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Real-time estimation of destination and travel time for taxis is of greatimportance for existing electronic dispatch systems. We present an approachbased on trip matching and ensemble learning, in which we leverage the patternsobserved in a dataset of roughly 1.7 million taxi journeys to predict thecorresponding final destination and travel time for ongoing taxi trips, as asolution for the ECML/PKDD Discovery Challenge 2015 competition. The results ofour empirical evaluation show that our approach is effective and very robust,which led our team -- BlueTaxi -- to the 3rd and 7th position of the finalrankings for the trip time and destination prediction tasks, respectively.Given the fact that the final rankings were computed using a very small testset (with only 320 trips) we believe that our approach is one of the mostrobust solutions for the challenge based on the consistency of our good resultsacross the test sets.

(Blue) Taxi Destination and Trip Time Prediction from Partial Trajectories