STCDM: Spatio-Temporal Conditional Diffusion Model for Traffic Data Filling

While the utilization of transportation systems is on the rise, significant data quality concerns persist, including data loss and noise arising from network transmission delays and detector malfunctions. Various methods for data imputation exist, among which diffusion-based approaches have demonstrated competitive outcomes. Nonetheless, diffusion models, primarily employed in matrix-structured data like images, fail to fully exploit the inherent graph structure of traffic data. To enhance the quality of data filling, we propose a novel method that combines spatio-temporal transformer and a conditional diffusion model (STCDM). The introduction of the conditional diffusion model involves using observable traffic data as conditional information in the reverse process, allowing it to learn the underlying probability distribution and guide the generation of high-quality data samples. The spatio-temporal transformer module is selected as the basic denoising function, capturing comprehensive spatio-temporal context information of traffic data. Our experimental results, conducted on public transportation datasets with various missing patterns and rates, indicate that STCDM exhibits superior performance by achieving up to a 1.11% improvement over the second-ranked conditional score-based diffusion model across popular performance metrics.