Ramp Metering with Real-Time Estimation of Parameters

Abstract

Demand exceeding the capacity of a bottleneck will create congestion upstream of that bottleneck. Once this congestion occurs, the maximum flow through this bottleneck decreases (capacity drop). By limiting the flow towards the bottleneck, one can prevent or postpone the capacity drop and the accompanying congestion. In case the bottleneck is caused by an on-ramp, a common approach is to meter the on-ramp flow. For metering to be effective the algorithm has to be tuned carefully. Normally, the parameters of a metering algorithm are fit for the situation. However, traffic is dynamic and external factors might change, which both lead to changes in parameters of the traffic process. This paper studies how these parameters can be updated dynamically in the control algorithm. It considers various ramp metering algorithms and introduces methods to adapt their parameters. They are tested with simulations using the METANET model. This shows that parameter adaptation improves traffic state. Gains in travel time due to parameter adaptation are typically several percent compared to non-adaptive ramp metering. Road authorities can use these findings to improve ramp metering algorithms and reduce delays