On-ramp neuro-fuzzy metering for urban freeway

The paper introduces the neural network and fuzzy logic methods into on-ramp metering of urban freeway. In which, the advantages of these two methods are considered, namely, the learning ability, optimization ability, and interconnection structure of the neural network, and the human-like thought manner and professional knowledge integration of fuzzy logic method. The suitable input and output variables are selected by optimization, and are get justification and unfuzzification. Then, the corresponding fuzzy inference rules are established, and the relation generating method and inference synthesis algorithm are also developed. The membership function styles and parameters are determined by the adaptive neuron training method. An example was also given to illustrate the proposed method. The results indicate an increase of operating efficiency and a decrease of accident rate with the application of on-ramp metering of neuro-fuzzy.