The proportion of High-Occupancy Vehicle (HOV) commuting trips in the Greater Toronto Area has been declining over recent years. In an effort to reverse this decline and make more efficient use of the available infrastructure, the Ministry of Transportation of Ontario has begun to implement HOV lanes on the 400-series highway system. The implementation of HOV lanes on portions of Highway 403 and 404 in late 2005 met with substantial success, being well-used in peak periods. In support of the planning and prioritization of future HOV lane implementation, the Ministry conducted several studies, one focused on the Greater Toronto Area west of Highway 427 and one focused on the area to the east, to forecast HOV use of potential future HOV network components. The current paper addresses the GTA East study. The paper begins by providing some background with respect to auto occupancy and HOV use trends in the Greater Toronto Area. The success of the Highway 403 and 404 HOV lanes is reviewed briefly as context to the development and testing of a broader network. The modeling process used to forecast future HOV use in response to potential HOV lane network configurations is presented as an integrated approach, incorporating both the EMME macroscopic travel demand model and the VISSIM and PARAMICS micro-simulation models. The development and validation of a model to estimate incremental increases in HOV use as a function of travel time savings is presented and the validation of this model, using before-andafter data from the Highway 403 and 404 projects is discussed. HOV forecasting is not a standardized tool in the transportation planning toolbox. Different approaches have been attempted with varying levels of success. This paper is a useful addition to the literature on the subject in that it discusses some of the key issues encountered and describes the development and adaptation of models and methods to address these issues. Issues discussed include making maximum use of available data, model validation with beforeand-after data, and the integration of macroscopic travel demand modeling with microsimulation to combine the strengths of both approaches.
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