Multi-Objective Adaptive Traffic Signal Control Using Fuzzy Control and Q-Learning

A multi-objective adaptive traffic signal control algorithm using fuzzy control and Q-learning was proposed to improve the efficiency, traffic safety, and operational stability of signalized intersections. In this algorithm, the signal cycle length was derived by fuzzy control, then, to minimize delay and conflicts, the green split of each phase was dynamically adjusted through Q-learning. A joint simulation of Python and VISSIM was adopted for traffic operational simulation and evaluation. The simulation results show that the proposed algorithm jointing fuzzy control and Q-learning, and compared with traffic actuated control and fixed timing, the delay, queue length and traffic conflict of the intersection are significantly and comprehensively optimized. In addition, the algorithm reduced the platoon crash risk at the intersection, improving the overall operational stability.