Iterative Learning Based Freeway Density Control

Freeway congestion problem can be addressed employing many different measures. Ramp metering is the most widely used control measure, and is an efficient way to control and upgrade freeway traffic by regulating the number of vehicles entering the freeway. This paper proposes an iterative learning approach for the freeway density control under ramp metering in a macroscopic level traffic environment. A discretized first-order macroscopic traffic flow model is firstly established. Then traffic density is chosen as the control variable. In conjunction with nonlinear feedback method and proportional-integral control, an iterative learning based density controller is designed. Finally, the controller is simulated in MATLAB software. The results show that this method can effectively reduce the oscillator of traffic density, and can achieve a desired traffic density along the freeway mainline. The main advantage of the learning-based density control is its ability to reject exogenous traffic perturbations. This approach is quite effective to freeway ramp metering.