Switching ARIMA model based forecasting for traffic flow

Switching dynamic linear models are commonly used methods to describe change in an evolving time series, where the switching ARIMA (autoregressive integrated moving average) model is a special case. Short-term forecasting of traffic flows is an essential part of intelligent traffic systems (ITS). We apply the switching ARIMA model to a traffic flow series. We have observed that the conventional switching model is inappropriate to describe the pattern changing. Thus, the variable of duration is introduced and we use the sigmoid function to describe the influence of duration to the transition probability of the patterns. Based on the switching ARIMA model, a forecasting algorithm is presented. We apply the proposed model to real data obtained from UTC/SCOOT systems in Beijing's traffic management bureau. The experiments show that our proposed model is applicable and effective.