Content downloading by mobile users is expected to significantly increase the cellular network load. Vehicular users, in particular, are likely to engage in information retrieval on the move: in this context, Intelligent Transportation Systems (ITS) can play an important role in offloading the cellular infrastructure. We investigate the effectiveness of ITS in this task, considering that roadside units (RSUs) can exploit mobility prediction to decide which data they should fetch from the Internet and schedule transmissions to vehicles, either potential relays or downloaders. Rather than presenting a specific prediction scheme, we propose a model that allows us to express and account for any prediction technique in a simple, yet effective, manner. We then provide a probabilistic graph-based representation of the system that accounts for the prediction uncertainty. We use such a representation to study the network dynamics by efficiently solving a (non-integer) LP problem. Our results show that the above approach to content downloading through ITS can achieve an 80% offload of the cellular network. Also, we investigate the dependency of the system performance on the accuracy of the mobility prediction, and which prediction errors have the largest impact.