Section traffic neural network prediction method based on EMD

A technology of neural network and prediction method, which is applied to biological neural network models, predictions, instruments, etc., can solve the problems of large amount of calculation and storage, and achieve the effects of saving time, improving prediction efficiency, and reducing calculation time

Inactive Publication Date: 2014-04-16
BEIJING JIAOTONG UNIV
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The computational complexity of the LM algorithm is O(n 3 /6), if n i

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  • Section traffic neural network prediction method based on EMD
  • Section traffic neural network prediction method based on EMD
  • Section traffic neural network prediction method based on EMD

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Embodiment

[0137] This paper analyzes and selects the passenger flow data of the section from Xidan to Fuxingmen of Beijing Metro Line 1 (downlink) measured on weekdays. Such as Figure 4 as shown, Figure 4 It is (example) 2012.3.5-3.9 Beijing Subway Line 1 (downlink) Xidan-Fuxingmen cross-section passenger flow statistics map for every 30 minutes. The present invention predicts the passenger flow at the Xidan-Fuxingmen section on March 9, 2012 based on the passenger flow data at the Xidan-Fuxingmen section from 2012.3.5 to 2012.3.8.

[0138] An EMD-based cross-sectional passenger flow neural network prediction method, the method steps are as follows:

[0139] Step 1: Obtain data eigenmode function components

[0140] Decompose the EMD empirical mode on the above data, as follows Figure 5 as shown, Figure 5 is a schematic diagram of the (embodiment) EMD empirical decomposition model. From Figure 5 It can be seen from the figure that the original cross-sectional passenger flow ...

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Abstract

The invention discloses a section traffic neural network prediction method based on the EMD, and belongs to the technical field of rail transportation. The method comprises the first step of obtaining components of an intrinsic mode function, wherein summary traffic between OD every 30 minutes every day is distributed into section traffic information to form a section traffic original sequence, the EMD processing is carried out on the original sequence, IMF components are obtained, and an IMF matrix is formed; the second step of identifying the components, wherein a Pearson correlation coefficient between each IMF component and the original traffic sequence is calculated, and the relevance between the coefficients and original data is analyzed; the third step of predicting a neural network, wherein a three-layer BP network model is built, test data serving as the input data of an input layer are substituted into the BP neural network to carry out prediction, and corresponding section traffic output can be obtained. According to the method, the EMD and the neural network are mixed, the characteristics of the traffic data are analyzed, and input variables are provided for the neural network prediction method. By means of the method, the prediction precision of the section traffic can be greater than 95%.

Description

field of invention [0001] The invention relates to an EMD-based neural network prediction method for section passenger flow, which belongs to the technical field of rail transportation. Background technique [0002] In recent years, my country's urban rail transit has developed rapidly, and the passenger volume of the rail transit network has continued to rise. Huge passenger flow and complex temporal and spatial distribution on the road network have brought huge challenges to the organization and safety of passenger flow in China's urban rail transit. Real-time and accurate short-term traffic flow prediction has become an urgent need for safe and efficient operation and management of China's urban rail transit network. solved problem. The results of traffic forecast will provide reference for transportation system management, for example, operation management planning, station passenger flow congestion control plan, etc. [0003] Over the years, researchers at home and ab...

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/30G06N3/02
Inventor 王子洋朱婕秦勇赵忠信钟玲玲于鸿飞杜渺李倩李文宇朱鹏李军刘靖袁敏正丁健隆
Owner BEIJING JIAOTONG UNIV
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