{"type":"ScholarlyArticle","title":"Coarse-to-Fine Latent Guidance: A Multi-Scale Diffusion Transformer for Traffic Flow Forecasting","authors":[{"name":"Zetao Li","slug":"zetao-li","given":"Zetao","family":"Li","affiliation":""},{"name":"Silin Zhou","slug":"silin-zhou","given":"Silin","family":"Zhou","affiliation":""},{"name":"Zheng Hu","slug":"zheng-hu","given":"Zheng","family":"Hu","affiliation":""},{"name":"Shimin Cai","slug":"shimin-cai","given":"Shimin","family":"Cai","affiliation":""},{"name":"Tao Zhou","slug":"tao-zhou","given":"Tao","family":"Zhou","affiliation":""}],"abstract":"Accurate traffic flow prediction is fundamental to Intelligent Transportation Systems (ITS). However, traffic dynamics exhibit inherent multi-scale heterogeneity, where stable global trends are often masked by stochastic local fluctuations. Existing methods struggle to reconcile these conflicting resolutions, leading to sub-optimal forecasting. To address this, we propose the Multi-Scale Spatial-Temporal Diffusion Transformer (MS-STDT). Deviating from previous diffusion approaches that treat generation as a monolithic task, we reframe the forward diffusion process as an intrinsic temporal coarse-graining operation. By leveraging the data's multi-scale hierarchy as a structural anchor, we introduce a coarse-to-fine latent guidance strategy that enables the model to reconstruct stable global trends before refining fine-grained details. This ensures physical consistency and generative stability without requiring external labels. Extensive experiments across six real-world datasets confirm that MS-STDT performs the best, demonstrating significant improvements in predictive accuracy and zero-shot robustness against sensor failures. We provide code and data at https://github.com/ZetaoLiPhD/MS-STDT.","doi":"10.24963/ijcai.2026/311","url":"https://ijcai.org/proceedings/2026/311","pdf":"https://ijcai.org/proceedings/2026/0311.pdf","markdown":"https://ijcai.org/proceedings/2026/311.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/311","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Data Mining","track":"Main Track","number":311,"pages":{"first":2798,"last":2806},"keywords":["Data Mining: Mining spatial and/or temporal data"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}