Adaptive Dynamic Neuro-fuzzy System for Traffic Signal Control

This paper aims at developing near optimal traffic signal control for multi-intersection in city. Fuzzy control is widely used in traffic signal control. For improving fuzzy control's adaptability in fluctuant states, a controller combined with neuro-fuzzy system and adaptive dynamic programming (ADP) is designed. This controller can be used for cooperative control of multi-intersection. The adaptive dynamic programming gives reinforcement for good neuro-fuzzy system behavior and punishment for poor behavior. The neuro-fuzzy system adjusts its parameters according to the reinforcement and punishment Then, those actions leading to better results tend to be chosen preferentially in the future. Comparing with traditional ADP, this controller uses neuro-fuzzy system as the action network. The neuro-fuzzy system offers some existing knowledge and reduces the randomness of traditional ADP. In this paper, the objective of the controller is to minimize the average vehicular delay. The controller can be trained to adapt fluctuant traffic states by real-time traffic data, and achieves a near optimal control result in a long run. Simulation results show that the trained controller achieves shorter average vehicular delay than the controller with initial membership function.