Improving Queue Size Estimation for Minnesota's Stratified Zone Metering Strategy

Abstract

Stratified zone metering (SZM), a successor of zone metering, was recently implemented in the United States in the Twin Cities. SZM not only restricts metering rates subject to the freeway capacity constraints but also limits the on-ramp waiting time to a predetermined threshold. In SZM, accurate queue size estimation is crucial because inaccurate queue size can violate the maximum wait time constraint or reduce the service quality of the main-line system. Currently, a uniform and pre-calibrated regression equation is adopted for ramp queue size estimation for all ramps. It has been verified that such an estimation method may lead to outstanding estimation errors for ramp queues. In this paper, different ramp queue estimation algorithms are proposed for ramps with different categories, depending on the counting errors of queue and passage detectors. For ramps with minor counting error, queue size is estimated on the basis of flow conservation. For ramps with significant counting error on passage detectors but minor counting error on queue detectors, the flow conservation model can still be applied, but the traffic counts of passage detectors are replaced by the “green counts,” which are calculated by the ramp metering rate. For ramps with significant count errors from both queue and passage detectors, a Kalman filtering model is adopted. To verify the proposed methods, surveillance video data were used to compare actual and estimated queue sizes. Results indicate that the proposed methods greatly improve the accuracy of queue size estimation as compared with the original SZM queue size estimation.