Traffic flow prediction method based on deep neural network integration

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...

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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