{"type":"ScholarlyArticle","title":"Towards Robust Trajectory Representations: Isolating Environmental Confounders with Causal Learning","authors":[{"name":"Kang Luo","slug":"kang-luo","given":"Kang","family":"Luo","affiliation":"Hong Kong University of Science and Technology (Guangzhou )"},{"name":"Yuanshao Zhu","slug":"yuanshao-zhu","given":"Yuanshao","family":"Zhu","affiliation":"Hong Kong University of Science and Technology (Guangzhou )"},{"name":"Wei Chen","slug":"wei-chen","given":"Wei","family":"Chen","affiliation":"Hong Kong University of Science and Technology (Guangzhou )"},{"name":"Kun Wang","slug":"kun-wang","given":"Kun","family":"Wang","affiliation":"University of Science and Technology of China"},{"name":"Zhengyang Zhou","slug":"zhengyang-zhou","given":"Zhengyang","family":"Zhou","affiliation":"University of Science and Technology of China"},{"name":"Sijie Ruan","slug":"sijie-ruan","given":"Sijie","family":"Ruan","affiliation":"Beijing Institute of Technology"},{"name":"Yuxuan Liang","slug":"yuxuan-liang","given":"Yuxuan","family":"Liang","affiliation":"Hong Kong University of Science and Technology (Guangzhou )"}],"abstract":"Trajectory modeling refers to characterizing human movement behavior, serving as a pivotal step in understanding mobility patterns. Nevertheless, existing studies typically ignore the confounding effects of geospatial context, leading to the acquisition of spurious correlations and limited generalization capabilities. To bridge this gap, we initially formulate a Structural Causal Model (SCM) to decipher the trajectory representation learning process from a causal perspective. Building upon the SCM, we further present a Trajectory modeling framework (TrajCL) based on Causal Learning,  which leverages the backdoor adjustment theory as an intervention tool to eliminate the spurious correlations between geospatial context and trajectories. Extensive experiments on two real-world datasets verify that TrajCL markedly enhances performance in trajectory classification tasks while showcasing superior generalization and interpretability.","doi":"10.24963/ijcai.2024/248","url":"https://ijcai.org/proceedings/2024/248","pdf":"https://ijcai.org/proceedings/2024/0248.pdf","markdown":"https://ijcai.org/proceedings/2024/248.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/248","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":248,"pages":{"first":2243,"last":2251},"keywords":["Data Mining: DM: Mining spatial and/or temporal data","Multidisciplinary Topics and Applications: MTA: Transportation"],"videos":[],"references":[{"position":1,"raw":"[Chang et al., 2023] Yanchuan Chang, Jianzhong Qi, Yux- uan Liang, and Egemen Tanin. 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