The Ohio Department of Transportation (ODOT) uses the Ohio Statewide Travel Demand Model (OSTDM) to produce benefit-cost analysis for various planning purposes, including as one of several criteria in their TRAC project evaluation process. Because the transportation projects evaluated typically have major cost components in addition to capacity/operating benefits, there was motivation to broaden the calculation of user benefits and costs and to more effectively integrate these measures with the OSTDM. Additional improvements to ODOT’s user benefits capabilities enhanced the treatment of travel time reliability and induced demand, while ensuring consistency between cost and utility terms throughout the model and subsequent economic analyses.
To accomplish this, ODOT’s UCOST tool was dramatically updated. This tool interfaces with OSTDM and offers backwards compatibility with standard Ohio MPO models for which the tool was originally developed. The new tool incorporates several recent innovations. First, travel time reliability is addressed using a buffer time equation to account for the additional time travelers must allow when taking unreliable routes. Secondly, the application of consumer surplus is broadened to include not just savings in travel time resulting from transportation improvements but also the effects on vehicle operating costs and safety costs. Consumer surplus is calculated by skimming benefits and costs, multiplying the resulting skim matrix by the trip table, and then applying the rule of halves. Third, the calculations for both vehicle operating costs and crash costs are expanded to account for the effects of deteriorated pavements expressed according to the International Roughness Index (IRI) and for rerouting effects due to load limits resulting from bridge deterioration. This last enhancement facilitated an add-on to UCOST2 that comparatively evaluates the effects of various ODOT actions – including do nothing, routine maintenance, rehabilitation, and capacity expansion – on user benefits and costs as well as costs incurred by ODOT. Finally, a genetic algorithm was used to estimate a new generalized cost equation for the statewide model’s assignment which improves consistency between the model and benefit/cost analysis by incorporating the impacts of user costs on drivers’ route choices.