Analysing flows in a transportation network is a complex task, especially when the system is congested. The underlying reason is that the traffic flows result from the interactions of all participants in the network. In this paper a simple simulation model for a congested transportation network is shortly described. In the model the individual interactions between the participants play a major role. The simplicity of the model makes it possible to focus attention on the effects of different kinds of information mechanisms on the resulting traffic flows. In the presented model each individual travel several periods in the network. The route and departure time choice are made by using individual stochastic utility functions. A learning mechanism is used in these functions to model the individuals' experience of the situation in the network in the past. This implies that every next period the route and departure time choice are based on a better knowledge of the congested network. The learning mechanism in the utility function can be changed to model more advanced kinds of information acquisition. Four different kinds of information mechanisms will be presented. One of these is a real-time information system (RTI) which has been in the centre of interest recently (e. g. the DRIVE-project). Finally some simulations with the model are carried out. This is done with a program written in the language C. The congested network, used for the simulations, represents the major roads around Amsterdam in 1989. The simulations will lead to some interesting results. It will be shown among others that, depending on the way information is obtained in a congested network, following the shortest route in time will not always lead to the shortest travel time in the whole network.