Many academics and transportation planners seem convinced that pricing schemes may be one of the most effective policy instruments to change travelers’ behavior, to minimize congestion and emissions, or to optimize system use otherwise. Consequently, much empirical work has been conducted, although it is primarily about single pricing policies. Travelers’ adaptive behavior toward accumulated transport charges has not yet received much attention; therefore, this study addressed this underresearched issue. This paper documents the construction, implementation, and analysis of a mixture-amount experiment involving three mixtures of pricing schemes—toll road, congestion pricing, and parking price—and three travel budget levels per day. Basic mixed-amount design applications were extended to include an attribute associated with each pricing policy to capture different levels of travel time savings. With seven mixtures of the simplex–lattice design, a second-degree polynomial model was estimated to predict choice of amount and mixture of expenditures to different pricing schemes. This procedure captured the trade-off with level of travel time savings. In February 2012 in the Netherlands, an Internet-based stated choice experiment was conducted with 304 respondents to collect data for the model. A mixed logit model was estimated to model behavioral response. Results indicate the negative attitude of the sample toward their willingness to pay for pricing policies. Respondents seemed more sensitive to congestion pricing than to the two other policies. Furthermore, the effect of sociodemographic variables on choice probabilities was investigated. Income and public transport accessibility for conducting the peak hour trip were the variables that produced the most effect on respondents’ preference.
A validated multi-agent simulation test bed to evaluate congestion pricing policies on population segments by time-of-day in New York City