Scaling Real-Time Traffic Forecasting with a Graph-Aware Transformer

What this post added

This post details the engineering decisions behind DeepETT, Uber's new real-time traffic forecasting system powered by a graph-aware transformer model. It highlights the system's ability to improve long-trip arrival time accuracy and forecast variance explained, serving millions of forecasts per second. Key technical contributions include the decision to decouple training from the routing loop and use fixed-size inputs from pre-aggregated observations for predictable inference and scalability. The post also discusses the system's contracts for segment-level and trip-level forecasts, the decomposition of metrics into resolution and calibration, and the model's design utilizing local receptive fields with spatiotemporal views and context features.