An Adaptive Three-stage Fuzzy Controller for Signalized Intersections Using Golden Ratio based Genetic Algorithm

Traffic signal fuzzy control is perceived as one of the most promising solutions to the next-generation traffic signal control problems. However, most of the studies use two-stage fuzzy controllers, whereas the uncertainty of traffic flow at intersections, and functional disability of controller learning are largely unexplored. In this study, an adaptive three-stage fuzzy logic controller for traffic signals is presented. This controller is able to update phase structure, fuzzy membership functions and fuzzy rules according to real-time traffic conditions with following two critical features: (1) to quickly capture the uncertainty of traffic flows, the controller introduces overlap phases of conflict-free flows to develop the traffic signal three-stage fuzzy model; and (2) the rolling horizon based optimization framework is designed to optimize fuzzy parameters with quasi online learning. Based on the observed second-by-second traffic data, a golden ratio based genetic algorithm is employed to efficiently yield the reliable solution of fuzzy parameters. Compared with five other traffic signal control approaches, extensive Paramics simulation experiments under different traffic conditions have demonstrated the potential of the developed controllers for adaptive traffic signal control at isolated intersections.