Traffic Flow Prediction Method Based on Deep Learning

Abstract

Accurate traffic flow forecasts provide an important data basis for traffic management departments. This paper proposes a traffic flow prediction model based on deep learning, which combines Convolutional Neural Network (CNN), Long Short-term Memory (LSTM) and Support Vector Regression (SVR) features: use CNN neural network to mine the spatial characteristics of traffic flow, and then input the time series features captured by LSTM neural network into the SVR model for traffic prediction. The actual traffic flow data of intersections in Mianyang City are selected to verify the CNN-LSTM-SVR hybrid model, and compare it with the CNN model, LSTM model, and SVR model. The results show that the proposed prediction model has higher prediction accuracy.