Short-term traffic flow forecasting based on a hybrid neural network model and SARIMA model

This paper proposes a hybrid model, which combines the seasonal time series autoregressive integrated moving average (SARIMA) and the generalized regression neural network (GRNN) models in order to forecast accurately urban short-term traffic flow. For comparison, the two component models, namely the SARIMA model and GRNN model, are used to forecast the short-term traffic flow. An empirical study shows the hybrid models have better forecast performance than the SARIMA model, but does not necessarily better than the GRNN mode. However, choosing proper input variables and output variables in the GRNN component of a hybrid plays an important role in improving the forecast ability of the model. The hybrid model constructed in this paper not only provides the best forecast performance but also has simple structure suitable for providing real-time and short-term traffic flow forecast.