Abstract: Q-learning uses the interaction with the external environment to carry out the traffic signal adaptive control of a single intersection. In the background of the increasingly congested urban traffic, in order to alleviate the traffic congestion, a Q-learning algorithm combined with the green signal ratio optimization method of SCOOT system is proposed. In this paper, the method of green signal ratio optimization in SCOOT system is combined with Q-learning, that is, a new mathematical model is established as the cost function of the algorithm by combining the time factors such as average vehicle delay rate, parking times and economic factors, and a continuous reward and punishment function is established. On this basis, the operation process of Q-learning algorithm on a single intersection is introduced in detail, and through the horizontal comparison with Webster delay rate and Q-learning based on the minimum average vehicle delay rate, it is verified that this algorithm is superior to the timing control and Q-learning algorithm based on average vehicle delay. Compared with these two algorithms, the algorithm proposed in this paper is more suitable for the single intersection green signal ratio optimization.

Key words: traffic signal control, Q-learning, single intersection, agent