Section 5: Conclusions
This study examined the performance of HOV lanes based on the goals and objectives under which those HOV lanes were designed to operate and the factors that can best contribute to the success of HOV lanes, through targeted and focused outreach to HOV operators. An HOV Lane Compendium was developed that documented the basic characteristics of current and proposed High-Occupancy Vehicle (HOV) lanes throughout the United States. A survey and follow-up interviews with HOV facility owners was conducted in order to reveal why HOV lanes are successful, why some owners are considering policy changes, and what are the future expectations of HOV lanes. Then, a Policy Options Evaluation Tool for Managed Lanes (POET-ML) was developed to quantify the impacts of pricing and other policy shifts on the operational performance of the nation’s HOV lanes.
The results of the HOV operator survey and interviews revealed similar operational challenges and common categories of performance characteristics across HOV systems nationally. Localized habits and usage trends produce significant differences in outcome expectations for policy changes. In general, there does not appear to be a “one size fits all” understanding of the approaches to HOV policy change for improved operations. There are, however, distinct characteristics and lessons learned that increase the chances for successful policy implementation under certain conditions. Identifying an appropriate policy change, or set of policy changes, is just an initial step in addressing HOV lane performance challenges. Bringing these changes to bear is the next challenge. The following is a summary of conclusions on factors and policy change, as identified by HOV operators:
The Policy Options Evaluation Tool for Managed Lanes (POET-ML) was developed to enable HOV operators and policy-makers the ability to observe how HOV policy changes will impact the performance of HOV facilities, employing both quantitative analyses and qualitative reality checks. This tool was designed to be flexible enough to allow a user with little information to gain a comprehensive understanding of the current operational effectiveness of a specific HOV facility and to evaluate the impacts of potential policy changes. HOV operators with more extensive input data, and a motivation for more customized results, are granted access to adjust a number of model assumptions in order to account for regional variation.Every HOV lane is unique in its demand composition and operations. Policy changes will leave a unique footprint with respect to operational performance and financial feasibility. It is important to recognize that the impacts of such policy changes will vary significantly depending on localized conditions including but not limited to travel times, trip purposes, and driver willingness to pay. In the documentation of POET-ML, we organized the multitude of potential inputs and outcomes into a manageable number of typical scenarios (Scenario 1: HOV & GP Lanes Both Under Capacity; Scenario 2: HOV Lane Under Capacity & GP Lanes Congested; and Scenario 3: HOV Lane & GP Lanes Over Capacity [Increased Restrictions]; and Scenario 4: HOV Lane & GP Lanes Over Capacity [Additional Capacity]). Each scenario varies with respect to mobility impacts, environmental impacts, and financial feasibility. POET-ML provides high-level impacts of proposed HOV policy adjustments – more detailed analysis is recommended prior to implementation of any policy changes identified in the tool.
United States Department of Transportation - Federal Highway Administration