Scalable Trajectory-User Linking with Dual-Stream Representation Networks
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915204753784832 |
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| author | Zhang, Hao Chen, Wei Zhao, Xingyu Qi, Jianpeng Jiang, Guiyuan Yu, Yanwei |
| author_facet | Zhang, Hao Chen, Wei Zhao, Xingyu Qi, Jianpeng Jiang, Guiyuan Yu, Yanwei |
| contents | Trajectory-user linking (TUL) aims to match anonymous trajectories to the most likely users who generated them, offering benefits for a wide range of real-world spatio-temporal applications. However, existing TUL methods are limited by high model complexity and poor learning of the effective representations of trajectories, rendering them ineffective in handling large-scale user trajectory data. In this work, we propose a novel $\underline{Scal}$abl$\underline{e}$ Trajectory-User Linking with dual-stream representation networks for large-scale $\underline{TUL}$ problem, named ScaleTUL. Specifically, ScaleTUL generates two views using temporal and spatial augmentations to exploit supervised contrastive learning framework to effectively capture the irregularities of trajectories. In each view, a dual-stream trajectory encoder, consisting of a long-term encoder and a short-term encoder, is designed to learn unified trajectory representations that fuse different temporal-spatial dependencies. Then, a TUL layer is used to associate the trajectories with the corresponding users in the representation space using a two-stage training model. Experimental results on check-in mobility datasets from three real-world cities and the nationwide U.S. demonstrate the superiority of ScaleTUL over state-of-the-art baselines for large-scale TUL tasks. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2503_15002 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Scalable Trajectory-User Linking with Dual-Stream Representation Networks Zhang, Hao Chen, Wei Zhao, Xingyu Qi, Jianpeng Jiang, Guiyuan Yu, Yanwei Machine Learning Trajectory-user linking (TUL) aims to match anonymous trajectories to the most likely users who generated them, offering benefits for a wide range of real-world spatio-temporal applications. However, existing TUL methods are limited by high model complexity and poor learning of the effective representations of trajectories, rendering them ineffective in handling large-scale user trajectory data. In this work, we propose a novel $\underline{Scal}$abl$\underline{e}$ Trajectory-User Linking with dual-stream representation networks for large-scale $\underline{TUL}$ problem, named ScaleTUL. Specifically, ScaleTUL generates two views using temporal and spatial augmentations to exploit supervised contrastive learning framework to effectively capture the irregularities of trajectories. In each view, a dual-stream trajectory encoder, consisting of a long-term encoder and a short-term encoder, is designed to learn unified trajectory representations that fuse different temporal-spatial dependencies. Then, a TUL layer is used to associate the trajectories with the corresponding users in the representation space using a two-stage training model. Experimental results on check-in mobility datasets from three real-world cities and the nationwide U.S. demonstrate the superiority of ScaleTUL over state-of-the-art baselines for large-scale TUL tasks. |
| title | Scalable Trajectory-User Linking with Dual-Stream Representation Networks |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2503.15002 |