Predicting stochastic travel times based on high-volume floating car data

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