A combined method for short-term traffic flow prediction based on recurrent neural network
Saiqun Lu, Qiyan Zhang, Guangsen Chen, Dewen Seng
Abstract
The accurate prediction of real-time traffic flow is indispensable to intelligent transport systems. However, the short-term prediction remains a thorny issue, due to the complexity and stochasticity of the traffic flow. To solve the problem, a combined prediction method for short-term traffic flow based on the autoregressive integral moving average (ARIMA) model and long short-term memory (LSTM) neural network was proposed. The method could make short-term predictions of future traffic flow based on historical traffic data. Firstly, the linear regression feature of the traffic data was captured using the rolling regression ARIMA model; then, backpropagation was used to train the LSTM network to capture the non-linear features of the traffic data; and finally, based on the dynamic weighting of sliding window combined the predicted effects of these two techniques. Using MAE, MSE RMSE and MAPE as evaluation indicators, the prediction performance of the combined method proposed was evaluated on three real highway data sets, and compared with the three comparative baselines of ARIMA and LSTM two single methods and equal weight combination. The experimental results show that the dynamic weighted combination model proposed has better prediction effect, which proves the versatility of this method.