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The purpose of this paper is to examine drivers’ willingness-to-pay (WTP) tolls using data from a survey of drivers in the Hampton Roads region of Southeastern Virginia. The theory of planned behavior is applied to understand the different factors contributing to WTP tolls. The study measures different dimensions of WTP, offers a two-stage approach that aligns correlates of WTP tolls in logical sequence, and assesses the role of price information (toll rates) as an anchor heuristic in WTP.

Three WTP measures are elicited via contingent valuation method using three survey questions that incorporate different price information. The study tests the role of price information as an anchor heuristic. WTP is analyzed using a two-stage decision process. Drivers first decide whether, in-principle, to support tolls, followed by the amount they are willing to pay (maximum and peak amounts). Three regression models are run to test the impact of ability to pay on amount WTP, impact of in-principle WTP on maximum WTP, and impact of maximum WTP on peak WTP given an anchor toll rate.

Attitudes supportive of tolls and the ability to pay are predictors of in-principle WTP, while in-principle WTP predicts amount (maximum and peak) WTP. Price information, as an anchor heuristic, reduces variability in amount WTP and conditions the amounts WTP.

The value and originality of this study lie in the application of the theory of planned behavior to study WTP tolls, the use of contingent valuation, and the effect of anchor heuristics.

The purpose of this paper is to compare public preferences for investment and spending on non-automobile infrastructures (mass transit and bicycling) to preferences for new roads and the repair of current highways. The study explores the factors that explain preferences for non-automobile infrastructure using a three-factor model including self-interest (personal transportation benefits), concern for community-wide benefits (political beliefs), and concern for the economic impact. The study uses a case study of the urban context of the Hampton Roads region of Southeastern Virginia (USA).

The analysis uses data from a 2013 telephone survey of urban residents in the Hampton Roads area. Survey respondents were asked to identify their two investment priorities from four options: repairing existing roads, bridges, and tunnels; constructing new or expanding roads, bridges, and tunnels; expanding mass transit; and expanding bicycle routes and improving bike safety.

Repairing existing highway infrastructure is the most popular spending priority (66 percent of residents). There is as much support (46 percent) for investing in non-automobile infrastructure as for investing in new roads, bridges, and tunnels. Significant predictors of support for non-automobile infrastructure, using the three-factor model, are: length of commute time, self-identification as liberal, use of light rail, and a belief that light rail contributes to economic development.

The study examines public preferences for both non-traditional and traditional transportation infrastructure investments. It highlights the factors that contribute to public support for different transportation spending options.