A Complementary Modularized Ramp Metering Approach Based on Iterative Learning Control and ALINEA

Ramp metering is an effective tool for traffic management on freeway networks. In this paper, we apply iterative learning control (ILC) to address rampmetering in amacroscopic-level freeway environment. By formulating the original ramp metering problem as an output regulating and disturbance rejection problem, ILC has been applied to control the traffic response. The learning mechanism is further combined with Asservissement Lineaire d'Entree Autoroutiere (ALINEA) in a complementary manner to achieve the desired control performance. The ILC-based rampmetering strategy and the modified modularized ramp metering approach based on ILC and ALINEA in the presence of input constraints are also analyzed to highlight the advantages and the robustness of the proposed methods. Extensive simulations are given to verify the effectiveness of the proposed approaches.