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Deep-learning-based data-driven forecasting methods have produced impressiveresults for traffic forecasting. A major limitation of these methods, however,is that they provide forecasts without estimates of uncertainty, which arecritical for real-time deployments. We focus on a diffusion convolutionalrecurrent neural network (DCRNN), a state-of-the-art method for short-termtraffic forecasting. We develop a scalable deep ensemble approach to quantifyuncertainties for DCRNN. Our approach uses a scalable Bayesian optimizationmethod to perform hyperparameter optimization, selects a set of high-performingconfigurations, fits a generative model to capture the joint distributions ofthe hyperparameter configurations, and trains an ensemble of models by samplinga new set of hyperparameter configurations from the generative model. Wedemonstrate the efficacy of the proposed methods by comparing them with otheruncertainty estimation techniques. We show that our generic and scalableapproach outperforms the current state-of-the-art Bayesian and a number ofother commonly used frequentist techniques.

Deep-Ensemble-Based Uncertainty Quantification in Spatiotemporal Graph Neural Networks for Traffic Forecasting