A Short-term Traffic Flow Intelligent Hybrid Forecasting Model and Its Application

Abstract

In order to transcend the limitation of existing individual traffic flow forecasting models on different traffic condition, a novel intelligent hybrid (IH) model for short-term traffic flow forecasting was presented. The IH model had three sub-models: Kalman filter (KF) model, artificial neural network (ANN) model and fuzzy combination (FC) model. The KF model forecasted the traffic flow by the linear iteration method based on the historical traffic data. Otherwise, the ANN model was a single-hiddenlayer feed-forward neural network built by some common S-function neurons. The two individual models reflecting practical problems from different respects were combined by fuzzy logic. The FC model mixed the two individual forecast results and its output was regarded as the final forecasting of the traffic flow. Practical application results show that the IH model can produce more precise forecasting than that of two individual models.