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

An Explainable Stacked Ensemble Model for Static Route-Free Estimation of Time of Arrival