Freeway Ramp Control Based on Genetic PI and Fuzzy Logic

An approach of fuzzy logic and genetic PI is proposed to regulate the number of vehicles entering a freeway entrance point. First, the freeway traffic flow model is built, and the objective of ramp metering is determined. In conjunction with the advantages of fuzzy logic in processing language information and PI control in processing error, a fuzzy-PI mix controller is then designed, and genetic algorithm is used to optimize the PI parameters. Density tracking error and error variation are chosen as the input variables of fuzzy logic control, and triangle curves are used for the membership functions of the fuzzy variables. Nine fuzzy control rules are established, and genetic optimization procedures are designed to tune the PI parameters. Finally, the mix controller is simulated. The results show that the mix controller has better dynamic and steady-state performance compared with conventional PID control, and can achieve a desired traffic density along the mainline of a freeway. This approach is quite effective to the on-ramp control.