# Short-term traffic flow prediction system based on SARIMA-GA-Elman combined model

## A traffic flow and combined model technology, which is applied in traffic flow detection, road vehicle traffic control system, traffic control system, etc., can solve the problem that wavelet neural network cannot effectively avoid overfitting

Pending Publication Date: 2021-05-07

LANZHOU UNIVERSITY OF TECHNOLOGY

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## Abstract

The invention discloses a short-time traffic flow prediction system based on an SARIMA-GA-Elman combined model, and belongs to the technical field of traffic flow prediction of an intelligent traffic system. The method is realized through the five steps of modeling of an SARIMA model, Elman-RNN prediction, SARIMA-GA-Elman combined prediction, linear prediction and nonlinear prediction. The invention provides a novel optimization method to solve the problem that training of the neural network often consumes a large amount of time cost. The method comprises the following steps: firstly, training an Elman-RNN weight and a threshold value based on GA to obtain an interval near value of an optimal solution, and then continuously training by using an Elman-RNN gradient descent algorithm to obtain a final weight and a final threshold value; the optimization mode is very similar to transfer learning, and the training efficiency of the model can be improved. According to the method disclosed by the invention, the Elman-RNN nonlinear model is constructed by utilizing a prediction error subjected to SARIMA prediction. The prediction error excludes the interference of a linear factor and a daily cycle factor, and comprises the nonlinear characteristics of the traffic flow. The two models fully consider periodic, linear and nonlinear characteristics of traffic flow.

Application Domain

Detection of traffic movementForecasting +2

Technology Topic

Linear predictionOperations research +11

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