{"type":"ScholarlyArticle","title":"DynaOD: Dynamic Origin-Destination Flow Generation with Discrete-to-Continuous Temporal Semantic Modeling","authors":[{"name":"Jie Zhao","slug":"jie-zhao","given":"Jie","family":"Zhao","affiliation":""},{"name":"Xianqi Dai","slug":"xianqi-dai","given":"Xianqi","family":"Dai","affiliation":""},{"name":"Jie Feng","slug":"jie-feng","given":"Jie","family":"Feng","affiliation":""},{"name":"Huandong Wang","slug":"huandong-wang","given":"Huandong","family":"Wang","affiliation":""},{"name":"Yong Li","slug":"yong-li","given":"Yong","family":"Li","affiliation":""}],"abstract":"Dynamic origin-destination (OD) flow generation seeks to synthesize realistic mobility dynamics from temporal context alone, without relying on historical OD observations. A key challenge is to translate semantic temporal signals into temporally coherent OD patterns while preserving the inherent spatial heterogeneity of urban regions. We propose DynaOD, a semantic-driven framework that models temporal dynamics through two complementary perspectives: discrete directional trends that characterize qualitative shifts in urban activity patterns, and continuous temporal evolution that captures how such shifts unfold over time. By jointly encoding these temporal semantics, the framework constructs time-varying region representations that condition pretrained static OD generators in a lightweight and plug-and-play fashion. This modular design further supports scalable deployment and cross-city transferability. Extensive experiments on large-scale real-world datasets show that our method consistently outperforms representative baselines in both predictive accuracy and distributional fidelity. Code is publicly available at https://github.com/csjiezhao/DynaOD.","doi":"10.24963/ijcai.2026/362","url":"https://ijcai.org/proceedings/2026/362","pdf":"https://ijcai.org/proceedings/2026/0362.pdf","markdown":"https://ijcai.org/proceedings/2026/362.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/362","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":362,"pages":{"first":3259,"last":3267},"keywords":["Data Mining: Mining spatial and/or temporal data"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}