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Traffic flow prediction method based on deep neural network integration

A deep neural network and traffic flow technology, applied in the field of traffic flow prediction based on deep neural network integration, can solve problems such as poor prediction performance, effective information affecting model performance, and inability to handle the nonlinear characteristics of traffic flow data well.

Inactive Publication Date: 2019-09-06
ZHEJIANG UNIV
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AI Technical Summary

Problems solved by technology

The invention solves the technical problem that the time series analysis method represented by the ARIMA model cannot handle the obvious nonlinear characteristics in the traffic flow data well and the prediction performance is poor, and solves the problem that the deep learning technology cannot fully The technical problem of extracting effective information from traffic flow data and affecting the performance of the model solves the technical problem of unstable training of the deep neural network

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  • Traffic flow prediction method based on deep neural network integration
  • Traffic flow prediction method based on deep neural network integration
  • Traffic flow prediction method based on deep neural network integration

Examples

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Effect test

Embodiment 1

[0089] Embodiment 1 uses a test set to test the predictive performance of the present invention.

[0090] Example 1

[0091] In the following, the prediction performance of the present invention is illustrated through experiments on the PeMS traffic flow data set, and the performance is compared with the SARIMA (seasonal sum autoregressive moving average) model. On the PeMS data set, the traffic flow data of an observation site on all working days from January 1, 2018 to September 30, 2018 was selected as the experimental data set, and a total of 4536 measurement data were obtained. Among them, the first 4036 data are taken to form the training set, and the last 500 data are used to form the test set, and all data are normalized by maximum and minimum values.

[0092] For the SARIMA prediction model, the model is constructed on the training set according to the Box-Jenkins analysis method, and finally a more suitable prediction model SARIMA(8,1,2)(0,1,1) is obtained 24 , the...

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Abstract

The invention discloses a traffic flow prediction method based on deep neural network integration. The method comprises the following steps: obtaining original traffic flow data, carrying out data preprocessing, constructing sample data, dividing the sample data into a training set and a test set, and enabling the sample data to be one-dimensional time series data consisting of a plurality of traffic flow measurement values; constructing a convolutional neural network prediction model for traffic flow prediction, inputting sample data into the model, training by using a gradient optimization algorithm, and calculating a variance of a prediction error for the trained model on the training set; using the convolutional neural network prediction model as an individual learner according to thevariance of the prediction error, and constructing a convolutional neural network integration model for traffic flow prediction; and predicting the traffic flow data to be tested by using the convolutional neural network integration model. Based on a convolutional neural network model, an integrated learning method is utilized, an improved traffic flow prediction method is provided, and the prediction accuracy is improved.

Description

technical field [0001] The invention belongs to the field of machine learning and intelligent traffic systems, and in particular relates to a traffic flow prediction method based on deep neural network integration. Background technique [0002] Intelligent transportation system is a comprehensive road traffic management system developed on the basis of advanced science and technology such as information technology and sensor technology. [0003] Traffic flow forecasting is an important technical link in intelligent transportation systems, and it is very important to realize accurate and reliable traffic flow forecasting. Traffic flow forecasting can make reasonable and accurate inferences about the changing trend of traffic flow in the future based on the current road condition information, which helps to implement accurate and effective traffic scheduling and improve transportation efficiency. It is an important technical link in the intelligent transportation system. Traf...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06N3/04
CPCG06Q10/04G06N3/045
Inventor 李春光权钲杰
Owner ZHEJIANG UNIV
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