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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2404.14073 |
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| _version_ | 1866911848602796032 |
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| author | Luo, Kang Zhu, Yuanshao Chen, Wei Wang, Kun Zhou, Zhengyang Ruan, Sijie Liang, Yuxuan |
| author_facet | Luo, Kang Zhu, Yuanshao Chen, Wei Wang, Kun Zhou, Zhengyang Ruan, Sijie Liang, Yuxuan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_14073 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards Robust Trajectory Representations: Isolating Environmental Confounders with Causal Learning Luo, Kang Zhu, Yuanshao Chen, Wei Wang, Kun Zhou, Zhengyang Ruan, Sijie Liang, Yuxuan Machine Learning Artificial Intelligence 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. |
| title | Towards Robust Trajectory Representations: Isolating Environmental Confounders with Causal Learning |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2404.14073 |