Routing games model the behavior of a large number of strategic users travelling in a transportation network. Each link of the network is endowed with a delay function that describes the travel time as a function of the flow, and users aim at selecting routes with minimum total travel time. An equilibrium of the game is a flow assignment whereby that no users have an incentive to deviate from their chosen route. This thesis is focused on Bayesian routing games, in which delays are stochastic functions that depend on a random variable representing the state of the world. In particular, we investigate how a central planner who observes the state of the world should provide information to the users in order to influence their routing behavior, possibly stirring it towards a system optimum.