This work is part of an ongoing effort to understand the dynamics of passenger loads in modern, multimodal transportation networks (TNs) and to mitigate the impact of perturbations. The challenge is that the percentage of passengers at any given point of the TN that have a certain destination, i.e. their distribution over different trip profiles, is unknown. We introduce a stochastic hybrid automaton model for multimodal TNs that allows to compute how such probabilistic load vectors are propagated through the TN, and develop a computation strategy for forecasting the network's load a certain time into the future.