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How Uber Calculates Real-Time ETAs Using ML and Live Traffic Data

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Uber's Estimated Time of Arrival system is a multi-layer engineering stack that combines live GPS data, map matching, and machine learning to predict travel times accurately. Raw GPS signals are first aligned to road networks using probabilistic map-matching techniques, after which routing engines compute candidate paths on a weighted road graph. Machine learning models then predict travel time for individual road segments by factoring in real-time congestion, historical speed patterns, weather, and road type. The system continuously refreshes ETA every few seconds as trips progress, adjusting for route deviations and sudden traffic changes. Accurate ETAs are critical to Uber's business, as even small errors can trigger cancellations, reduce driver-rider matching efficiency, and erode user trust at scale.

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