Methodology to Identify On-Ramps for Metering with Consideration of System-Wide Recurrent and Non-Recurrent Congestion

Abstract

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.