  Transportation planning depends on predictions of the travel times between
loading and unloading locations. While accurate techniques exist for making
deterministic predictions of travel times based on real-world data, making
stochastic predictions remains an open issue. This paper aims to fill this gap
by showing how floating car data from TomTom can be used to make stochastic
predictions of travel times. It also shows how these predictions are affected
by choices that can be made with respect to the level of aggregation of the
data in space and time, and by choices regarding the dependence between travel
times on different parts of the route.
