Recent advances in deep learning for traffic probabilistic prediction.

The editorial discusses recent advances in deep learning for traffic probabilistic prediction, emphasizing the importance of probabilistic models in handling uncertainties in traffic systems. Deep learning methods such as Bayesian Deep Learning, Deep Gaussian Processes, Quantile Regression, and diffusion models are explored for their effectiveness in providing a range of possible traffic states and their corresponding probabilities. The practical implications of probabilistic traffic prediction include proactive congestion control, refined passenger information systems, improved risk assessment for autonomous driving, and enhanced public transport planning. Future research directions focus on developing efficient and scalable deep Bayesian inference techniques, integrating various data sources for more precise predictions, and incorporating uncertainty estimates into decision-making frameworks for robust traffic management solutions. [Extracted from the article]