Convolutional neural network structure-based traffic flow prediction method

Abstract

The invention discloses a convolutional neural network structure-based traffic flow prediction method. The method comprises the following steps of 1) establishing a traffic flow data set and preprocessing the data set: establishing the traffic flow data set according to obtained traffic flow data, preprocessing the data set, constructing a data set sample matrix, and dividing the data set into a training set and a test set; 2) establishing a single-layer conventional convolutional neural network, removing a pooling layer, constructing a feature extraction network of a road traffic flow matrix,adding a sigmoid nonlinear regression layer to a full connection layer, and constructing a road traffic flow nonlinear regression prediction network; and 3) training the convolutional neural networkand realizing real-time prediction of short-term traffic flow: defining a model objective function, taking the training set as an input of a convolutional neural network model, solving an optimal parameter of the model to finish model training, and performing real-time traffic flow prediction on the test set by utilizing the trained convolutional neural network model. The short-term prediction accuracy of the traffic flow is improved.

Description

technical field [0001] The invention relates to a traffic flow prediction method based on a convolutional neural network structure, and belongs to the field of traffic prediction. Background technique [0002] Road traffic flow forecasting is a necessary prerequisite for inducing and controlling traffic flow. It not only facilitates travelers to make better travel plans, but also facilitates traffic management departments to make better management decisions. In addition, road traffic flow prediction also plays an irreplaceable role in intelligent transportation. [0003] In the existing road traffic flow prediction methods, the shallow model cannot mine the information in the traffic flow data well, and the time series model only considers the characteristics of traffic flow in time and ignores the influence of space. The convolutional neural network can not only extract temporal features but also spatial features through convolution, so the present invention proposes a tra...

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Problems solved by technology

[0003] In the existing road traffic flow prediction methods, the shallow model cannot mine the information in the traffic flow data well, and the time series model only considers the characteristics of traffic flow in time and ignores the influence of space

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