  In this paper, we consider the task of predicting travel times between two
arbitrary points in an urban scenario. We view this problem from two temporal
perspectives: long-term forecasting with a horizon of several days and
short-term forecasting with a horizon of one hour. Both of these perspectives
are relevant for planning tasks in the context of urban mobility and
transportation services. We utilize tree-based ensemble methods that we train
and evaluate on a dataset of taxi trip records from New York City. Through
extensive data analysis, we identify relevant temporal and spatial features. We
also engineer additional features based on weather and routing data. The latter
is obtained via a routing solver operating on the road network. The
computational results show that the addition of this routing data can be
beneficial to the model performance. Moreover, employing different models for
short and long-term prediction is useful as short-term models are better suited
to mirror current traffic conditions. In fact, we show that accurate short-term
predictions may be obtained with only little training data.
