A Mathematical Model for Evaluating the Conversion of High Occupancy Vehicle Lane to High Occupancy/ Toll Lane
A methodology for evaluating and quantifying the benefits/ costs of converting a given High Occupancy Vehicle (HOV) lane into a High Occupancy/ Toll (HOT) lane is presented in this study. A mathematical programming model that seeks the optimal pricing strategy, using a logit-like choice model embedded as constraints, forms the core of the methodology. A salient feature of this study is the incorporation of equity into the planning process by imposing constraints thus enabling planners to limit the inequities in vertical as well as temporal dimensions. A HOV lane on a corridor on I-80 in the San Francisco Bay Area was studied for conversion under different objectives – revenue maximization, total vehicular travel time minimization, total passenger time minimization, total cost minimization and minimization of total vehicle miles traveled. It was found that converting the HOV lane into a HOT lane would improve the objective function in all programs except for total cost minimization. It was also found that the capital and operating costs can be recovered in a reasonable amount of time (three-five yrs). The analysis revealed that there can be significant differences in the pricing strategies across different objective functions. The variation in the system performance measures across different programs was also studied and it was found that revenue was the most sensitive performance measure. The results of all the programs revealed that there is an inverse relationship between equity and efficiency, with the exact nature of this relationship being a function of the objective. Furthermore, in situations where there is no redistribution of revenues, the vertical equity situation cannot be improved even though all the user groups can be made better off after the conversion. Additionally, Dynamic Programming models were constructed to solve for the optimal sequence/ schedule of converting a given set of HOV lanes into HOT lanes. The optimal sequences here minimized the total conversion time for a self-sustaining/ self-financing sequence or minimized the total funding needed to complete all the conversions by a certain deadline.