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Warrants for ramp metering installation have been developed by a number of U.S. states. These warrants are generally simple and are based on the traffic, geometry, and safety conditions in the immediate vicinity of each ramp (local conditions). However, advanced applications of ramp metering use system-based metering algorithms that involve metering a number of on-ramps to address system bottlenecks. These algorithms have been proved to perform better compared with local ramp metering algorithms. This situation creates a disconnect between existing agency metering warrants to install the meters and the subsequent management and operations of the ramp meters. This study focused on assessing the need for system warrants in addition to local warrants for ramp metering installation to prevent traffic breakdown at bottleneck locations. A linear programming formulation combined with consideration of the stochasticity of bottleneck capacity was used to select the ramps to be metered on the basis of the system bottlenecks. The study found that the selection of these ramps for metering—in addition to those justified by local warrants—could delay the breakdown at the system bottleneck location and improve the performance of the freeway main line. Another important benefit of selecting ramps for metering based on system operations was its distribution of on-ramp delays because of metering of more ramps and thus to a reduction in the delay experienced on the ramps selected with the existing warrants that were based on local conditions.

State agencies have developed warrants and guidelines for the metering of freeway on-ramps. However, these warrants only consider the traffic conditions in the vicinity of each on-ramp without considering the need to meter multiple ramps to mitigate the impacts of bottlenecks downstream. The warrants do not employ detailed analyses of traffic conditions or take advantage of the increasing availability of data from multiple sources. In addition, the existing local warrants only consider recurrent conditions with no consideration of the benefit of metering during non-recurrent events such as incidents and adverse weather. This study aims to develop a methodology for the identification of the ramps to meter that considers system-wide recurrent and non-recurrent traffic conditions based on detailed analysis of traffic data. This methodology incorporates the stochastic nature of the demand and capacity and the impacts of incidents and weather using Monte Carlo simulation and a ramp selection procedure based on a linear programming formulation. The method allows the calculation of the minimum number of ramps that need to be metered to keep flows below capacity on the freeway mainline, while keeping the on-ramp queues from spilling back to the upstream arterial street segments. The methodology can be used in conjunction with the existing local warrants to identify the ramps that need to be metered. In addition, it can be used in benefit–cost analyses of ramp metering deployments and associated decisions such as which ramps to meter and when to activate metering.

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