A stochastic adaptive traffic signal control model based on fuzzy reinforcement learning

The signalized intersection system often exhibits severe nonlinear and time-varying characteristic due to the random fluctuation of traffic demand or some special event, therefore, it cannot be adequately controlled with some traditional ways. The traditional reinforcement learning was extended to the fuzzy pattern with defining the fuzzy reinforcement function by using the fuzzy state. A stochastic control scheme, based on fuzzy reinforcement learning, is introduced in the traffic signal control systems due to its powerful adaptability. The FRL-based adaptive controller can produced appropriate control policy to prevent the traffic network from becoming over-congested. The traditional intersection traffic model is extended to a new mode which taking some real aspects of traffic conditions into account, such as the turning fraction and the lanes scheme. The model is tested on a typical four-legged signalized intersection, and compared to both pre-timed control and full-actuated controller. Analyses of simulation results using this approach show significant improvement over traditional control, especially for the case of over-saturated traffic demand and special events such as incidents and blockages. Using the FRL model, the total mean delay of each vehicle has been reduced by 25.7% under the heavy demands compared to the FAC scheme.