Ramp metering based on on-line ADHDP (λ) controller

Increasing dependence on car-based travel has led to the daily occurrence of freeway congestions around the world. In order to improve the worse and worse traffic congestion situation and solve the problems brought with it, a new kind of effective, fast, and robust method should be presented. Ramp metering has been developed as a traffic management strategy to alleviate congestion on freeways. But, it doesnpsilat work well in uncertainty situations. In this paper, in order to solve the problems in uncertainty conditions, an on-line learning control method based on the fundamental principle of reinforcement learning is proposed. The method is ADP (adaptive dynamic programming) and in order to expedite the learning rate, the concept about eligibility traces is introduced here. Then eligibility trace and ADP is combined to present a new kind of traffic responsive control method. The new method is called action-dependent heuristic dynamic programming based on eligibility traces (ADHDP (lambda)). ADHDP (lambda) is an approximate optimal ramp metering method. Simulation studies on a hypothetical freeway indicate good control performance of the proposed real-time traffic controller.