How Uber Predicts Arrival Times - Xinyu Hu and Olcay Cirit | Stanford MLSys #64

Episode 64 of the Stanford MLSys Seminar Series!

DeepETA: How Uber Predicts Arrival Times Using Deep Learning
Speakers: Xinyu Hu and Olcay Cirit

Abstract:
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 talk, we describe an ETA post-processing system in which a deep residual ETA network (DeepETA) refines naive ETAs produced by a route planning algorithm. Offline experiments and online tests demonstrate that post-processing by DeepETA significantly improves upon the accuracy of naive ETAs as measured by mean and median absolute error. We further show that post-processing by DeepETA attains lower error than competitive baseline regression models.

Bio:
Xinyu Hu is a Senior Research Scientist at Uber, focusing on large-scale machine learning applications in spatial-temporal problems and causal inference. She currently works on projects in personalized incentives targeting, including user promotion targeting, spatial-temporal paid movement targeting, etc.. Prior to Uber, Xinyu graduated from Columbia University with a Ph.D. in Biostatistics. Olcay Cirit is a Staff Research Scientist at Uber AI focused on ML systems and large-scale deep learning problems. Prior to Uber AI, he worked on ad targeting at Google.

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