  To compare alternative taxi schedules and to compute them, as well as to
provide insights into an upcoming taxi trip to drivers and passengers, the
duration of a trip or its Estimated Time of Arrival (ETA) is predicted. To
reach a high prediction precision, machine learning models for ETA are state of
the art. One yet unexploited option to further increase prediction precision is
to combine multiple ETA models into an ensemble. While an increase of
prediction precision is likely, the main drawback is that the predictions made
by such an ensemble become less transparent due to the sophisticated ensemble
architecture. One option to remedy this drawback is to apply eXplainable
Artificial Intelligence (XAI). The contribution of this paper is three-fold.
First, we combine multiple machine learning models from our previous work for
ETA into a two-level ensemble model - a stacked ensemble model - which on its
own is novel; therefore, we can outperform previous state-of-the-art static
route-free ETA approaches. Second, we apply existing XAI methods to explain the
first- and second-level models of the ensemble. Third, we propose three joining
methods for combining the first-level explanations with the second-level ones.
Those joining methods enable us to explain stacked ensembles for regression
tasks. An experimental evaluation shows that the ETA models correctly learned
the importance of those input features driving the prediction.
