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Traffic flow prediction method

A forecasting method and traffic flow technology, applied in traffic flow detection, neural learning methods, biological neural network models, etc., can solve problems such as the impact of prediction model accuracy

Active Publication Date: 2018-05-08
BEIJING INSTITUTE OF TECHNOLOGYGY
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Problems solved by technology

Use historical traffic flow data to supervise the prediction model in order to obtain better generalization performance, but the non-stationary and nonlinear characteristics of the traffic flow will have a certain impact on the accuracy of the prediction model, which is also in the research of traffic flow prediction. must overcome

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Embodiment Construction

[0041] The present invention will be described in detail below with reference to the accompanying drawings and examples.

[0042] Please refer to figure 1 , figure 1 A flow chart of a traffic flow prediction method provided by an embodiment of the present invention is shown; the method includes the following steps:

[0043] S1. Acquire real-time traffic flow data, and use variational mode decomposition to decompose real-time traffic flow data into a set number of band-limited eigenmode components with different characteristic scales; the number of eigenmode components is set In order to minimize the sum of the estimated bandwidths of all eigenmode components.

[0044] In the embodiment of the present invention, using variational mode decomposition to decompose real-time traffic flow data into a set number of band-limited eigenmode components with different characteristic scales includes the following steps:

[0045] The number of eigenmode components obtained by decomposing...

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Abstract

The present invention discloses a traffic flow prediction method which can reduce the data complexity and improve the prediction precision. The method provided by the embodiment of the invention comprises the following steps of: collecting and obtaining real-time traffic flow data, and employing variation mode decomposition to decompose real-time traffic flow data to a preset number of band-limiting intrinsic mode components with different feature scales; and the number of the intrinsic mode components is set to allow the sum of estimation bandwidths of all the intrinsic mode components is theminimum. An increment-type over-limit learning network is employed to establish prediction models for each intrinsic mode component, and a pre-collected historical traffic flow data is employed to perform training of the prediction models. The trained prediction models are employed to perform traffic flow prediction of each intrinsic mode component and accumulate prediction results of all the intrinsic mode components to obtain a final traffic flow prediction result.

Description

technical field [0001] The invention relates to the technical field of road traffic monitoring, in particular to a traffic flow prediction method. Background technique [0002] Traffic can not only create favorable conditions for the stable development of the city, but also maintain the smooth progress of the city's economy. Good traffic conditions are the booster for the rapid development of the city, and also the guarantee for the happy life of the people. [0003] With the rapid development of the urban economy, the scale of the city continues to expand, and at the same time, many urban problems are becoming more and more prominent. Among them, the traffic problem is the most acute urban problem. The number of urban vehicles has increased sharply, but the urban transportation infrastructure and management level have not kept pace with the times, which has caused a series of traffic accidents and serious traffic congestion, seriously affecting the healthy development of ...

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Application Information

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IPC IPC(8): G08G1/01G06N3/04G06N3/08
CPCG06N3/08G08G1/0129G06N3/045
Inventor 邹伟东夏元清张金会翟弟华戴荔刘坤闫策
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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