Deep belief network-based support vector regression method for traffic flow forecasting

Abstract

Instability is a common problem in deep belief network–back propagation forecasting model, and the trend of traffic data will affect the forecasting results of the model. Therefore, this paper proposes a short-term traffic flow forecasting method based on deep belief network–support vector regression. Support vector regression classifier SVR is used at the top of the model. Data processing is from bottom to top. Firstly, at the bottom of the model, the input traffic flow data are processed differently; then, the DBN model is used to learn the traffic flow characteristics. Finally, SVR is used to predict the traffic flow at the top of the model. The average absolute error of the prediction is 9.57%, and the average relative error is 5.91%. The relationship between the predicted value and the actual traffic flow data is found through simulation experiments. The predicted value of the model proposed in this paper is in good agreement with the measured value, and the prediction accuracy is high. The model can effectively predict short-term traffic flow. Finally, compared with the traditional DBN prediction model and other common prediction models, the proposed prediction model has higher prediction accuracy.