Design and Simulation of Agent-Oriented Intersection

Modeling intersection with agent-oriented technology, this paper gives detail description of intersection agent structure and control strategy. It applies fuzzy theory and ant colony optimization (ACO) in intersection signal control and put forward an intersection fuzzy control model with self-learning mechanism. ACO is used to optimize fuzzy control rules, so the intersection agent has self-learning ability. After programming agent model and simulating, this paper compares the control effect of this new approach with the traditional fuzzy control method. Simulating result shows that the effect of the model is obviously better than the traditional ones.