nonlinear factors such as the economy. The SARIMA-RBF model method provided by the present invention fully considers the influence of airport cargo volume The linear and nonlinear factors in order to improve the prediction accuracy of airport cargo traffic.

[0046] The experimental data of the present invention selects the real operating data of a domestic hub airport from January 2010 to July 2014, as shown in Table 1

[0047] Table 1 Cargo volume of a hub airport from January 2010 to July 2014

[0048]

[0049] In another embodiment of the present invention, normalization processing can also be performed on the time series data of airport cargo volume in advance.

[0050] Depend on figure 2 It can be clearly seen that the cargo volume of the airport has a clear upward trend from 20...

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Abstract

The invention relates to an airport freight traffic prediction analysis method based on an autoregressive integrating moving average (SARIMA) and RBF neural network integration combination model. According to the method, an airport freight traffic linear part is predicted by using seasonal SARIMA; a non-linear airport freight traffic part is predicted by using an RBF neural network; and then the non-linear prediction result is used as compensation of the linear prediction result, thereby obtaining a final prediction result.

Description

technical field [0001] The invention relates to the technical field of transportation planning, design and management, in particular to an airport freight volume prediction and analysis method based on SARIMA and RBF neural network integrated combination model. Background technique [0002] With the rapid development of civil aviation business, the transportation scale of civil aviation airports has grown rapidly. The reasonable forecast of airport cargo volume can provide guidance for the development of airports, and can also provide decision support for airport managers. As a kind of time series data, the airport cargo volume data can be divided into two categories with the continuous breakthrough of technology: one is the traditional forecasting method, such as: econometric method, regression analysis method, gray forecasting method, autoregressive Differential moving average (Autoregressive Integrating Moving Average, ARIMA), etc., among which the autoregressive differen...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/30G06N3/02
Inventor 罗谦李学哲冯文星潘野罗沛郁二改张扬谭晶裴翔宇廖顺兵
Owner THE SECOND RES INST OF CIVIL AVIATION ADMINISTRATION OF CHINA
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