A Short-Term Traffic Flow Prediction Method Based on Long Short-Term Memory Network

摘要

In order to achieve the higher accuracy of the short-term traffic flow prediction, this paper proposed a prediction method based on the Long Short-Term Memory Network (LSTM) model. First, the original traffic flow data is processed by difference and scaling, so the trend is removed. And then the LSTM model is proposed to learn internal characteristic of the traffic flow and make the forecast. Comparing LSTM method with the traditional prediction model (back propagation neural network, BPNN), the experiment result shows that the proposed traffic flow prediction method has the better learnability for the short-term traffic flow and achieves higher accuracy for the prediction.