Fuzzy Combination Forecasting Model Based On Urban Traffic Flow Control System

The increasing number of traffic flux in recent years actually poses a great challenge. Thus, it is meaningful to improve the traffic efficieney in the intersections in order that traffic jam can be lessened. Since the traditional method with fixed signal for time distribution can not meet the requirement of the frequently changing situation, an intelligent traffic signal control is needed to guarantee the favorable passage of vehicles in a single intersection. Main works included in this dissertation are described as follows.1. NNCA-RBF forecasting model is designed. Study on the combined forecasting theory. Aim at demerit of the general combined forecasting model, a short-term traffic flow combined forecasting model based on radial basis function (RBF) neural network is designed. The simulation results show that it works well in the prediction of traffic flow.2. Two stages fuzzy control is designed which is based on short-term traffic flow prediction model. It includes three modules: red light loss module, green light income module, phase time changing module. It decides the green light time according to the short-term trffic flow prediction and the duration of the phases.3. The single intersection traffic control panel circuit has been structured which adopted AT89S51 as the core. The design of hardware is accomplished and come into a sample. And the two stages fuzzy control software design is realized.4. Take traffic flow of the Changsha city as experimental data. Simulate some traffic instances in the control panel circuit, so as to experimentize. The fuzzy control can effectively enhance the utilization ratio of phase time and enhance the utilization ratio of intersection traffic.