{"type":"ScholarlyArticle","title":"DASS: A Dual-Branch Attention-based Framework for Trajectory Similarity Learning with Spatial and Semantic Fusion","authors":[{"name":"Jiayi Li","slug":"jiayi-li","given":"Jiayi","family":"Li","affiliation":""},{"name":"Junhua Fang","slug":"junhua-fang","given":"Junhua","family":"Fang","affiliation":""},{"name":"Pingfu Chao","slug":"pingfu-chao","given":"Pingfu","family":"Chao","affiliation":""},{"name":"Jiajie Xu","slug":"jiajie-xu","given":"Jiajie","family":"Xu","affiliation":""},{"name":"Pengpeng Zhao","slug":"pengpeng-zhao","given":"Pengpeng","family":"Zhao","affiliation":""}],"abstract":"Trajectory similarity aims to identify pairs of similar trajectories, serving as a crucial operation in spatial-temporal data mining. Although several approaches have been proposed, they encounter the following two issues: 1) An overemphasis on spatial similarity in road networks while the rich semantic information embedded in trajectories is not fully exploited; 2) Dependence on Recurrent Neural Network (RNN) architectures would struggle to capture long-term dependencies. To address these limitations, we propose a Dual-branch Attention-based framework with Spatial and Semantic information (DASS) based on self-supervised learning. Specifically, DASS comprises two core components: 1) A trajectory representation module that models spatial-temporal adjacent relationships in the form of graph and converts semantics into numerical embeddings. 2) A backbone encoder with a co-attention module to independently process two features before they are integrated. Extensive experiments on real-world datasets demonstrate that DASS outperforms state-of-the-art methods, establishing itself as a novel paradigm.","doi":"10.24963/ijcai.2025/833","url":"https://ijcai.org/proceedings/2025/833","pdf":"https://ijcai.org/proceedings/2025/0833.pdf","markdown":"https://ijcai.org/proceedings/2025/833.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/833","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Multidisciplinary Topics and Applications","track":"Main Track","number":833,"pages":{"first":7491,"last":7499},"keywords":["Multidisciplinary Topics and Applications: MTA: Transportation"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}