An Analytical Framework for Managed Lane Facility Performance Evaluation

BY XIAOYUE CATHY LIU, E.I.T., YINHAI WANG, PH.D., BASTIAN J. SCHROEDER, PH.D., AND NAGUI M. ROUPHAIL, PH.D. Introduction According to the 2011 Urban Mobility Report,1 roadway congestion cost a total of $101 billion and induced a total delay of 4.8 billion hours in the United States in 2010, with approximately 60 percent of the congestion occurring on freeways. Strategies have been implemented to mitigate congestion by utilizing cutting-edge information technologies to proactively control and manage traffic system operations. Among them, managed lanes (MLs) are considered one of the most effective countermeasures against traffic congestion. MLs provide a control mode for managing traffic in response to real-time traffic condition changes without adding any physical capacity to the existing infrastructure. They allow the allocation of an appropriate portion of existing freeway facility capacities to special categories of roadway users. The restrictions imposed on user groups often include the number of occupants, types of vehicles, and tolling.2 Prevailing ML operational and design practices are high-occupancy vehicle (HOV), highoccupancy toll (HOT), truck-only tolling (TOT), and express tolling. While increasing numbers of ML facilities are in operation nationwide, the operational guidance for ML is minimal, especially in terms of the methodology or modeling technique to quantify its performance, as well as to model its interaction with the adjacent general-purpose lanes (GPLs). National Cooperative Highway Research Program (NCHRP) project 03-96, Analysis of Managed Lanes on Freeway Facilities, was geared toward developing an analytical framework to evaluate the performance of ML facilities along with parallel GPLs. This framework is consistent with the Highway Capacity Manual (HCM) to provide a more standardized basis for analyzing and quantifying ML facility performance under diverse traffic conditions. This paper, based on the research developed in the NCHRP 03-96 project, elaborates the analytical framework and methodology for modeling ML facilities.