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TULRN: Trajectory user linking on road networks

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Abstract

Linking trajectories to users who generate them with deep learning techniques has been a popular research topic in recent years, due to the large-scale trajectory data obtained by ubiquitous GPS-enabled devices and the widespread applications served by the study, such as route planning, next location prediction, and destination prediction. To address the TUL (Trajectory User Linking) problem more effectively, we propose a novel semi-supervised model TULRN (Trajectory User Linking on Road Networks) based on GNN (Graph Neural Network) and BiLSTM (Bi-directional Long Short-Term Memory). The main difference between our study and existing ones is that the TUL problem is extended onto road networks in this work, where both the structure of road networks and the sequential characteristics of trajectories will be fully utilized in a unified manner. The reason behind the extension is that many trajectories are usually generated on road networks in real life, and based on which we can model the relationships between trajectories and users more precisely. Specifically, our proposed model TULRN contains four main components: (1) transforming each trajectory into a sequence of road segments and constructing a road network-aware trajectory sequence graph RTSG; (2) learning the representation of a node in RTSG with a weight-aware GNN module; (3) learning the representation of a trajectory with a BiLSTM-based module; (4) linking trajectories to users based on the embedding of each trajectory. The extensive experiments conducted on a real-world dataset demonstrate that the proposed model TULRN performs better than the state-of-the-art methods.

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Notes

  1. https://github.com/bmwcarit/barefoot/

  2. https://en.wikipedia.org/wiki/Entropy

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Acknowledgements

This work is supported by National Natural Science Foundation of China (No. 61872166), Six Talent Peaks Project of Jiangsu Province (2019 XYDXX-161).

Funding

The funding concludes the National Natural Science Foundation of China (No.61872166), Six Talent Peaks Project of Jiangsu Province (2019 XYDXX-161).

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Authors and Affiliations

  1. School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China

    Yu Sang & Zhenping Xie

  2. School of Computer Science and Technology, Soochow University, Suzhou, China

    Wei Chen & Lei Zhao

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  1. Yu Sang
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  2. Zhenping Xie
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  3. Wei Chen
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  4. Lei Zhao
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Contributions

Yu Sang wrote the manuscript, Zhenping Xie modified the proposed model, Wei Chen and Lei Zhao polished the paper.

Corresponding author

Correspondence to Zhenping Xie.

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We declare that we have no conflict of interest.

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The road network and trajectory data in experiments are non-public datasets.

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This article belongs to the Topical Collection: Special Issue on Spatiotemporal Data Management and Analytics for Recommend

Guest Editors: Shuo Shang, Xiangliang Zhang and Panos Kalnis

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Sang, Y., Xie, Z., Chen, W. et al. TULRN: Trajectory user linking on road networks. World Wide Web 26, 1949–1965 (2023). https://doi.org/10.1007/s11280-022-01124-0

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