Development of the Next Generation Stratified Ramp Metering Algorithm Based on Freeway Design

A new coordinated, traffic-responsive ramp metering algorithm has been designed for Minnes ota's freeways based on density measurements, rather than flows. This is m otivated in view of recent research indicating that the critical value of density at which capacity is observed is less sensitive and more stable than the value of capacity, thereby resulting in more effective control. Firstly , we devel op a methodology to estimate de nsities with space and time based on data from loop detectors. The methodology is based on solving a flow conservation differential equation (using LWR theory) with intermediate (internal) freeway mainline boundaries, which is fast er and more accurate from previous resear ch using onl y external boundaries. To capture the capacity drop phenomenon into the first-order model we utilize a fundamental diagram with tw o values of capacity and we provi de a memory-based methodology to choose the appropriate value in the numerical solution of the problem. Secondly, with respect to ramp metering, the main goals of the al gorithm are to delay the onset of the breakdown and to accel erate system recovery when ramp metering is unable due to the vi olation of maximum allowable ram p waiting time. The effectiveness of the new c ontrol strategy is being assess ed by comparison with the currently deployed version of the Stratified Zone Algorithm (SZM) through microscopic simulation of a real 12-mile, 17 ramp freeway section. Simulations show a decrease in the delay s of mainline and ramp traffic, an im provement 8% in the o verall delays and avoidance of the maximum ramp delay violations. The contents of this report reflect the views of the authors, who are responsible for the facts and the accuracy of the information presented herein. This document is disseminated under the sponsorship of the Department of Transportation University Transportation Centers Program, in the interest of information exchange. The U.S. Government assumes no liability for the contents or use thereof. This report does not necessarily reflect the official views or policies of the University of Minnesota. The authors, the University of Minnesota, and the U.S. Government do not endorse products or manufacturers. Any trade or manufacturers' names that may appear herein do so solely because they are con sidered essential to this report.