  Estimated Time of Arrival (ETA) plays an important role in delivery and
ride-hailing platforms. For example, Uber uses ETAs to calculate fares,
estimate pickup times, match riders to drivers, plan deliveries, and more.
Commonly used route planning algorithms predict an ETA conditioned on the best
available route, but such ETA estimates can be unreliable when the actual route
taken is not known in advance. In this paper, we describe an ETA
post-processing system in which a deep residual ETA network (DeeprETA) refines
naive ETAs produced by a route planning algorithm. Offline experiments and
online tests demonstrate that post-processing by DeeprETA significantly
improves upon the accuracy of naive ETAs as measured by mean and median
absolute error. We further show that post-processing by DeeprETA attains lower
error than competitive baseline regression models.
