How Uber Predicts Your Driver's Arrival Time With Remarkable Accuracy

Uber calculates ETAs by dividing roads into roughly 100 million segments and measuring real-time crossing times using GPS pings sent by drivers every four seconds, effectively creating a free, city-wide traffic sensor network. Because live data only reflects the recent past, Uber built a deep learning system called DeepETA in 2022 to forecast traffic conditions up to three hours ahead, processing around two million requests per second. The model prioritizes fresh signals — such as a new accident — over historical patterns, and infers conditions on quiet roads from surrounding traffic data. A second correction model, trained on millions of completed trips, then adjusts for systematic errors like slow left turns or unaccounted stop signs. Uber reports that deploying DeepETA improved long-trip arrival accuracy by 6 percent, which the company estimates translates to approximately $100 million annually in retained gross bookings.