Travel Demand Model with Heterogeneous Users and Endogenous Congestion: An application to optimal pricing of bus services

We formulate and estimate a structural model for travel demand, in which users have heterogeneous preferences and make their transport decisions considering the network congestion. A key component in the model is that users have incomplete information about the preferences of other users in the network and they behave strategically when they make transportation decisions (mode and number of trips). Therefore, the congestion level is endogenously determinate in the equilibrium of the game played by users. For the estimation, we use the first order conditions of the users’ utility maximization problem to derive the likelihood function and apply Bayesian methods for inference. Using data from Santiago, Chile, the estimated demand elasticities are consistent with results reported in the literature and the parameters confirm the effect of the congestion on the individuals’ preferences. Finally, we compute optimal nonlinear prices for buses in Santiago, Chile. As a result, the nonlinear pricing schedule produces total benefits slightly greater than the linear pricing. Also, nonlinear pricing implies fewer individuals making trips by bus, but a higher number of trips per individual.