Prediction and Identification of Urban Traffic Flow Based on Features

Identifying and predicting the situation of traffic flow play an important role in traveler information broadcast and real-time traffic control. In this paper, to pick up the effective characteristic parameters of traffic, the features and the transition between different situations in traffic are studied and analyzed, A hybrid Elman neural network and fuzzy techniques are good at working out the nonlinear problem and identifying the state of system, so they can apply to predict and distinguish the traffic situation in short term. As a result, it proves that there are some advantages, e.g. simple configuration, good prediction and exact identification. So it is fit to online predict and identify the traffic flow in urban expressway