Real-time Traffic Forecasting

1 post · May 2026

What this trail covers

Uber has significantly enhanced its real-time traffic forecasting system, initially built over a decade ago. The latest iteration, DeepETT (Deep Estimated Travel Time), leverages deep learning, specifically a graph-aware transformer model, to improve arrival time accuracy, especially for longer trips. This multi-year effort focused on addressing challenges in rapidly changing traffic conditions, sparse road networks, and the compounding effects of segment-level inaccuracies on trip-level ETAs. The system now serves upwards of 2 million real-time forecasts per second, driving an estimated $100 million in incremental annual revenue.

All Uber trails

May 2026Scaling Real-Time Traffic Forecasting with a Graph-Aware TransformerThis post details the engineering decisions behind DeepETT, Uber's new real-time traffic forecasting system powered by deep learning. It introduces a graph-aware transformer model that improves long-trip arrival time accuracy by 6% and forecast variance explained by 19%. Key engineering decisions include decoupling the forecasting model from the routing engine to de-risk the project and using a fixed-size input based on pre-aggregated observations instead of a dynamic graph to maintain predictable inference latency and throughput at scale. The system defines contracts for segment-level and trip-level forecasts, measuring accuracy via MSE and MAE respectively, and analyzes the trade-offs between resolution and calibration. The model design leverages local receptive fields and spatiotemporal views for data aggregation, and the pipeline is designed for high throughput, serving 2 million forecasts per second.
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