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Travel time information plays an important role in transportation and logistics. Much research has been done in the field of travel time prediction in local areas, aiming at accurate short-term predictions based on the current traffic situation and historical data of the area. In contrast, literature on prediction methods for long-range trips in large areas is rare, although it is highly relevant for logistics companies to manage their fleet of vehicles. In this paper, we present a new algorithm for predicting the remaining travel times of long-range trips. It makes use of nonparametric distribution-free regression models, which are applicable only in the presence of a sufficiently large database. Since, in contrast to local areas, such a base is visionary for large areas, we bring into play a dynamic data preparation to artificially enlarge the database. The algorithm also takes into account that routes of long-range trips are not completely given in advance but are rather unknown and subject to change. We illustrate our algorithm by means of simulations and a real-life case study at a German logistics company. The latter shows that, by our algorithm, the average relative error can be halved compared with conventional methods.