{"type":"ScholarlyArticle","title":"DGCPath: Distribution-Aware Generative Contrastive Framework for Self-supervised Path Representation Learning","authors":[{"name":"Sean Bin Yang","slug":"sean-bin-yang","given":"Sean Bin","family":"Yang","affiliation":""},{"name":"Hao Miao","slug":"hao-miao","given":"Hao","family":"Miao","affiliation":""},{"name":"Zongyi Xu","slug":"zongyi-xu","given":"Zongyi","family":"Xu","affiliation":""},{"name":"Jilin Hu","slug":"jilin-hu","given":"Jilin","family":"Hu","affiliation":""},{"name":"Xiangmeng Wang","slug":"xiangmeng-wang","given":"Xiangmeng","family":"Wang","affiliation":""},{"name":"Hua Lu","slug":"hua-lu","given":"Hua","family":"Lu","affiliation":""},{"name":"Bin Yang","slug":"bin-yang","given":"Bin","family":"Yang","affiliation":""},{"name":"Christian S. Jensen","slug":"christian-s-jensen","given":"Christian S.","family":"Jensen","affiliation":""}],"abstract":"Due to proliferation of vehicle trajectory data from advanced sensing technologies, path representation learning has become a pivotal task in intelligent transportation systems. Although existing self-supervised approaches work to some extent, their dependence on deterministic contrastive learning paradigms and handcrafted view augmentation strategies inherently restricts cross-scenario generalization capabilities. To address these limitations, we present DGCPath – an innovative Distribution-aware Generative Contrastive learning framework for Path representation. This architecture establishes a synergistic connection between generative modeling and distributional contrastive learning, enabling the acquisition of robust and transferable feature embeddings. Specifically, our framework incorporates: (1) a diffusion-based view generator that autonomously produces semantically coherent yet diversified trajectory views from Gaussian noise distributions; (2) a variational contrastive mechanism enforcing latent feature alignment at the distribution level, transcending conventional instance-wise consistency; and (3) a novel generative cross-supervision module that reinforces view-level consistency through cross-view reconstruction learning. Comprehensive evaluations on three real-world trajectory datasets demonstrate DGCPath's superior performance over state-of-the-art baselines in two distinct downstream tasks, validating its enhanced generalization capacity and representation effectiveness.","doi":"10.24963/ijcai.2026/352","url":"https://ijcai.org/proceedings/2026/352","pdf":"https://ijcai.org/proceedings/2026/0352.pdf","markdown":"https://ijcai.org/proceedings/2026/352.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/352","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":352,"pages":{"first":3169,"last":3177},"keywords":["Data Mining: Mining spatial and/or temporal data"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}