Pricing urban congestion: A structural random utility model with traffic anticipation

We design and estimate a game theoretic congestion pricing mechanism in which the regulator aims at reducing urban traffic congestion by price discriminating travelers according to their value of time (VOT). Travelersʹ preferences depend on their observable characteristics, on the endogenous amount of congestion anticipated, on their marginal utility (MU) of income and on some unobserved factors. Using a French household survey, we estimate the demand models to simulate different pricing mechanisms. We find that unobserved determinants of transportation demand are significant and are used to measure the anticipated time spent in traffic and the comfort of traveling: diverging from these expectations is felt as more discomfort than if no expectations were formed a priori. However, some of this discomfort is derived from travelersʹ marginal utility of income: the lost time in traffic is clearly “unpleasant” because of its opportunity cost. When the regulator and the transportation provider share common objectives, we show that a great welfare improvement can be achieved when implementing a homogenous pricing that accurately accounts for travelers VOT.