  Stop-and-go waves are a fundamental phenomenon in freeway traffic flow,
contributing to inefficiencies, crashes, and emissions. Recent advancements in
high-fidelity sensor technologies have improved the ability to capture detailed
traffic dynamics, yet such systems remain scarce and costly. In contrast,
conventional traffic sensors are widely deployed but suffer from relatively
coarse-grain data resolution, potentially impeding accurate analysis of
stop-and-go waves. This article explores whether generative AI models can
enhance the resolution of conventional traffic sensor to approximate the
quality of high-fidelity observations. We present a novel approach using a
conditional diffusion denoising model, designed to reconstruct fine-grained
traffic speed field from radar-based conventional sensors via iterative
refinement. We introduce a new dataset, I24-WaveX, comprising 132 hours of data
from both low and high-fidelity sensor systems, totaling over 2 million vehicle
miles traveled. Our approach leverages this dataset to formulate the traffic
measurement enhancement problem as a spatio-temporal super-resolution task. We
demonstrate that our model can effectively reproduce the patterns of
stop-and-go waves, achieving high accuracy in capturing these critical traffic
dynamics. Our results show promising advancements in traffic data enhancement,
offering a cost-effective way to leverage existing low spatio-temporal
resolution sensor networks for improved traffic analysis and management. We
also open-sourced our trained model and code to facilitate further research and
applications.
