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Transfer-learning-based representation learning for trajectory similarity search

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Abstract

Trajectory similarity search is one of the most fundamental tasks in spatial-temporal data analysis. Classical methods are based on predefined trajectory similarity measures, consuming high time and space costs. To accelerate similarity computation, some deep metric learning methods have recently been proposed to approximate predefined measures based on the learned representation of trajectories. However, instead of predefined measures, real applications may require personalized measures, which cannot be effectively learned by existing models due to insufficient labels. Thus, this paper proposes a transfer-learning-based model FTL-Traj, which addresses this problem by effectively transferring knowledge from several existing measures as source measures. Particularly, a ProbSparse self-attention-based GRU unit is designed to extract the spatial and structural information of each trajectory. Confronted with diverse source measures, the priority modeling assists the model for the rational ensemble. Then, sparse labels are enriched with rank knowledge and collaboration knowledge via transfer learning. Extensive experiments on two real-world datasets demonstrate the superiority of our model.

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Availability of Data and Materials

Both datasets analyzed during the current study are available in the following resources: https://drive.google.com/drive/folders/1wGDt81o80yfbWx3Dmb66Q9VgQdP2-mqr?usp=drive_link

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Funding

This work was supported by the National Natural Science Foundation of China (Grant No. 62102276), the Natural Science Foundation of Jiangsu Province (Grant No. BK20210705), China Postdoctoral Science Foundation(Grant No. 2023M732563)and the Natural Science Foundation of Educational Commission of Jiangsu Province, China (Grant No. 21KJD520005)

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Authors and Affiliations

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

    Danling Lai, Jianfeng Qu & Xi Chen

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

    Yu Sang

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  1. Danling Lai
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  2. Jianfeng Qu
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  3. Yu Sang
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  4. Xi Chen
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Contributions

Danling Lai wrote the main manuscript text. Jianfeng Qu, Yu Sang and Xi Chen participated in model design and technical discussion. All contributing authors reviewed the manuscript.

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Correspondence to Danling Lai or Jianfeng Qu.

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Cite this article

Lai, D., Qu, J., Sang, Y. et al. Transfer-learning-based representation learning for trajectory similarity search. Geoinformatica (2024). https://doi.org/10.1007/s10707-024-00515-x

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