{"type":"ScholarlyArticle","title":"A Graph-based Representation Framework for Trajectory Recovery via Spatiotemporal Interval-Informed Seq2Seq","authors":[{"name":"Yaya Zhao","slug":"yaya-zhao","given":"Yaya","family":"Zhao","affiliation":"Center for Applied Statistics , School of Statistics , Renmin University of China"},{"name":"Kaiqi Zhao","slug":"kaiqi-zhao","given":"Kaiqi","family":"Zhao","affiliation":"The University of Auckland"},{"name":"Zhiqian Chen","slug":"zhiqian-chen","given":"Zhiqian","family":"Chen","affiliation":"Mississippi State University"},{"name":"Yuanyuan Zhang","slug":"yuanyuan-zhang","given":"Yuanyuan","family":"Zhang","affiliation":"Beijing Baixingkefu Network Technology Co. , Ltd."},{"name":"Yalei Du","slug":"yalei-du","given":"Yalei","family":"Du","affiliation":"Beijing Baixingkefu Network Technology Co. , Ltd."},{"name":"Xiaoling Lu","slug":"xiaoling-lu","given":"Xiaoling","family":"Lu","affiliation":"Center for Applied Statistics , School of Statistics , Renmin University of China"}],"abstract":"The prevalent issue in urban trajectory data usage, notably in low-sample rate datasets, revolves around the accuracy of travel time estimations, traffic flow predictions, and trajectory similarity measurements. Conventional methods, often relying on simplistic mixes of static road networks and raw GPS data, fail to adequately integrate both network and trajectory dimensions. Addressing this, the innovative GRFTrajRec framework offers a graph-based solution for trajectory recovery. Its key feature is a trajectory-aware graph representation, enhancing the understanding of trajectory-road network interactions and facilitating the extraction of detailed embedding features for road segments. Additionally, GRFTrajRec's trajectory representation acutely captures spatiotemporal attributes of trajectory points. Central to this framework is a novel spatiotemporal interval-informed seq2seq model, integrating an attention-enhanced transformer and a feature differences-aware decoder. This model specifically excels in handling spatiotemporal intervals, crucial for restoring missing GPS points in low-sample datasets. Validated through extensive experiments   on two large real-life trajectory datasets, GRFTrajRec has proven its efficacy in significantly boosting prediction accuracy and spatial consistency.","doi":"10.24963/ijcai.2024/286","url":"https://ijcai.org/proceedings/2024/286","pdf":"https://ijcai.org/proceedings/2024/0286.pdf","markdown":"https://ijcai.org/proceedings/2024/286.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/286","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Data Mining","track":"Main Track","number":286,"pages":{"first":2588,"last":2597},"keywords":["Data Mining: DM: Exploratory data mining","Data Mining: DM: Mining spatial and/or temporal data","Data Mining: DM: Applications"],"videos":[],"references":[{"position":1,"raw":"[Chen et al., 2023] Y. 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