We estimate a structural model of congestion costs using a Bayesian Nash Equilibrium approach: the individual's preference for traveling depends on the anticipated level of congestion, which in turn is determined by travelers' decisions of mobility. The model is estimated using a French transportation household survey. Results confirm the presence of incomplete information and show that aversion to congestion is 6.6% lower during peak time than during off-peak time. A traveler's willingness to pay to save one minute in traffic is estimated at 0.73 euros during peak time and .25 euros during off-peak time.

This paper considers the problem of identification and estimation in panel-data sample-selection models with a binary selection rule when the latent equations contain possibly predetermined variables, lags of the dependent variables, and unobserved individual effects. The selection equation contains lags of the dependent variables from both the latent and the selection equations as well as other possibly predetermined variables relative to the latent equations. We derive a set of conditional moment restrictions that are then exploited to construct a three-step sieve estimator for the parameters of the main equation including a nonparametric estimator of the sample-selection term. In the second step the unknown parameters of the selection equation are consistently estimated using a transformation approach in the spirit of Berkson's minimum chi-square sieve method and a first-step kernel estimator for the selection probability. This second-step estimator is of interest in its own right. It can be used to semiparametrically estimate a panel-data binary response model with a nonparametric individual specific effect without making any other distributional assumptions. We show that both estimators (second and third stage) are √n-consistent and asymptotically normal.

We relax the assumption of constancy of the marginal utility of income into a structural model of urban transportation with endogenous congestion. We examine the impact of unobserved heterogeneity in Marginal Utility (MU) of income on the determinants of travel by estimating the model using household survey data. We show that the value of time is no more statistically different across time slots and that the model is robust to all other results.

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). Travelers' 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 travelers' 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.

In order to treat a natural schedule matching problem related with worker-firm matchings, we generalize some theorems of Baiou--Balinski and Alkan--Gale by applying a fixed point method of Fleiner.