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Comentropy-based integrated prediction model of traffic flow

A forecasting model and traffic flow technology, applied in traffic flow detection, special data processing applications, instruments, etc., can solve the problems of not being able to adjust the weight in time, and not being able to track the actual flow change results in time

Inactive Publication Date: 2012-09-12
JISHOU UNIVERSITY
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AI Technical Summary

Problems solved by technology

However, the information entropy technology cannot track the actual flow change results in time, and cannot adjust the weight in time, so it is necessary to adjust the weight obtained by the information entropy technology online to improve the prediction accuracy

Method used

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  • Comentropy-based integrated prediction model of traffic flow
  • Comentropy-based integrated prediction model of traffic flow
  • Comentropy-based integrated prediction model of traffic flow

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Experimental program
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Embodiment Construction

[0042] The specific implementation of the program is as follows:

[0043] 1). Traffic flow forecasting method based on gray theory: obtain equidistant traffic flow sequence , where n To model the length of the sequence, a cumulative generation is performed with . The following first-order differential equation can be established to describe its development trend, with

[0044] , (1)

[0045] In the formula r For the development coefficient to reflect the development trend of the predicted value, u The gray action reflects the sparseness of data changes. The discretization of the differential equation has

[0046] . (2)

[0047] Take from the formula , then there is , which can be expressed as a matrix ,in ,

[0048] .

[0049] It can be estimated by the method of least squares that r , u , as shown in formula (4)

[0050] .(3)

[0051] The traffic flow prediction model based on gray theory can be ...

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Abstract

Provided is a comentropy-based integrated prediction model of traffic flow aiming at factors influencing traffic flow and having characteristics as uncertainty, and the like. A flow prediction model is established by separately utilizing a grey theory having online correction capability and mixture-kernel-based SVM. In order to improve the accuracy of the SVM prediction model, parameters of the SVM prediction model such as penalty coefficient, nuclear parameter, weighting coefficient, and the like are optimized by using an immune clonal optimization algorithm with gradient information. Then an integrated prediction model is established by using the comentropy technology, in order to further improve the accuracy of the prediction model, the weight obtained by the comentropy is corrected online. According to the invention, influences caused by uncertainty factors influencing a traffic flow can be reduced to some extent, the traffic flow can be predicted more accurately, and a preferable guidance is made for the control of the traffic flow.

Description

technical field [0001] The invention relates to a prediction method adapted to traffic flow, which belongs to the field of prediction control in automation. Background technique [0002] Traffic flow forecasting is a necessary condition for traffic flow control, and its real-time and reliability are directly related to the effect of traffic management and control. Therefore, short-term traffic flow forecasting has always been a research hotspot. Traffic flow is cyclical and sudden, which is similar to the law of network traffic, but its changing law is more complex and is largely affected by the external environment such as weather and temperature. [0003] Because the factors affecting traffic flow are characterized by uncertainty, suddenness, and complexity, and the time series forecasting model is based on statistical laws and does not require a clear connotation, so most of the methods proposed so far are based on time series forecasting . Early forecasting methods mai...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F19/00G08G1/01
Inventor 丁雷杨正华侯冬晴
Owner JISHOU UNIVERSITY
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