Single Neuron Based Freeway Traffic Density Control via Ramp Metering

In this work, we apply single neuron method to address the traffic density control problem in a macroscopic level freeway environment with ramp metering. The second-order traffic flow model is firstly formulated. Then traffic density is selected as the control variable in place of traffic occupancy. Based on the traffic flow model and in conjunction with nonlinear feedback theory, a single neuron based traffic density controller is designed, and the learning algorithm of single neuron is given in detail. Finally, the single neuron based feedback controller is simulated in Matlab software. The results show that this method can effectively deal with this class of control problem. It has good dynamic and steady-state performance, and can achieve an almost perfect tracking performance.