Short Term Traffic Flow Forecasting Based on Artificial Neural Network Combined Predictor

The artificial neural network combined predictor (ANNCP) is proposed for short term traffic flow forecasting based on the combination of an artificial neural network (ANN) predictor and an autoregressive integrated moving average (ARIMA) model based predictor. The method is tested using traffic flow data collected on Guangshen freeway in Guangdong, China. Statistical analysis of prediction errors reveals that the two singular predictors perform differently depending on different time and different prediction horizons. The combined prediction is a weighted sum of the ANN and ARIMA prediction. According to different time and the forecasting results of the two singular predictor, the weights are decided by ANN dynamically. The proposed method outperforms both singular ANN predictor and singular ARIMA predictor.