Abstract
This paper is devoted to the question of whether drivers can learn rational expectations from repeated observations of traffic conditions in a stationary environment. The learning problem is placed in the context of an iterative adjustment process which achieves equilibrium if drivers have rational expectations. Route choice models with rational expectations find a new justification if these models appear as limits of drivers' learning procedures. This paper investigates whether drivers can learn how to form rational expectations using standard Bayesian estimation techniques. The main result is that even if drivers begin with no knowledge of their environment, there exists an estimation procedure which converges to rational expectations when the environment satisfies a certain regularity conditions. The regularity conditions is shown to be generic.
