{"type":"ScholarlyArticle","title":"Two-Stage Fine-Grained Trajectory Generation Constrained by Road Networks","authors":[{"name":"Zewu Lv","slug":"zewu-lv","given":"Zewu","family":"Lv","affiliation":""},{"name":"Zipei Fan","slug":"zipei-fan","given":"Zipei","family":"Fan","affiliation":""},{"name":"Zhiwen Zhang","slug":"zhiwen-zhang","given":"Zhiwen","family":"Zhang","affiliation":""},{"name":"Xuan Song","slug":"xuan-song","given":"Xuan","family":"Song","affiliation":""}],"abstract":"Trajectory generation is a pivotal technique for mitigating data sparsity, but existing methods struggle to simultaneously achieve strict road network alignment and capture realistic movement characteristics. To bridge this gap, we propose RNTrajGen, a two-stage fine-grained trajectory generation framework constrained by road networks. Specifically, we first develop a Road Network Knowledge-Enhanced Encoder (RNKEE) to provide semantically rich representations for trajectory generation. By leveraging graph attention networks, RNKEE encodes static prior knowledge of the road network while integrating self-supervised learning to extract latent movement patterns from real-world trajectories. Subsequently, RNTrajGen follows a two-stage generation strategy: it first generates a sequence of road segments to ensure strict topological alignment, and then infers the moving ratios of trajectory points along road segments to capture fine-grained movement characteristics. Extensive experiments on two real-world datasets demonstrate that RNTrajGen significantly outperforms state-of-the-art baselines, and the generated trajectories exhibit high utility in downstream prediction tasks. Our code is available at https://github.com/jkzh986/RNTrajGen.","doi":"10.24963/ijcai.2026/324","url":"https://ijcai.org/proceedings/2026/324","pdf":"https://ijcai.org/proceedings/2026/0324.pdf","markdown":"https://ijcai.org/proceedings/2026/324.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/324","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":324,"pages":{"first":2915,"last":2923},"keywords":["Data Mining: Mining spatial and/or temporal data","Machine Learning: Sequence and graph learning","Machine Learning: Representation learning"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}